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ynput__OpenPype

assignments_and_allocations.rst

Tutorial / Subdoc

Working with assignments and allocations

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/assignments_and_allocations.rst

Working with assignments and allocations The API exposes assignments and allocations relationships on objects in the project hierarchy. You can use these to retrieve the allocated or assigned resources, which can be either groups or users. Allocations can be used to allocate users or groups to a project team, while assignments are more explicit and is used to assign users to tasks. Both assignment and allocations are modelled as Appointment objects, with a type attribute indicating the type of the appoinment. The following example retrieves all users part of the project team: # Retrieve a project project = session.query('Project').first() # Set to hold all users part of the project team project_team = set() # Add all allocated groups and users for allocation in project['allocations']: # Resource may be either a group or a user resource = allocation['resource'] # If the resource is a group, add its members if isinstance(resource, session.types['Group']): for membership in resource['memberships']: user = membership['user'] project_team.add(user) # The resource is a user, add it. else: user = resource project_team.add(user) The next example shows how to assign the current user to a task: # Retrieve a task and the current user task = session.query('Task').first() current_user = session.query( u'User where username is {0}'.format(session.api_user) ).one() # Create a new Appointment of type assignment. session.create('Appointment', { 'context': task, 'resource': current_user, 'type': 'assignment' }) # Finally, persist the new assignment session.commit() To list all users assigned to a task, see the following example: task = session.query('Task').first() users = session.query( 'select first_name, last_name from User ' 'where assignments any (context_id = "{0}")'.format(task['id']) ) for user in users: print user['first_name'], user['last_name'] To list the current user's assigned tasks, see the example below: assigned_tasks = session.query( 'select link from Task ' 'where assignments any (resource.username = "{0}")'.format(session.api_user) ) for task in assigned_tasks: print u' / '.join(item['name'] for item in task['link'])

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

custom_attribute.rst

Tutorial / Subdoc

Using custom attributes

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/custom_attribute.rst

Using custom attributes Custom attributes can be written and read from entities using the custom_attributes property. The custom_attributes property provides a similar interface to a dictionary. Keys can be printed using the keys method: >>> task['custom_attributes'].keys() [u'my_text_field'] or access keys and values as items: >>> print task['custom_attributes'].items() [(u'my_text_field', u'some text')] Read existing custom attribute values: >>> print task['custom_attributes']['my_text_field'] 'some text' Updating a custom attributes can also be done similar to a dictionary: task['custom_attributes']['my_text_field'] = 'foo' To query for tasks with a custom attribute, my_text_field, you can use the key from the configuration: for task in session.query( 'Task where custom_attributes any ' '(key is "my_text_field" and value is "bar")' ): print task['name'] Limitations Expression attributes Expression attributes are not yet supported and the reported value will always be the non-evaluated expression. Hierarchical attributes Hierarchical attributes are not yet fully supported in the API. Hierarchical attributes support both read and write, but when read they are not calculated and instead the raw value is returned: # The hierarchical attribute `my_attribute` is set on Shot but this will not # be reflected on the children. Instead the raw value is returned. print shot['custom_attributes']['my_attribute'] 'foo' print task['custom_attributes']['my_attribute'] None To work around this limitation it is possible to use the legacy api for hierarchical attributes or to manually query the parents for values and use the first value that is set. Validation Custom attributes are validated on the ftrack server before persisted. The validation will check that the type of the data is correct for the custom attribute. - number - int or float - text - str or unicode - enumerator - list - boolean - bool - date - datetime.datetime or datetime.date If the value set is not valid a ftrack_api.exception.ServerError is raised with debug information: shot['custom_attributes']['fstart'] = 'test' Traceback (most recent call last): ... ftrack_api.exception.ServerError: Server reported error: ValidationError(Custom attribute value for "fstart" must be of type number. Got "test" of type <type 'unicode'>)

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

encode_media.rst

Tutorial / Subdoc

Encoding media

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/encode_media.rst

Encoding media Media such as images and video can be encoded by the ftrack server to allow playing it in the ftrack web interface. Media can be encoded using ftrack_api.session.Session.encode_media which accepts a path to a file or an existing component in the ftrack.server location. Here is an example of how to encode a video and read the output: job = session.encode_media('/PATH/TO/MEDIA') job_data = json.loads(job['data']) print 'Source component id', job_data['source_component_id'] print 'Keeping original component', job_data['keep_original'] for output in job_data['output']: print u'Output component - id: {0}, format: {1}'.format( output['component_id'], output['format'] ) You can also call the corresponding helper method on an asset version <ftrack_api.entity.asset_version.AssetVersion.encode_media>, to have the encoded components automatically associated with the version: job = asset_version.encode_media('/PATH/TO/MEDIA') It is also possible to get the URL to an encoded component once the job has finished: job = session.encode_media('/PATH/TO/MEDIA') # Wait for job to finish. location = session.query('Location where name is "ftrack.server"').one() for component in job['job_components']: print location.get_url(component) Media can also be an existing component in another location. Before encoding it, the component needs to be added to the ftrack.server location: location = session.query('Location where name is "ftrack.server"').one() location.add_component(component) session.commit() job = session.encode_media(component)

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

web_review.rst

Tutorial / Subdoc

Publishing for web review

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/web_review.rst

Publishing for web review Follow the example/encode_media example if you want to upload and encode media using ftrack. If you already have a file encoded in the correct format and want to bypass the built-in encoding in ftrack, you can create the component manually and add it to the ftrack.server location: # Retrieve or create version. version = session.query('AssetVersion', 'SOME-ID') server_location = session.query('Location where name is "ftrack.server"').one() filepath = '/path/to/local/file.mp4' component = version.create_component( path=filepath, data={ 'name': 'ftrackreview-mp4' }, location=server_location ) # Meta data needs to contain *frameIn*, *frameOut* and *frameRate*. component['metadata']['ftr_meta'] = json.dumps({ 'frameIn': 0, 'frameOut': 150, 'frameRate': 25 }) component.session.commit() To publish an image for review the steps are similar: # Retrieve or create version. version = session.query('AssetVersion', 'SOME-ID') server_location = session.query('Location where name is "ftrack.server"').one() filepath = '/path/to/image.jpg' component = version.create_component( path=filepath, data={ 'name': 'ftrackreview-image' }, location=server_location ) # Meta data needs to contain *format*. component['metadata']['ftr_meta'] = json.dumps({ 'format': 'image' }) component.session.commit() Here is a list of components names and how they should be used: - ftrackreview-image (Images reviewable in the browser) - ftrackreview-mp4 (H.264/mp4 video reviewable in browser) - ftrackreview-webm (WebM video reviewable in browser) Note Make sure to use the pre-defined component names and set the ftr_meta on the components or review will not work.

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

sync_ldap_users.rst

Tutorial / Subdoc

Sync users with LDAP

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/sync_ldap_users.rst

Sync users with LDAP If ftrack is configured to connect to LDAP you may trigger a synchronization through the api using the ftrack_api.session.Session.call: result = session.call([ dict( action='delayed_job', job_type='SYNC_USERS_LDAP' ) ]) job = result[0]['data] You will get a ftrack_api.entity.job.Job instance back which can be used to check the success of the job: if job.get('status') == 'failed': # The job failed get the error. logging.error(job.get('data'))

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

publishing.rst

Tutorial / Subdoc

Publishing versions

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/publishing.rst

Publishing versions To know more about publishing and the concepts around publishing, read the ftrack article about publishing. To publish an asset you first need to get the context where the asset should be published: # Get a task from a given id. task = session.get('Task', '423ac382-e61d-4802-8914-dce20c92b740') And the parent of the task which will be used to publish the asset on: asset_parent = task['parent'] Then we create an asset and a version on the asset: asset_type = session.query('AssetType where name is "Geometry"').one() asset = session.create('Asset', { 'name': 'My asset', 'type': asset_type, 'parent': asset_parent }) asset_version = session.create('AssetVersion', { 'asset': asset, 'task': task }) Note The task is not used as the parent of the asset, instead the task is linked directly to the AssetVersion. Then when we have a version where we can create the components: asset_version.create_component( '/path/to/a/file.mov', location='auto' ) asset_version.create_component( '/path/to/a/another-file.mov', location='auto' ) session.commit() This will automatically create a new component and add it to the location which has been configured as the first in priority. Components can also be named and added to a custom location like this: location = session.query('Location where name is "my-location"') asset_version.create_component( '/path/to/a/file.mov', data={ 'name': 'foobar' }, location=location )

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

security_roles.rst

Tutorial / Subdoc

Working with user security roles

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/security_roles.rst

Working with user security roles The API exposes SecurityRole and UserSecurityRole that can be used to specify who should have access to certain data on different projects. List all available security roles like this: security_roles = session.query( 'select name from SecurityRole where type is "PROJECT"' ) Note We only query for project roles since those are the ones we can add to a user for certain projects. Other types include API and ASSIGNED. Type API can only be added to global API keys, which is currently not supported via the api and type ASSIGNED only applies to assigned tasks. To get all security roles from a user we can either use relations like this: for user_security_role in user['user_security_roles']: if user_security_role['is_all_projects']: result_string = 'all projects' else: result_string = ', '.join( [project['full_name'] for project in user_security_role['projects']] ) print 'User has security role "{0}" which is valid on {1}.'.format( user_security_role['security_role']['name'], result_string ) or query them directly like this: user_security_roles = session.query( 'UserSecurityRole where user.username is "{0}"'.format(session.api_user) ).all() User security roles can also be added to a user for all projects like this: project_manager_role = session.query( 'SecurityRole where name is "Project Manager"' ).one() session.create('UserSecurityRole', { 'is_all_projects': True, 'user': user, 'security_role': project_manager_role }) session.commit() or for certain projects only like this: projects = session.query( 'Project where full_name is "project1" or full_name is "project2"' ).all()[:] session.create('UserSecurityRole', { 'user': user, 'security_role': project_manager_role, 'projects': projects }) session.commit()

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

list.rst

Tutorial / Subdoc

Using lists

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/list.rst

Using lists Lists can be used to create a collection of asset versions or objects such as tasks. It could be a list of items that should be sent to client, be included in todays review session or items that belong together in way that is different from the project hierarchy. There are two types of lists, one for asset versions and one for other objects such as tasks. To create a list use Session.create: user = # Get a user from ftrack. project = # Get a project from ftrack. list_category = # Get a list category from ftrack. asset_version_list = session.create('AssetVersionList', { 'owner': user, 'project': project, 'category': list_category }) task_list = session.create('TypedContextList', { 'owner': user, 'project': project, 'category': list_category }) Then add items to the list like this: asset_version_list['items'].append(asset_version) task_list['items'].append(task) And remove items from the list like this: asset_version_list['items'].remove(asset_version) task_list['items'].remove(task)

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

review_session.rst

Tutorial / Subdoc

Using review sessions

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/review_session.rst

Using review sessions Client review sessions can either be queried manually or by using a project instance. review_sessions = session.query( 'ReviewSession where name is "Weekly review"' ) project_review_sessions = project['review_sessions'] To create a new review session on a specific project use Session.create. review_session = session.create('ReviewSession', { 'name': 'Weekly review', 'description': 'See updates from last week.', 'project': project }) To add objects to a review session create them using Session.create and reference a review session and an asset version. review_session = session.create('ReviewSessionObject', { 'name': 'Compositing', 'description': 'Fixed shadows.', 'version': 'Version 3', 'review_session': review_session, 'asset_version': asset_version }) To list all objects in a review session. review_session_objects = review_session['review_session_objects'] Listing and adding collaborators to review session can be done using Session.create and the review_session_invitees relation on a review session. invitee = session.create('ReviewSessionInvitee', { 'name': 'John Doe', 'email': 'john.doe@example.com', 'review_session': review_session }) session.commit() invitees = review_session['review_session_invitees'] To remove a collaborator simply delete the object using Session.delete. session.delete(invitee) To send out an invite email to a signle collaborator use Session.send_review_session_invite. session.send_review_session_invite(invitee) Multiple invitees can have emails sent to them in one batch using Session.send_review_session_invites. session.send_review_session_invites(a_list_of_invitees)

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

timer.rst

Tutorial / Subdoc

Using timers

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/timer.rst

Using timers Timers can be used to track how much time has been spend working on something. To start a timer for a user: user = # Get a user from ftrack. task = # Get a task from ftrack. user.start_timer(task) A timer has now been created for that user and should show up in the ftrack web UI. To stop the currently running timer for a user and create a timelog from it: user = # Get a user from ftrack. timelog = user.stop_timer() Note Starting a timer when a timer is already running will raise in an exception. Use the force parameter to automatically stop the running timer first. user.start_timer(task, force=True)

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

metadata.rst

Tutorial / Subdoc

Using metadata

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/example/metadata.rst

Using metadata Key/value metadata can be written to entities using the metadata property and also used to query entities. The metadata property has a similar interface as a dictionary and keys can be printed using the keys method: >>> print new_sequence['metadata'].keys() ['frame_padding', 'focal_length'] or items: >>> print new_sequence['metadata'].items() [('frame_padding': '4'), ('focal_length': '70')] Read existing metadata: >>> print new_sequence['metadata']['frame_padding'] '4' Setting metadata can be done in a few ways where that later one will replace any existing metadata: new_sequence['metadata']['frame_padding'] = '5' new_sequence['metadata'] = { 'frame_padding': '4' } Entities can also be queried using metadata: session.query( 'Sequence where metadata any (key is "frame_padding" and value is "4")' )

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

working_with_entities.rst

Directory summarization

Working with entities

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/working_with_entities.rst

Working with entities Entity <ftrack_api.entity.base.Entity> instances are Python dict-like objects whose keys correspond to attributes for that type in the system. They may also provide helper methods to perform common operations such as replying to a note: note = session.query('Note').first() print note.keys() print note['content'] note['content'] = 'A different message!' reply = note.create_reply(...) Attributes Each entity instance is typed according to its underlying entity type on the server and configured with appropriate attributes. For example, a task will be represented by a Task class and have corresponding attributes. You can customise entity classes <working_with_entities/entity_types> to alter attribute access or provide your own helper methods. To see the available attribute names on an entity use the ~ftrack_api.entity.base.Entity.keys method on the instance: >>> task = session.query('Task').first() >>> print task.keys() ['id', 'name', ...] If you need more information about the type of attribute, examine the attributes property on the corresponding class: >>> for attribute in type(task).attributes: ... print attribute <ftrack_api.attribute.ScalarAttribute(id) object at 66701296> <ftrack_api.attribute.ScalarAttribute(name) object at 66702192> <ftrack_api.attribute.ReferenceAttribute(status) object at 66701240> <ftrack_api.attribute.CollectionAttribute(timelogs) object at 66701184> <ftrack_api.attribute.KeyValueMappedCollectionAttribute(metadata) object at 66701632> ... Notice that there are different types of attribute such as ~ftrack_api.attribute.ScalarAttribute for plain values or ~ftrack_api.attribute.ReferenceAttribute for relationships. These different types are reflected in the behaviour on the entity instance when accessing a particular attribute by key: >>> # Scalar >>> print task['name'] 'model' >>> task['name'] = 'comp' >>> # Single reference >>> print task['status'] <Status(e610b180-4e64-11e1-a500-f23c91df25eb)> >>> new_status = session.query('Status').first() >>> task['status'] = new_status >>> # Collection >>> print task['timelogs'] <ftrack_api.collection.Collection object at 0x00000000040D95C0> >>> print task['timelogs'][:] [<dynamic ftrack Timelog object 72322240>, ...] >>> new_timelog = session.create('Timelog', {...}) >>> task['timelogs'].append(new_timelog) Bi-directional relationships Some attributes refer to different sides of a bi-directional relationship. In the current version of the API bi-directional updates are not propagated automatically to the other side of the relationship. For example, setting a parent will not update the parent entity's children collection locally. There are plans to support this behaviour better in the future. For now, after commit, populate <working_with_entities/populating> the reverse side attribute manually. Creating entities In order to create a new instance of an entity call Session.create passing in the entity type to create and any initial attribute values: new_user = session.create('User', {'username': 'martin'}) If there are any default values that can be set client side then they will be applied at this point. Typically this will be the unique entity key: >>> print new_user['id'] 170f02a4-6656-4f15-a5cb-c4dd77ce0540 At this point no information has been sent to the server. However, you are free to continue updating <working_with_entities/updating> this object locally until you are ready to persist the changes by calling Session.commit. If you are wondering about what would happen if you accessed an unset attribute on a newly created entity, go ahead and give it a go: >>> print new_user['first_name'] NOT_SET The session knows that it is a newly created entity that has not yet been persisted so it doesn't try to fetch any attributes on access even when session.auto_populate is turned on. Updating entities Updating an entity is as simple as modifying the values for specific keys on the dict-like instance and calling Session.commit when ready. The entity to update can either be a new entity or a retrieved entity: task = session.query('Task').first() task['bid'] = 8 Remember that, for existing entities, accessing an attribute will load it from the server automatically. If you are interested in just setting values without first fetching them from the server, turn auto-population <understanding_sessions/auto_population> off temporarily: >>> with session.auto_populating(False): ... task = session.query('Task').first() ... task['bid'] = 8 Server side reset of entity attributes or settings. =========================== Some entities support resetting of attributes, for example to reset a users api key: session.reset_remote( 'api_key', entity=session.query('User where username is "test_user"').one() ) Note Currently the only attribute possible to reset is 'api_key' on the user entity type. Deleting entities To delete an entity you need an instance of the entity in your session (either from having created one or retrieving one). Then call Session.delete on the entity and Session.commit when ready: task_to_delete = session.query('Task').first() session.delete(task_to_delete) ... session.commit() Note Even though the entity is deleted, you will still have access to the local instance and any local data stored on that instance whilst that instance remains in memory. Keep in mind that some deletions, when propagated to the server, will cause other entities to be deleted also, so you don't have to worry about deleting an entire hierarchy manually. For example, deleting a Task will also delete all Notes on that task. Populating entities When an entity is retrieved via Session.query or Session.get it will have some attributes prepopulated. The rest are dynamically loaded when they are accessed. If you need to access many attributes it can be more efficient to request all those attributes be loaded in one go. One way to do this is to use a projections <querying/projections> in queries. However, if you have entities that have been passed to you from elsewhere you don't have control over the query that was issued to get those entities. In this case you can you can populate those entities in one go using Session.populate which works exactly like projections <querying/projections> in queries do, but operating against known entities: >>> users = session.query('User') >>> session.populate(users, 'first_name, last_name') >>> with session.auto_populating(False): # Turn off for example purpose. ... for user in users: ... print 'Name: {0}'.format(user['first_name']) ... print 'Email: {0}'.format(user['email']) Name: Martin Email: NOT_SET ... Note You can populate a single or many entities in one call so long as they are all the same entity type. Entity states Operations on entities are recorded in the session <understanding_sessions/unit_of_work> as they happen. At any time you can inspect an entity to determine its current state from those pending operations. To do this, use ftrack_api.inspection.state: >>> import ftrack_api.inspection >>> new_user = session.create('User', {}) >>> print ftrack_api.inspection.state(new_user) CREATED >>> existing_user = session.query('User').first() >>> print ftrack_api.inspection.state(existing_user) NOT_SET >>> existing_user['email'] = 'martin@example.com' >>> print ftrack_api.inspection.state(existing_user) MODIFIED >>> session.delete(new_user) >>> print ftrack_api.inspection.state(new_user) DELETED Customising entity types Each type of entity in the system is represented in the Python client by a dedicated class. However, because the types of entities can vary these classes are built on demand using schema information retrieved from the server. Many of the default classes provide additional helper methods which are mixed into the generated class at runtime when a session is started. In some cases it can be useful to tailor the custom classes to your own pipeline workflows. Perhaps you want to add more helper functions, change attribute access rules or even providing a layer of backwards compatibility for existing code. The Python client was built with this in mind and makes such customisations as easy as possible. When a Session is constructed it fetches schema details from the connected server and then calls an Entity factory <ftrack_api.entity.factory.Factory> to create classes from those schemas. It does this by emitting a synchronous event, ftrack.api.session.construct-entity-type, for each schema and expecting a class object to be returned. In the default setup, a construct_entity_type.py <../resource/plugin/construct_entity_type.py> plugin is placed on the FTRACK_EVENT_PLUGIN_PATH. This plugin will register a trivial subclass of ftrack_api.entity.factory.StandardFactory to create the classes in response to the construct event. The simplest way to get started is to edit this default plugin as required. understanding_sessions/plugins Default projections When a query <querying> is issued without any projections <querying/projections>, the session will automatically add default projections according to the type of the entity. For example, the following shows that for a User, only id is fetched by default when no projections added to the query: >>> user = session.query('User').first() >>> with session.auto_populating(False): # For demonstration purpose only. ... print user.items() [ (u'id', u'59f0963a-15e2-11e1-a5f1-0019bb4983d8') (u'username', Symbol(NOT_SET)), (u'first_name', Symbol(NOT_SET)), ... ] Note These default projections are also used when you access a relationship attribute using the dictionary key syntax. If you want to default to fetching username for a Task as well then you can change the default_projections* in your class factory plugin: class Factory(ftrack_api.entity.factory.StandardFactory): '''Entity class factory.''' def create(self, schema, bases=None): '''Create and return entity class from *schema*.''' cls = super(Factory, self).create(schema, bases=bases) # Further customise cls before returning. if schema['id'] == 'User': cls.default_projections = ['id', 'username'] return cls Now a projection-less query will also query username by default: Note You will need to start a new session to pick up the change you made: session = ftrack_api.Session() >>> user = session.query('User').first() >>> with session.auto_populating(False): # For demonstration purpose only. ... print user.items() [ (u'id', u'59f0963a-15e2-11e1-a5f1-0019bb4983d8') (u'username', u'martin'), (u'first_name', Symbol(NOT_SET)), ... ] Note that if any specific projections are applied in a query, those override the default projections entirely. This allows you to also reduce the data loaded on demand: >>> session = ftrack_api.Session() # Start new session to avoid cache. >>> user = session.query('select id from User').first() >>> with session.auto_populating(False): # For demonstration purpose only. ... print user.items() [ (u'id', u'59f0963a-15e2-11e1-a5f1-0019bb4983d8') (u'username', Symbol(NOT_SET)), (u'first_name', Symbol(NOT_SET)), ... ] Helper methods If you want to add additional helper methods to the constructed classes to better support your pipeline logic, then you can simply patch the created classes in your factory, much like with changing the default projections: def get_full_name(self): '''Return full name for user.''' return '{0} {1}'.format(self['first_name'], self['last_name']).strip() class Factory(ftrack_api.entity.factory.StandardFactory): '''Entity class factory.''' def create(self, schema, bases=None): '''Create and return entity class from *schema*.''' cls = super(Factory, self).create(schema, bases=bases) # Further customise cls before returning. if schema['id'] == 'User': cls.get_full_name = get_full_name return cls Now you have a new helper method get_full_name on your User entities: >>> session = ftrack_api.Session() # New session to pick up changes. >>> user = session.query('User').first() >>> print user.get_full_name() Martin Pengelly-Phillips If you'd rather not patch the existing classes, or perhaps have a lot of helpers to mixin, you can instead inject your own class as the base class. The only requirement is that it has the base ~ftrack_api.entity.base.Entity class in its ancestor classes: import ftrack_api.entity.base class CustomUser(ftrack_api.entity.base.Entity): '''Represent user.''' def get_full_name(self): '''Return full name for user.''' return '{0} {1}'.format(self['first_name'], self['last_name']).strip() class Factory(ftrack_api.entity.factory.StandardFactory): '''Entity class factory.''' def create(self, schema, bases=None): '''Create and return entity class from *schema*.''' # Alter base class for constructed class. if bases is None: bases = [ftrack_api.entity.base.Entity] if schema['id'] == 'User': bases = [CustomUser] cls = super(Factory, self).create(schema, bases=bases) return cls The resulting effect is the same: >>> session = ftrack_api.Session() # New session to pick up changes. >>> user = session.query('User').first() >>> print user.get_full_name() Martin Pengelly-Phillips Note Your custom class is not the leaf class which will still be a dynamically generated class. Instead your custom class becomes the base for the leaf class: >>> print type(user).__mro__ (<dynamic ftrack class 'User'>, <dynamic ftrack class 'CustomUser'>, ...)

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

tutorial.rst

Tutorial

A quick dive into using the API

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/tutorial.rst

Tutorial This tutorial provides a quick dive into using the API and the broad stroke concepts involved. First make sure the ftrack Python API is installed <installing>. Then start a Python session and import the ftrack API: >>> import ftrack_api The API uses sessions <understanding_sessions> to manage communication with an ftrack server. Create a session that connects to your ftrack server (changing the passed values as appropriate): >>> session = ftrack_api.Session( ... server_url='https://mycompany.ftrackapp.com', ... api_key='7545384e-a653-11e1-a82c-f22c11dd25eq', ... api_user='martin' ... ) Note A session can use environment variables <understanding_sessions/connection> to configure itself. Now print a list of the available entity types retrieved from the server: >>> print session.types.keys() [u'TypedContext', u'ObjectType', u'Priority', u'Project', u'Sequence', u'Shot', u'Task', u'Status', u'Type', u'Timelog', u'User'] Now the list of possible entity types is known, query <querying> the server to retrieve entities of a particular type by using the Session.query method: >>> projects = session.query('Project') Each project retrieved will be an entity <working_with_entities> instance that behaves much like a standard Python dictionary. For example, to find out the available keys for an entity, call the ~ftrack_api.entity.Entity.keys method: >>> print projects[0].keys() [u'status', u'is_global', u'name', u'end_date', u'context_type', u'id', u'full_name', u'root', u'start_date'] Now, iterate over the retrieved entities and print each ones name: >>> for project in projects: ... print project['name'] test client_review tdb man_test ftrack bunny Note Many attributes for retrieved entities are loaded on demand when the attribute is first accessed. Doing this lots of times in a script can be inefficient, so it is worth using projections <querying/projections> in queries or pre-populating <working_with_entities/populating> entities where appropriate. You can also customise default projections <working_with_entities/entity_types/default_projections> to help others pre-load common attributes. To narrow a search, add criteria <querying/criteria> to the query: >>> active_projects = session.query('Project where status is active') Combine criteria for more powerful queries: >>> import arrow >>> >>> active_projects_ending_before_next_week = session.query( ... 'Project where status is active and end_date before "{0}"' ... .format(arrow.now().replace(weeks=+1)) ... ) Some attributes on an entity will refer to another entity or collection of entities, such as children on a Project being a collection of Context entities that have the project as their parent: >>> project = session.query('Project').first() >>> print project['children'] <ftrack_api.collection.Collection object at 0x00000000045B1438> And on each Context there is a corresponding parent attribute which is a link back to the parent: >>> child = project['children'][0] >>> print child['parent'] is project True These relationships can also be used in the criteria for a query: >>> results = session.query( ... 'Context where parent.name like "te%"' ... ) To create new entities in the system use Session.create: >>> new_sequence = session.create('Sequence', { ... 'name': 'Starlord Reveal' ... }) The created entity is not yet persisted to the server, but it is still possible to modify it. >>> new_sequence['description'] = 'First hero character reveal.' The sequence also needs a parent. This can be done in one of two ways: - Set the parent attribute on the sequence: >>> new_sequence['parent'] = project - Add the sequence to a parent's children attribute: >>> project['children'].append(new_sequence) When ready, persist to the server using Session.commit: >>> session.commit() When finished with a Session, it is important to ~Session.close it in order to release resources and properly unsubscribe any registered event listeners. It is also possible to use the session as a context manager in order to have it closed automatically after use: >>> with ftrack_api.Session() as session: ... print session.query('User').first() <User(0154901c-eaf9-11e5-b165-00505681ec7a)> >>> print session.closed True Once a Session is closed, any operations that attempt to use the closed connection to the ftrack server will fail: >>> session.query('Project').first() ConnectionClosedError: Connection closed. Continue to the next section to start learning more about the API in greater depth or jump over to the usage examples <example> if you prefer to learn by example.

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

understanding_sessions.rst

Module doc

Understanding sessions

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/understanding_sessions.rst

Understanding sessions All communication with an ftrack server takes place through a Session. This allows more opportunity for configuring the connection, plugins etc. and also makes it possible to connect to multiple ftrack servers from within the same Python process. Connection A session can be manually configured at runtime to connect to a server with certain credentials: >>> session = ftrack_api.Session( ... server_url='https://mycompany.ftrackapp.com', ... api_key='7545384e-a653-11e1-a82c-f22c11dd25eq', ... api_user='martin' ... ) Alternatively, a session can use the following environment variables to configure itself: - FTRACK_SERVER - FTRACK_API_USER - FTRACK_API_KEY When using environment variables, no server connection arguments need to be passed manually: >>> session = ftrack_api.Session() Unit of work Each session follows the unit of work pattern. This means that many of the operations performed using a session will happen locally and only be persisted to the server at certain times, notably when calling Session.commit. This approach helps optimise calls to the server and also group related logic together in a transaction: user = session.create('User', {}) user['username'] = 'martin' other_user = session.create('User', {'username': 'bjorn'}) other_user['email'] = 'bjorn@example.com' Behind the scenes a series of operations <ftrack_api.operation.Operation> are recorded reflecting the changes made. You can take a peek at these operations if desired by examining the Session.recorded_operations property: >>> for operation in session.recorded_operations: ... print operation <ftrack_api.operation.CreateEntityOperation object at 0x0000000003EC49B0> <ftrack_api.operation.UpdateEntityOperation object at 0x0000000003E16898> <ftrack_api.operation.CreateEntityOperation object at 0x0000000003E16240> <ftrack_api.operation.UpdateEntityOperation object at 0x0000000003E16128> Calling Session.commit persists all recorded operations to the server and clears the operation log: session.commit() Note The commit call will optimise operations to be as efficient as possible without breaking logical ordering. For example, a create followed by updates on the same entity will be compressed into a single create. Queries are special and always issued on demand. As a result, a query may return unexpected results if the relevant local changes have not yet been sent to the server: >>> user = session.create('User', {'username': 'some_unique_username'}) >>> query = 'User where username is "{0}"'.format(user['username']) >>> print len(session.query(query)) 0 >>> session.commit() >>> print len(session.query(query)) 1 Where possible, query results are merged in with existing data transparently with any local changes preserved: >>> user = session.query('User').first() >>> user['email'] = 'me@example.com' # Not yet committed to server. >>> retrieved = session.query( ... 'User where id is "{0}"'.format(user['id']) ... ).one() >>> print retrieved['email'] # Displays locally set value. 'me@example.com' >>> print retrieved is user True This is possible due to the smart caching layer in the session. Auto-population Another important concept in a session is that of auto-population. By default a session is configured to auto-populate missing attribute values on access. This means that the first time you access an attribute on an entity instance a query will be sent to the server to fetch the value: user = session.query('User').first() # The next command will issue a request to the server to fetch the # 'username' value on demand at this is the first time it is accessed. print user['username'] Once a value has been retrieved it is cached <caching> locally in the session and accessing it again will not issue more server calls: # On second access no server call is made. print user['username'] You can control the auto population behaviour of a session by either changing the Session.auto_populate attribute on a session or using the provided context helper Session.auto_populating to temporarily change the setting. When turned off you may see a special ~ftrack_api.symbol.NOT_SET symbol that represents a value has not yet been fetched: >>> with session.auto_populating(False): ... print user['email'] NOT_SET Whilst convenient for simple scripts, making many requests to the server for each attribute can slow execution of a script. To support optimisation the API includes methods for batch fetching attributes. Read about them in querying/projections and working_with_entities/populating. Entity types When a session has successfully connected to the server it will automatically download schema information and create appropriate classes <working_with_entities/entity_types> for use. This is important as different servers can support different entity types and configurations. This information is readily available and useful if you need to check that the entity types you expect are present. Here's how to print a list of all entity types registered for use in the current API session: >>> print session.types.keys() [u'Task', u'Shot', u'TypedContext', u'Sequence', u'Priority', u'Status', u'Project', u'User', u'Type', u'ObjectType'] Each entity type is backed by a customisable class <working_with_entities/entity_types> that further describes the entity type and the attributes that are available. Hint If you need to use an isinstance check, always go through the session as the classes are built dynamically: >>> isinstance(entity, session.types['Project']) Configuring plugins Plugins are used by the API to extend it with new functionality, such as locations <location> or adding convenience methods to understanding_sessions/entity_types. In addition to new API functionality, event plugins may also be used for event processing by listening to ftrack update events <handling_events> or adding custom functionality to ftrack by registering actions <action>. When starting a new Session either pass the plugins_paths to search explicitly or rely on the environment variable FTRACK_EVENT_PLUGIN_PATH. As each session is independent of others, you can configure plugins per session. The paths will be searched for plugins <plugin>, python files which expose a register function. These functions will be evaluated and can be used extend the API with new functionality, such as locations or actions. If you do not specify any override then the session will attempt to discover and use the default plugins. Plugins are discovered using ftrack_api.plugin.discover with the session instance passed as the sole positional argument. Most plugins should take the form of a mount function that then subscribes to specific events <handling_events> on the session: def configure_locations(event): '''Configure locations for session.''' session = event['data']['session'] # Find location(s) and customise instances. def register(session): '''Register plugin with *session*.''' session.event_hub.subscribe( 'topic=ftrack.api.session.configure-location', configure_locations ) Additional keyword arguments can be passed as plugin_arguments to the Session on instantiation. These are passed to the plugin register function if its signature supports them: # a_plugin.py def register(session, reticulate_splines=False): '''Register plugin with *session*.''' ... # main.py session = ftrack_api.Session( plugin_arguments={ 'reticulate_splines': True, 'some_other_argument': 42 } ) Lists of events which you can subscribe to in your plugins are available both for synchronous event published by the python API <event_list> and asynchronous events published by the server <ftrack:developing/events/list> Quick setup 1. Create a directory where plugins will be stored. Place any plugins you want loaded automatically in an API session here. [image] 2. Configure the FTRACK_EVENT_PLUGIN_PATH to point to the directory. Detailed setup Start out by creating a directory on your machine where you will store your plugins. Download example_plugin.py </resource/example_plugin.py> and place it in the directory. Open up a terminal window, and ensure that plugin is picked up when instantiating the session and manually setting the plugin_paths: >>> # Set up basic logging >>> import logging >>> logging.basicConfig() >>> plugin_logger = logging.getLogger('com.example.example-plugin') >>> plugin_logger.setLevel(logging.DEBUG) >>> >>> # Configure the API, loading plugins in the specified paths. >>> import ftrack_api >>> plugin_paths = ['/path/to/plugins'] >>> session = ftrack_api.Session(plugin_paths=plugin_paths) If everything is working as expected, you should see the following in the output: DEBUG:com.example.example-plugin:Plugin registered Instead of specifying the plugin paths when instantiating the session, you can also specify the FTRACK_EVENT_PLUGIN_PATH to point to the directory. To specify multiple directories, use the path separator for your operating system.

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

ynput__OpenPype

querying.rst

Subdoc to file

Querying

MIT License

ynput__OpenPype/openpype/modules/ftrack/python2_vendor/ftrack-python-api/doc/querying.rst

Querying The API provides a simple, but powerful query language in addition to iterating directly over entity attributes. Using queries can often substantially speed up your code as well as reduce the amount of code written. A query is issued using Session.query and returns a list of matching entities. The query always has a single target entity type that the query is built against. This means that you cannot currently retrieve back a list of different entity types in one query, though using projections <querying/projections> does allow retrieving related entities of a different type in one go. The syntax for a query is: select <projections> from <entity type> where <criteria> However, both the selection of projections and criteria are optional. This means the most basic query is just to fetch all entities of a particular type, such as all projects in the system: projects = session.query('Project') A query always returns a ~ftrack_api.query.QueryResult instance that acts like a list with some special behaviour. The main special behaviour is that the actual query to the server is not issued until you iterate or index into the query results: for project in projects: print project['name'] You can also explicitly call ~ftrack_api.query.QueryResult.all on the result set: projects = session.query('Project').all() Note This behaviour exists in order to make way for efficient paging and other optimisations in future. Using criteria to narrow results Often you will have some idea of the entities you want to retrieve. In this case you can optimise your code by not fetching more data than you need. To do this, add criteria to your query: projects = session.query('Project where status is active') Each criteria follows the form: <attribute> <operator> <value> You can inspect the entity type or instance to find out which attributes <working_with_entities/attributes> are available to filter on for a particular entity type. The list of operators <querying/criteria/operators> that can be applied and the types of values they expect is listed later on. Combining criteria Multiple criteria can be applied in a single expression by joining them with either and or or: projects = session.query( 'Project where status is active and name like "%thrones"' ) You can use parenthesis to control the precedence when compound criteria are used (by default and takes precedence): projects = session.query( 'Project where status is active and ' '(name like "%thrones" or full_name like "%thrones")' ) Filtering on relationships Filtering on relationships is also intuitively supported. Simply follow the relationship using a dotted notation: tasks_in_project = session.query( 'Task where project.id is "{0}"'.format(project['id']) ) This works even for multiple strides across relationships (though do note that excessive strides can affect performance): tasks_completed_in_project = session.query( 'Task where project.id is "{0}" and ' 'status.type.name is "Done"' .format(project['id']) ) The same works for collections (where each entity in the collection is compared against the subsequent condition): import arrow tasks_with_time_logged_today = session.query( 'Task where timelogs.start >= "{0}"'.format(arrow.now().floor('day')) ) In the above query, each Task that has at least one Timelog with a start time greater than the start of today is returned. When filtering on relationships, the conjunctions has and any can be used to specify how the criteria should be applied. This becomes important when querying using multiple conditions on collection relationships. The relationship condition can be written against the following form: <not?> <relationship> <has|any> (<criteria>) For optimal performance has should be used for scalar relationships when multiple conditions are involved. For example, to find notes by a specific author when only name is known: notes_written_by_jane_doe = session.query( 'Note where author has (first_name is "Jane" and last_name is "Doe")' ) This query could be written without has, giving the same results: notes_written_by_jane_doe = session.query( 'Note where author.first_name is "Jane" and author.last_name is "Doe"' ) any should be used for collection relationships. For example, to find all projects that have at least one metadata instance that has key=some_key and value=some_value the query would be: projects_where_some_key_is_some_value = session.query( 'Project where metadata any (key=some_key and value=some_value)' ) If the query was written without any, projects with one metadata matching key and another matching the value would be returned. any can also be used to query for empty relationship collections: users_without_timelogs = session.query( 'User where not timelogs any ()' ) Supported operators This is the list of currently supported operators: ------------------------------------------------------------------------ Operators Description Example -------------- --------------- ----------------------------------------- = is Exactly equal. name is "martin" != is_not Not exactly name is_not "martin" equal. > after Greater than start after "2015-06-01" greater_than exclusive. < before Less than end before "2015-06-01" less_than exclusive. >= Greater than bid >= 10 inclusive. <= Less than bid <= 10 inclusive. in One of. status.type.name in ("In Progress", "Done") not_in Not one of. status.name not_in ("Omitted", "On Hold") like Matches name like "%thrones" pattern. not_like Does not match name not_like "%thrones" pattern. has Test scalar author has (first_name is "Jane" and relationship. last_name is "Doe") any Test collection metadata any (key=some_key and relationship. value=some_value) ------------------------------------------------------------------------ Optimising using projections In understanding_sessions we mentioned auto-population <understanding_sessions/auto_population> of attribute values on access. This meant that when iterating over a lot of entities and attributes a large number of queries were being sent to the server. Ultimately, this can cause your code to run slowly: >>> projects = session.query('Project') >>> for project in projects: ... print( ... # Multiple queries issued here for each attribute accessed for ... # each project in the loop! ... '{project[full_name]} - {project[status][name]})' ... .format(project=project) ... ) Fortunately, there is an easy way to optimise. If you know what attributes you are interested in ahead of time you can include them in your query string as projections in order to fetch them in one go: >>> projects = session.query( ... 'select full_name, status.name from Project' ... ) >>> for project in projects: ... print( ... # No additional queries issued here as the values were already ... # loaded by the above query! ... '{project[full_name]} - {project[status][name]})' ... .format(project=project) ... ) Notice how this works for related entities as well. In the example above, we also fetched the name of each Status entity attached to a project in the same query, which meant that no further queries had to be issued when accessing those nested attributes. Note There are no arbitrary limits to the number (or depth) of projections, but do be aware that excessive projections can ultimately result in poor performance also. As always, it is about choosing the right tool for the job. You can also customise the working_with_entities/entity_types/default_projections to use for each entity type when none are specified in the query string.

# :coding: utf-8 # :copyright: Copyright (c) 2014 ftrack from __future__ import absolute_import import json import logging import collections import datetime import os import getpass import functools import itertools import distutils.version import hashlib import appdirs import threading import atexit import requests import requests.auth import arrow import clique import ftrack_api import ftrack_api.exception import ftrack_api.entity.factory import ftrack_api.entity.base import ftrack_api.entity.location import ftrack_api.cache import ftrack_api.symbol import ftrack_api.query import ftrack_api.attribute import ftrack_api.collection import ftrack_api.event.hub import ftrack_api.event.base import ftrack_api.plugin import ftrack_api.inspection import ftrack_api.operation import ftrack_api.accessor.disk import ftrack_api.structure.origin import ftrack_api.structure.entity_id import ftrack_api.accessor.server import ftrack_api._centralized_storage_scenario import ftrack_api.logging from ftrack_api.logging import LazyLogMessage as L try: from weakref import WeakMethod except ImportError: from ftrack_api._weakref import WeakMethod class SessionAuthentication(requests.auth.AuthBase): '''Attach ftrack session authentication information to requests.''' def __init__(self, api_key, api_user): '''Initialise with *api_key* and *api_user*.''' self.api_key = api_key self.api_user = api_user super(SessionAuthentication, self).__init__() def __call__(self, request): '''Modify *request* to have appropriate headers.''' request.headers.update({ 'ftrack-api-key': self.api_key, 'ftrack-user': self.api_user }) return request class Session(object): '''An isolated session for interaction with an ftrack server.''' def __init__( self, server_url=None, api_key=None, api_user=None, auto_populate=True, plugin_paths=None, cache=None, cache_key_maker=None, auto_connect_event_hub=None, schema_cache_path=None, plugin_arguments=None ): '''Initialise session. *server_url* should be the URL of the ftrack server to connect to including any port number. If not specified attempt to look up from :envvar:`FTRACK_SERVER`. *api_key* should be the API key to use for authentication whilst *api_user* should be the username of the user in ftrack to record operations against. If not specified, *api_key* should be retrieved from :envvar:`FTRACK_API_KEY` and *api_user* from :envvar:`FTRACK_API_USER`. If *auto_populate* is True (the default), then accessing entity attributes will cause them to be automatically fetched from the server if they are not already. This flag can be changed on the session directly at any time. *plugin_paths* should be a list of paths to search for plugins. If not specified, default to looking up :envvar:`FTRACK_EVENT_PLUGIN_PATH`. *cache* should be an instance of a cache that fulfils the :class:`ftrack_api.cache.Cache` interface and will be used as the cache for the session. It can also be a callable that will be called with the session instance as sole argument. The callable should return ``None`` if a suitable cache could not be configured, but session instantiation can continue safely. .. note:: The session will add the specified cache to a pre-configured layered cache that specifies the top level cache as a :class:`ftrack_api.cache.MemoryCache`. Therefore, it is unnecessary to construct a separate memory cache for typical behaviour. Working around this behaviour or removing the memory cache can lead to unexpected behaviour. *cache_key_maker* should be an instance of a key maker that fulfils the :class:`ftrack_api.cache.KeyMaker` interface and will be used to generate keys for objects being stored in the *cache*. If not specified, a :class:`~ftrack_api.cache.StringKeyMaker` will be used. If *auto_connect_event_hub* is True then embedded event hub will be automatically connected to the event server and allow for publishing and subscribing to **non-local** events. If False, then only publishing and subscribing to **local** events will be possible until the hub is manually connected using :meth:`EventHub.connect <ftrack_api.event.hub.EventHub.connect>`. .. note:: The event hub connection is performed in a background thread to improve session startup time. If a registered plugin requires a connected event hub then it should check the event hub connection status explicitly. Subscribing to events does *not* require a connected event hub. Enable schema caching by setting *schema_cache_path* to a folder path. If not set, :envvar:`FTRACK_API_SCHEMA_CACHE_PATH` will be used to determine the path to store cache in. If the environment variable is also not specified then a temporary directory will be used. Set to `False` to disable schema caching entirely. *plugin_arguments* should be an optional mapping (dict) of keyword arguments to pass to plugin register functions upon discovery. If a discovered plugin has a signature that is incompatible with the passed arguments, the discovery mechanism will attempt to reduce the passed arguments to only those that the plugin accepts. Note that a warning will be logged in this case. ''' super(Session, self).__init__() self.logger = logging.getLogger( __name__ + '.' + self.__class__.__name__ ) self._closed = False if server_url is None: server_url = os.environ.get('FTRACK_SERVER') if not server_url: raise TypeError( 'Required "server_url" not specified. Pass as argument or set ' 'in environment variable FTRACK_SERVER.' ) self._server_url = server_url if api_key is None: api_key = os.environ.get( 'FTRACK_API_KEY', # Backwards compatibility os.environ.get('FTRACK_APIKEY') ) if not api_key: raise TypeError( 'Required "api_key" not specified. Pass as argument or set in ' 'environment variable FTRACK_API_KEY.' ) self._api_key = api_key if api_user is None: api_user = os.environ.get('FTRACK_API_USER') if not api_user: try: api_user = getpass.getuser() except Exception: pass if not api_user: raise TypeError( 'Required "api_user" not specified. Pass as argument, set in ' 'environment variable FTRACK_API_USER or one of the standard ' 'environment variables used by Python\'s getpass module.' ) self._api_user = api_user # Currently pending operations. self.recorded_operations = ftrack_api.operation.Operations() self.record_operations = True self.cache_key_maker = cache_key_maker if self.cache_key_maker is None: self.cache_key_maker = ftrack_api.cache.StringKeyMaker() # Enforce always having a memory cache at top level so that the same # in-memory instance is returned from session. self.cache = ftrack_api.cache.LayeredCache([ ftrack_api.cache.MemoryCache() ]) if cache is not None: if callable(cache): cache = cache(self) if cache is not None: self.cache.caches.append(cache) self._managed_request = None self._request = requests.Session() self._request.auth = SessionAuthentication( self._api_key, self._api_user ) self.auto_populate = auto_populate # Fetch server information and in doing so also check credentials. self._server_information = self._fetch_server_information() # Now check compatibility of server based on retrieved information. self.check_server_compatibility() # Construct event hub and load plugins. self._event_hub = ftrack_api.event.hub.EventHub( self._server_url, self._api_user, self._api_key, ) self._auto_connect_event_hub_thread = None if auto_connect_event_hub is True: # Connect to event hub in background thread so as not to block main # session usage waiting for event hub connection. self._auto_connect_event_hub_thread = threading.Thread( target=self._event_hub.connect ) self._auto_connect_event_hub_thread.daemon = True self._auto_connect_event_hub_thread.start() # To help with migration from auto_connect_event_hub default changing # from True to False. self._event_hub._deprecation_warning_auto_connect = False # Register to auto-close session on exit. atexit.register(WeakMethod(self.close)) self._plugin_paths = plugin_paths if self._plugin_paths is None: self._plugin_paths = os.environ.get( 'FTRACK_EVENT_PLUGIN_PATH', '' ).split(os.pathsep) self._discover_plugins(plugin_arguments=plugin_arguments) # TODO: Make schemas read-only and non-mutable (or at least without # rebuilding types)? if schema_cache_path is not False: if schema_cache_path is None: schema_cache_path = appdirs.user_cache_dir() schema_cache_path = os.environ.get( 'FTRACK_API_SCHEMA_CACHE_PATH', schema_cache_path ) schema_cache_path = os.path.join( schema_cache_path, 'ftrack_api_schema_cache.json' ) self.schemas = self._load_schemas(schema_cache_path) self.types = self._build_entity_type_classes(self.schemas) ftrack_api._centralized_storage_scenario.register(self) self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.ready', data=dict( session=self ) ), synchronous=True ) def __enter__(self): '''Return session as context manager.''' return self def __exit__(self, exception_type, exception_value, traceback): '''Exit session context, closing session in process.''' self.close() @property def _request(self): '''Return request session. Raise :exc:`ftrack_api.exception.ConnectionClosedError` if session has been closed and connection unavailable. ''' if self._managed_request is None: raise ftrack_api.exception.ConnectionClosedError() return self._managed_request @_request.setter def _request(self, value): '''Set request session to *value*.''' self._managed_request = value @property def closed(self): '''Return whether session has been closed.''' return self._closed @property def server_information(self): '''Return server information such as server version.''' return self._server_information.copy() @property def server_url(self): '''Return server ulr used for session.''' return self._server_url @property def api_user(self): '''Return username used for session.''' return self._api_user @property def api_key(self): '''Return API key used for session.''' return self._api_key @property def event_hub(self): '''Return event hub.''' return self._event_hub @property def _local_cache(self): '''Return top level memory cache.''' return self.cache.caches[0] def check_server_compatibility(self): '''Check compatibility with connected server.''' server_version = self.server_information.get('version') if server_version is None: raise ftrack_api.exception.ServerCompatibilityError( 'Could not determine server version.' ) # Perform basic version check. if server_version!= 'dev': min_server_version = '3.3.11' if ( distutils.version.LooseVersion(min_server_version) > distutils.version.LooseVersion(server_version) ): raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0} incompatible with this version of the ' 'API which requires a server version >= {1}'.format( server_version, min_server_version ) ) def close(self): '''Close session. Close connections to server. Clear any pending operations and local cache. Use this to ensure that session is cleaned up properly after use. ''' if self.closed: self.logger.debug('Session already closed.') return self._closed = True self.logger.debug('Closing session.') if self.recorded_operations: self.logger.warning( 'Closing session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Close connections. self._request.close() self._request = None try: self.event_hub.disconnect() if self._auto_connect_event_hub_thread: self._auto_connect_event_hub_thread.join() except ftrack_api.exception.EventHubConnectionError: pass self.logger.debug('Session closed.') def reset(self): '''Reset session clearing local state. Clear all pending operations and expunge all entities from session. Also clear the local cache. If the cache used by the session is a :class:`~ftrack_api.cache.LayeredCache` then only clear top level cache. Otherwise, clear the entire cache. Plugins are not rediscovered or reinitialised, but certain plugin events are re-emitted to properly configure session aspects that are dependant on cache (such as location plugins). .. warning:: Previously attached entities are not reset in memory and will retain their state, but should not be used. Doing so will cause errors. ''' if self.recorded_operations: self.logger.warning( 'Resetting session with pending operations not persisted.' ) # Clear pending operations. self.recorded_operations.clear() # Clear top level cache (expected to be enforced memory cache). self._local_cache.clear() # Re-configure certain session aspects that may be dependant on cache. self._configure_locations() self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.reset', data=dict( session=self ) ), synchronous=True ) def auto_populating(self, auto_populate): '''Temporarily set auto populate to *auto_populate*. The current setting will be restored automatically when done. Example:: with session.auto_populating(False): print entity['name'] ''' return AutoPopulatingContext(self, auto_populate) def operation_recording(self, record_operations): '''Temporarily set operation recording to *record_operations*. The current setting will be restored automatically when done. Example:: with session.operation_recording(False): entity['name'] = 'change_not_recorded' ''' return OperationRecordingContext(self, record_operations) @property def created(self): '''Return list of newly created entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.CREATED ] @property def modified(self): '''Return list of locally modified entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.MODIFIED ] @property def deleted(self): '''Return list of deleted entities.''' entities = self._local_cache.values() states = ftrack_api.inspection.states(entities) return [ entity for (entity, state) in itertools.izip(entities, states) if state is ftrack_api.symbol.DELETED ] def reset_remote(self, reset_type, entity=None): '''Perform a server side reset. *reset_type* is a server side supported reset type, passing the optional *entity* to perform the option upon. Please refer to ftrack documentation for a complete list of supported server side reset types. ''' payload = { 'action':'reset_remote', 'reset_type': reset_type } if entity is not None: payload.update({ 'entity_type': entity.entity_type, 'entity_key': entity.get('id') }) result = self.call( [payload] ) return result[0]['data'] def create(self, entity_type, data=None, reconstructing=False): '''Create and return an entity of *entity_type* with initial *data*. If specified, *data* should be a dictionary of key, value pairs that should be used to populate attributes on the entity. If *reconstructing* is False then create a new entity setting appropriate defaults for missing data. If True then reconstruct an existing entity. Constructed entity will be automatically :meth:`merged <Session.merge>` into the session. ''' entity = self._create(entity_type, data, reconstructing=reconstructing) entity = self.merge(entity) return entity def _create(self, entity_type, data, reconstructing): '''Create and return an entity of *entity_type* with initial *data*.''' try: EntityTypeClass = self.types[entity_type] except KeyError: raise ftrack_api.exception.UnrecognisedEntityTypeError(entity_type) return EntityTypeClass(self, data=data, reconstructing=reconstructing) def ensure(self, entity_type, data, identifying_keys=None): '''Retrieve entity of *entity_type* with *data*, creating if necessary. *data* should be a dictionary of the same form passed to :meth:`create`. By default, check for an entity that has matching *data*. If *identifying_keys* is specified as a list of keys then only consider the values from *data* for those keys when searching for existing entity. If *data* is missing an identifying key then raise :exc:`KeyError`. If no *identifying_keys* specified then use all of the keys from the passed *data*. Raise :exc:`ValueError` if no *identifying_keys* can be determined. Each key should be a string. .. note:: Currently only top level scalars supported. To ensure an entity by looking at relationships, manually issue the :meth:`query` and :meth:`create` calls. If more than one entity matches the determined filter criteria then raise :exc:`~ftrack_api.exception.MultipleResultsFoundError`. If no matching entity found then create entity using supplied *data*. If a matching entity is found, then update it if necessary with *data*. .. note:: If entity created or updated then a :meth:`commit` will be issued automatically. If this behaviour is undesired, perform the :meth:`query` and :meth:`create` calls manually. Return retrieved or created entity. Example:: # First time, a new entity with `username=martin` is created. entity = session.ensure('User', {'username':'martin'}) # After that, the existing entity is retrieved. entity = session.ensure('User', {'username':'martin'}) # When existing entity retrieved, entity may also be updated to # match supplied data. entity = session.ensure( 'User', {'username':'martin', 'email':'martin@example.com'} ) ''' if not identifying_keys: identifying_keys = data.keys() self.logger.debug(L( 'Ensuring entity {0!r} with data {1!r} using identifying keys ' '{2!r}', entity_type, data, identifying_keys )) if not identifying_keys: raise ValueError( 'Could not determine any identifying data to check against ' 'when ensuring {0!r} with data {1!r}. Identifying keys: {2!r}' .format(entity_type, data, identifying_keys) ) expression = '{0} where'.format(entity_type) criteria = [] for identifying_key in identifying_keys: value = data[identifying_key] if isinstance(value, basestring): value = '"{0}"'.format(value) elif isinstance( value, (arrow.Arrow, datetime.datetime, datetime.date) ): # Server does not store microsecond or timezone currently so # need to strip from query. # TODO: When datetime handling improved, update this logic. value = ( arrow.get(value).naive.replace(microsecond=0).isoformat() ) value = '"{0}"'.format(value) criteria.append('{0} is {1}'.format(identifying_key, value)) expression = '{0} {1}'.format( expression,'and '.join(criteria) ) try: entity = self.query(expression).one() except ftrack_api.exception.NoResultFoundError: self.logger.debug('Creating entity as did not already exist.') # Create entity. entity = self.create(entity_type, data) self.commit() else: self.logger.debug('Retrieved matching existing entity.') # Update entity if required. updated = False for key, target_value in data.items(): if entity[key]!= target_value: entity[key] = target_value updated = True if updated: self.logger.debug('Updating existing entity to match new data.') self.commit() return entity def delete(self, entity): '''Mark *entity* for deletion.''' if self.record_operations: self.recorded_operations.push( ftrack_api.operation.DeleteEntityOperation( entity.entity_type, ftrack_api.inspection.primary_key(entity) ) ) def get(self, entity_type, entity_key): '''Return entity of *entity_type* with unique *entity_key*. First check for an existing entry in the configured cache, otherwise issue a query to the server. If no matching entity found, return None. ''' self.logger.debug(L('Get {0} with key {1}', entity_type, entity_key)) primary_key_definition = self.types[entity_type].primary_key_attributes if isinstance(entity_key, basestring): entity_key = [entity_key] if len(entity_key)!= len(primary_key_definition): raise ValueError( 'Incompatible entity_key {0!r} supplied. Entity type {1} ' 'expects a primary key composed of {2} values ({3}).' .format( entity_key, entity_type, len(primary_key_definition), ', '.join(primary_key_definition) ) ) entity = None try: entity = self._get(entity_type, entity_key) except KeyError: # Query for matching entity. self.logger.debug( 'Entity not present in cache. Issuing new query.' ) condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) expression = '{0} where ({1})'.format( entity_type,'and '.join(condition) ) results = self.query(expression).all() if results: entity = results[0] return entity def _get(self, entity_type, entity_key): '''Return cached entity of *entity_type* with unique *entity_key*. Raise :exc:`KeyError` if no such entity in the cache. ''' # Check cache for existing entity emulating # ftrack_api.inspection.identity result object to pass to key maker. cache_key = self.cache_key_maker.key( (str(entity_type), map(str, entity_key)) ) self.logger.debug(L( 'Checking cache for entity with key {0}', cache_key )) entity = self.cache.get(cache_key) self.logger.debug(L( 'Retrieved existing entity from cache: {0} at {1}', entity, id(entity) )) return entity def query(self, expression, page_size=500): '''Query against remote data according to *expression*. *expression* is not executed directly. Instead return an :class:`ftrack_api.query.QueryResult` instance that will execute remote call on access. *page_size* specifies the maximum page size that the returned query result object should be configured with. .. seealso:: :ref:`querying` ''' self.logger.debug(L('Query {0!r}', expression)) # Add in sensible projections if none specified. Note that this is # done here rather than on the server to allow local modification of the # schema setting to include commonly used custom attributes for example. # TODO: Use a proper parser perhaps? if not expression.startswith('select'): entity_type = expression.split(' ', 1)[0] EntityTypeClass = self.types[entity_type] projections = EntityTypeClass.default_projections expression ='select {0} from {1}'.format( ', '.join(projections), expression ) query_result = ftrack_api.query.QueryResult( self, expression, page_size=page_size ) return query_result def _query(self, expression): '''Execute *query* and return (records, metadata). Records will be a list of entities retrieved via the query and metadata a dictionary of accompanying information about the result set. ''' # TODO: Actually support batching several queries together. # TODO: Should batches have unique ids to match them up later. batch = [{ 'action': 'query', 'expression': expression }] # TODO: When should this execute? How to handle background=True? results = self.call(batch) # Merge entities into local cache and return merged entities. data = [] merged = dict() for entity in results[0]['data']: data.append(self._merge_recursive(entity, merged)) return data, results[0]['metadata'] def merge(self, value, merged=None): '''Merge *value* into session and return merged value. *merged* should be a mapping to record merges during run and should be used to avoid infinite recursion. If not set will default to a dictionary. ''' if merged is None: merged = {} with self.operation_recording(False): return self._merge(value, merged) def _merge(self, value, merged): '''Return merged *value*.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if isinstance(value, ftrack_api.entity.base.Entity): log_debug and self.logger.debug( 'Merging entity into session: {0} at {1}' .format(value, id(value)) ) return self._merge_entity(value, merged=merged) elif isinstance(value, ftrack_api.collection.Collection): log_debug and self.logger.debug( 'Merging collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection elif isinstance(value, ftrack_api.collection.MappedCollectionProxy): log_debug and self.logger.debug( 'Merging mapped collection into session: {0!r} at {1}' .format(value, id(value)) ) merged_collection = [] for entry in value.collection: merged_collection.append( self._merge(entry, merged=merged) ) return merged_collection else: return value def _merge_recursive(self, entity, merged=None): '''Merge *entity* and all its attributes recursivly.''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} attached = self.merge(entity, merged) for attribute in entity.attributes: # Remote attributes. remote_value = attribute.get_remote_value(entity) if isinstance( remote_value, ( ftrack_api.entity.base.Entity, ftrack_api.collection.Collection, ftrack_api.collection.MappedCollectionProxy ) ): log_debug and self.logger.debug( 'Merging remote value for attribute {0}.'.format(attribute) ) if isinstance(remote_value, ftrack_api.entity.base.Entity): self._merge_recursive(remote_value, merged=merged) elif isinstance( remote_value, ftrack_api.collection.Collection ): for entry in remote_value: self._merge_recursive(entry, merged=merged) elif isinstance( remote_value, ftrack_api.collection.MappedCollectionProxy ): for entry in remote_value.collection: self._merge_recursive(entry, merged=merged) return attached def _merge_entity(self, entity, merged=None): '''Merge *entity* into session returning merged entity. Merge is recursive so any references to other entities will also be merged. *entity* will never be modified in place. Ensure that the returned merged entity instance is used. ''' log_debug = self.logger.isEnabledFor(logging.DEBUG) if merged is None: merged = {} with self.auto_populating(False): entity_key = self.cache_key_maker.key( ftrack_api.inspection.identity(entity) ) # Check whether this entity has already been processed. attached_entity = merged.get(entity_key) if attached_entity is not None: log_debug and self.logger.debug( 'Entity already processed for key {0} as {1} at {2}' .format(entity_key, attached_entity, id(attached_entity)) ) return attached_entity else: log_debug and self.logger.debug( 'Entity not already processed for key {0}.' .format(entity_key) ) # Check for existing instance of entity in cache. log_debug and self.logger.debug( 'Checking for entity in cache with key {0}'.format(entity_key) ) try: attached_entity = self.cache.get(entity_key) log_debug and self.logger.debug( 'Retrieved existing entity from cache: {0} at {1}' .format(attached_entity, id(attached_entity)) ) except KeyError: # Construct new minimal instance to store in cache. attached_entity = self._create( entity.entity_type, {}, reconstructing=True ) log_debug and self.logger.debug( 'Entity not present in cache. Constructed new instance: ' '{0} at {1}'.format(attached_entity, id(attached_entity)) ) # Mark entity as seen to avoid infinite loops. merged[entity_key] = attached_entity changes = attached_entity.merge(entity, merged=merged) if changes: self.cache.set(entity_key, attached_entity) self.logger.debug('Cache updated with merged entity.') else: self.logger.debug( 'Cache not updated with merged entity as no differences ' 'detected.' ) return attached_entity def populate(self, entities, projections): '''Populate *entities* with attributes specified by *projections*. Any locally set values included in the *projections* will not be overwritten with the retrieved remote value. If this'synchronise' behaviour is required, first clear the relevant values on the entity by setting them to :attr:`ftrack_api.symbol.NOT_SET`. Deleting the key will have the same effect:: >>> print(user['username']) martin >>> del user['username'] >>> print(user['username']) Symbol(NOT_SET) .. note:: Entities that have been created and not yet persisted will be skipped as they have no remote values to fetch. ''' self.logger.debug(L( 'Populate {0!r} projections for {1}.', projections, entities )) if not isinstance( entities, (list, tuple, ftrack_api.query.QueryResult) ): entities = [entities] # TODO: How to handle a mixed collection of different entity types # Should probably fail, but need to consider handling hierarchies such # as User and Group both deriving from Resource. Actually, could just # proceed and ignore projections that are not present in entity type. entities_to_process = [] for entity in entities: if ftrack_api.inspection.state(entity) is ftrack_api.symbol.CREATED: # Created entities that are not yet persisted have no remote # values. Don't raise an error here as it is reasonable to # iterate over an entities properties and see that some of them # are NOT_SET. self.logger.debug(L( 'Skipping newly created entity {0!r} for population as no ' 'data will exist in the remote for this entity yet.', entity )) continue entities_to_process.append(entity) if entities_to_process: reference_entity = entities_to_process[0] entity_type = reference_entity.entity_type query ='select {0} from {1}'.format(projections, entity_type) primary_key_definition = reference_entity.primary_key_attributes entity_keys = [ ftrack_api.inspection.primary_key(entity).values() for entity in entities_to_process ] if len(primary_key_definition) > 1: # Composite keys require full OR syntax unfortunately. conditions = [] for entity_key in entity_keys: condition = [] for key, value in zip(primary_key_definition, entity_key): condition.append('{0} is "{1}"'.format(key, value)) conditions.append('({0})'.format('and '.join(condition))) query = '{0} where {1}'.format(query,'or '.join(conditions)) else: primary_key = primary_key_definition[0] if len(entity_keys) > 1: query = '{0} where {1} in ({2})'.format( query, primary_key, ','.join([ str(entity_key[0]) for entity_key in entity_keys ]) ) else: query = '{0} where {1} is {2}'.format( query, primary_key, str(entity_keys[0][0]) ) result = self.query(query) # Fetch all results now. Doing so will cause them to populate the # relevant entities in the cache. result.all() # TODO: Should we check that all requested attributes were # actually populated? If some weren't would we mark that to avoid # repeated calls or perhaps raise an error? # TODO: Make atomic. def commit(self): '''Commit all local changes to the server.''' batch = [] with self.auto_populating(False): for operation in self.recorded_operations: # Convert operation to payload. if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): # At present, data payload requires duplicating entity # type in data and also ensuring primary key added. entity_data = { '__entity_type__': operation.entity_type, } entity_data.update(operation.entity_key) entity_data.update(operation.entity_data) payload = OperationPayload({ 'action': 'create', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.UpdateEntityOperation ): entity_data = { # At present, data payload requires duplicating entity # type. '__entity_type__': operation.entity_type, operation.attribute_name: operation.new_value } payload = OperationPayload({ 'action': 'update', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values(), 'entity_data': entity_data }) elif isinstance( operation, ftrack_api.operation.DeleteEntityOperation ): payload = OperationPayload({ 'action': 'delete', 'entity_type': operation.entity_type, 'entity_key': operation.entity_key.values() }) else: raise ValueError( 'Cannot commit. Unrecognised operation type {0} ' 'detected.'.format(type(operation)) ) batch.append(payload) # Optimise batch. # TODO: Might be better to perform these on the operations list instead # so all operation contextual information available. # If entity was created and deleted in one batch then remove all # payloads for that entity. created = set() deleted = set() for payload in batch: if payload['action'] == 'create': created.add( (payload['entity_type'], str(payload['entity_key'])) ) elif payload['action'] == 'delete': deleted.add( (payload['entity_type'], str(payload['entity_key'])) ) created_then_deleted = deleted.intersection(created) if created_then_deleted: optimised_batch = [] for payload in batch: entity_type = payload.get('entity_type') entity_key = str(payload.get('entity_key')) if (entity_type, entity_key) in created_then_deleted: continue optimised_batch.append(payload) batch = optimised_batch # Remove early update operations so that only last operation on # attribute is applied server side. updates_map = set() for payload in reversed(batch): if payload['action'] in ('update', ): for key, value in payload['entity_data'].items(): if key == '__entity_type__': continue identity = ( payload['entity_type'], str(payload['entity_key']), key ) if identity in updates_map: del payload['entity_data'][key] else: updates_map.add(identity) # Remove NOT_SET values from entity_data. for payload in batch: entity_data = payload.get('entity_data', {}) for key, value in entity_data.items(): if value is ftrack_api.symbol.NOT_SET: del entity_data[key] # Remove payloads with redundant entity_data. optimised_batch = [] for payload in batch: entity_data = payload.get('entity_data') if entity_data is not None: keys = entity_data.keys() if not keys or keys == ['__entity_type__']: continue optimised_batch.append(payload) batch = optimised_batch # Collapse updates that are consecutive into one payload. Also, collapse # updates that occur immediately after creation into the create payload. optimised_batch = [] previous_payload = None for payload in batch: if ( previous_payload is not None and payload['action'] == 'update' and previous_payload['action'] in ('create', 'update') and previous_payload['entity_type'] == payload['entity_type'] and previous_payload['entity_key'] == payload['entity_key'] ): previous_payload['entity_data'].update(payload['entity_data']) continue else: optimised_batch.append(payload) previous_payload = payload batch = optimised_batch # Process batch. if batch: result = self.call(batch) # Clear recorded operations. self.recorded_operations.clear() # As optimisation, clear local values which are not primary keys to # avoid redundant merges when merging references. Note: primary keys # remain as needed for cache retrieval on new entities. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): for attribute in entity: if attribute not in entity.primary_key_attributes: del entity[attribute] # Process results merging into cache relevant data. for entry in result: if entry['action'] in ('create', 'update'): # Merge returned entities into local cache. self.merge(entry['data']) elif entry['action'] == 'delete': # TODO: Detach entity - need identity returned? # TODO: Expunge entity from cache. pass # Clear remaining local state, including local values for primary # keys on entities that were merged. with self.auto_populating(False): with self.operation_recording(False): for entity in self._local_cache.values(): entity.clear() def rollback(self): '''Clear all recorded operations and local state. Typically this would be used following a failed :meth:`commit` in order to revert the session to a known good state. Newly created entities not yet persisted will be detached from the session / purged from cache and no longer contribute, but the actual objects are not deleted from memory. They should no longer be used and doing so could cause errors. ''' with self.auto_populating(False): with self.operation_recording(False): # Detach all newly created entities and remove from cache. This # is done because simply clearing the local values of newly # created entities would result in entities with no identity as # primary key was local while not persisted. In addition, it # makes no sense for failed created entities to exist in session # or cache. for operation in self.recorded_operations: if isinstance( operation, ftrack_api.operation.CreateEntityOperation ): entity_key = str(( str(operation.entity_type), operation.entity_key.values() )) try: self.cache.remove(entity_key) except KeyError: pass # Clear locally stored modifications on remaining entities. for entity in self._local_cache.values(): entity.clear() self.recorded_operations.clear() def _fetch_server_information(self): '''Return server information.''' result = self.call([{'action': 'query_server_information'}]) return result[0] def _discover_plugins(self, plugin_arguments=None): '''Find and load plugins in search paths. Each discovered module should implement a register function that accepts this session as first argument. Typically the function should register appropriate event listeners against the session's event hub. def register(session): session.event_hub.subscribe( 'topic=ftrack.api.session.construct-entity-type', construct_entity_type ) *plugin_arguments* should be an optional mapping of keyword arguments and values to pass to plugin register functions upon discovery. ''' plugin_arguments = plugin_arguments or {} ftrack_api.plugin.discover( self._plugin_paths, [self], plugin_arguments ) def _read_schemas_from_cache(self, schema_cache_path): '''Return schemas and schema hash from *schema_cache_path*. *schema_cache_path* should be the path to the file containing the schemas in JSON format. ''' self.logger.debug(L( 'Reading schemas from cache {0!r}', schema_cache_path )) if not os.path.exists(schema_cache_path): self.logger.info(L( 'Cache file not found at {0!r}.', schema_cache_path )) return [], None with open(schema_cache_path, 'r') as schema_file: schemas = json.load(schema_file) hash_ = hashlib.md5( json.dumps(schemas, sort_keys=True) ).hexdigest() return schemas, hash_ def _write_schemas_to_cache(self, schemas, schema_cache_path): '''Write *schemas* to *schema_cache_path*. *schema_cache_path* should be a path to a file that the schemas can be written to in JSON format. ''' self.logger.debug(L( 'Updating schema cache {0!r} with new schemas.', schema_cache_path )) with open(schema_cache_path, 'w') as local_cache_file: json.dump(schemas, local_cache_file, indent=4) def _load_schemas(self, schema_cache_path): '''Load schemas. First try to load schemas from cache at *schema_cache_path*. If the cache is not available or the cache appears outdated then load schemas from server and store fresh copy in cache. If *schema_cache_path* is set to `False`, always load schemas from server bypassing cache. ''' local_schema_hash = None schemas = [] if schema_cache_path: try: schemas, local_schema_hash = self._read_schemas_from_cache( schema_cache_path ) except (IOError, TypeError, AttributeError, ValueError): # Catch any known exceptions when trying to read the local # schema cache to prevent API from being unusable. self.logger.exception(L( 'Schema cache could not be loaded from {0!r}', schema_cache_path )) # Use `dictionary.get` to retrieve hash to support older version of # ftrack server not returning a schema hash. server_hash = self._server_information.get( 'schema_hash', False ) if local_schema_hash!= server_hash: self.logger.debug(L( 'Loading schemas from server due to hash not matching.' 'Local: {0!r}!= Server: {1!r}', local_schema_hash, server_hash )) schemas = self.call([{'action': 'query_schemas'}])[0] if schema_cache_path: try: self._write_schemas_to_cache(schemas, schema_cache_path) except (IOError, TypeError): self.logger.exception(L( 'Failed to update schema cache {0!r}.', schema_cache_path )) else: self.logger.debug(L( 'Using cached schemas from {0!r}', schema_cache_path )) return schemas def _build_entity_type_classes(self, schemas): '''Build default entity type classes.''' fallback_factory = ftrack_api.entity.factory.StandardFactory() classes = {} for schema in schemas: results = self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.construct-entity-type', data=dict( schema=schema, schemas=schemas ) ), synchronous=True ) results = [result for result in results if result is not None] if not results: self.logger.debug(L( 'Using default StandardFactory to construct entity type ' 'class for "{0}"', schema['id'] )) entity_type_class = fallback_factory.create(schema) elif len(results) > 1: raise ValueError( 'Expected single entity type to represent schema "{0}" but ' 'received {1} entity types instead.' .format(schema['id'], len(results)) ) else: entity_type_class = results[0] classes[entity_type_class.entity_type] = entity_type_class return classes def _configure_locations(self): '''Configure locations.''' # First configure builtin locations, by injecting them into local cache. # Origin. location = self.create( 'Location', data=dict( name='ftrack.origin', id=ftrack_api.symbol.ORIGIN_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.OriginLocationMixin, name='OriginLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 100 # Unmanaged. location = self.create( 'Location', data=dict( name='ftrack.unmanaged', id=ftrack_api.symbol.UNMANAGED_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() # location.resource_identifier_transformer = ( # ftrack_api.resource_identifier_transformer.internal.InternalResourceIdentifierTransformer(session) # ) location.priority = 90 # Review. location = self.create( 'Location', data=dict( name='ftrack.review', id=ftrack_api.symbol.REVIEW_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.UnmanagedLocationMixin, name='UnmanagedLocation' ) location.accessor = ftrack_api.accessor.disk.DiskAccessor(prefix='') location.structure = ftrack_api.structure.origin.OriginStructure() location.priority = 110 # Server. location = self.create( 'Location', data=dict( name='ftrack.server', id=ftrack_api.symbol.SERVER_LOCATION_ID ), reconstructing=True ) ftrack_api.mixin( location, ftrack_api.entity.location.ServerLocationMixin, name='ServerLocation' ) location.accessor = ftrack_api.accessor.server._ServerAccessor( session=self ) location.structure = ftrack_api.structure.entity_id.EntityIdStructure() location.priority = 150 # Master location based on server scenario. storage_scenario = self.server_information.get('storage_scenario') if ( storage_scenario and storage_scenario.get('scenario') ): self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.storage-scenario.activate', data=dict( storage_scenario=storage_scenario ) ), synchronous=True ) # Next, allow further configuration of locations via events. self.event_hub.publish( ftrack_api.event.base.Event( topic='ftrack.api.session.configure-location', data=dict( session=self ) ), synchronous=True ) @ftrack_api.logging.deprecation_warning( 'Session._call is now available as public method Session.call. The ' 'private method will be removed in version 2.0.' ) def _call(self, data): '''Make request to server with *data* batch describing the actions. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.call(data) def call(self, data): '''Make request to server with *data* batch describing the actions.''' url = self._server_url + '/api' headers = { 'content-type': 'application/json', 'accept': 'application/json' } data = self.encode(data, entity_attribute_strategy='modified_only') self.logger.debug(L('Calling server {0} with {1!r}', url, data)) response = self._request.post( url, headers=headers, data=data ) self.logger.debug(L('Call took: {0}', response.elapsed.total_seconds())) self.logger.debug(L('Response: {0!r}', response.text)) try: result = self.decode(response.text) except Exception: error_message = ( 'Server reported error in unexpected format. Raw error was: {0}' .format(response.text) ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) else: if 'exception' in result: # Handle exceptions. error_message = 'Server reported error: {0}({1})'.format( result['exception'], result['content'] ) self.logger.exception(error_message) raise ftrack_api.exception.ServerError(error_message) return result def encode(self, data, entity_attribute_strategy='set_only'): '''Return *data* encoded as JSON formatted string. *entity_attribute_strategy* specifies how entity attributes should be handled. The following strategies are available: * *all* - Encode all attributes, loading any that are currently NOT_SET. * *set_only* - Encode only attributes that are currently set without loading any from the remote. * *modified_only* - Encode only attributes that have been modified locally. * *persisted_only* - Encode only remote (persisted) attribute values. ''' entity_attribute_strategies = ( 'all','set_only','modified_only', 'persisted_only' ) if entity_attribute_strategy not in entity_attribute_strategies: raise ValueError( 'Unsupported entity_attribute_strategy "{0}". Must be one of ' '{1}'.format( entity_attribute_strategy, ', '.join(entity_attribute_strategies) ) ) return json.dumps( data, sort_keys=True, default=functools.partial( self._encode, entity_attribute_strategy=entity_attribute_strategy ) ) def _encode(self, item, entity_attribute_strategy='set_only'): '''Return JSON encodable version of *item*. *entity_attribute_strategy* specifies how entity attributes should be handled. See :meth:`Session.encode` for available strategies. ''' if isinstance(item, (arrow.Arrow, datetime.datetime, datetime.date)): return { '__type__': 'datetime', 'value': item.isoformat() } if isinstance(item, OperationPayload): data = dict(item.items()) if "entity_data" in data: for key, value in data["entity_data"].items(): if isinstance(value, ftrack_api.entity.base.Entity): data["entity_data"][key] = self.entity_reference(value) return data if isinstance(item, ftrack_api.entity.base.Entity): data = self.entity_reference(item) with self.auto_populating(True): for attribute in item.attributes: value = ftrack_api.symbol.NOT_SET if entity_attribute_strategy == 'all': value = attribute.get_value(item) elif entity_attribute_strategy =='set_only': if attribute.is_set(item): value = attribute.get_local_value(item) if value is ftrack_api.symbol.NOT_SET: value = attribute.get_remote_value(item) elif entity_attribute_strategy =='modified_only': if attribute.is_modified(item): value = attribute.get_local_value(item) elif entity_attribute_strategy == 'persisted_only': if not attribute.computed: value = attribute.get_remote_value(item) if value is not ftrack_api.symbol.NOT_SET: if isinstance( attribute, ftrack_api.attribute.ReferenceAttribute ): if isinstance(value, ftrack_api.entity.base.Entity): value = self.entity_reference(value) data[attribute.name] = value return data if isinstance( item, ftrack_api.collection.MappedCollectionProxy ): # Use proxied collection for serialisation. item = item.collection if isinstance(item, ftrack_api.collection.Collection): data = [] for entity in item: data.append(self.entity_reference(entity)) return data raise TypeError('{0!r} is not JSON serializable'.format(item)) def entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. ''' reference = { '__entity_type__': entity.entity_type } with self.auto_populating(False): reference.update(ftrack_api.inspection.primary_key(entity)) return reference @ftrack_api.logging.deprecation_warning( 'Session._entity_reference is now available as public method ' 'Session.entity_reference. The private method will be removed ' 'in version 2.0.' ) def _entity_reference(self, entity): '''Return entity reference that uniquely identifies *entity*. Return a mapping containing the __entity_type__ of the entity along with the key, value pairs that make up it's primary key. .. note:: This private method is now available as public method :meth:`entity_reference`. This alias remains for backwards compatibility, but will be removed in version 2.0. ''' return self.entity_reference(entity) def decode(self, string): '''Return decoded JSON *string* as Python object.''' with self.operation_recording(False): return json.loads(string, object_hook=self._decode) def _decode(self, item): '''Return *item* transformed into appropriate representation.''' if isinstance(item, collections.Mapping): if '__type__' in item: if item['__type__'] == 'datetime': item = arrow.get(item['value']) elif '__entity_type__' in item: item = self._create( item['__entity_type__'], item, reconstructing=True ) return item def _get_locations(self, filter_inaccessible=True): '''Helper to returns locations ordered by priority. If *filter_inaccessible* is True then only accessible locations will be included in result. ''' # Optimise this call. locations = self.query('Location') # Filter. if filter_inaccessible: locations = filter( lambda location: location.accessor, locations ) # Sort by priority. locations = sorted( locations, key=lambda location: location.priority ) return locations def pick_location(self, component=None): '''Return suitable location to use. If no *component* specified then return highest priority accessible location. Otherwise, return highest priority accessible location that *component* is available in. Return None if no suitable location could be picked. ''' if component: return self.pick_locations([component])[0] else: locations = self._get_locations() if locations: return locations[0] else: return None def pick_locations(self, components): '''Return suitable locations for *components*. Return list of locations corresponding to *components* where each picked location is the highest priority accessible location for that component. If a component has no location available then its corresponding entry will be None. ''' candidate_locations = self._get_locations() availabilities = self.get_component_availabilities( components, locations=candidate_locations ) locations = [] for component, availability in zip(components, availabilities): location = None for candidate_location in candidate_locations: if availability.get(candidate_location['id']) > 0.0: location = candidate_location break locations.append(location) return locations def create_component( self, path, data=None, location='auto' ): '''Create a new component from *path* with additional *data* .. note:: This is a helper method. To create components manually use the standard :meth:`Session.create` method. *path* can be a string representing a filesystem path to the data to use for the component. The *path* can also be specified as a sequence string, in which case a sequence component with child components for each item in the sequence will be created automatically. The accepted format for a sequence is '{head}{padding}{tail} [{ranges}]'. For example:: '/path/to/file.%04d.ext [1-5, 7, 8, 10-20]' .. seealso:: `Clique documentation <http://clique.readthedocs.org>`_ *data* should be a dictionary of any additional data to construct the component with (as passed to :meth:`Session.create`). If *location* is specified then automatically add component to that location. The default of 'auto' will automatically pick a suitable location to add the component to if one is available. To not add to any location specifiy locations as None. .. note:: A :meth:`Session.commit<ftrack_api.session.Session.commit>` may be automatically issued as part of the components registration in the location. ''' if data is None: data = {} if location == 'auto': # Check if the component name matches one of the ftrackreview # specific names. Add the component to the ftrack.review location if # so. This is used to not break backwards compatibility. if data.get('name') in ( 'ftrackreview-mp4', 'ftrackreview-webm', 'ftrackreview-image' ): location = self.get( 'Location', ftrack_api.symbol.REVIEW_LOCATION_ID ) else: location = self.pick_location() try: collection = clique.parse(path) except ValueError: # Assume is a single file. if'size' not in data: data['size'] = self._get_filesystem_size(path) data.setdefault('file_type', os.path.splitext(path)[-1]) return self._create_component( 'FileComponent', path, data, location ) else: # Calculate size of container and members. member_sizes = {} container_size = data.get('size') if container_size is not None: if len(collection.indexes) > 0: member_size = int( round(container_size / len(collection.indexes)) ) for item in collection: member_sizes[item] = member_size else: container_size = 0 for item in collection: member_sizes[item] = self._get_filesystem_size(item) container_size += member_sizes[item] # Create sequence component container_path = collection.format('{head}{padding}{tail}') data.setdefault('padding', collection.padding) data.setdefault('file_type', os.path.splitext(container_path)[-1]) data.setdefault('size', container_size) container = self._create_component( 'SequenceComponent', container_path, data, location=None ) # Create member components for sequence. for member_path in collection: member_data = { 'name': collection.match(member_path).group('index'), 'container': container, 'size': member_sizes[member_path], 'file_type': os.path.splitext(member_path)[-1] } component = self._create_component( 'FileComponent', member_path, member_data, location=None ) container['members'].append(component) if location: origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) location.add_component( container, origin_location, recursive=True ) return container def _create_component(self, entity_type, path, data, location): '''Create and return component. See public function :py:func:`createComponent` for argument details. ''' component = self.create(entity_type, data) # Add to special origin location so that it is possible to add to other # locations. origin_location = self.get( 'Location', ftrack_api.symbol.ORIGIN_LOCATION_ID ) origin_location.add_component(component, path, recursive=False) if location: location.add_component(component, origin_location, recursive=False) return component def _get_filesystem_size(self, path): '''Return size from *path*''' try: size = os.path.getsize(path) except OSError: size = 0 return size def get_component_availability(self, component, locations=None): '''Return availability of *component*. If *locations* is set then limit result to availability of *component* in those *locations*. Return a dictionary of {location_id:percentage_availability} ''' return self.get_component_availabilities( [component], locations=locations )[0] def get_component_availabilities(self, components, locations=None): '''Return availabilities of *components*. If *locations* is set then limit result to availabilities of *components* in those *locations*. Return a list of dictionaries of {location_id:percentage_availability}. The list indexes correspond to those of *components*. ''' availabilities = [] if locations is None: locations = self.query('Location') # Separate components into two lists, those that are containers and # those that are not, so that queries can be optimised. standard_components = [] container_components = [] for component in components: if'members' in component.keys(): container_components.append(component) else: standard_components.append(component) # Perform queries. if standard_components: self.populate( standard_components, 'component_locations.location_id' ) if container_components: self.populate( container_components, 'members, component_locations.location_id' ) base_availability = {} for location in locations: base_availability[location['id']] = 0.0 for component in components: availability = base_availability.copy() availabilities.append(availability) is_container ='members' in component.keys() if is_container and len(component['members']): member_availabilities = self.get_component_availabilities( component['members'], locations=locations ) multiplier = 1.0 / len(component['members']) for member, member_availability in zip( component['members'], member_availabilities ): for location_id, ratio in member_availability.items(): availability[location_id] += ( ratio * multiplier ) else: for component_location in component['component_locations']: location_id = component_location['location_id'] if location_id in availability: availability[location_id] = 100.0 for location_id, percentage in availability.items(): # Avoid quantization error by rounding percentage and clamping # to range 0-100. adjusted_percentage = round(percentage, 9) adjusted_percentage = max(0.0, min(adjusted_percentage, 100.0)) availability[location_id] = adjusted_percentage return availabilities @ftrack_api.logging.deprecation_warning( 'Session.delayed_job has been deprecated in favour of session.call. ' 'Please refer to the release notes for more information.' ) def delayed_job(self, job_type): '''Execute a delayed job on the server, a `ftrack.entity.job.Job` is returned. *job_type* should be one of the allowed job types. There is currently only one remote job type "SYNC_USERS_LDAP". ''' if job_type not in (ftrack_api.symbol.JOB_SYNC_USERS_LDAP, ): raise ValueError( u'Invalid Job type: {0}.'.format(job_type) ) operation = { 'action': 'delayed_job', 'job_type': job_type.name } try: result = self.call( [operation] )[0] except ftrack_api.exception.ServerError as error: raise return result['data'] def get_widget_url(self, name, entity=None, theme=None): '''Return an authenticated URL for widget with *name* and given options. The returned URL will be authenticated using a token which will expire after 6 minutes. *name* should be the name of the widget to return and should be one of 'info', 'tasks' or 'tasks_browser'. Certain widgets require an entity to be specified. If so, specify it by setting *entity* to a valid entity instance. *theme* sets the theme of the widget and can be either 'light' or 'dark' (defaulting to 'dark' if an invalid option given). ''' operation = { 'action': 'get_widget_url', 'name': name, 'theme': theme } if entity: operation['entity_type'] = entity.entity_type operation['entity_key'] = ( ftrack_api.inspection.primary_key(entity).values() ) try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_widget_url\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "get_widget_url", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise else: return result[0]['widget_url'] def encode_media(self, media, version_id=None, keep_original='auto'): '''Return a new Job that encode *media* to make it playable in browsers. *media* can be a path to a file or a FileComponent in the ftrack.server location. The job will encode *media* based on the file type and job data contains information about encoding in the following format:: { 'output': [{ 'format': 'video/mp4', 'component_id': 'e2dc0524-b576-11d3-9612-080027331d74' }, { 'format': 'image/jpeg', 'component_id': '07b82a97-8cf9-11e3-9383-20c9d081909b' }], 'source_component_id': 'e3791a09-7e11-4792-a398-3d9d4eefc294', 'keep_original': True } The output components are associated with the job via the job_components relation. An image component will always be generated if possible that can be used as a thumbnail. If *media* is a file path, a new source component will be created and added to the ftrack server location and a call to :meth:`commit` will be issued. If *media* is a FileComponent, it will be assumed to be in available in the ftrack.server location. If *version_id* is specified, the new components will automatically be associated with the AssetVersion. Otherwise, the components will not be associated to a version even if the supplied *media* belongs to one. A server version of 3.3.32 or higher is required for the version_id argument to function properly. If *keep_original* is not set, the original media will be kept if it is a FileComponent, and deleted if it is a file path. You can specify True or False to change this behavior. ''' if isinstance(media, basestring): # Media is a path to a file. server_location = self.get( 'Location', ftrack_api.symbol.SERVER_LOCATION_ID ) if keep_original == 'auto': keep_original = False component_data = None if keep_original: component_data = dict(version_id=version_id) component = self.create_component( path=media, data=component_data, location=server_location ) # Auto commit to ensure component exists when sent to server. self.commit() elif ( hasattr(media, 'entity_type') and media.entity_type in ('FileComponent',) ): # Existing file component. component = media if keep_original == 'auto': keep_original = True else: raise ValueError( 'Unable to encode media of type: {0}'.format(type(media)) ) operation = { 'action': 'encode_media', 'component_id': component['id'], 'version_id': version_id, 'keep_original': keep_original } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'encode_media\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support "encode_media", ' 'please update server and try again.'.format( self.server_information.get('version') ) ) else: raise return self.get('Job', result[0]['job_id']) def get_upload_metadata( self, component_id, file_name, file_size, checksum=None ): '''Return URL and headers used to upload data for *component_id*. *file_name* and *file_size* should match the components details. The returned URL should be requested using HTTP PUT with the specified headers. The *checksum* is used as the Content-MD5 header and should contain the base64-encoded 128-bit MD5 digest of the message (without the headers) according to RFC 1864. This can be used as a message integrity check to verify that the data is the same data that was originally sent. ''' operation = { 'action': 'get_upload_metadata', 'component_id': component_id, 'file_name': file_name, 'file_size': file_size, 'checksum': checksum } try: result = self.call([operation]) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'get_upload_metadata\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"get_upload_metadata", please update server and try ' 'again.'.format( self.server_information.get('version') ) ) else: raise return result[0] def send_user_invite(self, user): '''Send a invitation to the provided *user*. *user* is a User instance ''' self.send_user_invites( [user] ) def send_user_invites(self, users): '''Send a invitation to the provided *user*. *users* is a list of User instances ''' operations = [] for user in users: operations.append( { 'action':'send_user_invite', 'user_id': user['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_user_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_user_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise def send_review_session_invite(self, invitee): '''Send an invite to a review session to *invitee*. *invitee* is a instance of ReviewSessionInvitee. .. note:: The *invitee* must be committed. ''' self.send_review_session_invites([invitee]) def send_review_session_invites(self, invitees): '''Send an invite to a review session to a list of *invitees*. *invitee* is a list of ReviewSessionInvitee objects. .. note:: All *invitees* must be committed. ''' operations = [] for invitee in invitees: operations.append( { 'action':'send_review_session_invite', 'review_session_invitee_id': invitee['id'] } ) try: self.call(operations) except ftrack_api.exception.ServerError as error: # Raise informative error if the action is not supported. if 'Invalid action u\'send_review_session_invite\'' in error.message: raise ftrack_api.exception.ServerCompatibilityError( 'Server version {0!r} does not support ' '"send_review_session_invite", please update server and ' 'try again.'.format( self.server_information.get('version') ) ) else: raise class AutoPopulatingContext(object): '''Context manager for temporary change of session auto_populate value.''' def __init__(self, session, auto_populate): '''Initialise context.''' super(AutoPopulatingContext, self).__init__() self._session = session self._auto_populate = auto_populate self._current_auto_populate = None def __enter__(self): '''Enter context switching to desired auto populate setting.''' self._current_auto_populate = self._session.auto_populate self._session.auto_populate = self._auto_populate def __exit__(self, exception_type, exception_value, traceback): '''Exit context resetting auto populate to original setting.''' self._session.auto_populate = self._current_auto_populate class OperationRecordingContext(object): '''Context manager for temporary change of session record_operations.''' def __init__(self, session, record_operations): '''Initialise context.''' super(OperationRecordingContext, self).__init__() self._session = session self._record_operations = record_operations self._current_record_operations = None def __enter__(self): '''Enter context.''' self._current_record_operations = self._session.record_operations self._session.record_operations = self._record_operations def __exit__(self, exception_type, exception_value, traceback): '''Exit context.''' self._session.record_operations = self._current_record_operations class OperationPayload(collections.MutableMapping): '''Represent operation payload.''' def __init__(self, *args, **kwargs): '''Initialise payload.''' super(OperationPayload, self).__init__() self._data = dict() self.update(dict(*args, **kwargs)) def __str__(self): '''Return string representation.''' return '<{0} {1}>'.format( self.__class__.__name__, str(self._data) ) def __getitem__(self, key): '''Return value for *key*.''' return self._data[key] def __setitem__(self, key, value): '''Set *value* for *key*.''' self._data[key] = value def __delitem__(self, key): '''Remove *key*.''' del self._data[key] def __iter__(self): '''Iterate over all keys.''' return iter(self._data) def __len__(self): '''Return count of keys.''' return len(self._data)

westpa__westpa

ploterr.rst

Manual

Ploterr command

MIT License

westpa__westpa/doc/documentation/cli/ploterr.rst

ploterr usage: ploterr [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version] {help,d.kinetics,d.probs,rw.probs,rw.kinetics,generic} ... Plots error ranges for weighted ensemble datasets. Command-line options optional arguments: -h, --help show this help message and exit general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --debug enable extra checks and emit copious information --version show program's version number and exit supported input formats: {help,d.kinetics,d.probs,rw.probs,rw.kinetics,generic} help print help for this command or individual subcommands d.kinetics output of w_direct kinetics d.probs output of w_direct probs rw.probs output of w_reweight probs rw.kinetics output of w_reweight kinetics generic arbitrary HDF5 file and dataset

import logging import os import re import h5py import numpy as np from westpa.tools import WESTMasterCommand, WESTSubcommand, ProgressIndicatorComponent, Plotter from westpa.core import h5io if os.environ.get('DISPLAY') is not None: from matplotlib import pyplot log = logging.getLogger('ploterrs') class CommonPloterrs(WESTSubcommand): def __init__(self, parent): super().__init__(parent) self.progress = ProgressIndicatorComponent() self.xscale = None self.yscale = None self.xrange = None self.yrange = None self.xlabel = None self.ylabel = None self.title = None self.plot_options_group = None def add_args(self, parser): self.progress.add_args(parser) pogroup = self.plot_options_group = parser.add_argument_group('plot options') pogroup.add_argument( '--xscale', choices=['linear', 'log','symlog'], default='linear', help='''Use "linear", "log", or "symlog" scaling for the x axis. (Default: %(default)s).''', ) pogroup.add_argument( '--yscale', choices=['linear', 'log','symlog'], default='linear', help='''Use "linear", "log", or "symlog" scaling for the y axis. (Default: %(default)s).''', ) pogroup.add_argument( '--xrange', help='''Restrict X range to XRANGE, which must be formatted as "xmin,xmax". (Default: determined by input data.)''', ) pogroup.add_argument( '--yrange', help='''Restrict Y range to YRANGE, which must be formatted as "ymin,ymax". (Default: determined by input data.)''', ) pogroup.add_argument('--xlabel', help='''Use XLABEL for the x-axis label. (Default: varies.)''') pogroup.add_argument('--ylabel', help='''Use YLABEL for the y-axis label. (Default: varies.)''') pogroup.add_argument('--title', help='''Use TITLE for the plot title. (Default: varies.)''') pogroup.add_argument('--terminal', '-t', dest='plotting', action='store_true', help='''Plot output in terminal.''') def process_args(self, args): self.progress.process_args(args) if args.xrange: self.xrange = self.parse_range(args.xrange) if args.yrange: self.yrange = self.parse_range(args.yrange) self.xscale = args.xscale self.yscale = args.yscale self.xlabel = args.xlabel or 'Iteration' self.ylabel = args.ylabel self.title = args.title if args.plotting or os.environ.get('DISPLAY') is None: self.interface = 'text' else: self.interface ='matplotlib' def parse_range(self, rangespec): try: (lbt, ubt) = rangespec.split(',') return float(lbt), float(ubt) except (ValueError, TypeError) as e: raise ValueError('invalid range specification {!r}: {!s}'.format(rangespec, e)) def do_plot(self, data, output_filename, title=None, x_range=None, y_range=None, x_label=None, y_label=None): if not output_filename: return title = title or self.title x_range = x_range or self.xrange y_range = y_range or self.yrange x_label = x_label or self.xlabel y_label = y_label or self.ylabel iters = data['iter_stop'] - 1 pyplot.figure() pyplot.plot(iters, data['expected'], color='black') pyplot.plot(iters, data['ci_lbound'], color='gray') pyplot.plot(iters, data['ci_ubound'], color='gray') pyplot.gca().set_xscale(self.xscale) pyplot.gca().set_yscale(self.yscale) if title: pyplot.title(title) if x_range is not None: pyplot.xlim(x_range) if y_range is not None: pyplot.ylim(y_range) if x_label: pyplot.xlabel(x_label) if y_label: pyplot.ylabel(y_label) pyplot.savefig(output_filename) class GenericIntervalSubcommand(CommonPloterrs): description = '''\ Plots generic expectation/CI data. A path to the HDF5 file and the dataset within it must be provided. This path takes the form **FILENAME/PATH[SLICE]**. If the dataset is not a vector (one dimensional) then a slice must be provided. For example, to access the state 0 to state 1 rate evolution calculated by ``w_kinavg``, one would use ``kinavg.h5/rate_evolution[:,0,1]``. ----------------------------------------------------------------------------- Command-line arguments ----------------------------------------------------------------------------- ''' subcommand = 'generic' help_text = 'arbitrary HDF5 file and dataset' def __init__(self, parent): super().__init__(parent) self.h5file = None self.h5dset = None self.dset_slice = None self.output_filename = None def add_args(self, parser): iogroup = parser.add_argument_group('input/output options') iogroup.add_argument( '-o', '--output', default='errbars.pdf', help='''Write plot to OUTPUT (default: %(default)s), whose format will be determined by filename extension.''', ) iogroup.add_argument( 'dsspec', help='''Use data located at DSSPEC, which must be formatted as FILENAME/PATH[SLICE]. FILENAME is the HDF5 file to read, PATH is the HDF5 path to the dataset, and SLICE, if provided, must be the Numpy-style slice (including brackets) which selects a vector of data of the appropriate type.''', ) def process_args(self, args): self.output_filename = args.output (pathname, slicestr) = re.search(r'([^[]+)(\[[^\]]+\])?$', args.dsspec).groups() if slicestr: sl = eval('np.index_exp' + slicestr) else: sl = np.index_exp[...] self.h5file, self.h5dset = h5io.resolve_filepath(pathname, mode='r') self.dset_slice = sl def load_and_validate_data(self): reqd_fields = set(['iter_start', 'iter_stop', 'expected', 'ci_lbound', 'ci_ubound']) self.progress.indicator.new_operation('loading data') data = self.h5dset[self.dset_slice] if data.ndim!= 1: raise TypeError('dataset to be plotted must be 1-dimensional') try: fieldnames = set(data.dtype.fields.keys()) except AttributeError: raise TypeError('dataset has inappropriate type') else: if len(fieldnames & reqd_fields) < len(reqd_fields): raise TypeError('dataset does not contain correct fields') return data def go(self): with self.progress.indicator: data = self.load_and_validate_data() self.progress.indicator.new_operation('plotting') self.do_plot(data, self.output_filename) class DirectKinetics(CommonPloterrs): subcommand = 'd.kinetics' help_text = 'output of w_direct kinetics' input_filename = 'direct.h5' flux_output_filename = 'flux_evolution_d_{state_label}.pdf' rate_output_filename = 'rate_evolution_d_{istate_label}_{fstate_label}.pdf' description = '''\ Plot evolution of state-to-state rates and total flux into states as generated by ``w_{direct/reweight} kinetics`` (when used with the ``--evolution-mode`` option). Plots are generated for all rates/fluxes calculated. Output filenames require (and plot titles and axis labels support) substitution based on which flux/rate is being plotted: istate_label, fstate_label *(String, for rates)* Names of the initial and final states, as originally given to ``w_assign``. istate_index, fstate_index *(Integer, for rates)* Indices of initial and final states. state_label *(String, for fluxes)* Name of state state_index *(Integer, for fluxes)* Index of state ''' def __init__(self, parent): super().__init__(parent) self.kinavg_file = None self.dset_slice = None self.rate_output_pattern = None self.flux_output_pattern = None self.state_labels = None def add_args(self, parser): iogroup = parser.add_argument_group('input/output') iogroup.add_argument( '-i', '--input', default=self.input_filename, help='''Read kinetics results from INPUT (default: %(default)s).''' ) iogroup.add_argument( '--rate-output', default=self.rate_output_filename, help='''Filename pattern for rate evolution output. See above for valid field names. (Default: %(default)r).''', ) iogroup.add_argument( '--flux-output', default=self.flux_output_filename, help='''Filename pattern for flux evolution output. See above for valid field names. (Default: %(default)r).''', ) def process_args(self, args): self.kinavg_file = h5py.File(args.input, 'r') self.state_labels = list(self.kinavg_file['state_labels'][...]) self.rate_output_pattern = args.rate_output self.flux_output_pattern = args.flux_output def plot_flux(self, istate): label = self.state_labels[istate] data = self.kinavg_file['target_flux_evolution'][:, istate] if (data['iter_start'] == 0).all(): # No data return subdict = dict(state_label=label, state_index=istate) output_filename = self.flux_output_pattern.format(**subdict) if self.flux_output_pattern else None title = self.title if self.title is not None else 'Flux into state "{state_label}"' title = title.format(**subdict) x_label = self.xlabel.format(**subdict) if self.xlabel else None y_label = self.ylabel if self.ylabel is not None else r'Flux $(\tau^{{-1}})$' y_label = y_label.format(**subdict) self.do_plot(data, output_filename, title, x_label=x_label, y_label=y_label) def plot_rate(self, istate, jstate): ilabel = self.state_labels[istate] jlabel = self.state_labels[jstate] data = self.kinavg_file['rate_evolution'][:, istate, jstate] if (data['iter_start'] == 0).all(): # No data return subdict = dict(istate_label=ilabel, istate_index=istate, fstate_label=jlabel, fstate_index=jstate) output_filename = self.rate_output_pattern.format(**subdict) if self.rate_output_pattern else None title = self.title if self.title is not None else 'Rate from state "{istate_label}" to state "{fstate_label}"' title = title.format(**subdict) x_label = self.xlabel.format(**subdict) if self.xlabel else None y_label = self.ylabel if self.ylabel is not None else r'Rate $(\tau^{{-1}})$' y_label = y_label.format(**subdict) self.do_plot(data, output_filename, title, x_label=x_label, y_label=y_label) def go(self): pi = self.progress.indicator nstates = len(self.state_labels) if self.interface =='matplotlib': with pi: # if --evolution-mode wasn't specified, neither of these exist: if 'target_flux_evolution' in self.kinavg_file: pi.new_operation('plotting fluxes', nstates) for istate in range(nstates): self.plot_flux(istate) pi.progress += 1 # if --evolution-mode wasn't specified, we won't get this either if 'rate_evolution' in self.kinavg_file: pi.new_operation('plotting rates', nstates * nstates) for istate in range(nstates): for jstate in range(nstates): self.plot_rate(istate, jstate) pi.progress += 1 else: print('rate evolution not available') else: plotter = Plotter(self.kinavg_file, 'rate_evolution', iteration=-1, interface='text') for istate in range(nstates): for jstate in range(nstates): if istate!= jstate: plotter.plot(istate, jstate) plotter = Plotter(self.kinavg_file, 'conditional_flux_evolution', iteration=-1, interface='text') for istate in range(nstates): for jstate in range(nstates): if istate!= jstate: plotter.plot(istate, jstate) class DirectStateprobs(CommonPloterrs): subcommand = 'd.probs' help_text = 'output of w_direct probs' input_filename = 'direct.h5' pop_output_filename = 'pop_evolution_d_{state_label}.pdf' color_output_filename = 'color_evolution_d_{state_label}.pdf' description = '''\ Plot evolution of macrostate populations and associated uncertainties. Plots are generated for all states calculated. Output filenames require (and plot titles and axis labels support) substitution based on which state is being plotted: state_label *(String, for fluxes)* Name of state state_index *(Integer, for fluxes)* Index of state ''' def __init__(self, parent): super().__init__(parent) self.stateprobs_file = None self.dset_slice = None self.rate_output_pattern = None self.flux_output_pattern = None self.state_labels = None def add_args(self, parser): iogroup = parser.add_argument_group('input/output') iogroup.add_argument( '-i', '--input', default=self.input_filename, help='''Read w_kinavg results from INPUT (default: %(default)s).''' ) iogroup.add_argument( '--population-output', default=self.pop_output_filename, help='''Filename pattern for population evolution output. See above for valid field names. (Default: %(default)r).''', ) iogroup.add_argument( '--color-output', default=self.color_output_filename, help='''Filename pattern for ensemble evolution output. See above for valid field names. (Default: %(default)r).''', ) def process_args(self, args): self.stateprobs_file = h5py.File(args.input, 'r') self.state_labels = list(self.stateprobs_file['state_labels'][...]) self.pop_output_pattern = args.population_output self.color_output_pattern = args.color_output def plot_pop(self, istate): label = self.state_labels[istate] data = self.stateprobs_file['state_pop_evolution'][:, istate] if (data['iter_start'] == 0).all(): # No data return subdict = dict(state_label=label, state_index=istate) output_filename = self.pop_output_pattern.format(**subdict) if self.pop_output_pattern else None title = self.title if self.title is not None else 'Population in state "{state_label}"' title = title.format(**subdict) x_label = self.xlabel.format(**subdict) if self.xlabel else None y_label = self.ylabel if self.ylabel is not None else r'Population' y_label = y_label.format(**subdict) self.do_plot(data, output_filename, title, x_label=x_label, y_label=y_label) def plot_color(self, istate): label = self.state_labels[istate] data = self.stateprobs_file['color_prob_evolution'][:, istate] if (data['iter_start'] == 0).all(): # No data return subdict = dict(state_label=label, state_index=istate) output_filename = self.color_output_pattern.format(**subdict) if self.color_output_pattern else None title = self.title if self.title is not None else 'Population in ensemble "{state_label}"' title = title.format(**subdict) x_label = self.xlabel.format(**subdict) if self.xlabel else None y_label = self.ylabel if self.ylabel is not None else r'Population' y_label = y_label.format(**subdict) self.do_plot(data, output_filename, title, x_label=x_label, y_label=y_label) def go(self): pi = self.progress.indicator nstates = len(self.state_labels) if self.interface =='matplotlib': with pi: if'state_pop_evolution' in self.stateprobs_file: pi.new_operation('plotting populations', nstates) for istate in range(nstates): self.plot_pop(istate) pi.progress += 1 if 'color_prob_evolution' in self.stateprobs_file: pi.new_operation('plotting ensemble populations', nstates) for istate in range(nstates): self.plot_color(istate) pi.progress += 1 else: print('population evolution not available') else: plotter = Plotter(self.stateprobs_file,'state_pop_evolution', iteration=-1, interface='text') for istate in range(nstates): plotter.plot(istate) plotter = Plotter(self.stateprobs_file, 'color_prob_evolution', iteration=-1, interface='text') for istate in range(nstates): plotter.plot(istate) class ReweightStateprobs(DirectStateprobs): subcommand = 'rw.probs' help_text = 'output of w_reweight probs' input_filename ='reweight.h5' pop_output_filename = 'pop_evolution_rw_{state_label}.pdf' color_output_filename = 'color_evolution_rw_{state_label}.pdf' class ReweightKinetics(DirectKinetics): subcommand = 'rw.kinetics' help_text = 'output of w_reweight kinetics' input_filename ='reweight.h5' flux_output_filename = 'flux_evolution_rw_{state_label}.pdf' rate_output_filename = 'rate_evolution_rw_{istate_label}_{fstate_label}.pdf' class PloterrsTool(WESTMasterCommand): prog = 'ploterrs' subcommands = [DirectKinetics, DirectStateprobs, ReweightStateprobs, ReweightKinetics, GenericIntervalSubcommand] subparsers_title ='supported input formats' description = '''\ Plots error ranges for weighted ensemble datasets. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def entry_point(): PloterrsTool().main() if __name__ == '__main__': entry_point()

westpa__westpa

plothist.rst

Manual

Plothist command

MIT License

westpa__westpa/doc/documentation/cli/plothist.rst

plothist Use the plothist tool to plot the results of w_pdist. This tool uses an hdf5 file as its input (i.e. the output of another analysis tool), and outputs a pdf image. The plothist tool operates in one of three (mutually exclusive) plotting modes: - evolution: Plots the relevant data as a time evolution over specified number of simulation iterations - average: Plots the relevant data as a time average over a specified number of iterations - instant: Plots the relevant data for a single specified iteration Overview The basic usage, independent of plotting mode, is as follows: usage: | ``plothist [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version]`` | ``                         {instant,average,evolution} input ...`` Note that the user must specify a plotting mode (i.e. 'instant', 'average', or 'evolution') and an input file, input. Therefore, this tool is always called as: plothist mode input_file [``other`` ``options``] 'instant' mode usage: | ``plothist instant [-h] input [-o PLOT_OUTPUT]`` | ``                                [--hdf5-output HDF5_OUTPUT] [--text-output TEXT_OUTPUT]`` | ``                                [--title TITLE] [--range RANGE] [--linear | --energy | --log10]`` | ``                                [--iter N_ITER] `` | ``                                [DIMENSION] [ADDTLDIM]`` 'average' mode usage: | ``plothist average [-h] input [-o PLOT_OUTPUT]`` | ``                                [--hdf5-output HDF5_OUTPUT] [--text-output TEXT_OUTPUT]`` | ``                                [--title TITLE] [--range RANGE] [--linear | --energy | --log10]`` | ``                                [--first-iter N_ITER] [--last-iter N_ITER]                           `` | ``                                [DIMENSION] [ADDTLDIM]`` 'evolution' mode usage: | ``plothist evolution [-h] input [-o PLOT_OUTPUT]`` | ``                                  [--hdf5-output HDF5_OUTPUT]`` | ``                                  [--title TITLE] [--range RANGE] [--linear | --energy | --log10]`` | ``                                  [--first-iter N_ITER] [--last-iter N_ITER]`` | ``                                  [--step-iter STEP]                                   `` | ``                                  [DIMENSION]`` Command-Line Options See the command-line tool index <command_line_tool_index> for more information on the general options. Unless specified (as a Note in the command-line option description), the command-line options below are shared for all three plotting modes Input/output options No matter the mode, an input hdf5 file must be specified. There are three possible outputs that are mode or user-specified: A text file, an hdf5 file, and a pdf image. Specifying input file *``input``* Specify the input hdf5 file ''input. This is the output file from a previous analysis tool (e.g. 'pdist.h5') Output plot pdf file ``-o ''plot_output'', --plot_output ''plot_output''`` Specify the name of the pdf plot image output (Default: 'hist.pdf'). Note: You can suppress plotting entirely by specifying an empty string as plot_output (i.e. -o '' or --plot_output '') Additional output options Note: plothist provides additional, optional arguments to output the data points used to construct the plot: ``--hdf5-output ''hdf5_output''`` Output plot data hdf5 file 'hdf5_output' (Default: No hdf5 output file) ``--text-output ''text_output''`` Output plot data as a text file named 'text_output' (Default: No text output file) Note: This option is only available for 1 dimensional histogram plots (that is, 'average' and 'instant' modes only) Plotting options The following options allow the user to specify a plot title, the type of plot (i.e. energy or probability distribution), whether to apply a log transformation to the data, and the range of data values to include. ``--title ''title'' `` Optionally specify a title, ``title``, for the plot (Default: No title) ``--range ''<nowiki>'</nowiki>LB, UB<nowiki>'</nowiki>''`` Optionally specify the data range to be plotted as "LB, UB" (e.g. ' --range "-1, 10" ' - note that the quotation marks are necessary if specifying a negative bound). For 1 dimensional histograms, the range affects the y axis. For 2 dimensional plots (e.g. evolution plot with 1 dimensional progress coordinate), it corresponds to the range of the color bar Mutually exclusive plotting options The following three options determine how the plotted data is represented (Default: '--energy') ``--energy `` Plots the probability distribution on an inverted natural log scale (i.e. -ln[P(x)] ), corresponding to the free energy (Default) ``--linear `` Plots the probability distribution function as a linear scale ``--log10 `` Plots the (base-10) logarithm of the probability distribution Iteration selection options Depending on plotting mode, you can select either a range or a single iteration to plot. ``'instant'`` mode only: ``--iter ''n_iter'' `` Plot the distribution for iteration ''n_iter'' (Default: Last completed iteration) ``'average'`` and ``'evolution'`` modes only: ``--first-iter ''first_iter'' `` Begin averaging or plotting at iteration ``first_iter`` (Default: 1) ``--last-iter ''last_iter'' `` Average or plot up to and including ``last_iter`` (Default: Last completed iteration) ``'evolution'`` mode only: ``--iter_step ''n_step'' `` Average every ``n_step`` iterations together when plotting in 'evolution' mode (Default: 1 - i.e. plot each iteration) Specifying progress coordinate dimension For progress coordinates with dimensions greater than 1, you can specify the dimension of the progress coordinate to use, the of progress coordinate values to include, and the progress coordinate axis label with a single positional argument: ``dimension `` Specify 'dimension' as 'int[:[LB,UB]:label]', where 'int' specifies the dimension (starting at 0), and, optionally, 'LB,UB' specifies the lower and upper range bounds, and/or 'label' specifies the axis label (Default: int = 0, full range, default label is 'dimension int'; e.g 'dimension 0') For 'average' and 'instant' modes, you can plot two dimensions at once using a color map if this positional argument is specified: ``addtl_dimension `` Specify the other dimension to include as 'addtl_dimension' Examples These examples assume the input file is created using w_pdist and is named 'pdist.h5' Basic plotting Plot the energy ( -ln(P(x)) ) for the last iteration plothist instant pdist.h5 Plot the evolution of the log10 of the probability distribution over all iterations plothist evolution pdist.h5 --log10  Plot the average linear probability distribution over all iterations plothist average pdist.h5 --linear Specifying progress coordinate Plot the average probability distribution as the energy, label the x-axis 'pcoord', over the entire range of the progress coordinate plothist average pdist.h5 0::pcoord Same as above, but only plot the energies for with progress coordinate between 0 and 10 plothist average pdist.h5 '0:0,10:pcoord' (Note: the quotes are needed if specifying a range that includes a negative bound) (For a simulation that uses at least 2 progress coordinates) plot the probability distribution for the 5th iteration, representing the first two progress coordinates as a heatmap plothist instant pdist.h5 0 1 --iter 5 --linear

import logging import os import re import h5py import numpy as np import matplotlib from matplotlib import pyplot from matplotlib.image import NonUniformImage from westpa.tools import WESTMasterCommand, WESTSubcommand from westpa.core import h5io, textio from westpa.fasthist import normhistnd from westpa.core.extloader import get_object log = logging.getLogger('plothist') # Suppress divide-by-zero in log np.seterr(divide='ignore', invalid='ignore') def sum_except_along(array, axes): '''Reduce the given array by addition over all axes except those listed in the scalar or iterable ``axes``''' try: iter(axes) except TypeError: axes = [axes] kept = set(axes) summed = list(set(range(array.ndim)) - kept) # Reorder axes so that the kept axes are first, and in the order they # were given array = np.transpose(array, list(axes) + summed).copy() # Now, the last len(summed) axes are summed over for _ in range(len(summed)): array = np.add.reduce(array, axis=-1) return array class PlotHistBase(WESTSubcommand): def __init__(self, parent): super().__init__(parent) self.input_arg_group = None self.output_arg_group = None self.input_h5 = None self.opmode = None self.plotscale = None self.enerzero = None self.plotrange = None self.plottitle = None self.postprocess_function = None self.plot_contour = None # Iteration range for average/evolution self.avail_iter_start = None self.avail_iter_stop = None self.avail_iter_step = None self.iter_start = None self.iter_stop = None self.iter_step = None # Iteration for single point self.n_iter = None # An array of dicts describing what dimensions to work with and # what their ranges should be for the plots. self.dimensions = [] self.plot_output_filename = None self.text_output_filename = None self.hdf5_output_filename = None def add_args(self, parser): igroup = self.input_arg_group = parser.add_argument_group('input options') igroup.add_argument('input', help='HDF5 file containing histogram data') igroup.add_argument( 'firstdim', nargs='?', metavar='DIMENSION', help='''Plot for the given DIMENSION, specified as INT[:[LB,UB]:LABEL], where INT is a zero-based integer identifying the dimension in the histogram, LB and UB are lower and upper bounds for plotting, and LABEL is the label for the plot axis. (Default: dimension 0, full range.)''', ) ogroup = self.output_arg_group = parser.add_argument_group('output options') ogroup.add_argument( '-o', '--output', '--plot-output', dest='plot_output', default='hist.pdf', metavar='PLOT_OUTPUT', help='''Store plot as PLOT_OUTPUT. This may be set to an empty string (e.g. --plot-output='') to suppress plotting entirely. The output format is determined by filename extension (and thus defaults to PDF). Default: "%(default)s".''', ) ogroup.add_argument('--hdf5-output', help='''Store plot data in the HDF5 file HDF5_OUTPUT.''') ogroup.add_argument( '--plot-contour', dest='plot_contour', action='store_const', const=True, default=False, help='''Determines whether or not to superimpose a contour plot over the heatmap for 2D objects.''', ) pgroup = parser.add_argument_group('plot options') pmgroup = pgroup.add_mutually_exclusive_group() pgroup.add_argument('--title', dest='title', help='Include TITLE as the top-of-graph title') pmgroup.add_argument( '--linear', dest='plotscale', action='store_const', const='linear', help='Plot the histogram on a linear scale.' ) pmgroup.add_argument( '--energy', dest='plotscale', action='store_const', const='energy', help='Plot the histogram on an inverted natural log scale, corresponding to (free) energy (default).', ) pmgroup.add_argument( '--zero-energy', dest='enerzero', metavar='E', default='min', help='Set the zero of energy to E, which may be a scalar, "min" or "max"', ) pmgroup.add_argument( '--log10', dest='plotscale', action='store_const', const='log10', help='Plot the histogram on a base-10 log scale.' ) pgroup.add_argument( '--range', help='''Plot histogram ordinates over the given RANGE, specified as "LB,UB", where LB and UB are the lower and upper bounds, respectively. For 1-D plots, this is the Y axis. For 2-D plots, this is the colorbar axis. (Default: full range.)''', ) pgroup.add_argument( '--postprocess-function', help='''Names a function (as in module.function) that will be called just prior to saving the plot. The function will be called as ``postprocess(hist, midpoints, binbounds)`` where ``hist`` is the histogram that was plotted, ``midpoints`` is the bin midpoints for each dimension, and ``binbounds`` is the bin boundaries for each dimension for 2-D plots, or None otherwise. The plot must be modified in place using the pyplot stateful interface.''', ) parser.set_defaults(plotscale='energy') def process_args(self, args): self.plotscale = args.plotscale self.input_h5 = h5py.File(args.input, 'r') self.plot_output_filename = args.plot_output self.hdf5_output_filename = args.hdf5_output self.plot_contour = args.plot_contour if args.title: self.plottitle = args.title if args.range: self.plotrange = self.parse_range(args.range) if args.firstdim: self.dimensions.append(self.parse_dimspec(args.firstdim)) if not args.firstdim: self.dimensions.append({'idim': 0, 'label': 'dimension 0'}) if args.enerzero: lenerzero = args.enerzero.lower() if lenerzero not in ('min','max'): try: self.enerzero = float(args.enerzero) except ValueError: raise ValueError('invalid energy zero point {!r}'.format(args.enerzero)) else: self.enerzero = lenerzero else: self.enerzero ='min' self.avail_iter_start, self.avail_iter_stop = h5io.get_iter_range(self.input_h5['histograms']) try: self.avail_iter_step = h5io.get_iter_step(self.input_h5['histograms']) except KeyError: self.avail_iter_step = 1 log.info( 'HDF5 file {!r} contains data for iterations {} -- {} with a step of {}'.format( args.input, self.avail_iter_start, self.avail_iter_stop, self.avail_iter_step ) ) if args.postprocess_function: self.postprocess_function = get_object(args.postprocess_function, path=['.']) def parse_dimspec(self, dimspec): dimdata = {} match = re.match(r'([0-9]+)(?::(?:([^,]+),([^:,]+))?(?::(.*))?)?', dimspec) if not match: raise ValueError('invalid dimension specification {!r}'.format(dimspec)) (idim_txt, lb_txt, ub_txt, label) = match.groups() try: dimdata['idim'] = int(idim_txt) if lb_txt: dimdata['lb'] = float(lb_txt) if ub_txt: dimdata['ub'] = float(ub_txt) if label: dimdata['label'] = label else: dimdata['label'] = 'dimension {}'.format(dimdata['idim']) except ValueError as e: raise ValueError('invalid dimension specification {!r}: {!r}'.format(dimspec, e)) return dimdata def parse_range(self, rangespec): try: (lbt, ubt) = rangespec.split(',') return float(lbt), float(ubt) except (ValueError, TypeError) as e: raise ValueError('invalid range specification {!r}: {!r}'.format(rangespec, e)) def _ener_zero(self, hist): hist = -np.log(hist) if self.enerzero =='min': np.subtract(hist, hist.min(), out=hist, casting="unsafe") elif self.enerzero =='max': np.subtract(hist, hist.max(), out=hist, casting="unsafe") else: np.subtract(hist, self.enerzero, out=hist, casting="unsafe") return hist class PlotSupports2D(PlotHistBase): def __init__(self, parent): super().__init__(parent) def add_args(self, parser): self.input_arg_group.add_argument( 'seconddim', nargs='?', metavar='ADDTLDIM', help='''For instantaneous/average plots, plot along the given additional dimension, producing a color map.''', ) self.output_arg_group.add_argument( '--text-output', help='''Store plot data in a text format at TEXT_OUTPUT. This option is only valid for 1-D histograms. (Default: no text output.)''', ) def process_args(self, args): self.text_output_filename = args.text_output if args.seconddim is not None: self.dimensions.append(self.parse_dimspec(args.seconddim)) def _do_1d_output(self, hist, idim, midpoints): enehist = self._ener_zero(hist) log10hist = np.log10(hist) if self.hdf5_output_filename: with h5py.File(self.hdf5_output_filename, 'w') as output_h5: h5io.stamp_creator_data(output_h5) output_h5.attrs['source_data'] = os.path.abspath(self.input_h5.filename) output_h5.attrs['source_dimension'] = idim output_h5['midpoints'] = midpoints output_h5['histogram'] = hist if self.text_output_filename: with textio.NumericTextOutputFormatter(self.text_output_filename) as output_file: output_file.write_header('source data: {} dimension {}'.format(os.path.abspath(self.input_h5.filename), idim)) output_file.write_header('column 0: midpoint of bin') output_file.write_header('column 1: probability in bin') output_file.write_header('column 2: -ln P') output_file.write_header('column 3: log10 P') np.savetxt(output_file, np.column_stack([midpoints, hist, enehist, log10hist])) if self.plot_output_filename: if self.plotscale == 'energy': plothist = enehist label = r'$-\ln\,P(x)$' elif self.plotscale == 'log10': plothist = log10hist label = r'$\log_{10}\ P(x)$' else: plothist = hist label = r'$P(x)$' pyplot.figure() pyplot.plot(midpoints, plothist) pyplot.xlim(self.dimensions[0].get('lb'), self.dimensions[0].get('ub')) if self.plotrange: pyplot.ylim(*self.plotrange) pyplot.xlabel(self.dimensions[0]['label']) pyplot.ylabel(label) if self.plottitle: pyplot.title(self.plottitle) if self.postprocess_function: self.postprocess_function(plothist, midpoints, None) pyplot.savefig(self.plot_output_filename) def _do_2d_output(self, hist, idims, midpoints, binbounds): enehist = self._ener_zero(hist) log10hist = np.log10(hist) if self.hdf5_output_filename: with h5py.File(self.hdf5_output_filename, 'w') as output_h5: h5io.stamp_creator_data(output_h5) output_h5.attrs['source_data'] = os.path.abspath(self.input_h5.filename) output_h5.attrs['source_dimensions'] = np.array(idims, np.min_scalar_type(max(idims))) output_h5.attrs['source_dimension_labels'] = np.array([dim['label'] for dim in self.dimensions]) for idim in idims: output_h5['midpoints_{}'.format(idim)] = midpoints[idim] output_h5['histogram'] = hist if self.plot_output_filename: if self.plotscale == 'energy': plothist = enehist label = r'$-\ln\,P(x)$' elif self.plotscale == 'log10': plothist = log10hist label = r'$\log_{10}\ P(\vec{x})$' else: plothist = hist plothist[~np.isfinite(plothist)] = np.nan label = r'$P(\vec{x})$' try: vmin, vmax = self.plotrange except TypeError: vmin, vmax = None, None pyplot.figure() # Transpose input so that axis 0 is displayed as x and axis 1 is displayed as y # pyplot.imshow(plothist.T, interpolation='nearest', aspect='auto', # extent=(midpoints[0][0], midpoints[0][-1], midpoints[1][0], midpoints[1][-1]), # origin='lower', vmin=vmin, vmax=vmax) # The following reproduces the former calls to imshow and colorbar norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax) ax = pyplot.gca() nui = NonUniformImage( ax, extent=(midpoints[0][0], midpoints[0][-1], midpoints[1][0], midpoints[1][-1]), origin='lower', norm=norm ) nui.set_data(midpoints[0], midpoints[1], plothist.T) ax.add_image(nui) ax.set_xlim(midpoints[0][0], midpoints[0][-1]) ax.set_ylim(midpoints[1][0], midpoints[1][-1]) cb = pyplot.colorbar(nui) cb.set_label(label) pyplot.xlabel(self.dimensions[0]['label']) pyplot.xlim(self.dimensions[0].get('lb'), self.dimensions[0].get('ub')) pyplot.ylabel(self.dimensions[1]['label']) pyplot.ylim(self.dimensions[1].get('lb'), self.dimensions[1].get('ub')) if self.plottitle: pyplot.title(self.plottitle) if self.postprocess_function: self.postprocess_function(plothist, midpoints, binbounds) if self.plot_contour: pyplot.contour(midpoints[0], midpoints[1], plothist.T) pyplot.savefig(self.plot_output_filename) class InstantPlotHist(PlotSupports2D): subcommand = 'instant' help_text = 'plot probability distribution for a single WE iteration' description = '''\ Plot a probability distribution for a single WE iteration. The probability distribution must have been previously extracted with ``w_pdist`` (or, at least, must be compatible with the output format of ``w_pdist``; see ``w_pdist --help`` for more information). ''' def add_args(self, parser): self.input_arg_group.add_argument( '--iter', metavar='N_ITER', dest='n_iter', type=int, help='''Plot distribution for iteration N_ITER (default: last completed iteration).''', ) def process_args(self, args): if args.n_iter: self.n_iter = min(args.n_iter, self.avail_iter_stop - 1) else: self.n_iter = self.avail_iter_stop - 1 def do_instant_plot_1d(self): '''Plot the histogram for iteration self.n_iter''' idim = self.dimensions[0]['idim'] n_iters = self.input_h5['n_iter'][...] iiter = np.searchsorted(n_iters, self.n_iter) binbounds = self.input_h5['binbounds_{}'.format(idim)][...] midpoints = self.input_h5['midpoints_{}'.format(idim)][...] hist = self.input_h5['histograms'][iiter] # Average over other dimensions hist = sum_except_along(hist, idim) normhistnd(hist, [binbounds]) self._do_1d_output(hist, idim, midpoints) def do_instant_plot_2d(self): '''Plot the histogram for iteration self.n_iter''' idim0 = self.dimensions[0]['idim'] idim1 = self.dimensions[1]['idim'] n_iters = self.input_h5['n_iter'][...] iiter = np.searchsorted(n_iters, self.n_iter) binbounds_0 = self.input_h5['binbounds_{}'.format(idim0)][...] midpoints_0 = self.input_h5['midpoints_{}'.format(idim0)][...] binbounds_1 = self.input_h5['binbounds_{}'.format(idim1)][...] midpoints_1 = self.input_h5['midpoints_{}'.format(idim1)][...] hist = self.input_h5['histograms'][iiter] # Average over other dimensions hist = sum_except_along(hist, [idim0, idim1]) normhistnd(hist, [binbounds_0, binbounds_1]) self._do_2d_output(hist, [idim0, idim1], [midpoints_0, midpoints_1], [binbounds_0, binbounds_1]) def go(self): if len(self.dimensions) == 2: self.do_instant_plot_2d() else: self.do_instant_plot_1d() class AveragePlotHist(PlotSupports2D): subcommand = 'average' help_text = 'plot average of a probability distribution over a WE simulation' description = '''\ Plot a probability distribution averaged over multiple iterations. The probability distribution must have been previously extracted with ``w_pdist`` (or, at least, must be compatible with the output format of ``w_pdist``; see ``w_pdist --help`` for more information). ''' def add_args(self, parser): igroup = self.input_arg_group igroup.add_argument( '--first-iter', dest='first_iter', type=int, metavar='N_ITER', default=1, help='''Begin averaging at iteration N_ITER (default: %(default)d).''', ) igroup.add_argument( '--last-iter', dest='last_iter', type=int, metavar='N_ITER', help='''Conclude averaging with N_ITER, inclusive (default: last completed iteration).''', ) def process_args(self, args): if args.first_iter: self.iter_start = max(args.first_iter, self.avail_iter_start) else: self.iter_start = self.avail_iter_start if args.last_iter: self.iter_stop = min(args.last_iter + 1, self.avail_iter_stop) else: self.iter_stop = self.avail_iter_stop def do_average_plot_1d(self): '''Plot the average histogram for iterations self.iter_start to self.iter_stop''' idim = self.dimensions[0]['idim'] n_iters = self.input_h5['n_iter'][...] iiter_start = np.searchsorted(n_iters, self.iter_start) iiter_stop = np.searchsorted(n_iters, self.iter_stop) binbounds = self.input_h5['binbounds_{}'.format(idim)][...] midpoints = self.input_h5['midpoints_{}'.format(idim)][...] # hist = self.input_h5['histograms'][iiter_start:iiter_stop] for iiter in range(iiter_start, iiter_stop): iter_hist = sum_except_along(self.input_h5['histograms'][iiter], idim) if iiter == iiter_start: hist = iter_hist else: hist += iter_hist del iter_hist normhistnd(hist, [binbounds]) self._do_1d_output(hist, idim, midpoints) def do_average_plot_2d(self): '''Plot the histogram for iteration self.n_iter''' idim0 = self.dimensions[0]['idim'] idim1 = self.dimensions[1]['idim'] n_iters = self.input_h5['n_iter'][...] iiter_start = np.searchsorted(n_iters, self.iter_start) iiter_stop = np.searchsorted(n_iters, self.iter_stop) binbounds_0 = self.input_h5['binbounds_{}'.format(idim0)][...] midpoints_0 = self.input_h5['midpoints_{}'.format(idim0)][...] binbounds_1 = self.input_h5['binbounds_{}'.format(idim1)][...] midpoints_1 = self.input_h5['midpoints_{}'.format(idim1)][...] for iiter in range(iiter_start, iiter_stop): iter_hist = sum_except_along(self.input_h5['histograms'][iiter], [idim0, idim1]) if iiter == iiter_start: hist = iter_hist else: hist += iter_hist normhistnd(hist, [binbounds_0, binbounds_1]) self._do_2d_output(hist, [idim0, idim1], [midpoints_0, midpoints_1], [binbounds_0, binbounds_1]) def go(self): if len(self.dimensions) == 2: self.do_average_plot_2d() else: self.do_average_plot_1d() class EvolutionPlotHist(PlotHistBase): subcommand = 'evolution' help_text = 'plot evolution of a probability distribution over the course of a WE simulation' description = '''\ Plot a probability distribution as it evolves over iterations. The probability distribution must have been previously extracted with ``w_pdist`` (or, at least, must be compatible with the output format of ``w_pdist``; see ``w_pdist --help`` for more information). ''' def add_args(self, parser): igroup = self.input_arg_group igroup.add_argument( '--first-iter', dest='first_iter', type=int, metavar='N_ITER', default=1, help='''Begin analysis at iteration N_ITER (default: %(default)d).''', ) igroup.add_argument( '--last-iter', dest='last_iter', type=int, metavar='N_ITER', help='''Conclude analysis with N_ITER, inclusive (default: last completed iteration).''', ) igroup.add_argument( '--step-iter', dest='step_iter', type=int, metavar='STEP', help='''Average in blocks of STEP iterations.''' ) def process_args(self, args): if args.first_iter: self.iter_start = max(args.first_iter, self.avail_iter_start) else: self.iter_start = self.avail_iter_start if args.last_iter: self.iter_stop = min(args.last_iter + 1, self.avail_iter_stop) else: self.iter_stop = self.avail_iter_stop if args.step_iter: self.iter_step = max(args.step_iter, self.avail_iter_step) else: self.iter_step = self.avail_iter_step log.info('using data for iterations {} -- {} with a step of {}'.format(self.iter_start, self.iter_stop, self.iter_step)) def go(self): '''Plot the evolution of the histogram for iterations self.iter_start to self.iter_stop''' idim = self.dimensions[0]['idim'] n_iters = self.input_h5['n_iter'][...] iiter_start = np.searchsorted(n_iters, self.iter_start) iiter_stop = np.searchsorted(n_iters, self.iter_stop) binbounds = self.input_h5['binbounds_{}'.format(idim)][...] midpoints = self.input_h5['midpoints_{}'.format(idim)][...] hists_ds = self.input_h5['histograms'] itercount = self.iter_stop - self.iter_start # We always round down, so that we don't have a dangling partial block at the end nblocks = itercount // self.iter_step block_iters = np.empty((nblocks, 2), dtype=n_iters.dtype) blocked_hists = np.zeros((nblocks, hists_ds.shape[1 + idim]), dtype=hists_ds.dtype) for iblock, istart in enumerate(range(iiter_start, iiter_start + nblocks * self.iter_step, self.iter_step)): istop = min(istart + self.iter_step, iiter_stop) histslice = hists_ds[istart:istop] # Sum over time histslice = np.add.reduce(histslice, axis=0) # Sum over other dimensions blocked_hists[iblock] = sum_except_along(histslice, idim) # Normalize normhistnd(blocked_hists[iblock], [binbounds]) block_iters[iblock, 0] = n_iters[istart] block_iters[iblock, 1] = n_iters[istop - 1] + 1 # enehists = -np.log(blocked_hists) enehists = self._ener_zero(blocked_hists) log10hists = np.log10(blocked_hists) if self.hdf5_output_filename: with h5py.File(self.hdf5_output_filename, 'w') as output_h5: h5io.stamp_creator_data(output_h5) output_h5.attrs['source_data'] = os.path.abspath(self.input_h5.filename) output_h5.attrs['source_dimension'] = idim output_h5['midpoints'] = midpoints output_h5['histograms'] = blocked_hists output_h5['n_iter'] = block_iters if self.plot_output_filename: if self.plotscale == 'energy': plothist = enehists label = r'$-\ln\,P(x)$' elif self.plotscale == 'log10': plothist = log10hists label = r'$\log_{10}\ P(x)$' else: plothist = blocked_hists label = r'$P(x)$' try: vmin, vmax = self.plotrange except TypeError: vmin, vmax = None, None pyplot.figure() norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax) ax = pyplot.gca() nui = NonUniformImage( ax, extent=(midpoints[0], midpoints[-1], block_iters[0, -1], block_iters[-1, -1]), origin='lower', norm=norm ) # not sure why plothist works but plothist.T doesn't, and the opposite is true # for _do_2d_output nui.set_data(midpoints, block_iters[:, -1], plothist) ax.add_image(nui) ax.set_xlim(midpoints[0], midpoints[-1]) ax.set_ylim(block_iters[0, -1], block_iters[-1, -1]) cb = pyplot.colorbar(nui) cb.set_label(label) pyplot.xlabel(self.dimensions[0]['label']) pyplot.xlim(self.dimensions[0].get('lb'), self.dimensions[0].get('ub')) pyplot.ylabel('WE Iteration') if self.plottitle: pyplot.title(self.plottitle) if self.postprocess_function: self.postprocess_function(plothist, midpoints, binbounds) pyplot.savefig(self.plot_output_filename) class PlotHistTool(WESTMasterCommand): prog = 'plothist' subparsers_title = 'plotting modes' subcommands = [InstantPlotHist, AveragePlotHist, EvolutionPlotHist] description = '''\ Plot probability density functions (histograms) generated by w_pdist or other programs conforming to the same output format. This program operates in one of three modes: instant Plot 1-D and 2-D histograms for an individual iteration. See ``plothist instant --help`` for more information. average Plot 1-D and 2-D histograms, averaged over several iterations. See ``plothist average --help`` for more information. evolution Plot the time evolution 1-D histograms as waterfall (heat map) plots. See ``plothist evolution --help`` for more information. This program takes the output of ``w_pdist`` as input (see ``w_pdist --help`` for more information), and can generate any kind of graphical output that matplotlib supports. ------------------------------------------------------------------------------ Command-line options ------------------------------------------------------------------------------ ''' def entry_point(): PlotHistTool().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_assign.rst

Manual

w_assign command

MIT License

westpa__westpa/doc/documentation/cli/w_assign.rst

w_assign w_assign uses simulation output to assign walkers to user-specified bins and macrostates. These assignments are required for some other simulation tools, namely w_kinetics and w_kinavg. w_assign supports parallelization (see general work manager options for more on command line options to specify a work manager). Overview Usage: w_assign [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version]                [-W WEST_H5FILE] [-o OUTPUT]                [--bins-from-system | --bins-from-expr BINS_FROM_EXPR | --bins-from-function BINS_FROM_FUNCTION]                [-p MODULE.FUNCTION]                [--states STATEDEF [STATEDEF ...] | --states-from-file STATEFILE | --states-from-function STATEFUNC]                [--wm-work-manager WORK_MANAGER] [--wm-n-workers N_WORKERS]                [--wm-zmq-mode MODE] [--wm-zmq-info INFO_FILE]                [--wm-zmq-task-endpoint TASK_ENDPOINT]                [--wm-zmq-result-endpoint RESULT_ENDPOINT]                [--wm-zmq-announce-endpoint ANNOUNCE_ENDPOINT]                [--wm-zmq-listen-endpoint ANNOUNCE_ENDPOINT]                [--wm-zmq-heartbeat-interval INTERVAL]                [--wm-zmq-task-timeout TIMEOUT]                [--wm-zmq-client-comm-mode MODE] Command-Line Options See the general command-line tool reference for more information on the general options. Input/output Options -W, --west-data /path/to/file Read simulation result data from file *file*. (**Default:** The *hdf5* file specified in the configuration file, by default **west.h5**) -o, --output /path/to/file Write assignment results to file *outfile*. (**Default:** *hdf5* file **assign.h5**) Binning Options Specify how binning is to be assigned to the dataset.: --bins-from-system Use binning scheme specified by the system driver; system driver can be found in the west configuration file, by default named **west.cfg** (**Default binning**) --bins-from-expr bin_expr Use binning scheme specified in *``bin_expr``*, which takes the form a Python list of lists, where each inner list corresponds to the binning a given dimension. (for example, "[[0,1,2,4,inf],[-inf,0,inf]]" specifies bin boundaries for two dimensional progress coordinate. Note that this option accepts the special symbol 'inf' for floating point infinity --bins-from-function bin_func Bins specified by calling an external function *``bin_func``*. *``bin_func``* should be formatted as '[PATH:]module.function', where the function 'function' in module 'module' will be used Macrostate Options You can optionally specify how to assign user-defined macrostates. Note that macrostates must be assigned for subsequent analysis tools, namely w_kinetics and w_kinavg.: --states statedef [statedef ...] Specify a macrostate for a single bin as *``statedef``*, formatted as a coordinate tuple where each coordinate specifies the bin to which it belongs, for instance: '[1.0, 2.0]' assigns a macrostate corresponding to the bin that contains the (two-dimensional) progress coordinates 1.0 and 2.0. Note that a macrostate label can optionally by specified, for instance: 'bound:[1.0, 2.0]' assigns the corresponding bin containing the given coordinates the macrostate named 'bound'. Note that multiple assignments can be specified with this command, but only one macrostate per bin is possible - if you wish to specify multiple bins in a single macrostate, use the *``--states-from-file``* option. --states-from-file statefile Read macrostate assignments from *yaml* file *``statefile``*. This option allows you to assign multiple bins to a single macrostate. The following example shows the contents of *``statefile``* that specify two macrostates, bound and unbound, over multiple bins with a two-dimensional progress coordinate: --- states:   - label: unbound     coords:       - [9.0, 1.0]       - [9.0, 2.0]   - label: bound     coords:       - [0.1, 0.0] Specifying Progress Coordinate By default, progress coordinate information for each iteration is taken from pcoord dataset in the specified input file (which, by default is west.h5). Optionally, you can specify a function to construct the progress coordinate for each iteration - this may be useful to consolidate data from several sources or otherwise preprocess the progress coordinate data.: --construct-pcoord module.function, -p module.function Use the function *module.function* to construct the progress coordinate for each iteration. This will be called once per iteration as *function(n_iter, iter_group)* and should return an array indexable as [seg_id][timepoint][dimension]. The **default** function returns the 'pcoord' dataset for that iteration (i.e. the function executes return iter_group['pcoord'][...])

import logging import math import os import numpy as np from numpy import index_exp from westpa.core.data_manager import seg_id_dtype, weight_dtype from westpa.core.binning import index_dtype, assign_and_label, accumulate_labeled_populations from westpa.tools import WESTParallelTool, WESTDataReader, WESTDSSynthesizer, BinMappingComponent, ProgressIndicatorComponent import westpa from westpa.core import h5io from westpa.core.h5io import WESTPAH5File from westpa.core.extloader import get_object log = logging.getLogger('w_assign') # Changes to keep it alive... def parse_pcoord_value(pc_str): namespace = {'math': math, 'numpy': np, 'np': np, 'inf': float('inf')} arr = np.array(eval(pc_str, namespace)) if arr.ndim == 0: arr.shape = (1, 1) elif arr.ndim == 1: arr.shape = (1,) + arr.shape else: raise ValueError('too many dimensions') return arr def _assign_label_pop( n_iter, lb, ub, mapper, nstates, state_map, last_labels, parent_id_dsspec, weight_dsspec, pcoord_dsspec, subsample ): nbins = len(state_map) - 1 parent_ids = parent_id_dsspec.get_iter_data(n_iter, index_exp[lb:ub]) weights = weight_dsspec.get_iter_data(n_iter, index_exp[lb:ub]) pcoords = pcoord_dsspec.get_iter_data(n_iter, index_exp[lb:ub]) assignments, trajlabels, statelabels = assign_and_label( lb, ub, parent_ids, mapper.assign, nstates, state_map, last_labels, pcoords, subsample ) pops = np.zeros((nstates + 1, nbins + 1), weight_dtype) accumulate_labeled_populations(weights, assignments, trajlabels, pops) return (assignments, trajlabels, pops, lb, ub, statelabels) class WAssign(WESTParallelTool): prog = 'w_assign' description = '''\ Assign walkers to bins, producing a file (by default named "assign.h5") which can be used in subsequent analysis. For consistency in subsequent analysis operations, the entire dataset must be assigned, even if only a subset of the data will be used. This ensures that analyses that rely on tracing trajectories always know the originating bin of each trajectory. ----------------------------------------------------------------------------- Source data ----------------------------------------------------------------------------- Source data is provided either by a user-specified function (--construct-dataset) or a list of "data set specifications" (--dsspecs). If neither is provided, the progress coordinate dataset ''pcoord'' is used. To use a custom function to extract or calculate data whose probability distribution will be calculated, specify the function in standard Python MODULE.FUNCTION syntax as the argument to --construct-dataset. This function will be called as function(n_iter,iter_group), where n_iter is the iteration whose data are being considered and iter_group is the corresponding group in the main WEST HDF5 file (west.h5). The function must return data which can be indexed as [segment][timepoint][dimension]. To use a list of data set specifications, specify --dsspecs and then list the desired datasets one-by-one (space-separated in most shells). These data set specifications are formatted as NAME[,file=FILENAME,slice=SLICE], which will use the dataset called NAME in the HDF5 file FILENAME (defaulting to the main WEST HDF5 file west.h5), and slice it with the Python slice expression SLICE (as in [0:2] to select the first two elements of the first axis of the dataset). The ``slice`` option is most useful for selecting one column (or more) from a multi-column dataset, such as arises when using a progress coordinate of multiple dimensions. ----------------------------------------------------------------------------- Specifying macrostates ----------------------------------------------------------------------------- Optionally, kinetic macrostates may be defined in terms of sets of bins. Each trajectory will be labeled with the kinetic macrostate it was most recently in at each timepoint, for use in subsequent kinetic analysis. This is required for all kinetics analysis (w_kintrace and w_kinmat). There are three ways to specify macrostates: 1. States corresponding to single bins may be identified on the command line using the --states option, which takes multiple arguments, one for each state (separated by spaces in most shells). Each state is specified as a coordinate tuple, with an optional label prepended, as in ``bound:1.0`` or ``unbound:(2.5,2.5)``. Unlabeled states are named ``stateN``, where N is the (zero-based) position in the list of states supplied to --states. 2. States corresponding to multiple bins may use a YAML input file specified with --states-from-file. This file defines a list of states, each with a name and a list of coordinate tuples; bins containing these coordinates will be mapped to the containing state. For instance, the following file:: --- states: - label: unbound coords: - [9.0, 1.0] - [9.0, 2.0] - label: bound coords: - [0.1, 0.0] produces two macrostates: the first state is called "unbound" and consists of bins containing the (2-dimensional) progress coordinate values (9.0, 1.0) and (9.0, 2.0); the second state is called "bound" and consists of the single bin containing the point (0.1, 0.0). 3. Arbitrary state definitions may be supplied by a user-defined function, specified as --states-from-function=MODULE.FUNCTION. This function is called with the bin mapper as an argument (``function(mapper)``) and must return a list of dictionaries, one per state. Each dictionary must contain a vector of coordinate tuples with key "coords"; the bins into which each of these tuples falls define the state. An optional name for the state (with key "label") may also be provided. ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file (-o/--output, by default "assign.h5") contains the following attributes datasets: ``nbins`` attribute *(Integer)* Number of valid bins. Bin assignments range from 0 to *nbins*-1, inclusive. ``nstates`` attribute *(Integer)* Number of valid macrostates (may be zero if no such states are specified). Trajectory ensemble assignments range from 0 to *nstates*-1, inclusive, when states are defined. ``/assignments`` [iteration][segment][timepoint] *(Integer)* Per-segment and -timepoint assignments (bin indices). ``/npts`` [iteration] *(Integer)* Number of timepoints in each iteration. ``/nsegs`` [iteration] *(Integer)* Number of segments in each iteration. ``/labeled_populations`` [iterations][state][bin] *(Floating-point)* Per-iteration and -timepoint bin populations, labeled by most recently visited macrostate. The last state entry (*nstates-1*) corresponds to trajectories initiated outside of a defined macrostate. ``/bin_labels`` [bin] *(String)* Text labels of bins. When macrostate assignments are given, the following additional datasets are present: ``/trajlabels`` [iteration][segment][timepoint] *(Integer)* Per-segment and -timepoint trajectory labels, indicating the macrostate which each trajectory last visited. ``/state_labels`` [state] *(String)* Labels of states. ``/state_map`` [bin] *(Integer)* Mapping of bin index to the macrostate containing that bin. An entry will contain *nbins+1* if that bin does not fall into a macrostate. Datasets indexed by state and bin contain one more entry than the number of valid states or bins. For *N* bins, axes indexed by bin are of size *N+1*, and entry *N* (0-based indexing) corresponds to a walker outside of the defined bin space (which will cause most mappers to raise an error). More importantly, for *M* states (including the case *M=0* where no states are specified), axes indexed by state are of size *M+1* and entry *M* refers to trajectories initiated in a region not corresponding to a defined macrostate. Thus, ``labeled_populations[:,:,:].sum(axis=1)[:,:-1]`` gives overall per-bin populations, for all defined bins and ``labeled_populations[:,:,:].sum(axis=2)[:,:-1]`` gives overall per-trajectory-ensemble populations for all defined states. ----------------------------------------------------------------------------- Parallelization ----------------------------------------------------------------------------- This tool supports parallelized binning, including reading/calculating input data. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def __init__(self): super().__init__() # Parallel processing by default (this is not actually necessary, but it is # informative!) self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager self.data_reader = WESTDataReader() self.dssynth = WESTDSSynthesizer(default_dsname='pcoord') self.binning = BinMappingComponent() self.progress = ProgressIndicatorComponent() self.output_file = None self.output_filename = None self.states = [] self.subsample = False def add_args(self, parser): self.data_reader.add_args(parser) self.binning.add_args(parser) self.dssynth.add_args(parser) sgroup = parser.add_argument_group('macrostate definitions').add_mutually_exclusive_group() sgroup.add_argument( '--states', nargs='+', metavar='STATEDEF', help='''Single-bin kinetic macrostate, specified by a coordinate tuple (e.g. '1.0' or '[1.0,1.0]'), optionally labeled (e.g. 'bound:[1.0,1.0]'). States corresponding to multiple bins must be specified with --states-from-file.''', ) sgroup.add_argument( '--states-from-file', metavar='STATEFILE', help='''Load kinetic macrostates from the YAML file STATEFILE. See description above for the appropriate structure.''', ) sgroup.add_argument( '--states-from-function', metavar='STATEFUNC', help='''Load kinetic macrostates from the function STATEFUNC, specified as module_name.func_name. This function is called with the bin mapper as an argument, and must return a list of dictionaries {'label': state_label, 'coords': 2d_array_like} one for each macrostate; the 'coords' entry must contain enough rows to identify all bins in the macrostate.''', ) agroup = parser.add_argument_group('other options') agroup.add_argument( '-o', '--output', dest='output', default='assign.h5', help='''Store results in OUTPUT (default: %(default)s).''' ) agroup.add_argument( '--subsample', dest='subsample', action='store_const', const=True, help='''Determines whether or not the data should be subsampled. This is rather useful for analysing steady state simulations.''', ) agroup.add_argument( '--config-from-file', dest='config_from_file', action='store_true', help='''Load bins/macrostates from a scheme specified in west.cfg.''', ) agroup.add_argument('--scheme-name', dest='scheme', help='''Name of scheme specified in west.cfg.''') def process_args(self, args): self.progress.process_args(args) self.data_reader.process_args(args) # Necessary to open the file to get the current iteration # if we want to use the mapper in the file self.data_reader.open(mode='r+') self.n_iter = self.data_reader.current_iteration # If we decide to use this option for iteration selection: # getattr(args,'bins_from_h5file',None) or self.data_reader.current_iteration with self.data_reader: self.dssynth.h5filename = self.data_reader.we_h5filename self.dssynth.process_args(args) if args.config_from_file is False: self.binning.set_we_h5file_info(self.n_iter, self.data_reader) self.binning.process_args(args) self.output_filename = args.output if args.config_from_file: if not args.scheme: raise ValueError('A scheme must be specified.') else: self.load_config_from_west(args.scheme) elif args.states: self.parse_cmdline_states(args.states) elif args.states_from_file: self.load_state_file(args.states_from_file) elif args.states_from_function: self.load_states_from_function(get_object(args.states_from_function, path=['.'])) if self.states and len(self.states) < 2: raise ValueError('zero, two, or more macrostates are required') # self.output_file = WESTPAH5File(args.output, 'w', creating_program=True) log.debug('state list: {!r}'.format(self.states)) self.subsample = args.subsample if args.subsample is not None else False def parse_cmdline_states(self, state_strings): states = [] for istring, state_string in enumerate(state_strings): try: (label, coord_str) = state_string.split(':') except ValueError: label ='state{}'.format(istring) coord_str = state_string coord = parse_pcoord_value(coord_str) states.append({'label': label, 'coords': coord}) self.states = states def load_config_from_west(self, scheme): try: config = westpa.rc.config['west']['analysis'] except Exception: raise ValueError('There is no configuration file specified.') ystates = config['analysis_schemes'][scheme]['states'] self.states_from_dict(ystates) try: self.subsample = config['subsample'] except Exception: pass from westpa.core._rc import bins_from_yaml_dict self.binning.mapper = bins_from_yaml_dict(config['analysis_schemes'][scheme]['bins'][0]) path = os.path.join(os.getcwd(), config['directory'], scheme) try: os.mkdir(config['directory']) os.mkdir(path) except Exception: pass self.output_filename = os.path.join(path, 'assign.h5') def load_state_file(self, state_filename): import yaml ydict = yaml.load(open(state_filename, 'rt'), Loader=yaml.Loader) ystates = ydict['states'] self.states_from_dict(ystates) def states_from_dict(self, ystates): states = [] for istate, ystate in enumerate(ystates): state = {} state['label'] = ystate.get('label','state{}'.format(istate)) # coords can be: # - a scalar, in which case it is one bin, 1-D # - a single list, which is rejected as ambiguous # - a list of lists, which is a list of coordinate tuples coords = np.array(ystate['coords']) if coords.ndim == 0: coords.shape = (1, 1) elif coords.ndim == 1: raise ValueError( 'list {!r} is ambiguous (list of 1-d coordinates, or single multi-d coordinate?)'.format(ystate['coords']) ) elif coords.ndim > 2: raise ValueError('coordinates must be 2-D') state['coords'] = coords states.append(state) self.states = states def load_states_from_function(self, statefunc): states = statefunc(self.binning.mapper) for istate, state in enumerate(states): state.setdefault('label','state{}'.format(istate)) try: state['coords'] = np.array(state['coords']) except KeyError: raise ValueError('state function {!r} returned a state {!r} without coordinates'.format(statefunc, state)) self.states = states log.debug('loaded states: {!r}'.format(self.states)) def assign_iteration(self, n_iter, nstates, nbins, state_map, last_labels): '''Method to encapsulate the segment slicing (into n_worker slices) and parallel job submission Submits job(s), waits on completion, splices them back together Returns: assignments, trajlabels, pops for this iteration''' futures = [] iter_group = self.data_reader.get_iter_group(n_iter) nsegs, npts = iter_group['pcoord'].shape[:2] n_workers = self.work_manager.n_workers or 1 assignments = np.empty((nsegs, npts), dtype=index_dtype) trajlabels = np.empty((nsegs, npts), dtype=index_dtype) statelabels = np.empty((nsegs, npts), dtype=index_dtype) pops = np.zeros((nstates + 1, nbins + 1), dtype=weight_dtype) # Submit jobs to work manager blocksize = nsegs // n_workers if nsegs % n_workers > 0: blocksize += 1 def task_gen(): if __debug__: checkset = set() for lb in range(0, nsegs, blocksize): ub = min(nsegs, lb + blocksize) if __debug__: checkset.update(set(range(lb, ub))) args = () kwargs = dict( n_iter=n_iter, lb=lb, ub=ub, mapper=self.binning.mapper, nstates=nstates, state_map=state_map, last_labels=last_labels, parent_id_dsspec=self.data_reader.parent_id_dsspec, weight_dsspec=self.data_reader.weight_dsspec, pcoord_dsspec=self.dssynth.dsspec, subsample=self.subsample, ) yield (_assign_label_pop, args, kwargs) # futures.append(self.work_manager.submit(_assign_label_pop, # kwargs=) if __debug__: assert checkset == set(range(nsegs)),'segments missing: {}'.format(set(range(nsegs)) - checkset) # for future in self.work_manager.as_completed(futures): for future in self.work_manager.submit_as_completed(task_gen(), queue_size=self.max_queue_len): assign_slice, traj_slice, slice_pops, lb, ub, state_slice = future.get_result(discard=True) assignments[lb:ub, :] = assign_slice trajlabels[lb:ub, :] = traj_slice statelabels[lb:ub, :] = state_slice pops += slice_pops del assign_slice, traj_slice, slice_pops, state_slice del futures return (assignments, trajlabels, pops, statelabels) def go(self): assert self.data_reader.parent_id_dsspec._h5file is None assert self.data_reader.weight_dsspec._h5file is None if hasattr(self.dssynth.dsspec, '_h5file'): assert self.dssynth.dsspec._h5file is None pi = self.progress.indicator pi.operation = 'Initializing' with pi, self.data_reader, WESTPAH5File(self.output_filename, 'w', creating_program=True) as self.output_file: assign = self.binning.mapper.assign # We always assign the entire simulation, so that no trajectory appears to start # in a transition region that doesn't get initialized in one. iter_start = 1 iter_stop = self.data_reader.current_iteration h5io.stamp_iter_range(self.output_file, iter_start, iter_stop) nbins = self.binning.mapper.nbins self.output_file.attrs['nbins'] = nbins state_map = np.empty((self.binning.mapper.nbins + 1,), index_dtype) state_map[:] = 0 # state_id == nstates => unknown state # Recursive mappers produce a generator rather than a list of labels # so consume the entire generator into a list labels = [np.string_(label) for label in self.binning.mapper.labels] self.output_file.create_dataset('bin_labels', data=labels, compression=9) if self.states: nstates = len(self.states) state_map[:] = nstates # state_id == nstates => unknown state state_labels = [np.string_(state['label']) for state in self.states] for istate, sdict in enumerate(self.states): assert state_labels[istate] == np.string_(sdict['label']) # sanity check state_assignments = assign(sdict['coords']) for assignment in state_assignments: state_map[assignment] = istate self.output_file.create_dataset('state_map', data=state_map, compression=9, shuffle=True) self.output_file['state_labels'] = state_labels # + ['(unknown)'] else: nstates = 0 self.output_file.attrs['nstates'] = nstates # Stamp if this has been subsampled. self.output_file.attrs['subsampled'] = self.subsample iter_count = iter_stop - iter_start nsegs = np.empty((iter_count,), seg_id_dtype) npts = np.empty((iter_count,), seg_id_dtype) # scan for largest number of segments and largest number of points pi.new_operation('Scanning for segment and point counts', iter_stop - iter_start) for iiter, n_iter in enumerate(range(iter_start, iter_stop)): iter_group = self.data_reader.get_iter_group(n_iter) nsegs[iiter], npts[iiter] = iter_group['pcoord'].shape[0:2] pi.progress += 1 del iter_group pi.new_operation('Preparing output') # create datasets self.output_file.create_dataset('nsegs', data=nsegs, shuffle=True, compression=9) self.output_file.create_dataset('npts', data=npts, shuffle=True, compression=9) max_nsegs = nsegs.max() max_npts = npts.max() assignments_shape = (iter_count, max_nsegs, max_npts) assignments_dtype = np.min_scalar_type(nbins) assignments_ds = self.output_file.create_dataset( 'assignments', dtype=assignments_dtype, shape=assignments_shape, compression=4, shuffle=True, chunks=h5io.calc_chunksize(assignments_shape, assignments_dtype), fillvalue=nbins, ) if self.states: trajlabel_dtype = np.min_scalar_type(nstates) trajlabels_ds = self.output_file.create_dataset( 'trajlabels', dtype=trajlabel_dtype, shape=assignments_shape, compression=4, shuffle=True, chunks=h5io.calc_chunksize(assignments_shape, trajlabel_dtype), fillvalue=nstates, ) statelabels_ds = self.output_file.create_dataset( 'statelabels', dtype=trajlabel_dtype, shape=assignments_shape, compression=4, shuffle=True, chunks=h5io.calc_chunksize(assignments_shape, trajlabel_dtype), fillvalue=nstates, ) pops_shape = (iter_count, nstates + 1, nbins + 1) pops_ds = self.output_file.create_dataset( 'labeled_populations', dtype=weight_dtype, shape=pops_shape, compression=4, shuffle=True, chunks=h5io.calc_chunksize(pops_shape, weight_dtype), ) h5io.label_axes(pops_ds, [np.string_(i) for i in ['iteration','state', 'bin']]) pi.new_operation('Assigning to bins', iter_stop - iter_start) last_labels = None # mapping of seg_id to last macrostate inhabited for iiter, n_iter in enumerate(range(iter_start, iter_stop)): # get iteration info in this block if iiter == 0: last_labels = np.empty((nsegs[iiter],), index_dtype) last_labels[:] = nstates # unknown state # Slices this iteration into n_workers groups of segments, submits them to wm, splices results back together assignments, trajlabels, pops, statelabels = self.assign_iteration(n_iter, nstates, nbins, state_map, last_labels) # Do stuff with this iteration's results last_labels = trajlabels[:, -1].copy() assignments_ds[iiter, 0 : nsegs[iiter], 0 : npts[iiter]] = assignments pops_ds[iiter] = pops if self.states: trajlabels_ds[iiter, 0 : nsegs[iiter], 0 : npts[iiter]] = trajlabels statelabels_ds[iiter, 0 : nsegs[iiter], 0 : npts[iiter]] = statelabels pi.progress += 1 del assignments, trajlabels, pops, statelabels for dsname in 'assignments', 'npts', 'nsegs', 'labeled_populations','statelabels': h5io.stamp_iter_range(self.output_file[dsname], iter_start, iter_stop) def entry_point(): WAssign().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_bins.rst

Manual

w_bins command

MIT License

westpa__westpa/doc/documentation/cli/w_bins.rst

w_bins w_bins deals with binning modification and statistics Overview Usage: w_bins [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version]              [-W WEST_H5FILE]              {info,rebin} ... Display information and statistics about binning in a WEST simulation, or modify the binning for the current iteration of a WEST simulation. Command-Line Options See the general command-line tool reference for more information on the general options. Options Under 'info' Usage: w_bins info [-h] [-n N_ITER] [--detail]                   [--bins-from-system | --bins-from-expr BINS_FROM_EXPR | --bins-from-function BINS_FROM_FUNCTION | --bins-from-file] Positional options: info Display information about binning. Options for 'info': -n N_ITER, --n-iter N_ITER Consider initial points of segment N_ITER (default: current iteration). --detail Display detailed per-bin information in addition to summary information. Binning options for 'info': --bins-from-system Bins are constructed by the system driver specified in the WEST configuration file (default where stored bin definitions not available). --bins-from-expr BINS_FROM_EXPR, --binbounds BINS_FROM_EXPR Construct bins on a rectilinear grid according to the given BINEXPR. This must be a list of lists of bin boundaries (one list of bin boundaries for each dimension of the progress coordinate), formatted as a Python expression. E.g. "[[0,1,2,4,inf],[-inf,0,inf]]". The numpy module and the special symbol "inf" (for floating-point infinity) are available for use within BINEXPR. --bins-from-function BINS_FROM_FUNCTION, --binfunc BINS_FROM_FUNCTION Supply an external function which, when called, returns a properly constructed bin mapper which will then be used for bin assignments. This should be formatted as "[PATH:]MODULE.FUNC", where the function FUNC in module MODULE will be used; the optional PATH will be prepended to the module search path when loading MODULE. --bins-from-file Load bin specification from the data file being examined (default where stored bin definitions available). Options Under 'rebin' Usage: w_bins rebin [-h] [--confirm] [--detail]                    [--bins-from-system | --bins-from-expr BINS_FROM_EXPR | --bins-from-function BINS_FROM_FUNCTION]                    [--target-counts TARGET_COUNTS | --target-counts-from FILENAME] Positional option: rebin Rebuild current iteration with new binning. Options for 'rebin': --confirm Commit the revised iteration to HDF5; without this option, the effects of the new binning are only calculated and printed. --detail Display detailed per-bin information in addition to summary information. Binning options for 'rebin'; Same as the binning options for 'info'. Bin target count options for 'rebin';: --target-counts TARGET_COUNTS Use TARGET_COUNTS instead of stored or system driver target counts. TARGET_COUNTS is a comma-separated list of integers. As a special case, a single integer is acceptable, in which case the same target count is used for all bins. --target-counts-from FILENAME Read target counts from the text file FILENAME instead of using stored or system driver target counts. FILENAME must contain a list of integers, separated by arbitrary whitespace (including newlines). Input Options -W WEST_H5FILE, --west_data WEST_H5FILE Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in west.cfg).

import logging import sys import numpy as np from westpa.tools import WESTTool, WESTDataReader, BinMappingComponent import westpa from westpa.tools.binning import write_bin_info log = logging.getLogger('w_bins') class WBinTool(WESTTool): prog = 'w_bins' description = '''\ Display information and statistics about binning in a WEST simulation, or modify the binning for the current iteration of a WEST simulation. ------------------------------------------------------------------------------- ''' def __init__(self): super().__init__() self.subcommand = None self.data_reader = WESTDataReader() self.binning = BinMappingComponent() self.args = None self.n_iter = None # Interface for command-line tools def add_args(self, parser): self.data_reader.add_args(parser) subparsers = parser.add_subparsers(help='available commands') info_parser = subparsers.add_parser('info', help='Display information about binning.') info_parser.add_argument( '-n', '--n-iter', type=int, help='''Consider initial points of segment N_ITER (default: current iteration).''' ) info_parser.add_argument( '--detail', action='store_true', help='''Display detailed per-bin information in addition to summary information.''', ) self.binning.add_args(info_parser) info_parser.set_defaults(func=self.cmd_info) rebin_parser = subparsers.add_parser('rebin', help='Rebuild current iteration with new binning.') rebin_parser.add_argument( '--confirm', action='store_true', help='''Commit the revised iteration to HDF5; without this option, the effects of the new binning are only calculated and printed.''', ) rebin_parser.add_argument( '--detail', action='store_true', help='''Display detailed per-bin information in addition to summary information.''', ) rebin_parser.add_argument( '-n', '--n-iter', type=int, help='''Consider initial points of segment N_ITER (default: current iteration).''' ) self.binning.add_args(rebin_parser, suppress=['--bins-from-file']) self.binning.add_target_count_args(rebin_parser) rebin_parser.set_defaults(func=self.cmd_rebin) def process_args(self, args): self.data_reader.process_args(args) self.data_reader.open(mode='r+') self.n_iter = getattr(args, 'n_iter', None) or self.data_reader.current_iteration # we cannot read bin information during rebins # interesting note: '==' is required here; 'is' fails if args.func == self.cmd_rebin: self.binning.target_counts_required = True else: self.binning.set_we_h5file_info(self.n_iter, self.data_reader) self.binning.process_args(args) self.args = args self.subcommand = args.func def go(self): self.subcommand() def cmd_info(self): mapper = self.binning.mapper # Get target states and their assignments target_states = self.data_reader.get_target_states(self.n_iter) n_target_states = len(target_states) iter_group = self.data_reader.get_iter_group(self.n_iter) # bin initial pcoords for iteration n_iter initial_pcoords = iter_group['pcoord'][:, 0, :] assignments = mapper.assign(initial_pcoords) del initial_pcoords print('Bin information for iteration {:d}'.format(self.n_iter)) # Get bin counts and weights weights = iter_group['seg_index']['weight'] write_bin_info(mapper, assignments, weights, n_target_states, detailed=self.args.detail) def cmd_rebin(self): mapper = self.binning.mapper assert mapper is not None if self.n_iter == 1: sys.stderr.write('rebin is not supported for the first iteration; reinitialize with w_init instead\n') sys.exit(1) n_target_states = len(self.data_reader.get_target_states(self.n_iter)) we_driver = westpa.rc.get_we_driver() data_manager = self.data_reader.data_manager segments = data_manager.get_segments(self.n_iter, load_pcoords=True) last_iter_segments = data_manager.get_segments(self.n_iter - 1, load_pcoords=False) # Bin on this iteration's initial points # We don't have to worry about recycling because we are binning on # initial points rather than final points, so recycling has already # occurred for this iteration. # We do need initial states, in case we merge a newly-created walker out of existence # avail_initial_states = {state.state_id: state # for state in data_manager.get_unused_initial_states(n_iter = self.n_iter)} avail_initial_states = data_manager.get_unused_initial_states(n_iter=self.n_iter) used_initial_states = data_manager.get_segment_initial_states(segments) we_driver.new_iteration( initial_states=avail_initial_states, bin_mapper=mapper, bin_target_counts=self.binning.bin_target_counts ) we_driver.used_initial_states = {state.state_id: state for state in used_initial_states} we_driver.assign(segments, initializing=True) we_driver.rebin_current(parent_segments=last_iter_segments) weights = np.array([segment.weight for segment in we_driver.next_iter_segments]) assignments = np.fromiter(we_driver.next_iter_assignments, dtype=int, count=len(weights)) write_bin_info(mapper, assignments, weights, n_target_states, detailed=self.args.detail) if self.args.confirm: data_manager.prepare_iteration(self.n_iter, list(we_driver.next_iter_segments)) # manually update endpoint statuses only endpoint_types = sorted([(segment.seg_id, segment.endpoint_type) for segment in last_iter_segments]) last_iter_group = data_manager.get_iter_group(self.n_iter - 1) last_iter_index = last_iter_group['seg_index'][...] last_iter_index['endpoint_type'] = [pair[1] for pair in endpoint_types] last_iter_group['seg_index'][...] = last_iter_index data_manager.save_iter_binning( self.n_iter, self.binning.mapper_hash, self.binning.mapper_pickle, we_driver.bin_target_counts ) data_manager.update_initial_states(we_driver.all_initial_states) data_manager.flush_backing() def entry_point(): WBinTool().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_crawl.rst

Manual

w_crawl command

MIT License

westpa__westpa/doc/documentation/cli/w_crawl.rst

w_crawl usage: w_crawl [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version] [--max-queue-length MAX_QUEUE_LENGTH] [-W WEST_H5FILE] [--first-iter N_ITER] [--last-iter N_ITER] [-c CRAWLER_INSTANCE] [--serial | --parallel | --work-manager WORK_MANAGER] [--n-workers N_WORKERS] [--zmq-mode MODE] [--zmq-comm-mode COMM_MODE] [--zmq-write-host-info INFO_FILE] [--zmq-read-host-info INFO_FILE] [--zmq-upstream-rr-endpoint ENDPOINT] [--zmq-upstream-ann-endpoint ENDPOINT] [--zmq-downstream-rr-endpoint ENDPOINT] [--zmq-downstream-ann-endpoint ENDPOINT] [--zmq-master-heartbeat MASTER_HEARTBEAT] [--zmq-worker-heartbeat WORKER_HEARTBEAT] [--zmq-timeout-factor FACTOR] [--zmq-startup-timeout STARTUP_TIMEOUT] [--zmq-shutdown-timeout SHUTDOWN_TIMEOUT] task_callable Crawl a weighted ensemble dataset, executing a function for each iteration. This can be used for postprocessing of trajectories, cleanup of datasets, or anything else that can be expressed as "do X for iteration N, then do something with the result". Tasks are parallelized by iteration, and no guarantees are made about evaluation order. Command-line options optional arguments: -h, --help show this help message and exit general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --debug enable extra checks and emit copious information --version show program's version number and exit parallelization options: --max-queue-length MAX_QUEUE_LENGTH Maximum number of tasks that can be queued. Useful to limit RAM use for tasks that have very large requests/response. Default: no limit. WEST input data options: -W WEST_H5FILE, --west-data WEST_H5FILE Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in west.cfg). iteration range: --first-iter N_ITER Begin analysis at iteration N_ITER (default: 1). --last-iter N_ITER Conclude analysis with N_ITER, inclusive (default: last completed iteration). task options: -c CRAWLER_INSTANCE, --crawler-instance CRAWLER_INSTANCE Use CRAWLER_INSTANCE (specified as module.instance) as an instance of WESTPACrawler to coordinate the calculation. Required only if initialization, finalization, or task result processing is required. task_callable Run TASK_CALLABLE (specified as module.function) on each iteration. Required. parallelization options: --serial run in serial mode --parallel run in parallel mode (using processes) --work-manager WORK_MANAGER use the given work manager for parallel task distribution. Available work managers are ('serial', 'threads', 'processes', 'zmq'); default is 'serial' --n-workers N_WORKERS Use up to N_WORKERS on this host, for work managers which support this option. Use 0 for a dedicated server. (Ignored by work managers which do not support this option.) options for ZeroMQ ("zmq") work manager (master or node): --zmq-mode MODE Operate as a master (server) or a node (workers/client). "server" is a deprecated synonym for "master" and "client" is a deprecated synonym for "node". --zmq-comm-mode COMM_MODE Use the given communication mode -- TCP or IPC (Unix-domain) -- sockets for communication within a node. IPC (the default) may be more efficient but is not available on (exceptionally rare) systems without node-local storage (e.g. /tmp); on such systems, TCP may be used instead. --zmq-write-host-info INFO_FILE Store hostname and port information needed to connect to this instance in INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly. Downstream nodes read this file with --zmq-read-host-info and know where how to connect. --zmq-read-host-info INFO_FILE Read hostname and port information needed to connect to the master (or other coordinating node) from INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly, writing that information with --zmq-write-host-info for this instance to read. --zmq-upstream-rr-endpoint ENDPOINT ZeroMQ endpoint to which to send request/response (task and result) traffic toward the master. --zmq-upstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to receive announcement (heartbeat and shutdown notification) traffic from the master. --zmq-downstream-rr-endpoint ENDPOINT ZeroMQ endpoint on which to listen for request/response (task and result) traffic from subsidiary workers. --zmq-downstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to send announcement (heartbeat and shutdown notification) traffic toward workers. --zmq-master-heartbeat MASTER_HEARTBEAT Every MASTER_HEARTBEAT seconds, the master announces its presence to workers. --zmq-worker-heartbeat WORKER_HEARTBEAT Every WORKER_HEARTBEAT seconds, workers announce their presence to the master. --zmq-timeout-factor FACTOR Scaling factor for heartbeat timeouts. If the master doesn't hear from a worker in WORKER_HEARTBEAT*FACTOR, the worker is assumed to have crashed. If a worker doesn't hear from the master in MASTER_HEARTBEAT*FACTOR seconds, the master is assumed to have crashed. Both cases result in shutdown. --zmq-startup-timeout STARTUP_TIMEOUT Amount of time (in seconds) to wait for communication between the master and at least one worker. This may need to be changed on very large, heavily-loaded computer systems that start all processes simultaneously. --zmq-shutdown-timeout SHUTDOWN_TIMEOUT Amount of time (in seconds) to wait for workers to shut down.

import logging from westpa.tools import WESTParallelTool, WESTDataReader, IterRangeSelection, ProgressIndicatorComponent import westpa from westpa.core.extloader import get_object log = logging.getLogger('w_crawl') class WESTPACrawler: '''Base class for general crawling execution. This class only exists on the master.''' def initialize(self, iter_start, iter_stop): '''Initialize this crawling process.''' pass def finalize(self): '''Finalize this crawling process.''' pass def process_iter_result(self, n_iter, result): '''Process the result of a per-iteration task.''' pass def _remote_task(n_iter, taskfn): data_manager = westpa.rc.get_data_manager() # gaahhh...globals data_manager.open_backing(mode='r') return n_iter, taskfn(n_iter, data_manager.get_iter_group(n_iter)) class WCrawl(WESTParallelTool): prog = 'w_crawl' description = '''\ Crawl a weighted ensemble dataset, executing a function for each iteration. This can be used for postprocessing of trajectories, cleanup of datasets, or anything else that can be expressed as "do X for iteration N, then do something with the result". Tasks are parallelized by iteration, and no guarantees are made about evaluation order. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def __init__(self): super().__init__() # These are used throughout self.progress = ProgressIndicatorComponent() self.data_reader = WESTDataReader() self.iter_range = IterRangeSelection(self.data_reader) self.crawler = None self.task_callable = None def add_args(self, parser): self.data_reader.add_args(parser) self.iter_range.add_args(parser) tgroup = parser.add_argument_group('task options') tgroup.add_argument( '-c', '--crawler-instance', help='''Use CRAWLER_INSTANCE (specified as module.instance) as an instance of WESTPACrawler to coordinate the calculation. Required only if initialization, finalization, or task result processing is required.''', ) tgroup.add_argument( 'task_callable', help='''Run TASK_CALLABLE (specified as module.function) on each iteration. Required.''', ) self.progress.add_args(parser) def process_args(self, args): self.progress.process_args(args) self.data_reader.process_args(args) with self.data_reader: self.iter_range.process_args(args) self.task_callable = get_object(args.task_callable, path=['.']) if args.crawler_instance is not None: self.crawler = get_object(args.crawler_instance, path=['.']) else: self.crawler = WESTPACrawler() def go(self): iter_start = self.iter_range.iter_start iter_stop = self.iter_range.iter_stop iter_count = iter_stop - iter_start self.data_reader.open('r') pi = self.progress.indicator with pi: pi.operation = 'Initializing' self.crawler.initialize(iter_start, iter_stop) try: pi.new_operation('Dispatching tasks & processing results', iter_count) task_gen = ((_remote_task, (n_iter, self.task_callable), {}) for n_iter in range(iter_start, iter_stop)) for future in self.work_manager.submit_as_completed(task_gen, self.max_queue_len): n_iter, result = future.get_result(discard=True) if self.crawler is not None: self.crawler.process_iter_result(n_iter, result) pi.progress += 1 finally: pi.new_operation('Finalizing') self.crawler.finalize() def entry_point(): WCrawl().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_direct.rst

Manual

w_direct command

MIT License

westpa__westpa/doc/documentation/cli/w_direct.rst

w_direct usage: w_direct [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version] [--max-queue-length MAX_QUEUE_LENGTH] [--serial | --parallel | --work-manager WORK_MANAGER] [--n-workers N_WORKERS] [--zmq-mode MODE] [--zmq-comm-mode COMM_MODE] [--zmq-write-host-info INFO_FILE] [--zmq-read-host-info INFO_FILE] [--zmq-upstream-rr-endpoint ENDPOINT] [--zmq-upstream-ann-endpoint ENDPOINT] [--zmq-downstream-rr-endpoint ENDPOINT] [--zmq-downstream-ann-endpoint ENDPOINT] [--zmq-master-heartbeat MASTER_HEARTBEAT] [--zmq-worker-heartbeat WORKER_HEARTBEAT] [--zmq-timeout-factor FACTOR] [--zmq-startup-timeout STARTUP_TIMEOUT] [--zmq-shutdown-timeout SHUTDOWN_TIMEOUT] {help,init,average,kinetics,probs,all} ... optional arguments: -h, --help show this help message and exit general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --debug enable extra checks and emit copious information --version show program's version number and exit parallelization options: --max-queue-length MAX_QUEUE_LENGTH Maximum number of tasks that can be queued. Useful to limit RAM use for tasks that have very large requests/response. Default: no limit. direct kinetics analysis schemes: {help,init,average,kinetics,probs,all} help print help for this command or individual subcommands init calculate state-to-state kinetics by tracing trajectories average Averages and returns fluxes, rates, and color/state populations. kinetics Generates rate and flux values from a WESTPA simulation via tracing. probs Calculates color and state probabilities via tracing. all Runs the full suite, including the tracing of events. parallelization options: --serial run in serial mode --parallel run in parallel mode (using processes) --work-manager WORK_MANAGER use the given work manager for parallel task distribution. Available work managers are ('serial', 'threads', 'processes', 'zmq'); default is 'serial' --n-workers N_WORKERS Use up to N_WORKERS on this host, for work managers which support this option. Use 0 for a dedicated server. (Ignored by work managers which do not support this option.) options for ZeroMQ ("zmq") work manager (master or node): --zmq-mode MODE Operate as a master (server) or a node (workers/client). "server" is a deprecated synonym for "master" and "client" is a deprecated synonym for "node". --zmq-comm-mode COMM_MODE Use the given communication mode -- TCP or IPC (Unix-domain) -- sockets for communication within a node. IPC (the default) may be more efficient but is not available on (exceptionally rare) systems without node-local storage (e.g. /tmp); on such systems, TCP may be used instead. --zmq-write-host-info INFO_FILE Store hostname and port information needed to connect to this instance in INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly. Downstream nodes read this file with --zmq-read-host-info and know where how to connect. --zmq-read-host-info INFO_FILE Read hostname and port information needed to connect to the master (or other coordinating node) from INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly, writing that information with --zmq-write-host-info for this instance to read. --zmq-upstream-rr-endpoint ENDPOINT ZeroMQ endpoint to which to send request/response (task and result) traffic toward the master. --zmq-upstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to receive announcement (heartbeat and shutdown notification) traffic from the master. --zmq-downstream-rr-endpoint ENDPOINT ZeroMQ endpoint on which to listen for request/response (task and result) traffic from subsidiary workers. --zmq-downstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to send announcement (heartbeat and shutdown notification) traffic toward workers. --zmq-master-heartbeat MASTER_HEARTBEAT Every MASTER_HEARTBEAT seconds, the master announces its presence to workers. --zmq-worker-heartbeat WORKER_HEARTBEAT Every WORKER_HEARTBEAT seconds, workers announce their presence to the master. --zmq-timeout-factor FACTOR Scaling factor for heartbeat timeouts. If the master doesn't hear from a worker in WORKER_HEARTBEAT*FACTOR, the worker is assumed to have crashed. If a worker doesn't hear from the master in MASTER_HEARTBEAT*FACTOR seconds, the master is assumed to have crashed. Both cases result in shutdown. --zmq-startup-timeout STARTUP_TIMEOUT Amount of time (in seconds) to wait for communication between the master and at least one worker. This may need to be changed on very large, heavily-loaded computer systems that start all processes simultaneously. --zmq-shutdown-timeout SHUTDOWN_TIMEOUT Amount of time (in seconds) to wait for workers to shut down.

import logging import numpy as np from westpa.core.data_manager import weight_dtype from westpa.tools import WESTMasterCommand, WESTParallelTool from westpa.core import h5io from westpa.core.kinetics import sequence_macro_flux_to_rate, WKinetics from westpa.tools.kinetics_tool import WESTKineticsBase, AverageCommands from westpa.mclib import mcbs_ci_correl, _1D_simple_eval_block, _2D_simple_eval_block # From w_stateprobs from westpa.core.binning import accumulate_state_populations_from_labeled log = logging.getLogger('w_direct') # This block is responsible for submitting a set of calculations to be bootstrapped over for a particular type of calculation. # A property which wishes to be calculated should adhere to this format. def _rate_eval_block(iblock, start, stop, nstates, data_input, name, mcbs_alpha, mcbs_nsets, mcbs_acalpha, do_correl, mcbs_enable): # Our rate estimator is a little more complex, so we've defined a custom evaluation block for it, # instead of just using the block evalutors that we've imported. results = [] for istate in range(nstates): for jstate in range(nstates): if istate == jstate: continue kwargs = {'istate': istate, 'jstate': jstate} # Why are we sending in the total population dataset, instead of a sliced one? # It's a requirement of our estimator; we need to pull from any given i to j state in order to properly normalize # and avoid i to j rate constants which are affected by a third state k. # That is, we need the populations for both i and j, and it's easier to just send in the entire dataset. dataset = {'dataset': data_input['dataset'][:, istate, jstate], 'pops': data_input['pops']} ci_res = mcbs_ci_correl( dataset, estimator=sequence_macro_flux_to_rate, alpha=mcbs_alpha, n_sets=mcbs_nsets, autocorrel_alpha=mcbs_acalpha, subsample=np.mean, do_correl=do_correl, mcbs_enable=mcbs_enable, estimator_kwargs=kwargs, ) results.append((name, iblock, istate, jstate, (start, stop) + ci_res)) return results # The old w_kinetics class DKinetics(WESTKineticsBase, WKinetics): subcommand = 'init' default_kinetics_file = 'direct.h5' default_output_file = 'direct.h5' help_text = 'calculate state-to-state kinetics by tracing trajectories' description = '''\ Calculate state-to-state rates and transition event durations by tracing trajectories. A bin assignment file (usually "assign.h5") including trajectory labeling is required (see "w_assign --help" for information on generating this file). This subcommand for w_direct is used as input for all other w_direct subcommands, which will convert the flux data in the output file into average rates/fluxes/populations with confidence intervals. ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file (-o/--output, by default "direct.h5") contains the following datasets: ``/conditional_fluxes`` [iteration][state][state] *(Floating-point)* Macrostate-to-macrostate fluxes. These are **not** normalized by the population of the initial macrostate. ``/conditional_arrivals`` [iteration][stateA][stateB] *(Integer)* Number of trajectories arriving at state *stateB* in a given iteration, given that they departed from *stateA*. ``/total_fluxes`` [iteration][state] *(Floating-point)* Total flux into a given macrostate. ``/arrivals`` [iteration][state] *(Integer)* Number of trajectories arriving at a given state in a given iteration, regardless of where they originated. ``/duration_count`` [iteration] *(Integer)* The number of event durations recorded in each iteration. ``/durations`` [iteration][event duration] *(Structured -- see below)* Event durations for transition events ending during a given iteration. These are stored as follows: istate *(Integer)* Initial state of transition event. fstate *(Integer)* Final state of transition event. duration *(Floating-point)* Duration of transition, in units of tau. weight *(Floating-point)* Weight of trajectory at end of transition, **not** normalized by initial state population. Because state-to-state fluxes stored in this file are not normalized by initial macrostate population, they cannot be used as rates without further processing. The ``w_direct kinetics`` command is used to perform this normalization while taking statistical fluctuation and correlation into account. See ``w_direct kinetics --help`` for more information. Target fluxes (total flux into a given state) require no such normalization. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def __init__(self, parent): super().__init__(parent) def open_files(self): self.output_file = h5io.WESTPAH5File(self.output_filename, 'a', creating_program=True) h5io.stamp_creator_data(self.output_file) self.assignments_file = h5io.WESTPAH5File(self.assignments_filename, 'r') #, driver='core', backing_store=False) if not self.iter_range.check_data_iter_range_least(self.assignments_file): raise ValueError('assignments data do not span the requested iterations') def go(self): pi = self.progress.indicator with pi: self.w_kinetics() # The old w_kinavg class DKinAvg(AverageCommands): subcommand = 'kinetics' help_text = 'Generates rate and flux values from a WESTPA simulation via tracing.' default_kinetics_file = 'direct.h5' description = '''\ Calculate average rates/fluxes and associated errors from weighted ensemble data. Bin assignments (usually "assign.h5") and kinetics data (usually "direct.h5") data files must have been previously generated (see "w_assign --help" and "w_direct init --help" for information on generating these files). The evolution of all datasets may be calculated, with or without confidence intervals. ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file (-o/--output, usually "direct.h5") contains the following dataset: /avg_rates [state,state] (Structured -- see below) State-to-state rates based on entire window of iterations selected. /avg_total_fluxes [state] (Structured -- see below) Total fluxes into each state based on entire window of iterations selected. /avg_conditional_fluxes [state,state] (Structured -- see below) State-to-state fluxes based on entire window of iterations selected. If --evolution-mode is specified, then the following additional datasets are available: /rate_evolution [window][state][state] (Structured -- see below). State-to-state rates based on windows of iterations of varying width. If --evolution-mode=cumulative, then these windows all begin at the iteration specified with --start-iter and grow in length by --step-iter for each successive element. If --evolution-mode=blocked, then these windows are all of width --step-iter (excluding the last, which may be shorter), the first of which begins at iteration --start-iter. /target_flux_evolution [window,state] (Structured -- see below). Total flux into a given macro state based on windows of iterations of varying width, as in /rate_evolution. /conditional_flux_evolution [window,state,state] (Structured -- see below). State-to-state fluxes based on windows of varying width, as in /rate_evolution. The structure of these datasets is as follows: iter_start (Integer) Iteration at which the averaging window begins (inclusive). iter_stop (Integer) Iteration at which the averaging window ends (exclusive). expected (Floating-point) Expected (mean) value of the observable as evaluated within this window, in units of inverse tau. ci_lbound (Floating-point) Lower bound of the confidence interval of the observable within this window, in units of inverse tau. ci_ubound (Floating-point) Upper bound of the confidence interval of the observable within this window, in units of inverse tau. stderr (Floating-point) The standard error of the mean of the observable within this window, in units of inverse tau. corr_len (Integer) Correlation length of the observable within this window, in units of tau. Each of these datasets is also stamped with a number of attributes: mcbs_alpha (Floating-point) Alpha value of confidence intervals. (For example, *alpha=0.05* corresponds to a 95% confidence interval.) mcbs_nsets (Integer) Number of bootstrap data sets used in generating confidence intervals. mcbs_acalpha (Floating-point) Alpha value for determining correlation lengths. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def w_kinavg(self): pi = self.progress.indicator # pi = None # We're initializing the various datasets... self.open_files() self.open_assignments() # Obviously, this is for the conditional and total fluxes. This is really all we need to sort for this. cond_fluxes = h5io.IterBlockedDataset(self.kinetics_file['conditional_fluxes']) cond_fluxes.cache_data() total_fluxes = h5io.IterBlockedDataset(self.kinetics_file['total_fluxes']) # This is necessary for both color and state populations... #... but we also need this for the kinetics calculations. pops = h5io.IterBlockedDataset(self.assignments_file['labeled_populations']) pops.cache_data() pops.data = pops.data.sum(axis=2) submit_kwargs = dict( pi=pi, nstates=self.nstates, start_iter=self.start_iter, stop_iter=self.stop_iter, step_iter=self.step_iter ) # Calculate averages for the simulation, then report, if necessary. submit_kwargs['dataset'] = {'dataset': cond_fluxes, 'pops': pops} avg_rates = self.run_calculation( eval_block=_rate_eval_block, name='Rate Evolution', dim=2, do_averages=True, **submit_kwargs ) self.output_file.replace_dataset('avg_rates', data=avg_rates[1]) submit_kwargs['dataset'] = {'dataset': cond_fluxes} avg_conditional_fluxes = self.run_calculation( eval_block=_2D_simple_eval_block, name='Conditional Flux Evolution', dim=2, do_averages=True, **submit_kwargs ) self.output_file.replace_dataset('avg_conditional_fluxes', data=avg_conditional_fluxes[1]) submit_kwargs['dataset'] = {'dataset': total_fluxes} avg_total_fluxes = self.run_calculation( eval_block=_1D_simple_eval_block, name='Target Flux Evolution', dim=1, do_averages=True, **submit_kwargs ) self.output_file.replace_dataset('avg_total_fluxes', data=avg_total_fluxes[1]) # Now, print them! # We've returned an average, but it still exists in a timeslice format. So we need to return the 'last' value. if self.display_averages: self.print_averages(avg_total_fluxes[1], '\nfluxes into macrostates:', dim=1) self.print_averages(avg_conditional_fluxes[1], '\nfluxes from state to state:', dim=2) self.print_averages(avg_rates[1], '\nrates from state to state:', dim=2) # Do a bootstrap evolution. submit_kwargs['dataset'] = {'dataset': cond_fluxes, 'pops': pops} rate_evol = self.run_calculation(eval_block=_rate_eval_block, name='Rate Evolution', dim=2, **submit_kwargs) self.output_file.replace_dataset('rate_evolution', data=rate_evol, shuffle=True, compression=9) submit_kwargs['dataset'] = {'dataset': cond_fluxes} rate_evol = self.run_calculation( eval_block=_2D_simple_eval_block, name='Conditional Flux Evolution', dim=2, **submit_kwargs ) self.output_file.replace_dataset('conditional_flux_evolution', data=rate_evol, shuffle=True, compression=9) submit_kwargs['dataset'] = {'dataset': total_fluxes} rate_evol = self.run_calculation(eval_block=_1D_simple_eval_block, name='Target Flux Evolution', dim=1, **submit_kwargs) self.output_file.replace_dataset('target_flux_evolution', data=rate_evol, shuffle=True, compression=9) def go(self): pi = self.progress.indicator with pi: self.w_kinavg() # The old w_stateprobs class DStateProbs(AverageCommands): subcommand = 'probs' help_text = 'Calculates color and state probabilities via tracing.' default_kinetics_file = 'direct.h5' description = '''\ Calculate average populations and associated errors in state populations from weighted ensemble data. Bin assignments, including macrostate definitions, are required. (See "w_assign --help" for more information). ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file (-o/--output, usually "direct.h5") contains the following dataset: /avg_state_probs [state] (Structured -- see below) Population of each state across entire range specified. /avg_color_probs [state] (Structured -- see below) Population of each ensemble across entire range specified. If --evolution-mode is specified, then the following additional datasets are available: /state_pop_evolution [window][state] (Structured -- see below). State populations based on windows of iterations of varying width. If --evolution-mode=cumulative, then these windows all begin at the iteration specified with --start-iter and grow in length by --step-iter for each successive element. If --evolution-mode=blocked, then these windows are all of width --step-iter (excluding the last, which may be shorter), the first of which begins at iteration --start-iter. /color_prob_evolution [window][state] (Structured -- see below). Ensemble populations based on windows of iterations of varying width. If --evolution-mode=cumulative, then these windows all begin at the iteration specified with --start-iter and grow in length by --step-iter for each successive element. If --evolution-mode=blocked, then these windows are all of width --step-iter (excluding the last, which may be shorter), the first of which begins at iteration --start-iter. The structure of these datasets is as follows: iter_start (Integer) Iteration at which the averaging window begins (inclusive). iter_stop (Integer) Iteration at which the averaging window ends (exclusive). expected (Floating-point) Expected (mean) value of the observable as evaluated within this window, in units of inverse tau. ci_lbound (Floating-point) Lower bound of the confidence interval of the observable within this window, in units of inverse tau. ci_ubound (Floating-point) Upper bound of the confidence interval of the observable within this window, in units of inverse tau. stderr (Floating-point) The standard error of the mean of the observable within this window, in units of inverse tau. corr_len (Integer) Correlation length of the observable within this window, in units of tau. Each of these datasets is also stamped with a number of attributes: mcbs_alpha (Floating-point) Alpha value of confidence intervals. (For example, *alpha=0.05* corresponds to a 95% confidence interval.) mcbs_nsets (Integer) Number of bootstrap data sets used in generating confidence intervals. mcbs_acalpha (Floating-point) Alpha value for determining correlation lengths. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def calculate_state_populations(self, pops): #... but then this is how the state populations are done. # This was taken, more or less, from the old w_stateprobs iter_count = self.stop_iter - self.start_iter all_state_pops = np.empty((iter_count, self.nstates + 1), weight_dtype) iter_state_pops = np.empty((self.nstates + 1,), weight_dtype) avg_state_pops = np.zeros((self.nstates + 1,), weight_dtype) pops.cache_data(max_size='available') state_map = self.assignments_file['state_map'][...] try: for iiter, n_iter in enumerate(range(self.start_iter, self.stop_iter)): iter_state_pops.fill(0) labeled_pops = pops.iter_entry(n_iter) accumulate_state_populations_from_labeled(labeled_pops, state_map, iter_state_pops, check_state_map=False) all_state_pops[iiter] = iter_state_pops avg_state_pops += iter_state_pops del labeled_pops finally: pops.drop_cache() state_pops = h5io.IterBlockedDataset.empty_like(pops) state_pops.data = all_state_pops return state_pops def w_stateprobs(self): pi = self.progress.indicator self.open_files() self.open_assignments() # So far, we definitely need this boilerplate... # pi.new_operation('Reading data') # This is necessary for both color and state populations... pops = h5io.IterBlockedDataset(self.assignments_file['labeled_populations']) state_pops = self.calculate_state_populations(pops) # This now sorts it for the color populations pops.cache_data() pops.data = pops.data.sum(axis=2) submit_kwargs = dict( pi=pi, nstates=self.nstates, start_iter=self.start_iter, stop_iter=self.stop_iter, step_iter=self.step_iter, eval_block=_1D_simple_eval_block, ) # Calculate and print averages submit_kwargs['dataset'] = {'dataset': pops} color_evol_avg = self.run_calculation(name='Color Probability Evolution', dim=1, do_averages=True, **submit_kwargs) self.output_file.replace_dataset('avg_color_probs', data=color_evol_avg[1], shuffle=True, compression=9) submit_kwargs['dataset'] = {'dataset': state_pops} state_evol_avg = self.run_calculation(name='State Probability Evolution', dim=1, do_averages=True, **submit_kwargs) self.output_file.replace_dataset(name='avg_state_probs', data=state_evol_avg[1], shuffle=True, compression=9) # Print! if self.display_averages: self.print_averages(color_evol_avg[1], '\naverage color probabilities:', dim=1) self.print_averages(state_evol_avg[1], '\naverage state probabilities:', dim=1) # Now, do a bootstrap evolution submit_kwargs['dataset'] = {'dataset': pops} pop_evol = self.run_calculation(name='Color Probability Evolution', dim=1, **submit_kwargs) self.output_file.replace_dataset('color_prob_evolution', data=pop_evol, shuffle=True, compression=9) submit_kwargs['dataset'] = {'dataset': state_pops} pop_evol = self.run_calculation(name='State Probability Evolution', dim=1, **submit_kwargs) self.output_file.replace_dataset(name='state_pop_evolution', data=pop_evol, shuffle=True, compression=9) def go(self): pi = self.progress.indicator with pi: self.w_stateprobs() # Just a convenience class to run everything. class DAll(DStateProbs, DKinAvg, DKinetics): subcommand = 'all' help_text = 'Runs the full suite, including the tracing of events.' default_kinetics_file = 'direct.h5' description = '''\ A convenience function to run init/kinetics/probs. Bin assignments, including macrostate definitions, are required. (See "w_assign --help" for more information). For more information on the individual subcommands this subs in for, run w_direct {init/kinetics/probs} --help. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def go(self): # One minor issue; as this stands now, since it's inheriting from all the other classes, it needs # a kinetics file to instantiate the other attributes. We'll need to modify how the loading works, there. pi = self.progress.indicator with pi: self.w_kinetics() self.w_kinavg() self.w_stateprobs() # Just a convenience class to average the observables. class DAverage(DStateProbs, DKinAvg): subcommand = 'average' help_text = 'Averages and returns fluxes, rates, and color/state populations.' default_kinetics_file = 'direct.h5' description = '''\ A convenience function to run kinetics/probs. Bin assignments, including macrostate definitions, are required. (See "w_assign --help" for more information). For more information on the individual subcommands this subs in for, run w_direct {kinetics/probs} --help. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def go(self): pi = self.progress.indicator with pi: self.w_kinavg() self.w_stateprobs() class WDirect(WESTMasterCommand, WESTParallelTool): prog = 'w_direct' # subcommands = [AvgTraceSubcommand,AvgMatrixSubcommand] subcommands = [DKinetics, DAverage, DKinAvg, DStateProbs, DAll] subparsers_title = 'direct kinetics analysis schemes' def entry_point(): WDirect().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_eddist.rst

Manual

w_eddist command

MIT License

westpa__westpa/doc/documentation/cli/w_eddist.rst

w_eddist usage: w_eddist [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version] [--max-queue-length MAX_QUEUE_LENGTH] [-b BINEXPR] [-C] [--loose] --istate ISTATE --fstate FSTATE [--first-iter ITER_START] [--last-iter ITER_STOP] [-k KINETICS] [-o OUTPUT] [--serial | --parallel | --work-manager WORK_MANAGER] [--n-workers N_WORKERS] [--zmq-mode MODE] [--zmq-comm-mode COMM_MODE] [--zmq-write-host-info INFO_FILE] [--zmq-read-host-info INFO_FILE] [--zmq-upstream-rr-endpoint ENDPOINT] [--zmq-upstream-ann-endpoint ENDPOINT] [--zmq-downstream-rr-endpoint ENDPOINT] [--zmq-downstream-ann-endpoint ENDPOINT] [--zmq-master-heartbeat MASTER_HEARTBEAT] [--zmq-worker-heartbeat WORKER_HEARTBEAT] [--zmq-timeout-factor FACTOR] [--zmq-startup-timeout STARTUP_TIMEOUT] [--zmq-shutdown-timeout SHUTDOWN_TIMEOUT] Calculate time-resolved transition-event duration distribution from kinetics results Source data Source data is collected from the results of 'w_kinetics trace' (see w_kinetics trace --help for more information on generating this dataset). Histogram binning By default, histograms are constructed with 100 bins in each dimension. This can be overridden by specifying -b/--bins, which accepts a number of different kinds of arguments: a single integer N N uniformly spaced bins will be used in each dimension. a sequence of integers N1,N2,... (comma-separated) N1 uniformly spaced bins will be used for the first dimension, N2 for the second, and so on. a list of lists [[B11, B12, B13, ...], [B21, B22, B23, ...], ...] The bin boundaries B11, B12, B13, ... will be used for the first dimension, B21, B22, B23, ... for the second dimension, and so on. These bin boundaries need not be uniformly spaced. These expressions will be evaluated with Python's ``eval`` construct, with ``np`` available for use [e.g. to specify bins using np.arange()]. The first two forms (integer, list of integers) will trigger a scan of all data in each dimension in order to determine the minimum and maximum values, which may be very expensive for large datasets. This can be avoided by explicitly providing bin boundaries using the list-of-lists form. Note that these bins are NOT at all related to the bins used to drive WE sampling. Output format The output file produced (specified by -o/--output, defaulting to "pdist.h5") may be fed to plothist to generate plots (or appropriately processed text or HDF5 files) from this data. In short, the following datasets are created: ``histograms`` Normalized histograms. The first axis corresponds to iteration, and remaining axes correspond to dimensions of the input dataset. ``/binbounds_0`` Vector of bin boundaries for the first (index 0) dimension. Additional datasets similarly named (/binbounds_1, /binbounds_2, ...) are created for additional dimensions. ``/midpoints_0`` Vector of bin midpoints for the first (index 0) dimension. Additional datasets similarly named are created for additional dimensions. ``n_iter`` Vector of iteration numbers corresponding to the stored histograms (i.e. the first axis of the ``histograms`` dataset). Subsequent processing The output generated by this program (-o/--output, default "pdist.h5") may be plotted by the plothist program. See plothist --help for more information. Parallelization This tool supports parallelized binning, including reading of input data. Parallel processing is the default. For simple cases (reading pre-computed input data, modest numbers of segments), serial processing (--serial) may be more efficient. Command-line options optional arguments: -h, --help show this help message and exit -b BINEXPR, --bins BINEXPR Use BINEXPR for bins. This may be an integer, which will be used for each dimension of the progress coordinate; a list of integers (formatted as [n1,n2,...]) which will use n1 bins for the first dimension, n2 for the second dimension, and so on; or a list of lists of boundaries (formatted as [[a1, a2, ...], [b1, b2, ...], ... ]), which will use [a1, a2, ...] as bin boundaries for the first dimension, [b1, b2, ...] as bin boundaries for the second dimension, and so on. (Default: 100 bins in each dimension.) -C, --compress Compress histograms. May make storage of higher-dimensional histograms more tractable, at the (possible extreme) expense of increased analysis time. (Default: no compression.) --loose Ignore values that do not fall within bins. (Risky, as this can make buggy bin boundaries appear as reasonable data. Only use if you are sure of your bin boundary specification.) --istate ISTATE Initial state defining transition event --fstate FSTATE Final state defining transition event general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --debug enable extra checks and emit copious information --version show program's version number and exit parallelization options: --max-queue-length MAX_QUEUE_LENGTH Maximum number of tasks that can be queued. Useful to limit RAM use for tasks that have very large requests/response. Default: no limit. iteration range options: --first-iter ITER_START Iteration to begin analysis (default: 1) --last-iter ITER_STOP Iteration to end analysis input/output options: -k KINETICS, --kinetics KINETICS Populations and transition rates (including evolution) are stored in KINETICS (default: kintrace.h5). -o OUTPUT, --output OUTPUT Store results in OUTPUT (default: eddist.h5). parallelization options: --serial run in serial mode --parallel run in parallel mode (using processes) --work-manager WORK_MANAGER use the given work manager for parallel task distribution. Available work managers are ('serial', 'threads', 'processes', 'zmq'); default is 'processes' --n-workers N_WORKERS Use up to N_WORKERS on this host, for work managers which support this option. Use 0 for a dedicated server. (Ignored by work managers which do not support this option.) options for ZeroMQ ("zmq") work manager (master or node): --zmq-mode MODE Operate as a master (server) or a node (workers/client). "server" is a deprecated synonym for "master" and "client" is a deprecated synonym for "node". --zmq-comm-mode COMM_MODE Use the given communication mode -- TCP or IPC (Unix-domain) -- sockets for communication within a node. IPC (the default) may be more efficient but is not available on (exceptionally rare) systems without node-local storage (e.g. /tmp); on such systems, TCP may be used instead. --zmq-write-host-info INFO_FILE Store hostname and port information needed to connect to this instance in INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly. Downstream nodes read this file with --zmq-read-host-info and know where how to connect. --zmq-read-host-info INFO_FILE Read hostname and port information needed to connect to the master (or other coordinating node) from INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly, writing that information with --zmq-write-host-info for this instance to read. --zmq-upstream-rr-endpoint ENDPOINT ZeroMQ endpoint to which to send request/response (task and result) traffic toward the master. --zmq-upstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to receive announcement (heartbeat and shutdown notification) traffic from the master. --zmq-downstream-rr-endpoint ENDPOINT ZeroMQ endpoint on which to listen for request/response (task and result) traffic from subsidiary workers. --zmq-downstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to send announcement (heartbeat and shutdown notification) traffic toward workers. --zmq-master-heartbeat MASTER_HEARTBEAT Every MASTER_HEARTBEAT seconds, the master announces its presence to workers. --zmq-worker-heartbeat WORKER_HEARTBEAT Every WORKER_HEARTBEAT seconds, workers announce their presence to the master. --zmq-timeout-factor FACTOR Scaling factor for heartbeat timeouts. If the master doesn't hear from a worker in WORKER_HEARTBEAT*FACTOR, the worker is assumed to have crashed. If a worker doesn't hear from the master in MASTER_HEARTBEAT*FACTOR seconds, the master is assumed to have crashed. Both cases result in shutdown. --zmq-startup-timeout STARTUP_TIMEOUT Amount of time (in seconds) to wait for communication between the master and at least one worker. This may need to be changed on very large, heavily-loaded computer systems that start all processes simultaneously. --zmq-shutdown-timeout SHUTDOWN_TIMEOUT Amount of time (in seconds) to wait for workers to shut down.

import logging import h5py import numpy as np from westpa.tools import WESTParallelTool, ProgressIndicatorComponent from westpa.fasthist import histnd, normhistnd from westpa.core import h5io log = logging.getLogger('w_eddist') class DurationDataset: '''A facade for the 'dsspec' dataclass that incorporates the mask into get_iter_data method''' def __init__(self, dataset, mask, iter_start=1): self.dataset = dataset self.mask = mask self.dtype = dataset.dtype self.iter_start = iter_start def get_iter_data(self, n_iter): try: assert n_iter >= self.iter_start dset = self.dataset[n_iter - 1][self.mask[n_iter - self.iter_start]] except (AssertionError, IndexError): raise ValueError("Iteration {} is not within the iteration range".format(n_iter)) nsegs = dset.shape[0] if nsegs == 0: return None else: return dset.reshape(nsegs, 1, 1) def isiterable(x): try: iter(x) except TypeError: return False else: return True def _remote_min_max(ndim, dset_dtype, n_iter, dsspec): try: minval = np.finfo(dset_dtype).min maxval = np.finfo(dset_dtype).max except ValueError: minval = np.iinfo(dset_dtype).min maxval = np.iinfo(dset_dtype).max data_range = [(maxval, minval) for _i in range(ndim)] dset = dsspec.get_iter_data(n_iter) if dset is None: return data_range for idim in range(ndim): dimdata = dset[:, :, idim] current_min, current_max = data_range[idim] current_min = min(current_min, dimdata.min()) current_max = max(current_max, dimdata.max()) data_range[idim] = (current_min, current_max) del dimdata del dset return data_range def _remote_bin_iter(iiter, n_iter, dsspec, wt_dsspec, initpoint, binbounds, ignore_out_of_range): iter_hist_shape = tuple(len(bounds) - 1 for bounds in binbounds) iter_hist = np.zeros(iter_hist_shape, dtype=np.float64) dset = dsspec.get_iter_data(n_iter) if dset is None: return iiter, n_iter, iter_hist else: npts = dset.shape[1] weights = wt_dsspec.get_iter_data(n_iter)[:, 0, 0] # dset = dset[:,initpoint:,:] for ipt in range(npts - initpoint): histnd(dset[:, ipt, :], binbounds, weights, out=iter_hist, binbound_check=False, ignore_out_of_range=ignore_out_of_range) del weights, dset # normalize histogram normhistnd(iter_hist, binbounds) return iiter, n_iter, iter_hist class WEDDist(WESTParallelTool): prog = 'w_eddist' description = '''\ Calculate time-resolved transition-event duration distribution from kinetics results ----------------------------------------------------------------------------- Source data ----------------------------------------------------------------------------- Source data is collected from the results of 'w_kinetics trace' (see w_kinetics trace --help for more information on generating this dataset). ----------------------------------------------------------------------------- Histogram binning ----------------------------------------------------------------------------- By default, histograms are constructed with 100 bins in each dimension. This can be overridden by specifying -b/--bins, which accepts a number of different kinds of arguments: a single integer N N uniformly spaced bins will be used in each dimension. a sequence of integers N1,N2,... (comma-separated) N1 uniformly spaced bins will be used for the first dimension, N2 for the second, and so on. a list of lists [[B11, B12, B13,...], [B21, B22, B23,...],...] The bin boundaries B11, B12, B13,... will be used for the first dimension, B21, B22, B23,... for the second dimension, and so on. These bin boundaries need not be uniformly spaced. These expressions will be evaluated with Python's ``eval`` construct, with ``np`` available for use [e.g. to specify bins using np.arange()]. The first two forms (integer, list of integers) will trigger a scan of all data in each dimension in order to determine the minimum and maximum values, which may be very expensive for large datasets. This can be avoided by explicitly providing bin boundaries using the list-of-lists form. Note that these bins are *NOT* at all related to the bins used to drive WE sampling. ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file produced (specified by -o/--output, defaulting to "pdist.h5") may be fed to plothist to generate plots (or appropriately processed text or HDF5 files) from this data. In short, the following datasets are created: ``histograms`` Normalized histograms. The first axis corresponds to iteration, and remaining axes correspond to dimensions of the input dataset. ``/binbounds_0`` Vector of bin boundaries for the first (index 0) dimension. Additional datasets similarly named (/binbounds_1, /binbounds_2,...) are created for additional dimensions. ``/midpoints_0`` Vector of bin midpoints for the first (index 0) dimension. Additional datasets similarly named are created for additional dimensions. ``n_iter`` Vector of iteration numbers corresponding to the stored histograms (i.e. the first axis of the ``histograms`` dataset). ----------------------------------------------------------------------------- Subsequent processing ----------------------------------------------------------------------------- The output generated by this program (-o/--output, default "pdist.h5") may be plotted by the ``plothist`` program. See ``plothist --help`` for more information. ----------------------------------------------------------------------------- Parallelization ----------------------------------------------------------------------------- This tool supports parallelized binning, including reading of input data. Parallel processing is the default. For simple cases (reading pre-computed input data, modest numbers of segments), serial processing (--serial) may be more efficient. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def __init__(self): super().__init__() # Parallel processing by default (this is not actually necessary, but it is # informative!) self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager # These are used throughout self.progress = ProgressIndicatorComponent() self.default_kinetics_file = 'kintrace.h5' self.kinetics_filename = None self.kinetics_file = None # Kinavg file self.istate = None self.fstate = None # Duration and weight dsspecs self.duration_dsspec = None self.wt_dsspec = None self.binspec = None self.output_filename = None self.output_file = None # These are used during histogram generation only self.iter_start = None self.iter_stop = None self.ndim = None # self.ntimepoints = None self.dset_dtype = None self.binbounds = None # bin boundaries for each dimension self.midpoints = None # bin midpoints for each dimension self.data_range = None # data range for each dimension, as the pairs (min,max) self.ignore_out_of_range = False self.compress_output = False def add_args(self, parser): parser.add_argument( '-b', '--bins', dest='bins', metavar='BINEXPR', default='100', help='''Use BINEXPR for bins. This may be an integer, which will be used for each dimension of the progress coordinate; a list of integers (formatted as [n1,n2,...]) which will use n1 bins for the first dimension, n2 for the second dimension, and so on; or a list of lists of boundaries (formatted as [[a1, a2,...], [b1, b2,...],... ]), which will use [a1, a2,...] as bin boundaries for the first dimension, [b1, b2,...] as bin boundaries for the second dimension, and so on. (Default: 100 bins in each dimension.)''', ) parser.add_argument( '-C', '--compress', action='store_true', help='''Compress histograms. May make storage of higher-dimensional histograms more tractable, at the (possible extreme) expense of increased analysis time. (Default: no compression.)''', ) parser.add_argument( '--loose', dest='ignore_out_of_range', action='store_true', help='''Ignore values that do not fall within bins. (Risky, as this can make buggy bin boundaries appear as reasonable data. Only use if you are sure of your bin boundary specification.)''', ) parser.add_argument('--istate', type=int, required=True, dest='istate', help='''Initial state defining transition event''') parser.add_argument('--fstate', type=int, required=True, dest='fstate', help='''Final state defining transition event''') itergroup = parser.add_argument_group('iteration range options') itergroup.add_argument( '--first-iter', default=1, dest='iter_start', type=int, help='''Iteration to begin analysis (default: 1)''' ) itergroup.add_argument('--last-iter', dest='iter_stop', type=int, help='''Iteration to end analysis''') iogroup = parser.add_argument_group('input/output options') # self.default_kinetics_file will be picked up as a class attribute from the appropriate subclass iogroup.add_argument( '-k', '--kinetics', default=self.default_kinetics_file, help='''Populations and transition rates (including evolution) are stored in KINETICS (default: %(default)s).''', ) iogroup.add_argument( '-o', '--output', dest='output', default='eddist.h5', help='''Store results in OUTPUT (default: %(default)s).''' ) self.progress.add_args(parser) def process_args(self, args): self.progress.process_args(args) self.kinetics_filename = args.kinetics self.istate = args.istate self.fstate = args.fstate self.kinetics_file = h5io.WESTPAH5File(self.kinetics_filename, 'r') self.iter_start = args.iter_start if args.iter_stop is None: self.iter_stop = self.kinetics_file.attrs['iter_stop'] else: self.iter_stop = args.iter_stop + 1 self.binspec = args.bins self.output_filename = args.output self.ignore_out_of_range = bool(args.ignore_out_of_range) self.compress_output = args.compress or False def go(self): pi = self.progress.indicator pi.operation = 'Initializing' with pi: self.duration = self.kinetics_file['durations'][self.iter_start - 1 : self.iter_stop - 1] # Only select transition events from specified istate to fstate mask = (self.duration['istate'] == self.istate) & (self.duration['fstate'] == self.fstate) self.duration_dsspec = DurationDataset(self.kinetics_file['durations']['duration'], mask, self.iter_start) self.wt_dsspec = DurationDataset(self.kinetics_file['durations']['weight'], mask, self.iter_start) self.output_file = h5py.File(self.output_filename, 'w') h5io.stamp_creator_data(self.output_file) # Construct bin boundaries self.construct_bins(self.parse_binspec(self.binspec)) for idim, (binbounds, midpoints) in enumerate(zip(self.binbounds, self.midpoints)): self.output_file['binbounds_{}'.format(idim)] = binbounds self.output_file['midpoints_{}'.format(idim)] = midpoints # construct histogram self.construct_histogram() # Record iteration range iter_range = np.arange(self.iter_start, self.iter_stop, 1, dtype=(np.min_scalar_type(self.iter_stop))) self.output_file['n_iter'] = iter_range self.output_file['histograms'].attrs['iter_start'] = self.iter_start self.output_file['histograms'].attrs['iter_stop'] = self.iter_stop self.output_file.close() @staticmethod def parse_binspec(binspec): namespace = {'numpy': np, 'np': np, 'inf': float('inf')} try: binspec_compiled = eval(binspec, namespace) except Exception as e: raise ValueError('invalid bin specification: {!r}'.format(e)) else: if log.isEnabledFor(logging.DEBUG): log.debug('bin specs: {!r}'.format(binspec_compiled)) return binspec_compiled def construct_bins(self, bins): ''' Construct bins according to ``bins``, which may be: 1) A scalar integer (for that number of bins in each dimension) 2) A sequence of integers (specifying number of bins for each dimension) 3) A sequence of sequences of bin boundaries (specifying boundaries for each dimension) Sets ``self.binbounds`` to a list of arrays of bin boundaries appropriate for passing to fasthist.histnd, along with ``self.midpoints`` to the midpoints of the bins. ''' if not isiterable(bins): self._construct_bins_from_scalar(bins) elif not isiterable(bins[0]): self._construct_bins_from_int_seq(bins) else: self._construct_bins_from_bound_seqs(bins) if log.isEnabledFor(logging.DEBUG): log.debug('binbounds: {!r}'.format(self.binbounds)) def scan_data_shape(self): if self.ndim is None: dset = self.duration_dsspec # self.ntimepoints = dset.shape[1] # self.ndim = dset.shape[2] self.ndim = 1 self.dset_dtype = dset.dtype def scan_data_range(self): '''Scan input data for range in each dimension. The number of dimensions is determined from the shape of the progress coordinate as of self.iter_start.''' self.progress.indicator.new_operation('Scanning for data range', self.iter_stop - self.iter_start) self.scan_data_shape() dset_dtype = self.dset_dtype ndim = self.ndim dsspec = self.duration_dsspec try: minval = np.finfo(dset_dtype).min maxval = np.finfo(dset_dtype).max except ValueError: minval = np.iinfo(dset_dtype).min maxval = np.iinfo(dset_dtype).max data_range = self.data_range = [(maxval, minval) for _i in range(self.ndim)] # futures = [] # for n_iter in xrange(self.iter_start, self.iter_stop): # _remote_min_max(ndim, dset_dtype, n_iter, dsspec) # futures.append(self.work_manager.submit(_remote_min_max, args=(ndim, dset_dtype, n_iter, dsspec))) # for future in self.work_manager.as_completed(futures): for future in self.work_manager.submit_as_completed( ((_remote_min_max, (ndim, dset_dtype, n_iter, dsspec), {}) for n_iter in range(self.iter_start, self.iter_stop)), self.max_queue_len, ): bounds = future.get_result(discard=True) for idim in range(ndim): current_min, current_max = data_range[idim] current_min = min(current_min, bounds[idim][0]) current_max = max(current_max, bounds[idim][1]) data_range[idim] = (current_min, current_max) self.progress.indicator.progress += 1 def _construct_bins_from_scalar(self, bins): if self.data_range is None: self.scan_data_range() # print(self.data_range) self.binbounds = [] self.midpoints = [] for idim in range(self.ndim): lb, ub = self.data_range[idim] # Advance just beyond the upper bound of the range, so that we catch # the maximum in the histogram ub *= 1.01 # lb -= 0.01 boundset = np.linspace(lb, ub, bins + 1) midpoints = (boundset[:-1] + boundset[1:]) / 2.0 self.binbounds.append(boundset) self.midpoints.append(midpoints) def _construct_bins_from_int_seq(self, bins): if self.data_range is None: self.scan_data_range() self.binbounds = [] self.midpoints = [] for idim in range(self.ndim): lb, ub = self.data_range[idim] # Advance just beyond the upper bound of the range, so that we catch # the maximum in the histogram ub *= 1.01 boundset = np.linspace(lb, ub, bins[idim] + 1) midpoints = (boundset[:-1] + boundset[1:]) / 2.0 self.binbounds.append(boundset) self.midpoints.append(midpoints) def _construct_bins_from_bound_seqs(self, bins): self.binbounds = [] self.midpoints = [] for boundset in bins: boundset = np.asarray(boundset) if (np.diff(boundset) <= 0).any(): raise ValueError('boundary set {!r} is not strictly monotonically increasing'.format(boundset)) self.binbounds.append(boundset) self.midpoints.append((boundset[:-1] + boundset[1:]) / 2.0) def construct_histogram(self): '''Construct a histogram using bins previously constructed with ``construct_bins()``. The time series of histogram values is stored in ``histograms``. Each histogram in the time series is normalized.''' self.scan_data_shape() iter_count = self.iter_stop - self.iter_start histograms_ds = self.output_file.create_dataset( 'histograms', dtype=np.float64, shape=((iter_count,) + tuple(len(bounds) - 1 for bounds in self.binbounds)), compression=9 if self.compress_output else None, ) binbounds = [np.require(boundset, self.dset_dtype, 'C') for boundset in self.binbounds] self.progress.indicator.new_operation('Constructing histograms', self.iter_stop - self.iter_start) task_gen = ( (_remote_bin_iter, (iiter, n_iter, self.duration_dsspec, self.wt_dsspec, 0, binbounds, self.ignore_out_of_range), {}) for (iiter, n_iter) in enumerate(range(self.iter_start, self.iter_stop)) ) # futures = set() # for iiter, n_iter in enumerate(xrange(self.iter_start, self.iter_stop)): # initpoint = 1 if iiter > 0 else 0 # futures.add(self.work_manager.submit(_remote_bin_iter, # args=(iiter, n_iter, self.dsspec, self.wt_dsspec, initpoint, binbounds))) # for future in self.work_manager.as_completed(futures): # future = self.work_manager.wait_any(futures) # for future in self.work_manager.submit_as_completed(task_gen, self.queue_size): log.debug('max queue length: {!r}'.format(self.max_queue_len)) for future in self.work_manager.submit_as_completed(task_gen, self.max_queue_len): iiter, n_iter, iter_hist = future.get_result(discard=True) self.progress.indicator.progress += 1 # store histogram histograms_ds[iiter] = iter_hist del iter_hist, future def entry_point(): WEDDist().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_fluxanl.rst

Manual

w_fluxanl command

MIT License

westpa__westpa/doc/documentation/cli/deprecated/w_fluxanl.rst

w_fluxanl w_fluxanl calculates the probability flux of a weighted ensemble simulation based on a pre-defined target state. Also calculates confidence interval of average flux. Monte Carlo bootstrapping techniques are used to account for autocorrelation between fluxes and/or errors that are not normally distributed. Overview usage: w_fluxanl [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version]                          [-W WEST_H5FILE] [-o OUTPUT]                          [--first-iter N_ITER] [--last-iter N_ITER]                          [-a ALPHA] [--autocorrel-alpha ACALPHA] [-N NSETS] [--evol] [--evol-step ESTEP] Note: All command line arguments are optional for w_fluxanl. Command-Line Options See the general command-line tool reference for more information on the general options. Input/output options These arguments allow the user to specify where to read input simulation result data and where to output calculated progress coordinate probability distribution data. Both input and output files are hdf5 format.: -W, --west-data file Read simulation result data from file *file*. (**Default:** The *hdf5* file specified in the configuration file) -o, --output file Store this tool's output in *file*. (**Default:** The *hdf5* file **pcpdist.h5**) Iteration range options Specify the range of iterations over which to construct the progress coordinate probability distribution.: --first-iter n_iter Construct probability distribution starting with iteration *n_iter* (**Default:** 1) --last-iter n_iter Construct probability distribution's time evolution up to (and including) iteration *n_iter* (**Default:** Last completed iteration) Confidence interval and bootstrapping options Specify alpha values of constructed confidence intervals.: -a alpha Calculate a (1 - *alpha*) confidence interval for the mean flux (**Default:** 0.05) --autocorrel-alpha ACalpha Identify autocorrelation of fluxes at *ACalpha* significance level. Note: Specifying an *ACalpha* level that is too small may result in failure to find autocorrelation in noisy flux signals (**Default:** Same level as *alpha*) -N n_sets, --nsets n_sets Use *n_sets* samples for bootstrapping (**Default:** Chosen based on *alpha*) --evol Calculate the time evolution of flux confidence intervals (**Warning:** computationally expensive calculation) --evol-step estep (if ``'--evol'`` specified) Calculate the time evolution of flux confidence intervals for every *estep* iterations (**Default:** 1) Examples Calculate the time evolution flux every 5 iterations: w_fluxanl --evol --evol-step 5 Calculate mean flux confidence intervals at 0.01 signicance level and calculate autocorrelations at 0.05 significance: w_fluxanl --alpha 0.01 --autocorrel-alpha 0.05 Calculate the mean flux confidence intervals using a custom bootstrap sample size of 500: w_fluxanl --n-sets 500

import h5py import numpy as np from scipy.signal import fftconvolve from warnings import warn import westpa from westpa.core.data_manager import weight_dtype, n_iter_dtype, vstr_dtype from westpa.core.we_driver import NewWeightEntry from westpa.core import h5io from westpa.tools import WESTTool, WESTDataReader, IterRangeSelection from westpa.tools.dtypes import iter_block_ci_dtype as ci_dtype import westpa.mclib as mclib fluxentry_dtype = np.dtype([('n_iter', n_iter_dtype), ('flux', weight_dtype), ('count', np.uint)]) target_index_dtype = np.dtype( [ ('target_label', vstr_dtype), ('mean_flux', weight_dtype), ('mean_flux_ci_lb', weight_dtype), ('mean_flux_ci_ub', weight_dtype), ('mean_flux_correl_len', np.uintc), ] ) def _extract_fluxes_fileversion_lt_7(iter_start, iter_stop, data_manager): '''Extract fluxes from old format, where groups for iterations where recyling occurs contain a'recycling' table.''' assert data_manager.we_h5file_version < 7 iter_count = iter_stop - iter_start target_count = data_manager.get_iter_group(iter_start)['recycling'].shape[0] fluxdata = np.zeros((iter_count,), dtype=fluxentry_dtype) if data_manager.we_h5file_version < 5: flux_field = 'weight' else: flux_field = 'flux' fluxdata = {itarget: np.zeros((iter_count,), dtype=fluxentry_dtype) for itarget in range(target_count)} for iiter, n_iter in enumerate(range(iter_start, iter_stop)): rdata = data_manager.get_iter_group(n_iter)['recycling'] for itarget in range(target_count): fluxdata[itarget][iiter]['n_iter'] = n_iter fluxdata[itarget][iiter]['flux'] = rdata[itarget][flux_field] fluxdata[itarget][iiter]['count'] = rdata[itarget]['count'] del rdata return fluxdata def _extract_fluxes_fileversion_7(iter_start, iter_stop, data_manager): '''Extract fluxes from HDF5 file version 7, where recycling information is stored in the "new_weights" group of the iteration *following* recycling events.''' assert data_manager.we_h5file_version >= 7 iter_count = iter_stop - iter_start iters = np.arange(iter_start, iter_stop, dtype=n_iter_dtype) # for each target by name, collect the iterations, fluxes, and counts # This is not the most foolproof way to do this, but it's good enough, and fast. # The most correct way to do this is tracing trajectories, # and warning if the boundary conditions change during the trace, # but that's for another tool. by_target = {} for iiter, n_iter in enumerate(range(iter_start, iter_stop)): target_states = data_manager.get_target_states(n_iter) try: new_weight_index = data_manager.get_iter_group(n_iter + 1)['new_weights']['index'] except KeyError: # no recycling data available continue for tstate in target_states: try: target_info = by_target[tstate.label] except KeyError: # State not seen before target_info = by_target[tstate.label] = np.zeros((iter_count,), dtype=fluxentry_dtype) # If the target happens not to exist in an iteration (for whatever reason), # store a count of -1 as a sentinel target_info['count'][:] = -1 target_info['n_iter'][:] = iters[:] recycled_from_tstate = (new_weight_index['source_type'] == NewWeightEntry.NW_SOURCE_RECYCLED) & ( new_weight_index['target_state_id'] == tstate.state_id ) recycle_count = recycled_from_tstate.sum() target_info['count'][iiter] = recycle_count if recycle_count: # flux is in units of per tau target_info['flux'][iiter] = new_weight_index[recycled_from_tstate]['weight'].sum() del new_weight_index, target_states # Find the last contiguous run where target is available for target_label in by_target: fluxdata = by_target[target_label] by_target[target_label] = fluxdata[np.searchsorted(fluxdata['count'], [0])[0] :] return by_target def extract_fluxes(iter_start=None, iter_stop=None, data_manager=None): '''Extract flux values from the WEST HDF5 file for iterations >= iter_start and < iter_stop, optionally using another data manager instance instead of the global one returned by ``westpa.rc.get_data_manager()``. Returns a dictionary mapping target names (if available, target index otherwise) to a 1-D array of type ``fluxentry_dtype``, which contains columns for iteration number, flux, and count. ''' data_manager = data_manager or westpa.rc.get_data_manager() iter_start = iter_start or 1 iter_stop = iter_stop or data_manager.current_iteration if data_manager.we_h5file_version < 7: return _extract_fluxes_fileversion_lt_7(iter_start, iter_stop, data_manager) else: return _extract_fluxes_fileversion_7(iter_start, iter_stop, data_manager) class WFluxanlTool(WESTTool): prog = 'w_fluxanl' description = '''\ Extract fluxes into pre-defined target states from WEST data, average, and construct confidence intervals. Monte Carlo bootstrapping is used to account for the correlated and possibly non-Gaussian statistical error in flux measurements. All non-graphical output (including that to the terminal and HDF5) assumes that the propagation/resampling period ``tau`` is equal to unity; to obtain results in familiar units, divide all fluxes and multiply all correlation lengths by the true value of ``tau``. ''' output_format_version = 2 def __init__(self): super().__init__() self.data_reader = WESTDataReader() self.iter_range = IterRangeSelection() self.output_h5file = None self.output_group = None self.target_groups = {} self.fluxdata = {} self.alpha = None self.autocorrel_alpha = None self.n_sets = None self.do_evol = False self.evol_step = 1 def add_args(self, parser): self.data_reader.add_args(parser) self.iter_range.add_args(parser) ogroup = parser.add_argument_group('output options') ogroup.add_argument( '-o', '--output', default='fluxanl.h5', help='Store intermediate data and analysis results to OUTPUT (default: %(default)s).', ) cgroup = parser.add_argument_group('calculation options') cgroup.add_argument( '--disable-bootstrap', '-db', dest='bootstrap', action='store_const', const=False, help='''Enable the use of Monte Carlo Block Bootstrapping.''', ) cgroup.add_argument( '--disable-correl', '-dc', dest='correl', action='store_const', const=False, help='''Disable the correlation analysis.''', ) cgroup.add_argument( '-a', '--alpha', type=float, default=0.05, help='''Calculate a (1-ALPHA) confidence interval on the average flux' (default: %(default)s)''', ) cgroup.add_argument( '--autocorrel-alpha', type=float, dest='acalpha', metavar='ACALPHA', help='''Evaluate autocorrelation of flux to (1-ACALPHA) significance. Note that too small an ACALPHA will result in failure to detect autocorrelation in a noisy flux signal. (Default: same as ALPHA.)''', ) cgroup.add_argument( '-N', '--nsets', type=int, help='''Use NSETS samples for bootstrapping (default: chosen based on ALPHA)''' ) cgroup.add_argument( '--evol', action='store_true', dest='do_evol', help='''Calculate time evolution of flux confidence intervals (expensive).''', ) cgroup.add_argument( '--evol-step', type=int, default=1, metavar='ESTEP', help='''Calculate time evolution of flux confidence intervals every ESTEP iterations (default: %(default)s)''', ) def process_args(self, args): self.data_reader.process_args(args) self.data_reader.open() self.iter_range.data_manager = self.data_reader self.iter_range.process_args(args) self.output_h5file = h5py.File(args.output, 'w') self.alpha = args.alpha # Disable the bootstrap or the correlation analysis. self.mcbs_enable = args.bootstrap if args.bootstrap is not None else True self.do_correl = args.correl if args.correl is not None else True self.autocorrel_alpha = args.acalpha or self.alpha self.n_sets = args.nsets or mclib.get_bssize(self.alpha) self.do_evol = args.do_evol self.evol_step = args.evol_step or 1 def calc_store_flux_data(self): westpa.rc.pstatus( 'Calculating mean flux and confidence intervals for iterations [{},{})'.format( self.iter_range.iter_start, self.iter_range.iter_stop ) ) fluxdata = extract_fluxes(self.iter_range.iter_start, self.iter_range.iter_stop, self.data_reader) # Create a group to store data in output_group = h5io.create_hdf5_group(self.output_h5file, 'target_flux', replace=False, creating_program=self.prog) self.output_group = output_group output_group.attrs['version_code'] = self.output_format_version self.iter_range.record_data_iter_range(output_group) n_targets = len(fluxdata) index = np.empty((len(fluxdata),), dtype=target_index_dtype) avg_fluxdata = np.empty((n_targets,), dtype=ci_dtype) for itarget, (target_label, target_fluxdata) in enumerate(fluxdata.items()): # Create group and index entry index[itarget]['target_label'] = str(target_label) target_group = output_group.create_group('target_{}'.format(itarget)) self.target_groups[target_label] = target_group # Store per-iteration values target_group['n_iter'] = target_fluxdata['n_iter'] target_group['count'] = target_fluxdata['count'] target_group['flux'] = target_fluxdata['flux'] h5io.label_axes(target_group['flux'], ['n_iter'], units=['tau^-1']) # Calculate flux autocorrelation fluxes = target_fluxdata['flux'] mean_flux = fluxes.mean() fmm = fluxes - mean_flux acorr = fftconvolve(fmm, fmm[::-1]) acorr = acorr[len(acorr) // 2 :] acorr /= acorr[0] acorr_ds = target_group.create_dataset('flux_autocorrel', data=acorr) h5io.label_axes(acorr_ds, ['lag'], ['tau']) # Calculate overall averages and CIs # avg, lb_ci, ub_ci, correl_len = mclib.mcbs_ci_correl(fluxes, np.mean, self.alpha, self.n_sets, # autocorrel_alpha=self.autocorrel_alpha, subsample=np.mean) avg, lb_ci, ub_ci, sterr, correl_len = mclib.mcbs_ci_correl( {'dataset': fluxes}, estimator=(lambda stride, dataset: np.mean(dataset)), alpha=self.alpha, n_sets=self.n_sets, autocorrel_alpha=self.autocorrel_alpha, subsample=np.mean, do_correl=self.do_correl, mcbs_enable=self.mcbs_enable, ) avg_fluxdata[itarget] = (self.iter_range.iter_start, self.iter_range.iter_stop, avg, lb_ci, ub_ci, sterr, correl_len) westpa.rc.pstatus('target {!r}:'.format(target_label)) westpa.rc.pstatus(' correlation length = {} tau'.format(correl_len)) westpa.rc.pstatus(' mean flux and CI = {:e} ({:e},{:e}) tau^(-1)'.format(avg, lb_ci, ub_ci)) index[itarget]['mean_flux'] = avg index[itarget]['mean_flux_ci_lb'] = lb_ci index[itarget]['mean_flux_ci_ub'] = ub_ci index[itarget]['mean_flux_correl_len'] = correl_len # Write index and summary index_ds = output_group.create_dataset('index', data=index) index_ds.attrs['mcbs_alpha'] = self.alpha index_ds.attrs['mcbs_autocorrel_alpha'] = self.autocorrel_alpha index_ds.attrs['mcbs_n_sets'] = self.n_sets self.fluxdata = fluxdata self.output_h5file['avg_flux'] = avg_fluxdata def calc_evol_flux(self): westpa.rc.pstatus( 'Calculating cumulative evolution of flux confidence intervals every {} iteration(s)'.format(self.evol_step) ) for itarget, (target_label, target_fluxdata) in enumerate(self.fluxdata.items()): fluxes = target_fluxdata['flux'] target_group = self.target_groups[target_label] iter_start = target_group['n_iter'][0] iter_stop = target_group['n_iter'][-1] iter_count = iter_stop - iter_start n_blocks = iter_count // self.evol_step if iter_count % self.evol_step > 0: n_blocks += 1 cis = np.empty((n_blocks,), dtype=ci_dtype) for iblock in range(n_blocks): block_iter_stop = min(iter_start + (iblock + 1) * self.evol_step, iter_stop) istop = min((iblock + 1) * self.evol_step, len(target_fluxdata['flux'])) fluxes = target_fluxdata['flux'][:istop] # avg, ci_lb, ci_ub, correl_len = mclib.mcbs_ci_correl(fluxes, np.mean, self.alpha, self.n_sets, # autocorrel_alpha = self.autocorrel_alpha, # subsample=np.mean) avg, ci_lb, ci_ub, sterr, correl_len = mclib.mcbs_ci_correl( {'dataset': fluxes}, estimator=(lambda stride, dataset: np.mean(dataset)), alpha=self.alpha, n_sets=self.n_sets, autocorrel_alpha=self.autocorrel_alpha, subsample=np.mean, do_correl=self.do_correl, mcbs_enable=self.mcbs_enable, ) cis[iblock]['iter_start'] = iter_start cis[iblock]['iter_stop'] = block_iter_stop cis[iblock]['expected'], cis[iblock]['ci_lbound'], cis[iblock]['ci_ubound'] = avg, ci_lb, ci_ub cis[iblock]['corr_len'] = correl_len cis[iblock]['sterr'] = sterr del fluxes cis_ds = target_group.create_dataset('flux_evolution', data=cis) cis_ds.attrs['iter_step'] = self.evol_step cis_ds.attrs['mcbs_alpha'] = self.alpha cis_ds.attrs['mcbs_autocorrel_alpha'] = self.autocorrel_alpha cis_ds.attrs['mcbs_n_sets'] = self.n_sets def go(self): self.calc_store_flux_data() if self.do_evol: self.calc_evol_flux() def entry_point(): warn('w_fluxanl is being deprecated. Please use w_assign and w_direct instead.') WFluxanlTool().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_fork.rst

Manual

w_fork command

MIT License

westpa__westpa/doc/documentation/cli/w_fork.rst

w_fork usage: w_fork [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version] [-i INPUT_H5FILE] [-I N_ITER] [-o OUTPUT_H5FILE] [--istate-map ISTATE_MAP] [--no-headers] Prepare a new weighted ensemble simulation from an existing one at a particular point. A new HDF5 file is generated. In the case of executable propagation, it is the user's responsibility to prepare the new simulation directory appropriately, particularly making the old simulation's restart data from the appropriate iteration available as the new simulations initial state data; a mapping of old simulation segment to new simulation initial states is created, both in the new HDF5 file and as a flat text file, to aid in this. Target states and basis states for the new simulation are taken from those in the original simulation. optional arguments: -h, --help show this help message and exit -i INPUT_H5FILE, --input INPUT_H5FILE Create simulation from the given INPUT_H5FILE (default: read from configuration file. -I N_ITER, --iteration N_ITER Take initial distribution for new simulation from iteration N_ITER (default: last complete iteration). -o OUTPUT_H5FILE, --output OUTPUT_H5FILE Save new simulation HDF5 file as OUTPUT (default: forked.h5). --istate-map ISTATE_MAP Write text file describing mapping of existing segments to new initial states in ISTATE_MAP (default: istate_map.txt). --no-headers Do not write header to ISTATE_MAP general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --debug enable extra checks and emit copious information --version show program's version number and exit

import argparse import logging import numpy as np import westpa from westpa.core.segment import Segment from westpa.core.states import InitialState from westpa.core.data_manager import n_iter_dtype, seg_id_dtype log = logging.getLogger('w_fork') def entry_point(): parser = argparse.ArgumentParser( 'w_fork', description='''\ Prepare a new weighted ensemble simulation from an existing one at a particular point. A new HDF5 file is generated. In the case of executable propagation, it is the user's responsibility to prepare the new simulation directory appropriately, particularly making the old simulation's restart data from the appropriate iteration available as the new simulations initial state data; a mapping of old simulation segment to new simulation initial states is created, both in the new HDF5 file and as a flat text file, to aid in this. Target states and basis states for the new simulation are taken from those in the original simulation. ''', ) westpa.rc.add_args(parser) parser.add_argument( '-i', '--input', dest='input_h5file', help='''Create simulation from the given INPUT_H5FILE (default: read from configuration file.''', ) parser.add_argument( '-I', '--iteration', dest='n_iter', type=int, help='''Take initial distribution for new simulation from iteration N_ITER (default: last complete iteration).''', ) parser.add_argument( '-o', '--output', dest='output_h5file', default='forked.h5', help='''Save new simulation HDF5 file as OUTPUT (default: %(default)s).''', ) parser.add_argument( '--istate-map', default='istate_map.txt', help='''Write text file describing mapping of existing segments to new initial states in ISTATE_MAP (default: %(default)s).''', ) parser.add_argument('--no-headers', action='store_true', help='''Do not write header to ISTATE_MAP''') args = parser.parse_args() westpa.rc.process_args(args) # Open old HDF5 file dm_old = westpa.rc.new_data_manager() if args.input_h5file: dm_old.we_h5filename = args.input_h5file dm_old.open_backing(mode='r') # Get iteration if necessary n_iter = args.n_iter or dm_old.current_iteration - 1 # Create and open new HDF5 file dm_new = westpa.rc.new_data_manager() dm_new.we_h5filename = args.output_h5file dm_new.prepare_backing() dm_new.open_backing() # Copy target states target_states = dm_old.get_target_states(n_iter) dm_new.save_target_states(target_states, n_iter) # Copy basis states basis_states = dm_old.get_basis_states(n_iter) dm_new.create_ibstate_group(basis_states, n_iter=1) # Transform old segments into initial states and new segments # We produce one initial state and one corresponding # new segment for each old segment. Further adjustment # can be accomplished by using w_binning. old_iter_group = dm_old.get_iter_group(n_iter) old_index = old_iter_group['seg_index'][...] old_pcoord_ds = old_iter_group['pcoord'] n_segments = old_pcoord_ds.shape[0] pcoord_len = old_pcoord_ds.shape[1] pcoord_ndim = old_pcoord_ds.shape[2] old_final_pcoords = old_pcoord_ds[:, pcoord_len - 1, :] istates = dm_new.create_initial_states(n_segments, n_iter=1) segments = [] state_map_dtype = np.dtype([('old_n_iter', n_iter_dtype), ('old_seg_id', seg_id_dtype), ('new_istate_id', seg_id_dtype)]) state_map = np.empty((n_segments,), dtype=state_map_dtype) state_map['old_n_iter'] = n_iter for iseg, (index_row, pcoord) in enumerate(zip(old_index, old_final_pcoords)): istate = istates[iseg] istate.iter_created = 0 istate.iter_used = 1 istate.istate_type = InitialState.ISTATE_TYPE_RESTART istate.istate_status = InitialState.ISTATE_STATUS_PREPARED istate.pcoord = pcoord segment = Segment( n_iter=1, seg_id=iseg, weight=index_row['weight'], parent_id=-(istate.state_id + 1), wtg_parent_ids=[-(istate.state_id + 1)], status=Segment.SEG_STATUS_PREPARED, ) segment.pcoord = np.zeros((pcoord_len, pcoord_ndim), dtype=pcoord.dtype) segment.pcoord[0] = pcoord segments.append(segment) state_map[iseg]['old_seg_id'] = iseg state_map[iseg]['new_istate_id'] = istate.state_id dm_new.update_initial_states(istates, n_iter=0) dm_new.prepare_iteration(n_iter=1, segments=segments) # Update current iteration and close both files dm_new.current_iteration = 1 dm_new.close_backing() dm_old.close_backing() # Write state map istate_map_file = open(args.istate_map, 'wt') if not args.no_headers: istate_map_file.write('# mapping from previous segment IDs to new initial states\n') istate_map_file.write('# generated by w_fork\n') istate_map_file.write('# column 0: old simulation n_iter\n') istate_map_file.write('# column 1: old simulation seg_id\n') istate_map_file.write('# column 2: new simulation initial state ID\n') for row in state_map: istate_map_file.write( '{old_n_iter:20d} {old_seg_id:20d} {new_istate_id:20d}\n'.format( old_n_iter=int(row['old_n_iter']), old_seg_id=int(row['old_seg_id']), new_istate_id=int(row['new_istate_id']) ) ) if __name__ == '__main__': entry_point()

westpa__westpa

w_init.rst

Manual

w_init command

MIT License

westpa__westpa/doc/documentation/cli/w_init.rst

w_init w_init initializes the weighted ensemble simulation, creates the main HDF5 file and prepares the first iteration. Overview Usage: w_init [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version]              [--force] [--bstate-file BSTATE_FILE] [--bstate BSTATES]              [--tstate-file TSTATE_FILE] [--tstate TSTATES]              [--segs-per-state N] [--no-we] [--wm-work-manager WORK_MANAGER]              [--wm-n-workers N_WORKERS] [--wm-zmq-mode MODE]              [--wm-zmq-info INFO_FILE] [--wm-zmq-task-endpoint TASK_ENDPOINT]              [--wm-zmq-result-endpoint RESULT_ENDPOINT]              [--wm-zmq-announce-endpoint ANNOUNCE_ENDPOINT]              [--wm-zmq-heartbeat-interval INTERVAL]              [--wm-zmq-task-timeout TIMEOUT] [--wm-zmq-client-comm-mode MODE] Initialize a new WEST simulation, creating the WEST HDF5 file and preparing the first iteration's segments. Initial states are generated from one or more "basis states" which are specified either in a file specified with --bstates-from, or by one or more --bstate arguments. If neither --bstates-from nor at least one --bstate argument is provided, then a default basis state of probability one identified by the state ID zero and label "basis" will be created (a warning will be printed in this case, to remind you of this behavior, in case it is not what you wanted). Target states for (non- equilibrium) steady-state simulations are specified either in a file specified with --tstates-from, or by one or more --tstate arguments. If neither --tstates-from nor at least one --tstate argument is provided, then an equilibrium simulation (without any sinks) will be performed. Command-Line Options See the general command-line tool reference for more information on the general options. State Options --force Overwrites any existing simulation data --bstate BSTATES Add the given basis state (specified as a string 'label,probability[,auxref]') to the list of basis states (after those specified in --bstates-from, if any). This argument may be specified more than once, in which case the given states are appended in the order they are given on the command line. --bstate-file BSTATE_FILE, --bstates-from BSTATE_FILE Read basis state names, probabilities, and (optionally) data references from BSTATE_FILE. --tstate TSTATES Add the given target state (specified as a string 'label,pcoord0[,pcoord1[,...]]') to the list of target states (after those specified in the file given by --tstates-from, if any). This argument may be specified more than once, in which case the given states are appended in the order they appear on the command line. --tstate-file TSTATE_FILE, --tstates-from TSTATE_FILE Read target state names and representative progress coordinates from TSTATE_FILE. WESTPA uses the representative progress coordinate of a target state and converts the **entire** bin containing that progress coordinate into a recycling sink. --segs-per-state N Initialize N segments from each basis state (default: 1). --no-we, --shotgun Do not run the weighted ensemble bin/split/merge algorithm on newly-created segments.

import argparse import io import logging import sys import numpy as np import westpa from westpa.core.states import BasisState, TargetState import westpa.work_managers as work_managers from westpa.work_managers import make_work_manager log = logging.getLogger('w_init') EPS = np.finfo(np.float64).eps def entry_point(): parser = argparse.ArgumentParser( 'w_init', description='''\ Initialize a new WEST simulation, creating the WEST HDF5 file and preparing the first iteration's segments. Initial states are generated from one or more "basis states" which are specified either in a file specified with --bstates-from, or by one or more "--bstate" arguments. If neither --bstates-from nor at least one "--bstate" argument is provided, then a default basis state of probability one identified by the state ID zero and label "basis" will be created (a warning will be printed in this case, to remind you of this behavior, in case it is not what you wanted). Target states for (non-equilibrium) steady-state simulations are specified either in a file specified with --tstates-from, or by one or more --tstate arguments. If neither --tstates-from nor at least one --tstate argument is provided, then an equilibrium simulation (without any sinks) will be performed. ''', ) westpa.rc.add_args(parser) parser.add_argument('--force', dest='force', action='store_true', help='Overwrite any existing simulation data') parser.add_argument( '--bstate-file', '--bstates-from', metavar='BSTATE_FILE', help='Read basis state names, probabilities, and (optionally) data references from BSTATE_FILE.', ) parser.add_argument( '--bstate', action='append', dest='bstates', help='''Add the given basis state (specified as a string 'label,probability[,auxref]') to the list of basis states (after those specified in --bstates-from, if any). This argument may be specified more than once, in which case the given states are appended in the order they are given on the command line.''', ) parser.add_argument( '--tstate-file', '--tstates-from', metavar='TSTATE_FILE', help='''Read target state names and representative progress coordinates from TSTATE_FILE. WESTPA uses the representative progress coordinate of a target state and converts the **entire** bin containing that progress coordinate into a recycling sink.''', ) parser.add_argument( '--tstate', action='append', dest='tstates', help='''Add the given target state (specified as a string 'label,pcoord0[,pcoord1[,...]]') to the list of target states (after those specified in the file given by --tstates-from, if any). This argument may be specified more than once, in which case the given states are appended in the order they appear on the command line.''', ) parser.add_argument( '--sstate-file', '--sstates-from', metavar='SSTATE_FILE', help='Read start state names, probabilities, and (optionally) data references from SSTATE_FILE.', ) parser.add_argument( '--sstate', action='append', dest='sstates', help='''Add the given start state (specified as a string 'label,probability[,auxref]') to the list of start states (after those specified in --sstates-from, if any). This argument may be specified more than once, in which case the given states are appended in the order they are given on the command line.''', ) parser.add_argument( '--segs-per-state', type=int, metavar='N', default=1, help='''Initialize N segments from each basis state (default: %(default)s).''', ) parser.add_argument( '--no-we', '--shotgun', dest='shotgun', action='store_true', help='''Do not run the weighted ensemble bin/split/merge algorithm on newly-created segments.''', ) # TODO: Does this belong here or not? I like that it's parsing arguments, which is the purpose of entry_point. # I don't necessarily like that it's setting state across different parts of the program. work_managers.environment.add_wm_args(parser) args = parser.parse_args() westpa.rc.process_args(args) work_managers.environment.process_wm_args(args) initialize( args.tstates, args.tstate_file, args.bstates, args.bstate_file, args.sstates, args.sstate_file, args.segs_per_state, args.shotgun, ) def initialize(tstates, tstate_file, bstates, bstate_file, sstates=None, sstate_file=None, segs_per_state=1, shotgun=False): """ Initialize a WESTPA simulation. tstates : list of str tstate_file : str bstates : list of str bstate_file : str sstates : list of str sstate_file : str segs_per_state : int shotgun : bool """ westpa.rc.work_manager = work_manager = make_work_manager() system = westpa.rc.get_system_driver() sim_manager = westpa.rc.get_sim_manager() data_manager = westpa.rc.get_data_manager() data_manager.system = system with work_manager: if work_manager.is_master: # Process target states target_states = [] if tstate_file: target_states.extend(TargetState.states_from_file(tstate_file, system.pcoord_dtype)) if tstates: tstates_strio = io.StringIO('\n'.join(tstates).replace(',','')) target_states.extend(TargetState.states_from_file(tstates_strio, system.pcoord_dtype)) del tstates_strio # Process basis states basis_states = [] if bstate_file: basis_states.extend(BasisState.states_from_file(bstate_file)) if bstates: for bstate_str in bstates: fields = bstate_str.split(',') label = fields[0] probability = float(fields[1]) try: auxref = fields[2] except IndexError: auxref = None basis_states.append(BasisState(label=label, probability=probability, auxref=auxref)) # Process the list of start states, creating a BasisState from each start_states = [] if sstate_file: start_states.extend(BasisState.states_from_file(sstate_file)) if sstates: for sstate_str in sstates: fields = sstate_str.split(',') label = fields[0] probability = float(fields[1]) try: auxref = fields[2] except IndexError: auxref = None start_states.append(BasisState(label=label, probability=probability, auxref=auxref)) if not basis_states: log.error('At least one basis state is required') sys.exit(3) # Check that the total probability of basis states adds to one bstate_prob, sstate_prob = ( sum(bstate.probability for bstate in basis_states), sum(sstate.probability for sstate in start_states), ) # tprob = sum(bstate.probability for bstate in basis_states) tprob = bstate_prob + sstate_prob if abs(1.0 - tprob) > len(basis_states) * EPS: pscale = 1 / tprob log.warning( 'Basis state probabilities do not add to unity (basis: {:.2f}, start states: {:.2f}); rescaling by {:g}. If using start states, some rescaling is normal.'.format( bstate_prob, sstate_prob, pscale ) ) for bstate in basis_states: bstate.probability *= pscale for sstate in start_states: sstate.probability *= pscale # Prepare simulation sim_manager.initialize_simulation( basis_states=basis_states, target_states=target_states, start_states=start_states, segs_per_state=segs_per_state, suppress_we=shotgun, ) else: work_manager.run() if __name__ == '__main__': entry_point()

westpa__westpa

w_ipa.rst

Manual

w_ipa command

MIT License

westpa__westpa/doc/documentation/cli/w_ipa.rst

w_ipa The w_ipa is a (beta) WESTPA tool that automates analysis using analysis schemes and enables interactive analysis of WESTPA simulation data. The tool can do a variety of different types of analysis, including the following: * Calculate fluxes and rate constants * Adjust and use alternate state definitions * Trace trajectory segments, including statistical weights, position along the progress coordinate, and other auxiliary data * Plot all of the above in the terminal! If you are using w_ipa for kinetics automated kinetics analysis, keep in mind that w_ipa is running w_assign and w_direct using the scheme designated in your west.cfg file. For more diverse kinetics analysis options, consider using w_assign and w_direct manually. This can be useful if you'd like to use auxiliary coordinates that aren't your progress coordinate, in one or two dimension options. usage: w_ipa [-h] [-r RCFILE] [--quiet] [--verbose] [--version] [--max-queue-length MAX_QUEUE_LENGTH] [-W WEST_H5FILE] [--analysis-only] [--reanalyze] [--ignore-hash] [--debug] [--terminal] [--serial | --parallel | --work-manager WORK_MANAGER] [--n-workers N_WORKERS] [--zmq-mode MODE] [--zmq-comm-mode COMM_MODE] [--zmq-write-host-info INFO_FILE] [--zmq-read-host-info INFO_FILE] [--zmq-upstream-rr-endpoint ENDPOINT] [--zmq-upstream-ann-endpoint ENDPOINT] [--zmq-downstream-rr-endpoint ENDPOINT] [--zmq-downstream-ann-endpoint ENDPOINT] [--zmq-master-heartbeat MASTER_HEARTBEAT] [--zmq-worker-heartbeat WORKER_HEARTBEAT] [--zmq-timeout-factor FACTOR] [--zmq-startup-timeout STARTUP_TIMEOUT] [--zmq-shutdown-timeout SHUTDOWN_TIMEOUT] optional arguments: -h, --help show this help message and exit general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --version show program's version number and exit parallelization options: --max-queue-length MAX_QUEUE_LENGTH Maximum number of tasks that can be queued. Useful to limit RAM use for tasks that have very large requests/response. Default: no limit. WEST input data options: -W WEST_H5FILE, --west-data WEST_H5FILE Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in west.cfg). runtime options: --analysis-only, -ao Use this flag to run the analysis and return to the terminal. --reanalyze, -ra Use this flag to delete the existing files and reanalyze. --ignore-hash, -ih Ignore hash and don't regenerate files. --debug, -d Debug output largely intended for development. --terminal, -t Plot output in terminal. parallelization options: --serial run in serial mode --parallel run in parallel mode (using processes) --work-manager WORK_MANAGER use the given work manager for parallel task distribution. Available work managers are ('serial', 'threads', 'processes', 'zmq'); default is 'processes' --n-workers N_WORKERS Use up to N_WORKERS on this host, for work managers which support this option. Use 0 for a dedicated server. (Ignored by work managers which do not support this option.) options for ZeroMQ ("zmq") work manager (master or node): --zmq-mode MODE Operate as a master (server) or a node (workers/client). "server" is a deprecated synonym for "master" and "client" is a deprecated synonym for "node". --zmq-comm-mode COMM_MODE Use the given communication mode -- TCP or IPC (Unix-domain) -- sockets for communication within a node. IPC (the default) may be more efficient but is not available on (exceptionally rare) systems without node-local storage (e.g. /tmp); on such systems, TCP may be used instead. --zmq-write-host-info INFO_FILE Store hostname and port information needed to connect to this instance in INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly. Downstream nodes read this file with --zmq-read-host-info and know where how to connect. --zmq-read-host-info INFO_FILE Read hostname and port information needed to connect to the master (or other coordinating node) from INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly, writing that information with --zmq-write-host-info for this instance to read. --zmq-upstream-rr-endpoint ENDPOINT ZeroMQ endpoint to which to send request/response (task and result) traffic toward the master. --zmq-upstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to receive announcement (heartbeat and shutdown notification) traffic from the master. --zmq-downstream-rr-endpoint ENDPOINT ZeroMQ endpoint on which to listen for request/response (task and result) traffic from subsidiary workers. --zmq-downstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to send announcement (heartbeat and shutdown notification) traffic toward workers. --zmq-master-heartbeat MASTER_HEARTBEAT Every MASTER_HEARTBEAT seconds, the master announces its presence to workers. --zmq-worker-heartbeat WORKER_HEARTBEAT Every WORKER_HEARTBEAT seconds, workers announce their presence to the master. --zmq-timeout-factor FACTOR Scaling factor for heartbeat timeouts. If the master doesn't hear from a worker in WORKER_HEARTBEAT*FACTOR, the worker is assumed to have crashed. If a worker doesn't hear from the master in MASTER_HEARTBEAT*FACTOR seconds, the master is assumed to have crashed. Both cases result in shutdown. --zmq-startup-timeout STARTUP_TIMEOUT Amount of time (in seconds) to wait for communication between the master and at least one worker. This may need to be changed on very large, heavily-loaded computer systems that start all processes simultaneously. --zmq-shutdown-timeout SHUTDOWN_TIMEOUT Amount of time (in seconds) to wait for workers to shut down.

import base64 import codecs import hashlib import os import warnings import numpy as np import westpa from westpa.core import h5io from westpa.cli.tools import w_assign, w_direct, w_reweight from westpa.tools import WESTParallelTool, WESTDataReader, ProgressIndicatorComponent, Plotter from westpa.tools import WIPIDataset, __get_data_for_iteration__, WIPIScheme warnings.filterwarnings('ignore', category=DeprecationWarning) warnings.filterwarnings('ignore', category=RuntimeWarning) warnings.filterwarnings('ignore', category=FutureWarning) warnings.filterwarnings('ignore', category=ImportWarning) warnings.filterwarnings('ignore') class WIPI(WESTParallelTool): ''' Welcome to w_ipa (WESTPA Interactive Python Analysis)! From here, you can run traces, look at weights, progress coordinates, etc. This is considered a'stateful' tool; that is, the data you are pulling is always pulled from the current analysis scheme and iteration. By default, the first analysis scheme in west.cfg is used, and you are set at iteration 1. ALL PROPERTIES ARE ACCESSED VIA w or west To see the current iteration, try: w.iteration OR west.iteration to set it, simply plug in a new value. w.iteration = 100 To change/list the current analysis schemes: w.list_schemes w.scheme = OUTPUT FROM w.list_schemes To see the states and bins defined in the current analysis scheme: w.states w.bin_labels All information about the current iteration is available in an object called 'current': w.current walkers, summary, states, seg_id, weights, parents, kinavg, pcoord, bins, populations, and auxdata, if it exists. In addition, the function w.trace(seg_id) will run a trace over a seg_id in the current iteration and return a dictionary containing all pertinent information about that seg_id's history. It's best to store this, as the trace can be expensive. Run help on any function or property for more information! Happy analyzing! ''' def __init__(self): super().__init__() self.data_reader = WESTDataReader() self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager self.progress = ProgressIndicatorComponent() self._iter = 1 self.config_required = True self.version = "1.0B" # Set to matplotlib if you want that. But why would you? # Well, whatever, we'll just set it to that for now. self.interface ='matplotlib' self._scheme = None global iteration def add_args(self, parser): self.progress.add_args(parser) self.data_reader.add_args(parser) rgroup = parser.add_argument_group('runtime options') rgroup.add_argument( '--analysis-only', '-ao', dest='analysis_mode', action='store_true', help='''Use this flag to run the analysis and return to the terminal.''', ) rgroup.add_argument( '--reanalyze', '-ra', dest='reanalyze', action='store_true', help='''Use this flag to delete the existing files and reanalyze.''', ) rgroup.add_argument( '--ignore-hash', '-ih', dest='ignore_hash', action='store_true', help='''Ignore hash and don't regenerate files.''' ) rgroup.add_argument( '--debug', '-d', dest='debug_mode', action='store_true', help='''Debug output largely intended for development.''' ) rgroup.add_argument('--terminal', '-t', dest='plotting', action='store_true', help='''Plot output in terminal.''') # There is almost certainly a better way to handle this, but we'll sort that later. import argparse rgroup.add_argument('--f', '-f', dest='extra', default='blah', help=argparse.SUPPRESS) parser.set_defaults(compression=True) def process_args(self, args): self.progress.process_args(args) self.data_reader.process_args(args) with self.data_reader: self.niters = self.data_reader.current_iteration - 1 self.__config = westpa.rc.config self.__settings = self.__config['west']['analysis'] for ischeme, scheme in enumerate(self.__settings['analysis_schemes']): if ( self.__settings['analysis_schemes'][scheme]['enabled'] is True or self.__settings['analysis_schemes'][scheme]['enabled'] is None ): self.scheme = scheme self.data_args = args self.analysis_mode = args.analysis_mode self.reanalyze = args.reanalyze self.ignore_hash = args.ignore_hash self.debug_mode = args.debug_mode if args.plotting: self.interface = 'text' def hash_args(self, args, extra=None, path=None): '''Create unique hash stamp to determine if arguments/file is different from before.''' '''Combine with iteration to know whether or not file needs updating.''' # Why are we not loading this functionality into the individual tools? # While it may certainly be useful to store arguments (and we may well do that), # it's rather complex and nasty to deal with pickling and hashing arguments through # the various namespaces. # In addition, it's unlikely that the functionality is desired at the individual tool level, # since we'll always just rewrite a file when we call the function. # return hashlib.md5(pickle.dumps([args, extra])).hexdigest() # We don't care about the path, so we'll remove it. # Probably a better way to do this, but who cares. cargs = list(args) for iarg, arg in enumerate(cargs): if path in arg: cargs[iarg] = arg.replace(path, '').replace('/', '') if arg == '--disable-averages': cargs.remove('--disable-averages') to_hash = cargs + [extra] # print(args) # print(to_hash) # print(str(to_hash).encode('base64')) if self.debug_mode: for iarg, arg in enumerate(to_hash): if not isinstance(arg, list): print('arg {num:02d} -- {arg:<20}'.format(num=iarg, arg=arg)) else: for il, l in enumerate(arg): print('arg {num:02d} -- {arg:<20}'.format(num=il + iarg, arg=l)) # print('args: {}'.format(to_hash)) # This SHOULD produce the same output, maybe? That would be nice, anyway. # But we'll need to test it more. return hashlib.md5(base64.b64encode(str(to_hash).encode())).hexdigest() def stamp_hash(self, h5file_name, new_hash): '''Loads a file, stamps it, and returns the opened file in read only''' h5file = h5io.WESTPAH5File(h5file_name, 'r+') h5file.attrs['arg_hash'] = new_hash h5file.close() h5file = h5io.WESTPAH5File(h5file_name, 'r') return h5file def analysis_structure(self): ''' Run automatically on startup. Parses through the configuration file, and loads up all the data files from the different analysis schematics. If they don't exist, it creates them automatically by hooking in to existing analysis routines and going from there. It does this by calling in the make_parser_and_process function for w_{assign,reweight,direct} using a custom built list of args. The user can specify everything in the configuration file that would have been specified on the command line. For instance, were one to call w_direct as follows: w_direct --evolution cumulative --step-iter 1 --disable-correl the west.cfg would look as follows: west: analysis: w_direct: evolution: cumulative step_iter: 1 extra: ['disable-correl'] Alternatively, if one wishes to use the same options for both w_direct and w_reweight, the key 'w_direct' can be replaced with 'kinetics'. ''' # Make sure everything exists. try: os.mkdir(self.__settings['directory']) except Exception: pass # Now, check to see whether they exist, and then load them. self.__analysis_schemes__ = {} # We really need to implement some sort of default behavior if an analysis scheme isn't set. # Right now, we just crash. That isn't really graceful. for scheme in self.__settings['analysis_schemes']: if self.__settings['analysis_schemes'][scheme]['enabled']: if self.work_manager.running is False: self.work_manager.startup() path = os.path.join(os.getcwd(), self.__settings['directory'], scheme) # if 'postanalysis' in self.__settings['analysis_schemes'][scheme] and 'postanalysis' in self.__settings['postanalysis']: # Should clean this up. But it uses the default global setting if a by-scheme one isn't set. if 'postanalysis' in self.__settings: if 'postanalysis' in self.__settings['analysis_schemes'][scheme]: pass else: self.__settings['analysis_schemes'][scheme]['postanalysis'] = self.__settings['postanalysis'] try: os.mkdir(path) except Exception: pass self.__analysis_schemes__[scheme] = {} try: if ( self.__settings['analysis_schemes'][scheme]['postanalysis'] is True or self.__settings['postanalysis'] is True ): analysis_files = ['assign', 'direct','reweight'] else: analysis_files = ['assign', 'direct'] except Exception: analysis_files = ['assign', 'direct'] self.__settings['analysis_schemes'][scheme]['postanalysis'] = False reanalyze_kinetics = False assign_hash = None for name in analysis_files: arg_hash = None if self.reanalyze is True: reanalyze_kinetics = True try: os.remove(os.path.join(path, '{}.h5'.format(name))) except Exception: pass else: try: # Try to load the hash. If we fail to load the hash or the file, we need to reload. # if self.reanalyze == True: # raise ValueError('Reanalyze set to true.') self.__analysis_schemes__[scheme][name] = h5io.WESTPAH5File( os.path.join(path, '{}.h5'.format(name)), 'r' ) arg_hash = self.__analysis_schemes__[scheme][name].attrs['arg_hash'] if name == 'assign': assign_hash = arg_hash except Exception: pass # We shouldn't rely on this. # self.reanalyze = True if True: if name == 'assign': assign = w_assign.WAssign() w_assign_config = {'output': os.path.join(path, '{}.h5'.format(name))} try: w_assign_config.update(self.__settings['w_assign']) except Exception: pass try: w_assign_config.update(self.__settings['analysis_schemes'][scheme]['w_assign']) except Exception: pass args = [] for key, value in w_assign_config.items(): if key!= 'extra': args.append(str('--') + str(key).replace('_', '-')) args.append(str(value)) # This is for stuff like disabling correlation analysis, etc. if 'extra' in list(w_assign_config.keys()): # We're sorting to ensure that the order doesn't matter. for value in sorted(w_assign_config['extra']): args.append(str('--') + str(value).replace('_', '-')) # We're just calling the built in function. # This is a lot cleaner than what we had in before, and far more workable. args.append('--config-from-file') args.append('--scheme-name') args.append('{}'.format(scheme)) # Why are we calling this if we're not sure we're remaking the file? # We need to load up the bin mapper and states and see if they're the same. assign.make_parser_and_process(args=args) import pickle # new_hash = self.hash_args(args=args, path=path, extra=[self.niters, pickle.dumps(assign.binning.mapper), assign.states]) # We need to encode it properly to ensure that some OS specific thing doesn't kill us. Same goes for the args, ultimately. # Mostly, we just need to ensure that we're consistent. new_hash = self.hash_args( args=args, path=path, extra=[ int(self.niters), codecs.encode(pickle.dumps(assign.binning.mapper), "base64"), base64.b64encode(str(assign.states).encode()), ], ) # Let's check the hash. If the hash is the same, we don't need to reload. if self.debug_mode is True: print('{:<10}: old hash, new hash -- {}, {}'.format(name, arg_hash, new_hash)) if self.ignore_hash is False and (arg_hash!= new_hash or self.reanalyze is True): # If the hashes are different, or we need to reanalyze, delete the file. try: os.remove(os.path.join(path, '{}.h5'.format(name))) except Exception: pass print('Reanalyzing file {}.h5 for scheme {}.'.format(name, scheme)) # reanalyze_kinetics = True # We want to use the work manager we have here. Otherwise, just let the tool sort out what it needs, honestly. assign.work_manager = self.work_manager assign.go() assign.data_reader.close() # Stamp w/ hash, then reload as read only. self.__analysis_schemes__[scheme][name] = self.stamp_hash( os.path.join(path, '{}.h5'.format(name)), new_hash ) del assign # Update the assignment hash. assign_hash = new_hash # Since these are all contained within one tool, now, we want it to just... load everything. if name == 'direct' or name =='reweight': if name == 'direct': analysis = w_direct.WDirect() if name =='reweight': analysis = w_reweight.WReweight() analysis_config = { 'assignments': os.path.join(path, '{}.h5'.format('assign')), 'output': os.path.join(path, '{}.h5'.format(name)), 'kinetics': os.path.join(path, '{}.h5'.format(name)), } # Pull from general analysis options, then general SPECIFIC options for each analysis, # then general options for that analysis scheme, then specific options for the analysis type in the scheme. try: analysis_config.update(self.__settings['kinetics']) except Exception: pass try: analysis_config.update(self.__settings['w_{}'.format(name)]) except Exception: pass try: analysis_config.update(self.__settings['analysis_schemes'][scheme]['kinetics']) except Exception: pass try: analysis_config.update(self.__settings['analysis_schemes'][scheme]['w_{}'.format(name)]) except Exception: pass # We're pulling in a default set of arguments, then updating them with arguments from the west.cfg file, if appropriate, after setting the appropriate command # Then, we call the magic function'make_parser_and_process' with the arguments we've pulled in. # The tool has no real idea it's being called outside of its actual function, and we're good to go. args = ['all'] for key, value in analysis_config.items(): if key!= 'extra': args.append(str('--') + str(key).replace('_', '-')) args.append(str(value)) # This is for stuff like disabling correlation analysis, etc. if 'extra' in list(analysis_config.keys()): for value in sorted(analysis_config['extra']): args.append(str('--') + str(value).replace('_', '-')) # We want to not display the averages, so... args.append('--disable-averages') new_hash = self.hash_args(args=args, path=path, extra=[int(self.niters), assign_hash]) # if arg_hash!= new_hash or self.reanalyze == True or reanalyze_kinetics == True: if self.debug_mode is True: print('{:<10}: old hash, new hash -- {}, {}'.format(name, arg_hash, new_hash)) if self.ignore_hash is False and (arg_hash!= new_hash or reanalyze_kinetics is True): try: os.remove(os.path.join(path, '{}.h5'.format(name))) except Exception: pass print('Reanalyzing file {}.h5 for scheme {}.'.format(name, scheme)) analysis.make_parser_and_process(args=args) # We want to hook into the existing work manager. analysis.work_manager = self.work_manager analysis.go() # Open! self.__analysis_schemes__[scheme][name] = self.stamp_hash( os.path.join(path, '{}.h5'.format(name)), new_hash ) del analysis # Make sure this doesn't get too far out, here. We need to keep it alive as long as we're actually analyzing things. # self.work_manager.shutdown() print("") print("Complete!") @property def assign(self): return self.__analysis_schemes__[str(self.scheme)]['assign'] @property def direct(self): """ The output from w_kinavg.py from the current scheme. """ return self.__analysis_schemes__[str(self.scheme)]['direct'] @property def state_labels(self): print("State labels and definitions!") for istate, state in enumerate(self.assign['state_labels']): print('{}: {}'.format(istate, state)) print('{}: {}'.format(istate + 1, 'Unknown')) @property def bin_labels(self): print("Bin definitions! ") for istate, state in enumerate(self.assign['bin_labels']): print('{}: {}'.format(istate, state)) @property def west(self): return self.data_reader.data_manager.we_h5file @property def reweight(self): if self.__settings['analysis_schemes'][str(self.scheme)]['postanalysis'] is True: return self.__analysis_schemes__[str(self.scheme)]['reweight'] else: value = "This sort of analysis has not been enabled." current = { 'bin_prob_evolution': value, 'color_prob_evolution': value, 'conditional_flux_evolution': value, 'rate_evolution': value, 'state_labels': value, 'state_prob_evolution': value, } current.update({'bin_populations': value, 'iterations': value}) return current @property def scheme(self): ''' Returns and sets what scheme is currently in use. To see what schemes are available, run: w.list_schemes ''' # Let's do this a few different ways. # We want to return things about the DIFFERENT schemes, if possible. if self._scheme is None: self._scheme = WIPIScheme( scheme=self.__analysis_schemes__, name=self._schemename, parent=self, settings=self.__settings ) # This just ensures that when we call it, it's clean. self._scheme.name = None return self._scheme @scheme.setter def scheme(self, scheme): self._future = None self._current = None self._past = None if scheme in self.__settings['analysis_schemes']: pass else: for ischeme, schemename in enumerate(self.__settings['analysis_schemes']): if ischeme == scheme: scheme = schemename if ( self.__settings['analysis_schemes'][scheme]['enabled'] is True or self.__settings['analysis_schemes'][scheme]['enabled'] is None ): self._schemename = scheme else: print("Scheme cannot be changed to scheme: {}; it is not enabled!".format(scheme)) @property def list_schemes(self): ''' Lists what schemes are configured in west.cfg file. Schemes should be structured as follows, in west.cfg: west: system: analysis: directory: analysis analysis_schemes: scheme.1: enabled: True states: - label: unbound coords: [[7.0]] - label: bound coords: [[2.7]] bins: - type: RectilinearBinMapper boundaries: [[0.0, 2.80, 7, 10000]] ''' # print("The following schemes are available:") # print("") # for ischeme, scheme in enumerate(self.__settings['analysis_schemes']): # print('{}. Scheme: {}'.format(ischeme, scheme)) # print("") # print("Set via name, or via the index listed.") # print("") # print("Current scheme: {}".format(self.scheme)) self._scheme.list_schemes @property def iteration(self): ''' Returns/sets the current iteration. ''' # print("The current iteration is {}".format(self._iter)) return self._iter @iteration.setter def iteration(self, value): print("Setting iteration to iter {}.".format(value)) if value <= 0: print("Iteration must begin at 1.") value = 1 if value > self.niters: print("Cannot go beyond {} iterations!".format(self.niters)) print("Setting to {}".format(self.niters)) value = self.niters # We want to trigger a rebuild on our current/past/future bits. # The scheme should automatically reset to the proper iteration, but # future needs to be manually triggered. self._iter = value self._future = None return self._iter @property def current(self): ''' The current iteration. See help for __get_data_for_iteration__ ''' return self.scheme[self.scheme.scheme].current @property def past(self): ''' The previous iteration. See help for __get_data_for_iteration__ ''' return self.scheme[self.scheme.scheme].past def trace(self, seg_id): ''' Runs a trace on a seg_id within the current iteration, all the way back to the beginning, returning a dictionary containing all interesting information: seg_id, pcoord, states, bins, weights, iteration, auxdata (optional) sorted in chronological order. Call with a seg_id. ''' if seg_id >= self.current.walkers: print("Walker seg_id # {} is beyond the max count of {} walkers.".format(seg_id, self.current.walkers)) return 1 pi = self.progress.indicator with pi: pi.new_operation('Tracing scheme:iter:seg_id {}:{}:{}'.format(self.scheme, self.iteration, seg_id), self.iteration) current = {'seg_id': [], 'pcoord': [],'states': [], 'weights': [], 'iteration': [], 'bins': []} keys = [] try: current['auxdata'] = {} for key in list(self.current['auxdata'].keys()): current['auxdata'][key] = [] key = [] except Exception: pass for iter in reversed(list(range(1, self.iteration + 1))): iter_group = self.data_reader.get_iter_group(iter) current['pcoord'].append(iter_group['pcoord'][seg_id, :, :]) current['states'].append(self.assign['trajlabels'][iter - 1, seg_id, :]) current['bins'].append(self.assign['assignments'][iter - 1, seg_id, :]) current['seg_id'].append(seg_id) current['weights'].append(iter_group['seg_index']['weight'][seg_id]) current['iteration'].append(iter) try: for key in keys: current['auxdata'][key].append(iter_group['auxdata'][key][seg_id]) except Exception: pass seg_id = iter_group['seg_index']['parent_id'][seg_id] if seg_id < 0: # Necessary for steady state simulations. This means they started in that iteration. break pi.progress += 1 current['seg_id'] = list(reversed(current['seg_id'])) current['iteration'] = list(reversed(current['iteration'])) current['states'] = np.concatenate(np.array(list(reversed(current['states'])))) current['bins'] = np.concatenate(np.array(list(reversed(current['bins'])))) current['weights'] = np.array(list(reversed(current['weights']))) current['pcoord'] = np.concatenate(np.array(list(reversed(current['pcoord'])))) try: for key in keys(): current['auxdata'][key] = np.concatenate(np.array(list(reversed(current['auxdata'][key])))) except Exception: pass current['state_labels'] = self.assign['state_labels'] for i in ['pcoord','states', 'bins', 'weights']: current[i] = WIPIDataset(raw=current[i], key=i) if i == 'weights': current[i].plotter = Plotter( np.log10(current[i].raw), str('log10 of'+ str(i)), iteration=current[i].raw.shape[0], interface=self.interface ) else: current[i].plotter = Plotter(current[i].raw, i, iteration=current[i].raw.shape[0], interface=self.interface) current[i].plot = current[i].plotter.plot return WIPIDataset(raw=current, key=seg_id) @property def future(self, value=None): ''' Similar to current/past, but keyed differently and returns different datasets. See help for Future. ''' if self._future is None: self._future = self.Future(raw=self.__get_children__(), key=None) self._future.iteration = self.iteration + 1 return self._future class Future(WIPIDataset): # This isn't a real fancy one. def __getitem__(self, value): if isinstance(value, str): print(list(self.__dict__.keys())) try: return self.__dict__['raw'][value] except Exception: print('{} is not a valid data structure.'.format(value)) elif isinstance(value, int) or isinstance(value, np.int64): # Otherwise, we assume they're trying to index for a seg_id. # if value < self.parent.walkers: current = {} current['pcoord'] = self.__dict__['raw']['pcoord'][value] current['states'] = self.__dict__['raw']['states'][value] current['bins'] = self.__dict__['raw']['bins'][value] current['parents'] = self.__dict__['raw']['parents'][value] current['seg_id'] = self.__dict__['raw']['seg_id'][value] current['weights'] = self.__dict__['raw']['weights'][value] try: current['auxdata'] = {} for key in list(self.__dict__['raw']['auxdata'].keys()): current['auxdata'][key] = self.__dict__['raw']['auxdata'][key][value] except Exception: pass current = WIPIDataset(current, 'Segment {} in Iter {}'.format(value, self.iteration)) return current def __get_children__(self): ''' Returns all information about the children of a given walker in the current iteration. Used to generate and create the future object, if necessary. ''' if self.iteration == self.niters: print("Currently at iteration {}, which is the max. There are no children!".format(self.iteration)) return 0 iter_data = __get_data_for_iteration__(value=self.iteration + 1, parent=self) future = { 'weights': [], 'pcoord': [], 'parents': [], 'summary': iter_data['summary'], 'seg_id': [], 'walkers': iter_data['walkers'], 'states': [], 'bins': [], } for seg_id in range(0, self.current.walkers): children = np.where(iter_data['parents'] == seg_id)[0] if len(children) == 0: error = "No children for seg_id {}.".format(seg_id) future['weights'].append(error) future['pcoord'].append(error) future['parents'].append(error) future['seg_id'].append(error) future['states'].append(error) future['bins'].append(error) else: # Now, we're gonna put them in the thing. value = self.iteration + 1 future['weights'].append(iter_data['weights'][children]) future['pcoord'].append(iter_data['pcoord'][...][children, :, :]) try: aux_data = iter_data['auxdata'][...][children, :, :] try: future['aux_data'].append(aux_data) except Exception: future['aux_data'] = aux_data except Exception: pass future['parents'].append(iter_data['parents'][children]) future['seg_id'].append(iter_data['seg_id'][children]) future['states'].append(self.assign['trajlabels'][value - 1, children, :]) future['bins'].append(self.assign['assignments'][value - 1, children, :]) return future def go(self): ''' Function automatically called by main() when launched via the command line interface. Generally, call main, not this function. ''' w = self print("") print("Welcome to w_ipa (WESTPA Interactive Python Analysis) v. {}!".format(w.version)) print("Run w.introduction for a more thorough introduction, or w.help to see a list of options.") print("Running analysis & loading files.") self.data_reader.open() self.analysis_structure() # Seems to be consistent with other tools, such as w_assign. For setting the iterations. self.data_reader.open() self.niters = self.data_reader.current_iteration - 1 self.iteration = self.niters try: print('Your current scheme, system and iteration are : {}, {}, {}'.format(w.scheme, os.getcwd(), w.iteration)) except Exception: pass @property def introduction(self): ''' Just spits out an introduction, in case someone doesn't call help. ''' help_string = ''' Call as a dictionary item or a.attribute: w.past, w.current, w.future: {current} Raw schemes can be accessed as follows: w.scheme.{scheme_keys} and contain mostly the same datasets associated with w. The following give raw access to the h5 files associated with the current scheme w.west w.assign w.direct w.reweight OTHER: {w} '''.format( current=self.__format_keys__(self.current.__dir__(), split=' ', offset=12), scheme_keys=self.__format_keys__(list(self._scheme.raw.keys())), w=self.__format_keys__(self.__dir__(), offset=8, max_length=0, split='', prepend='w.'), ) print(help_string) # Just a little function to be used with the introduction. def __format_keys__(self, keys, split='/', offset=0, max_length=80, prepend=''): rtn = '' run_length = 0 for key in keys: rtn += prepend + str(key) + split run_length += len(str(key)) if run_length >= max_length: run_length = offset rtn += '\n' +'' * offset if rtn[-1] == split: return rtn[:-1] else: return rtn @property def help(self): '''Just a minor function to call help on itself. Only in here to really help someone get help.''' help(self) def _repr_pretty_(self, p, cycle): self.introduction return " " def __dir__(self): return_list = ['past', 'current', 'future'] # For the moment, don't expose direct, reweight, or assign, as these are scheme dependent files. # They do exist, and always link to the current scheme, however. return_list += ['iteration', 'niters','scheme', 'list_schemes', 'bin_labels','state_labels', 'west', 'trace'] return sorted(set(return_list)) def entry_point(): west = WIPI() w = west # We're gonna print some defaults. w.main() if w.analysis_mode is False: from IPython import embed import IPython # We're using this to set magic commands. # Mostly, we're using it to allow tab completion of objects stored in dictionaries. try: # Worked on MacOS. Probably just an older version. c = IPython.Config() except Exception: # Seems to be necessary on Linux, and likely on newer installs. c = IPython.terminal.ipapp.load_default_config() c.IPCompleter.greedy = True embed(banner1='', exit_msg='Leaving w_ipa... goodbye.', config=c) print("") if __name__ == '__main__': entry_point()

westpa__westpa

w_kinavg.rst

Manual

w_kinavg command

MIT License

westpa__westpa/doc/documentation/cli/deprecated/w_kinavg.rst

w_kinavg WARNING: w_kinavg is being deprecated. Please use w_direct instead. usage: w_kinavg trace [-h] [-W WEST_H5FILE] [--first-iter N_ITER] [--last-iter N_ITER] [--step-iter STEP] [-a ASSIGNMENTS] [-o OUTPUT] [-k KINETICS] [--disable-bootstrap] [--disable-correl] [--alpha ALPHA] [--autocorrel-alpha ACALPHA] [--nsets NSETS] [-e {cumulative,blocked,none}] [--window-frac WINDOW_FRAC] [--disable-averages] Calculate average rates/fluxes and associated errors from weighted ensemble data. Bin assignments (usually "assign.h5") and kinetics data (usually "direct.h5") data files must have been previously generated (see "w_assign --help" and "w_direct init --help" for information on generating these files). The evolution of all datasets may be calculated, with or without confidence intervals. Output format The output file (-o/--output, usually "direct.h5") contains the following dataset: /avg_rates [state,state] (Structured -- see below) State-to-state rates based on entire window of iterations selected. /avg_total_fluxes [state] (Structured -- see below) Total fluxes into each state based on entire window of iterations selected. /avg_conditional_fluxes [state,state] (Structured -- see below) State-to-state fluxes based on entire window of iterations selected. If --evolution-mode is specified, then the following additional datasets are available: /rate_evolution [window][state][state] (Structured -- see below). State-to-state rates based on windows of iterations of varying width. If --evolution-mode=cumulative, then these windows all begin at the iteration specified with --start-iter and grow in length by --step-iter for each successive element. If --evolution-mode=blocked, then these windows are all of width --step-iter (excluding the last, which may be shorter), the first of which begins at iteration --start-iter. /target_flux_evolution [window,state] (Structured -- see below). Total flux into a given macro state based on windows of iterations of varying width, as in /rate_evolution. /conditional_flux_evolution [window,state,state] (Structured -- see below). State-to-state fluxes based on windows of varying width, as in /rate_evolution. The structure of these datasets is as follows: iter_start (Integer) Iteration at which the averaging window begins (inclusive). iter_stop (Integer) Iteration at which the averaging window ends (exclusive). expected (Floating-point) Expected (mean) value of the observable as evaluated within this window, in units of inverse tau. ci_lbound (Floating-point) Lower bound of the confidence interval of the observable within this window, in units of inverse tau. ci_ubound (Floating-point) Upper bound of the confidence interval of the observable within this window, in units of inverse tau. stderr (Floating-point) The standard error of the mean of the observable within this window, in units of inverse tau. corr_len (Integer) Correlation length of the observable within this window, in units of tau. Each of these datasets is also stamped with a number of attributes: mcbs_alpha (Floating-point) Alpha value of confidence intervals. (For example, *alpha=0.05* corresponds to a 95% confidence interval.) mcbs_nsets (Integer) Number of bootstrap data sets used in generating confidence intervals. mcbs_acalpha (Floating-point) Alpha value for determining correlation lengths. Command-line options optional arguments: -h, --help show this help message and exit WEST input data options: -W WEST_H5FILE, --west-data WEST_H5FILE Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in west.cfg). iteration range: --first-iter N_ITER Begin analysis at iteration N_ITER (default: 1). --last-iter N_ITER Conclude analysis with N_ITER, inclusive (default: last completed iteration). --step-iter STEP Analyze/report in blocks of STEP iterations. input/output options: -a ASSIGNMENTS, --assignments ASSIGNMENTS Bin assignments and macrostate definitions are in ASSIGNMENTS (default: assign.h5). -o OUTPUT, --output OUTPUT Store results in OUTPUT (default: kinavg.h5). input/output options: -k KINETICS, --kinetics KINETICS Populations and transition rates are stored in KINETICS (default: kintrace.h5). confidence interval calculation options: --disable-bootstrap, -db Enable the use of Monte Carlo Block Bootstrapping. --disable-correl, -dc Disable the correlation analysis. --alpha ALPHA Calculate a (1-ALPHA) confidence interval' (default: 0.05) --autocorrel-alpha ACALPHA Evaluate autocorrelation to (1-ACALPHA) significance. Note that too small an ACALPHA will result in failure to detect autocorrelation in a noisy flux signal. (Default: same as ALPHA.) --nsets NSETS Use NSETS samples for bootstrapping (default: chosen based on ALPHA) calculation options: -e {cumulative,blocked,none}, --evolution-mode {cumulative,blocked,none} How to calculate time evolution of rate estimates. ``cumulative`` evaluates rates over windows starting with --start-iter and getting progressively wider to --stop- iter by steps of --step-iter. ``blocked`` evaluates rates over windows of width --step-iter, the first of which begins at --start-iter. ``none`` (the default) disables calculation of the time evolution of rate estimates. --window-frac WINDOW_FRAC Fraction of iterations to use in each window when running in ``cumulative`` mode. The (1 - frac) fraction of iterations will be discarded from the start of each window. misc options: --disable-averages, -da Whether or not the averages should be printed to the console (set to FALSE if flag is used).

from westpa.tools import WESTMasterCommand, WESTParallelTool from westpa.cli.tools.w_direct import DKinAvg from warnings import warn # Just a shim to make sure everything works and is backwards compatible. class WKinAvg(DKinAvg): subcommand = 'trace' help_text = 'averages and CIs for path-tracing kinetics analysis' default_kinetics_file = 'kintrace.h5' default_output_file = 'kinavg.h5' class WDirect(WESTMasterCommand, WESTParallelTool): prog = 'w_kinavg' subcommands = [WKinAvg] subparsers_title = 'direct kinetics analysis schemes' description = '''\ Calculate average rates and associated errors from weighted ensemble data. Bin assignments (usually "assignments.h5") and kinetics data (usually "kintrace.h5" or "kinmat.h5") data files must have been previously generated (see "w_assign --help" and "w_kinetics --help" for information on generating these files). ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file (-o/--output, usually "kinavg.h5") contains the following dataset: /avg_rates [state,state] (Structured -- see below) State-to-state rates based on entire window of iterations selected. For trace mode, the following additional datasets are generated: /avg_total_fluxes [state] (Structured -- see below) Total fluxes into each state based on entire window of iterations selected. /avg_conditional_fluxes [state,state] (Structured -- see below) State-to-state fluxes based on entire window of iterations selected. If --evolution-mode is specified, then the following additional dataset is available: /rate_evolution [window][state][state] (Structured -- see below). State-to-state rates based on windows of iterations of varying width. If --evolution-mode=cumulative, then these windows all begin at the iteration specified with --start-iter and grow in length by --step-iter for each successive element. If --evolution-mode=blocked, then these windows are all of width --step-iter (excluding the last, which may be shorter), the first of which begins at iteration --start-iter. If --evolution-mode is specified in trace mode, the following additional datasets are available: /target_flux_evolution [window,state] (Structured -- see below). Total flux into a given macro state based on windows of iterations of varying width, as in /rate_evolution. /conditional_flux_evolution [window,state,state] (Structured -- see below). State-to-state fluxes based on windows of varying width, as in /rate_evolution. The structure of these datasets is as follows: iter_start (Integer) Iteration at which the averaging window begins (inclusive). iter_stop (Integer) Iteration at which the averaging window ends (exclusive). expected (Floating-point) Expected (mean) value of the rate as evaluated within this window, in units of inverse tau. ci_lbound (Floating-point) Lower bound of the confidence interval on the rate within this window, in units of inverse tau. ci_ubound (Floating-point) Upper bound of the confidence interval on the rate within this window, in units of inverse tau. corr_len (Integer) Correlation length of the rate within this window, in units of tau. Each of these datasets is also stamped with a number of attributes: mcbs_alpha (Floating-point) Alpha value of confidence intervals. (For example, *alpha=0.05* corresponds to a 95% confidence interval.) mcbs_nsets (Integer) Number of bootstrap data sets used in generating confidence intervals. mcbs_acalpha (Floating-point) Alpha value for determining correlation lengths. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def entry_point(): warn('{} is being deprecated. Please use w_direct instead.'.format(WDirect.prog)) import sys try: if sys.argv[1]!= 'trace': sys.argv.insert(1, 'trace') except Exception: sys.argv.insert(1, 'trace') WDirect().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_kinetics.rst

Manual

w_kinetics command

MIT License

westpa__westpa/doc/documentation/cli/deprecated/w_kinetics.rst

w_kinetics WARNING: w_kinetics is being deprecated. Please use w_direct instead. usage: w_kinetics trace [-h] [-W WEST_H5FILE] [--first-iter N_ITER] [--last-iter N_ITER] [--step-iter STEP] [-a ASSIGNMENTS] [-o OUTPUT] Calculate state-to-state rates and transition event durations by tracing trajectories. A bin assignment file (usually "assign.h5") including trajectory labeling is required (see "w_assign --help" for information on generating this file). This subcommand for w_direct is used as input for all other w_direct subcommands, which will convert the flux data in the output file into average rates/fluxes/populations with confidence intervals. Output format The output file (-o/--output, by default "direct.h5") contains the following datasets: ``/conditional_fluxes`` [iteration][state][state] *(Floating-point)* Macrostate-to-macrostate fluxes. These are **not** normalized by the population of the initial macrostate. ``/conditional_arrivals`` [iteration][stateA][stateB] *(Integer)* Number of trajectories arriving at state *stateB* in a given iteration, given that they departed from *stateA*. ``/total_fluxes`` [iteration][state] *(Floating-point)* Total flux into a given macrostate. ``/arrivals`` [iteration][state] *(Integer)* Number of trajectories arriving at a given state in a given iteration, regardless of where they originated. ``/duration_count`` [iteration] *(Integer)* The number of event durations recorded in each iteration. ``/durations`` [iteration][event duration] *(Structured -- see below)* Event durations for transition events ending during a given iteration. These are stored as follows: istate *(Integer)* Initial state of transition event. fstate *(Integer)* Final state of transition event. duration *(Floating-point)* Duration of transition, in units of tau. weight *(Floating-point)* Weight of trajectory at end of transition, **not** normalized by initial state population. Because state-to-state fluxes stored in this file are not normalized by initial macrostate population, they cannot be used as rates without further processing. The w_direct kinetics command is used to perform this normalization while taking statistical fluctuation and correlation into account. See w_direct kinetics --help for more information. Target fluxes (total flux into a given state) require no such normalization. Command-line options optional arguments: -h, --help show this help message and exit WEST input data options: -W WEST_H5FILE, --west-data WEST_H5FILE Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in west.cfg). iteration range: --first-iter N_ITER Begin analysis at iteration N_ITER (default: 1). --last-iter N_ITER Conclude analysis with N_ITER, inclusive (default: last completed iteration). --step-iter STEP Analyze/report in blocks of STEP iterations. input/output options: -a ASSIGNMENTS, --assignments ASSIGNMENTS Bin assignments and macrostate definitions are in ASSIGNMENTS (default: assign.h5). -o OUTPUT, --output OUTPUT Store results in OUTPUT (default: kintrace.h5).

from westpa.tools import WESTMasterCommand, WESTParallelTool from warnings import warn from westpa.cli.tools.w_direct import DKinetics # Just a shim to make sure everything works and is backwards compatible. class WKinetics(DKinetics): subcommand = 'trace' help_text = 'averages and CIs for path-tracing kinetics analysis' default_output_file = 'kintrace.h5' class WDirect(WESTMasterCommand, WESTParallelTool): prog = 'w_kinetics' subcommands = [WKinetics] subparsers_title = 'calculate state-to-state kinetics by tracing trajectories' description = '''\ Calculate state-to-state rates and transition event durations by tracing trajectories. A bin assignment file (usually "assign.h5") including trajectory labeling is required (see "w_assign --help" for information on generating this file). The output generated by this program is used as input for the ``w_kinavg`` tool, which converts the flux data in the output file into average rates with confidence intervals. See ``w_kinavg trace --help`` for more information. ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file (-o/--output, by default "kintrace.h5") contains the following datasets: ``/conditional_fluxes`` [iteration][state][state] *(Floating-point)* Macrostate-to-macrostate fluxes. These are **not** normalized by the population of the initial macrostate. ``/conditional_arrivals`` [iteration][stateA][stateB] *(Integer)* Number of trajectories arriving at state *stateB* in a given iteration, given that they departed from *stateA*. ``/total_fluxes`` [iteration][state] *(Floating-point)* Total flux into a given macrostate. ``/arrivals`` [iteration][state] *(Integer)* Number of trajectories arriving at a given state in a given iteration, regardless of where they originated. ``/duration_count`` [iteration] *(Integer)* The number of event durations recorded in each iteration. ``/durations`` [iteration][event duration] *(Structured -- see below)* Event durations for transition events ending during a given iteration. These are stored as follows: istate *(Integer)* Initial state of transition event. fstate *(Integer)* Final state of transition event. duration *(Floating-point)* Duration of transition, in units of tau. weight *(Floating-point)* Weight of trajectory at end of transition, **not** normalized by initial state population. Because state-to-state fluxes stored in this file are not normalized by initial macrostate population, they cannot be used as rates without further processing. The ``w_kinavg`` command is used to perform this normalization while taking statistical fluctuation and correlation into account. See ``w_kinavg trace --help`` for more information. Target fluxes (total flux into a given state) require no such normalization. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def entry_point(): warn('{} is being deprecated. Please use w_direct instead.'.format(WDirect.prog)) import sys try: if sys.argv[1]!= 'trace': sys.argv.insert(1, 'trace') except Exception: sys.argv.insert(1, 'trace') WDirect().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_multi_west.rst

Manual

w_multi_west command

MIT License

westpa__westpa/doc/documentation/cli/w_multi_west.rst

w_multi_west The w_multi_west tool combines multiple WESTPA simulations into a single aggregate simulation to facilitate the analysis of the set of simulations. In particular, the tool creates a single west.h5 file that contains all of the data from the west.h5 files of the individual simulations. Each iteration x in the new file contains all of the segments from iteration x from each of the set of simulation, all normalized to the total weight. Overview usage: w_multi_west [-h] [-m master] [-n sims] [--quiet | --verbose | --debug] [--version] [-W WEST_H5FILE] [-a aux] [--auxall] [--ibstates] [--serial | --parallel | --work-manager WORK_MANAGER] [--n-workers N_WORKERS] [--zmq-mode MODE] [--zmq-comm-mode COMM_MODE] [--zmq-write-host-info INFO_FILE] [--zmq-read-host-info INFO_FILE] [--zmq-upstream-rr-endpoint ENDPOINT] [--zmq-upstream-ann-endpoint ENDPOINT] [--zmq-downstream-rr-endpoint ENDPOINT] [--zmq-downstream-ann-endpoint ENDPOINT] [--zmq-master-heartbeat MASTER_HEARTBEAT] [--zmq-worker-heartbeat WORKER_HEARTBEAT] [--zmq-timeout-factor FACTOR] [--zmq-startup-timeout STARTUP_TIMEOUT] [--zmq-shutdown-timeout SHUTDOWN_TIMEOUT] optional arguments: -h, --help show this help message and exit General options:: -m, --master directory Master path of simulations where all the smaller simulations are stored (default: Current Directory) -n, --sims n Number of simulation directories. Assumes leading zeros. (default: 0) --quiet emit only essential information --verbose emit extra information --version show program's version number and exit Command-Line Options See the general command-line tool reference for more information on the general options. Input/output options These arguments allow the user to specify where to read input simulation result data and where to output calculated progress coordinate probability distribution data. Both input and output files are hdf5 format: -W, --west, --WEST_H5FILE file The name of the main .h5 file inside each simulation directory. (Default: west.h5) -o, --output file Store this tool's output in file. (Default: multi.h5) -a, --aux auxdata Name of additional auxiliary dataset to be combined. Can be called multiple times. (Default: None) -aa, --auxall Combine all auxiliary datsets as labeled in ``west.h5`` in folder 01. (Default: False) -nr, --no-reweight Do not perform reweighting. (Default: False) -ib, --ibstates Attempt to combine ``ibstates`` dataset if the basis states are identical across all simulations. Needed when tracing with ``westpa.analysis``. (Default: False) Examples If you have five simulations, set up your directory such that you have five directories are named numerically with leading zeroes, and each directory contains a west.h5 file. For this example, each west.h5 also contains an auxiliary dataset called RMSD. If you run ls, you will see the following output: 01 02 03 04 05 To run the w_multi_west tool, do the following: w_multi_west.py -m . -n 5 --aux=RMSD If you used any custom WESTSystem, include that in the directory where you run the code. To proceed in analyzing the aggregated simulation data as a single simulation, rename the output file multi.h5 to west.h5.

import logging import numpy as np import pickle log = logging.getLogger(__name__) from westpa.tools.core import WESTTool from westpa.core.data_manager import n_iter_dtype, istate_dtype from westpa.tools.progress import ProgressIndicatorComponent from westpa.core import h5io from westpa.tools.core import WESTMultiTool # from westtools.dtypes import iter_block_ci_dtype as ci_dtype import gc # from pympler.tracker import SummaryTracker ci_dtype = np.dtype( [ ('iter_start', n_iter_dtype), ('iter_stop', n_iter_dtype), ('expected', np.float64), ('ci_lbound', np.float64), ('ci_ubound', np.float64), ('corr_len', n_iter_dtype), ('variance', np.float64), ('stderrormean', np.float64), ] ) # directory locations are stored in a.yaml file with this format: # --- # PATHS: ['/path/to/simulation/1','/path/to/simulation/2',..., # '/path/to/simulation/n'] # Straight up stolen from the data manager. In the future, maybe I can just sort it by subbing in the appropriate values. def get_bin_mapper(we_h5file, hashval): '''Look up the given hash value in the binning table, unpickling and returning the corresponding bin mapper if available, or raising KeyError if not.''' # Convert to a hex digest if we need to try: hashval = hashval.hexdigest() except AttributeError: pass while True: # these will raise KeyError if the group doesn't exist, which also means # that bin data is not available, so no special treatment here try: binning_group = we_h5file['/bin_topologies'] index = binning_group['index'] pkl = binning_group['pickles'] except KeyError: raise KeyError('hash {} not found. Could not retrieve binning group'.format(hashval)) n_entries = len(index) if n_entries == 0: raise KeyError('hash {} not found. No entries in index'.format(hashval)) chunksize = 1024 for istart in range(0, n_entries, chunksize): chunk = index[istart : min(istart + chunksize, n_entries)] for i in range(len(chunk)): if chunk[i]['hash'] == hashval: pkldat = bytes(pkl[istart + i, 0 : chunk[i]['pickle_len']].data) mapper = pickle.loads(pkldat) log.debug('loaded {!r} from {!r}'.format(mapper, binning_group)) log.debug('hash value {!r}'.format(hashval)) return mapper raise KeyError('hash {} not found'.format(hashval)) def create_idtype_array(input_array): '''Return a new array with the new istate_dtype while preserving old data.''' new_array = np.zeros(input_array.shape, dtype=istate_dtype) for j in input_array.dtype.names: new_array[j] = input_array[j].copy() # Need to turn 'basis_auxref' to empty bytestrings... new_array['basis_auxref'] = b'' return new_array class WMultiWest(WESTMultiTool): prog = 'w_multi_west' description = '''\ Tool designed to combine multiple WESTPA simulations while accounting for reweighting. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def __init__(self): super(WESTTool, self).__init__() self.progress = ProgressIndicatorComponent() # We no longer care about a lot of this. self.ntrials = 0 self.nstates = 0 self.kin_trial = {} self.west = {} self.niters = 0 def add_args(self, parser): self.progress.add_args(parser) iogroup = parser.add_argument_group('input/output options') iogroup.add_argument('-o', '--output-file', default='multi.h5', help='''The name of the output file to store results in.''') iogroup.add_argument( '-W', '--west', '--WEST_H5FILE', default='west.h5', help='''The name of the main.h5 file inside each simulation directory''', ) iogroup.add_argument('-a', '--aux', action='append', help='''Names of additional auxiliary datasets to be combined''') iogroup.add_argument('-aa', '--auxall', action='store_true', help='''Combine all auxiliary datasets. Default: False''') iogroup.add_argument('-nr', '--no-reweight', action='store_true', help='''Do not reweight. Default: False''') iogroup.add_argument( '-ib', '--ibstates', action='store_true', help='''Attempt to combine ibstates dataset. Default: False''' ) def open_files(self): self.output_file = h5io.WESTPAH5File(self.output_file, 'w', creating_program=True) h5io.stamp_creator_data(self.output_file) opened_files = self.generate_file_list([self.west]) self.westH5 = opened_files[self.west] # Just some temp things while I clean everything up... # west_files = self.westH5 # Determine max iteration... # We can't really use the old method anymore, as we need to calculate rates in the bootstrap. # Ergo, we're going to load things like w_kinavg, but that's all. # We'll just load them up and store them internally, for the moment. def process_args(self, args): self.progress.process_args(args) self.output_file = args.output_file self.output_file_name = args.output_file self.west = args.west self.sims = args.sims self.aux = args.aux self.auxall = args.auxall self.reweight = args.no_reweight self.ibstates = args.ibstates def total_number_of_walkers(self): self.total_walkers = [0] * self.niters for key, west in self.westH5.items(): # Sometimes, we're smaller or larger by one. Hm. try: self.total_walkers[:] += west['summary'][:-1]['n_particles'] except ValueError: self.total_walkers[:] += west['summary'][:-1]['n_particles'][: len(self.total_walkers)] class Segment: def __init__(self, weight=0, iteration=0, simid=0, recycled_in=0): self.weight = weight self.iteration = iteration self.simid = simid self.recycled_in = recycled_in def go(self): pi = self.progress.indicator self.istates = True # Assume serendipitously istates is same between runs... with pi: pi.new_operation('Initializing') self.open_files() self.total_number_of_walkers() if self.auxall is True: self.aux = list(self.westH5[1]['iterations/iter_00000001/auxdata'].keys()) # Create a giant WEST.h5 file, separating the individual walkers, and renormalizing the weights. # It should then be compatible with existing toolsets. # Isn't really going to start with auxdata, but we'll add it in. # self.niters = 500 # Initialize data manager... # Just bullshit for the current system. # self.niters = self.westH5[1].attrs['west_current_iteration'] - 1 # print(self.niters, len(self.westH5)) # self.data_manager = data_manager.WESTDataManager() westh5 = [] self.source_sinks = [] self.n_sims = {} istate_addition = [0] for ifile, (key, west) in enumerate(self.westH5.items()): d = {'west': west, 'wm': None, 'rt': None,'remove_next_cycle': [],'seg_index': None} # We're getting the bin mapper, then setting the recycling target... binhash = west['iterations/iter_{0:08d}'.format(2)].attrs['binhash'] bin_mapper = get_bin_mapper(west, bytes(binhash, 'utf-8')) try: d['rt'] = bin_mapper.assign(west['tstates']['0']['pcoord'][...])[0] self.source_sinks.append(bin_mapper.assign(west['tstates']['0']['pcoord'][...])[0]) except KeyError: d['rt'] = None self.source_sinks.append(None) pass # We're going to make a set of source and sink states that we can iterate through, eventually. # Keep a count of how many simulations for this particular recycling target we have... try: self.n_sims[d['rt']] += 1 except KeyError: self.n_sims[d['rt']] = 1 westh5.append(d) if ifile == 0: self.niters = west.attrs['west_current_iteration'] - 1 else: self.niters = min(west.attrs['west_current_iteration'] - 1, self.niters) istate_addition.append(istate_addition[-1] + len(west['ibstates/0/istate_index'])) # Check to see if all the bstates are identical if self.ibstates: check = [False, False] # Assuming they're false, so not accidentally outputing anything that errors out. try: check[0] = np.array_equal(bstate_index, west['ibstates/0/bstate_index'][:]) check[1] = np.array_equal(bstate_pcoord, west['ibstates/0/bstate_pcoord'][:]) if not np.all(check): print(f'File {ifile} used different bstates than the first file. Will skip exporting ibstates dataset.') self.ibstates = False except NameError: bstate_index = west['ibstates/0/bstate_index'][:] # noqa: F841 bstate_pcoord = west['ibstates/0/bstate_pcoord'][:] # noqa: F841 start_point = [] self.source_sinks = list(set(self.source_sinks)) # We'll need a global list of walkers to add to and take care of during the next round of simulations, as well as the current one. # We'll organize it by source and sink states. self.past_iter = {} self.futr_iter = {} self.past_rm = {} self.futr_rm = {} for i in self.source_sinks: self.past_iter[i] = [] self.futr_iter[i] = [] self.past_rm[i] = [] self.futr_rm[i] = [] print(pi.new_operation('Recreating...', self.niters)) # tracker = SummaryTracker() # self.output_file.close() if self.ibstates: # Copying the ibstates group from the first file as base self.output_file.copy(self.westH5[1]['ibstates'], self.output_file) del self.output_file['ibstates/0/istate_pcoord'] del self.output_file['ibstates/0/istate_index'] # Combining the rest of the istate datasets for ifile, (key, west) in enumerate(self.westH5.items()): if ifile == 0: final_istate_index = west['ibstates/0/istate_index'] final_istate_pcoord = west['ibstates/0/istate_pcoord'] if final_istate_index.dtype!= istate_dtype: final_istate_index = create_idtype_array(final_istate_index) else: addition = west['ibstates/0/istate_index'][:] if addition.dtype!= istate_dtype: addition = create_idtype_array(addition) final_istate_index = np.append(final_istate_index, addition) final_istate_pcoord = np.append(final_istate_pcoord, west['ibstates/0/istate_pcoord'][:]) # Saving them into self.output_file self.output_file['ibstates/0'].create_dataset('istate_index', data=final_istate_index, dtype=istate_dtype) self.output_file['ibstates/0'].create_dataset('istate_pcoord', data=final_istate_pcoord) for iter in range(self.niters): # We have the following datasets in each iteration: # ibstates, which can now be combined with --ibstates # pcoord # seg_index # wtgraph # wtgraph is going to be a little more complex to handle, but not too bad. # aux data specified iter += 1 ifile = 0 # self.output_file = h5io.WESTPAH5File(self.output_file_name, 'w', creating_program=True) # Determine how many simulations to append or remove per west file. # self.segments = {} # for key,value in self.n_sims.items(): # self.segments[key] = int(np.floor(len(self.past_iter[key]) / value)) # run_once = 0 # total_current_sims = 0 # for i in self.source_sinks: # total_current_sims += len(self.past_iter[i]) # total_current_sims += len(self.past_rm[i]) for ifile, west in enumerate(westh5): westdict = west['west'] seg_index = westdict['iterations/iter_{0:08d}'.format(iter)]['seg_index'][...] pcoord = westdict['iterations/iter_{0:08d}'.format(iter)]['pcoord'][...] wtgraph = westdict['iterations/iter_{0:08d}'.format(iter)]['wtgraph'][...] # new_weight = westdict['iterations/iter_{0:08d}'.format(iter)]['new_weight'][...] if self.aux: auxdata = {} for i in self.aux: auxdata[str(i)] = westdict['iterations/iter_{0:08d}'.format(iter)]['auxdata'][str(i)][...] if iter == 1 and ifile == 0: new_dtype = np.dtype(seg_index.dtype.descr + [('group', '<i8')]) new_seg_index = np.zeros(seg_index.shape, dtype=new_dtype) for dt, val in seg_index.dtype.fields.items(): new_seg_index[dt] = seg_index[dt] new_seg_index['group'] = ifile del seg_index seg_index = new_seg_index[...] del new_seg_index if ifile == 0: mseg = seg_index mpco = pcoord mwtg = wtgraph if self.aux: maux = {} for i in self.aux: maux[str(i)] = auxdata[str(i)] if iter == 1: summary = westdict['summary'][...] start_point.append(0) if ifile!= 0: # print(mseg.shape, seg_index.shape, ifile) # print(mpco.shape, pcoord.shape, ifile) # print(mwtg.shape, wtgraph.shape, ifile) if iter!= 1: addition = prev_start_point[ifile] # noqa: F821 else: addition = mseg.shape[0] seg_index['parent_id'][np.where(seg_index['parent_id'] >= 0)] += addition seg_index['parent_id'][np.where(seg_index['parent_id'] < 0)] -= istate_addition[ifile] seg_index['wtg_offset'] += mwtg.shape[0] start_point.append(mseg.shape[0]) wtgraph += mwtg.shape[0] mseg = np.concatenate((mseg, seg_index)) mpco = np.concatenate((mpco, pcoord)) mwtg = np.concatenate((mwtg, wtgraph)) if self.aux: for i in self.aux: maux[str(i)] = np.concatenate((maux[str(i)], auxdata[str(i)])) ifile += 1 del seg_index, pcoord, wtgraph, westdict if self.aux: del auxdata gc.collect() # Make a real copy to use in the next iteration. # self.past_iter = self.futr_iter.copy() # self.past_rm[i] = self.futr_rm.copy() prev_start_point = start_point # noqa: F841 start_point = [] # This is... maybe wrong, actually? Or at least, it's not ALL that is required for normalizing things. # We need to weight everything by 1/N, then just normalize if that normalization was wrong. Keep the relative weights sane. #... or actually, no, that's fine, nevermind, what's wrong with me? But we'll leave it in for now. # Normalize weight of each iteration, done unless specified not to. if not self.reweight: mseg['weight'] /= mseg['weight'].sum() summary['n_particles'][iter - 1] = mseg.shape[0] summary['norm'][iter - 1] = mseg['weight'].sum() summary['min_seg_prob'][iter - 1] = min(mseg['weight']) summary['max_seg_prob'][iter - 1] = max(mseg['weight']) curr_iter = self.output_file.create_group('iterations/iter_{0:08d}'.format(iter)) curr_iter.attrs['n_iter'] = iter # Hard-link ibstates dataset to the main one if self.ibstates: curr_iter['ibstates'] = self.output_file['ibstates/0'] ds_rate_evol = curr_iter.create_dataset('wtgraph', data=mwtg, shuffle=True, compression=9) ds_rate_evol = curr_iter.create_dataset('seg_index', data=mseg, shuffle=True, compression=9) ds_rate_evol = curr_iter.create_dataset('pcoord', data=mpco, shuffle=True, compression=9) if self.aux: aux_iter = self.output_file.create_group('iterations/iter_{0:08d}/auxdata'.format(iter)) for i in self.aux: ds_rate_evol = aux_iter.create_dataset(str(i), data=maux[str(i)], shuffle=True, compression=9) # We need to be careful about memory, here. We are blowing uppppp. # We're STILL blowing up. Criiiiiipes. # self.segments = {} del mseg, mpco, mwtg, ds_rate_evol, curr_iter #, self.segments if self.aux: del maux, aux_iter gc.collect() self.output_file.flush() # self.output_file.close() # print("How big is our summary?") # print(sys.getsizeof(summary)) # objgraph.show_most_common_types(limit=50) # objgraph.show_growth(limit=10) # objgraph.show_most_common_types(objects=objgraph.get_leaking_objects()) pi.progress += 1 pi.new_operation('Writing to file...') ds_rate_evol = self.output_file.create_dataset('summary', data=summary, shuffle=True, compression=9) # noqa: F841 self.output_file.attrs['west_current_iteration'] = self.niters self.output_file.attrs['west_file_format_version'] = 7 self.output_file.attrs['west_iter_prec'] = 8 self.output_file.attrs['westpa_fileformat_version'] = 7 self.output_file.attrs['westpa_iter_prec'] = 8 def entry_point(): WMultiWest().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_ntop.rst

Manual

w_ntop command

MIT License

westpa__westpa/doc/documentation/cli/w_ntop.rst

w_ntop usage: w_ntop [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version] [-W WEST_H5FILE] [--first-iter N_ITER] [--last-iter N_ITER] [-a ASSIGNMENTS] [-n COUNT] [-t TIMEPOINT] [--highweight | --lowweight | --random] [-o OUTPUT] Select walkers from bins . An assignment file mapping walkers to bins at each timepoint is required (seew_assign --help for further information on generating this file). By default, high-weight walkers are selected (hence the name w_ntop: select the N top-weighted walkers from each bin); however, minimum weight walkers and randomly-selected walkers may be selected instead. Output format The output file (-o/--output, by default "ntop.h5") contains the following datasets: ``/n_iter`` [iteration] *(Integer)* Iteration numbers for each entry in other datasets. ``/n_segs`` [iteration][bin] *(Integer)* Number of segments in each bin/state in the given iteration. This will generally be the same as the number requested with ``--n/--count`` but may be smaller if the requested number of walkers does not exist. ``/seg_ids`` [iteration][bin][segment] *(Integer)* Matching segments in each iteration for each bin. For an iteration ``n_iter``, only the first ``n_iter`` entries are valid. For example, the full list of matching seg_ids in bin 0 in the first stored iteration is ``seg_ids[0][0][:n_segs[0]]``. ``/weights`` [iteration][bin][segment] *(Floating-point)* Weights for each matching segment in ``/seg_ids``. Command-line arguments optional arguments: -h, --help show this help message and exit --highweight Select COUNT highest-weight walkers from each bin. --lowweight Select COUNT lowest-weight walkers from each bin. --random Select COUNT walkers randomly from each bin. general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --debug enable extra checks and emit copious information --version show program's version number and exit WEST input data options: -W WEST_H5FILE, --west-data WEST_H5FILE Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in west.cfg). iteration range: --first-iter N_ITER Begin analysis at iteration N_ITER (default: 1). --last-iter N_ITER Conclude analysis with N_ITER, inclusive (default: last completed iteration). input options: -a ASSIGNMENTS, --assignments ASSIGNMENTS Use assignments from the given ASSIGNMENTS file (default: assign.h5). selection options: -n COUNT, --count COUNT Select COUNT walkers from each iteration for each bin (default: 1). -t TIMEPOINT, --timepoint TIMEPOINT Base selection on the given TIMEPOINT within each iteration. Default (-1) corresponds to the last timepoint. output options: -o OUTPUT, --output OUTPUT Write output to OUTPUT (default: ntop.h5).

import h5py import numpy as np from westpa.tools import WESTTool, WESTDataReader, IterRangeSelection, ProgressIndicatorComponent import westpa from westpa.core import h5io from westpa.core.data_manager import seg_id_dtype, n_iter_dtype, weight_dtype from westpa.core.binning import assignments_list_to_table class WNTopTool(WESTTool): prog = 'w_ntop' description = '''\ Select walkers from bins. An assignment file mapping walkers to bins at each timepoint is required (see``w_assign --help`` for further information on generating this file). By default, high-weight walkers are selected (hence the name ``w_ntop``: select the N top-weighted walkers from each bin); however, minimum weight walkers and randomly-selected walkers may be selected instead. ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file (-o/--output, by default "ntop.h5") contains the following datasets: ``/n_iter`` [iteration] *(Integer)* Iteration numbers for each entry in other datasets. ``/n_segs`` [iteration][bin] *(Integer)* Number of segments in each bin/state in the given iteration. This will generally be the same as the number requested with ``--n/--count`` but may be smaller if the requested number of walkers does not exist. ``/seg_ids`` [iteration][bin][segment] *(Integer)* Matching segments in each iteration for each bin. For an iteration ``n_iter``, only the first ``n_iter`` entries are valid. For example, the full list of matching seg_ids in bin 0 in the first stored iteration is ``seg_ids[0][0][:n_segs[0]]``. ``/weights`` [iteration][bin][segment] *(Floating-point)* Weights for each matching segment in ``/seg_ids``. ----------------------------------------------------------------------------- Command-line arguments ----------------------------------------------------------------------------- ''' def __init__(self): super().__init__() self.data_reader = WESTDataReader() self.iter_range = IterRangeSelection() self.progress = ProgressIndicatorComponent() self.output_file = None self.assignments_filename = None self.output_filename = None self.what = None self.timepoint = None self.count = None def add_args(self, parser): self.data_reader.add_args(parser) self.iter_range.add_args(parser) igroup = parser.add_argument_group('input options') igroup.add_argument( '-a', '--assignments', default='assign.h5', help='''Use assignments from the given ASSIGNMENTS file (default: %(default)s).''', ) sgroup = parser.add_argument_group('selection options') sgroup.add_argument( '-n', '--count', type=int, default=1, help='''Select COUNT walkers from each iteration for each bin (default: %(default)s).''', ) sgroup.add_argument( '-t', '--timepoint', type=int, default=-1, help='''Base selection on the given TIMEPOINT within each iteration. Default (-1) corresponds to the last timepoint.''', ) cgroup = parser.add_mutually_exclusive_group() cgroup.add_argument( '--highweight', dest='select_what', action='store_const', const='highweight', help='''Select COUNT highest-weight walkers from each bin.''', ) cgroup.add_argument( '--lowweight', dest='select_what', action='store_const', const='lowweight', help='''Select COUNT lowest-weight walkers from each bin.''', ) cgroup.add_argument( '--random', dest='select_what', action='store_const', const='random', help='''Select COUNT walkers randomly from each bin.''', ) parser.set_defaults(select_what='highweight') ogroup = parser.add_argument_group('output options') ogroup.add_argument('-o', '--output', default='ntop.h5', help='''Write output to OUTPUT (default: %(default)s).''') self.progress.add_args(parser) def process_args(self, args): self.progress.process_args(args) self.data_reader.process_args(args) with self.data_reader: self.iter_range.process_args(args) self.what = args.select_what self.output_filename = args.output self.assignments_filename = args.assignments self.count = args.count self.timepoint = args.timepoint def go(self): self.data_reader.open('r') assignments_file = h5py.File(self.assignments_filename, mode='r') output_file = h5io.WESTPAH5File(self.output_filename, mode='w') pi = self.progress.indicator count = self.count timepoint = self.timepoint nbins = assignments_file.attrs['nbins'] + 1 assignments_ds = assignments_file['assignments'] iter_start, iter_stop = self.iter_range.iter_start, self.iter_range.iter_stop iter_count = iter_stop - iter_start h5io.check_iter_range_least(assignments_ds, iter_start, iter_stop) nsegs = assignments_file['nsegs'][h5io.get_iteration_slice(assignments_file['nsegs'], iter_start, iter_stop)] output_file.create_dataset('n_iter', dtype=n_iter_dtype, data=list(range(iter_start, iter_stop))) seg_count_ds = output_file.create_dataset('nsegs', dtype=np.uint, shape=(iter_count, nbins)) matching_segs_ds = output_file.create_dataset( 'seg_ids', shape=(iter_count, nbins, count), dtype=seg_id_dtype, chunks=h5io.calc_chunksize((iter_count, nbins, count), seg_id_dtype), shuffle=True, compression=9, ) weights_ds = output_file.create_dataset( 'weights', shape=(iter_count, nbins, count), dtype=weight_dtype, chunks=h5io.calc_chunksize((iter_count, nbins, count), weight_dtype), shuffle=True, compression=9, ) what = self.what with pi: pi.new_operation('Finding matching segments', extent=iter_count) for iiter, n_iter in enumerate(range(iter_start, iter_stop)): assignments = np.require( assignments_ds[h5io.get_iteration_entry(assignments_ds, n_iter) + np.index_exp[:, timepoint]], dtype=westpa.core.binning.index_dtype, ) all_weights = self.data_reader.get_iter_group(n_iter)['seg_index']['weight'] # the following Cython function just executes this loop: # for iseg in xrange(nsegs[iiter]): # segs_by_bin[iseg,assignments[iseg]] = True segs_by_bin = assignments_list_to_table(nsegs[iiter], nbins, assignments) for ibin in range(nbins): segs = np.nonzero(segs_by_bin[:, ibin])[0] seg_count_ds[iiter, ibin] = min(len(segs), count) if len(segs): weights = all_weights.take(segs) if what == 'lowweight': indices = np.argsort(weights)[:count] elif what == 'highweight': indices = np.argsort(weights)[::-1][:count] else: assert what == 'random' indices = np.random.permutation(len(weights)) matching_segs_ds[iiter, ibin, : len(segs)] = segs.take(indices) weights_ds[iiter, ibin, : len(segs)] = weights.take(indices) del segs, weights del assignments, segs_by_bin, all_weights pi.progress += 1 def entry_point(): WNTopTool().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_pdist.rst

Manual

w_pdist command

MIT License

westpa__westpa/doc/documentation/cli/w_pdist.rst

w_pdist w_pdist constructs and calculates the progress coordinate probability distribution's evolution over a user-specified number of simulation iterations. w_pdist supports progress coordinates with dimensionality ≥ 1. The resulting distribution can be viewed with the plothist tool. Overview Usage: w_pdist [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version]                        [-W WEST_H5FILE] [--first-iter N_ITER] [--last-iter N_ITER]                        [-b BINEXPR] [-o OUTPUT]                        [--construct-dataset CONSTRUCT_DATASET | --dsspecs DSSPEC [DSSPEC ...]]                        [--serial | --parallel | --work-manager WORK_MANAGER]                        [--n-workers N_WORKERS] [--zmq-mode MODE]                        [--zmq-info INFO_FILE] [--zmq-task-endpoint TASK_ENDPOINT]                        [--zmq-result-endpoint RESULT_ENDPOINT]                        [--zmq-announce-endpoint ANNOUNCE_ENDPOINT]                        [--zmq-listen-endpoint ANNOUNCE_ENDPOINT]                        [--zmq-heartbeat-interval INTERVAL]                        [--zmq-task-timeout TIMEOUT] [--zmq-client-comm-mode MODE] Note: This tool supports parallelization, which may be more efficient for especially large datasets. Command-Line Options See the general command-line tool reference for more information on the general options. Input/output options These arguments allow the user to specify where to read input simulation result data and where to output calculated progress coordinate probability distribution data. Both input and output files are hdf5 format: -W, --WEST_H5FILE file Read simulation result data from file *file*. (**Default:** The *hdf5* file specified in the configuration file (default config file is *west.h5*)) -o, --output file Store this tool's output in *file*. (**Default:** The *hdf5* file **pcpdist.h5**) Iteration range options Specify the range of iterations over which to construct the progress coordinate probability distribution.: --first-iter n_iter Construct probability distribution starting with iteration *n_iter* (**Default:** 1) --last-iter n_iter Construct probability distribution's time evolution up to (and including) iteration *n_iter* (**Default:** Last completed iteration) Probability distribution binning options Specify the number of bins to use when constructing the progress coordinate probability distribution. If using a multidimensional progress coordinate, different binning schemes can be used for the probability distribution for each progress coordinate.: -b binexpr *binexpr* specifies the number and formatting of the bins. Its format can be as follows: 1. an integer, in which case all distributions have that many equal sized bins 2. a python-style list of integers, of length corresponding to the number of dimensions of the progress coordinate, in which case each progress coordinate's probability distribution has the corresponding number of bins 3. a python-style list of lists of scalars, where the list at each index corresponds to each dimension of the progress coordinate and specifies specific bin boundaries for that progress coordinate's probability distribution. (**Default:** 100 bins for all progress coordinates) Examples Assuming simulation results are stored in west.h5 (which is specified in the configuration file named west.cfg), for a simulation with a 1-dimensional progress coordinate: Calculate a probability distribution histogram using all default options (output file: pdist.h5; histogram binning: 100 equal sized bins; probability distribution over the lowest reached progress coordinate to the largest; work is parallelized over all available local cores using the 'processes' work manager): w_pdist Same as above, except using the serial work manager (which may be more efficient for smaller datasets): w_pdist --serial

import logging import h5py import numpy as np from westpa.tools import ( WESTParallelTool, WESTDataReader, WESTDSSynthesizer, WESTWDSSynthesizer, IterRangeSelection, ProgressIndicatorComponent, ) from westpa.fasthist import histnd, normhistnd from westpa.core import h5io log = logging.getLogger('w_pdist') def isiterable(x): try: iter(x) except TypeError: return False else: return True def _remote_min_max(ndim, dset_dtype, n_iter, dsspec): try: minval = np.finfo(dset_dtype).min maxval = np.finfo(dset_dtype).max except ValueError: minval = np.iinfo(dset_dtype).min maxval = np.iinfo(dset_dtype).max data_range = [(maxval, minval) for _i in range(ndim)] dset = dsspec.get_iter_data(n_iter) for idim in range(ndim): dimdata = dset[:, :, idim] current_min, current_max = data_range[idim] current_min = min(current_min, dimdata.min()) current_max = max(current_max, dimdata.max()) data_range[idim] = (current_min, current_max) del dimdata del dset return data_range def _remote_bin_iter(iiter, n_iter, dsspec, wt_dsspec, initpoint, binbounds, ignore_out_of_range): iter_hist_shape = tuple(len(bounds) - 1 for bounds in binbounds) iter_hist = np.zeros(iter_hist_shape, dtype=np.float64) dset = dsspec.get_iter_data(n_iter) npts = dset.shape[1] weights = wt_dsspec.get_iter_data(n_iter) dset = dset[:, initpoint:, :] for ipt in range(npts - initpoint): histnd(dset[:, ipt, :], binbounds, weights, out=iter_hist, binbound_check=False, ignore_out_of_range=ignore_out_of_range) del weights, dset # normalize histogram normhistnd(iter_hist, binbounds) return iiter, n_iter, iter_hist class WPDist(WESTParallelTool): prog = 'w_pdist' description = '''\ Calculate time-resolved, multi-dimensional probability distributions of WE datasets. ----------------------------------------------------------------------------- Source data ----------------------------------------------------------------------------- Source data is provided either by a user-specified function (--construct-dataset) or a list of "data set specifications" (--dsspecs). If neither is provided, the progress coordinate dataset ''pcoord'' is used. To use a custom function to extract or calculate data whose probability distribution will be calculated, specify the function in standard Python MODULE.FUNCTION syntax as the argument to --construct-dataset. This function will be called as function(n_iter,iter_group), where n_iter is the iteration whose data are being considered and iter_group is the corresponding group in the main WEST HDF5 file (west.h5). The function must return data which can be indexed as [segment][timepoint][dimension]. To use a list of data set specifications, specify --dsspecs and then list the desired datasets one-by-one (space-separated in most shells). These data set specifications are formatted as NAME[,file=FILENAME,slice=SLICE], which will use the dataset called NAME in the HDF5 file FILENAME (defaulting to the main WEST HDF5 file west.h5), and slice it with the Python slice expression SLICE (as in [0:2] to select the first two elements of the first axis of the dataset). The ``slice`` option is most useful for selecting one column (or more) from a multi-column dataset, such as arises when using a progress coordinate of multiple dimensions. ----------------------------------------------------------------------------- Histogram binning ----------------------------------------------------------------------------- By default, histograms are constructed with 100 bins in each dimension. This can be overridden by specifying -b/--bins, which accepts a number of different kinds of arguments: a single integer N N uniformly spaced bins will be used in each dimension. a sequence of integers N1,N2,... (comma-separated) N1 uniformly spaced bins will be used for the first dimension, N2 for the second, and so on. a list of lists [[B11, B12, B13,...], [B21, B22, B23,...],...] The bin boundaries B11, B12, B13,... will be used for the first dimension, B21, B22, B23,... for the second dimension, and so on. These bin boundaries need not be uniformly spaced. These expressions will be evaluated with Python's ``eval`` construct, with ``np`` available for use [e.g. to specify bins using np.arange()]. The first two forms (integer, list of integers) will trigger a scan of all data in each dimension in order to determine the minimum and maximum values, which may be very expensive for large datasets. This can be avoided by explicitly providing bin boundaries using the list-of-lists form. Note that these bins are *NOT* at all related to the bins used to drive WE sampling. ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file produced (specified by -o/--output, defaulting to "pdist.h5") may be fed to plothist to generate plots (or appropriately processed text or HDF5 files) from this data. In short, the following datasets are created: ``histograms`` Normalized histograms. The first axis corresponds to iteration, and remaining axes correspond to dimensions of the input dataset. ``/binbounds_0`` Vector of bin boundaries for the first (index 0) dimension. Additional datasets similarly named (/binbounds_1, /binbounds_2,...) are created for additional dimensions. ``/midpoints_0`` Vector of bin midpoints for the first (index 0) dimension. Additional datasets similarly named are created for additional dimensions. ``n_iter`` Vector of iteration numbers corresponding to the stored histograms (i.e. the first axis of the ``histograms`` dataset). ----------------------------------------------------------------------------- Subsequent processing ----------------------------------------------------------------------------- The output generated by this program (-o/--output, default "pdist.h5") may be plotted by the ``plothist`` program. See ``plothist --help`` for more information. ----------------------------------------------------------------------------- Parallelization ----------------------------------------------------------------------------- This tool supports parallelized binning, including reading of input data. Parallel processing is the default. For simple cases (reading pre-computed input data, modest numbers of segments), serial processing (--serial) may be more efficient. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def __init__(self): super().__init__() # Parallel processing by default (this is not actually necessary, but it is # informative!) self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager # These are used throughout self.progress = ProgressIndicatorComponent() self.data_reader = WESTDataReader() self.input_dssynth = WESTDSSynthesizer(default_dsname='pcoord') self.input_wdssynth = WESTWDSSynthesizer(default_dsname='seg_index') self.iter_range = IterRangeSelection(self.data_reader) self.iter_range.include_args['iter_step'] = False self.binspec = None self.output_filename = None self.output_file = None self.dsspec = None self.wt_dsspec = None # dsspec for weights # These are used during histogram generation only self.iter_start = None self.iter_stop = None self.ndim = None self.ntimepoints = None self.dset_dtype = None self.binbounds = None # bin boundaries for each dimension self.midpoints = None # bin midpoints for each dimension self.data_range = None # data range for each dimension, as the pairs (min,max) self.ignore_out_of_range = False self.compress_output = False def add_args(self, parser): self.data_reader.add_args(parser) self.iter_range.add_args(parser) parser.add_argument( '-b', '--bins', dest='bins', metavar='BINEXPR', default='100', help='''Use BINEXPR for bins. This may be an integer, which will be used for each dimension of the progress coordinate; a list of integers (formatted as [n1,n2,...]) which will use n1 bins for the first dimension, n2 for the second dimension, and so on; or a list of lists of boundaries (formatted as [[a1, a2,...], [b1, b2,...],... ]), which will use [a1, a2,...] as bin boundaries for the first dimension, [b1, b2,...] as bin boundaries for the second dimension, and so on. (Default: 100 bins in each dimension.)''', ) parser.add_argument( '-o', '--output', dest='output', default='pdist.h5', help='''Store results in OUTPUT (default: %(default)s).''' ) parser.add_argument( '-C', '--compress', action='store_true', help='''Compress histograms. May make storage of higher-dimensional histograms more tractable, at the (possible extreme) expense of increased analysis time. (Default: no compression.)''', ) parser.add_argument( '--loose', dest='ignore_out_of_range', action='store_true', help='''Ignore values that do not fall within bins. (Risky, as this can make buggy bin boundaries appear as reasonable data. Only use if you are sure of your bin boundary specification.)''', ) igroup = parser.add_argument_group('input dataset options').add_mutually_exclusive_group(required=False) igroup.add_argument( '--construct-dataset', help='''Use the given function (as in module.function) to extract source data. This function will be called once per iteration as function(n_iter, iter_group) to construct data for one iteration. Data returned must be indexable as [seg_id][timepoint][dimension]''', ) igroup.add_argument( '--dsspecs', nargs='+', metavar='DSSPEC', help='''Construct probability distribution from one or more DSSPECs.''' ) wgroup = parser.add_argument_group('input weight dataset options').add_mutually_exclusive_group(required=False) wgroup.add_argument( '--construct-wdataset', help='''Use the given function (as in module.function) to extract weight data. This function will be called once per iteration as function(n_iter, iter_group) to construct data for one iteration. Data returned must be indexable as [seg_id]''', ) self.progress.add_args(parser) def process_args(self, args): self.progress.process_args(args) self.data_reader.process_args(args) self.input_dssynth.h5filename = self.data_reader.we_h5filename self.input_dssynth.process_args(args) self.dsspec = self.input_dssynth.dsspec # Carrying an open HDF5 file across a fork() seems to corrupt the entire HDF5 library # Open the WEST HDF5 file just long enough to process our iteration range, then close # and reopen in go() [which executes after the fork] with self.data_reader: self.iter_range.process_args(args) # Reading potential custom weights self.input_wdssynth.h5filename = self.data_reader.we_h5filename self.input_wdssynth.process_args(args) self.wt_dsspec = self.input_wdssynth.dsspec self.binspec = args.bins self.output_filename = args.output self.ignore_out_of_range = bool(args.ignore_out_of_range) self.compress_output = args.compress or False def go(self): self.data_reader.open('r') pi = self.progress.indicator pi.operation = 'Initializing' with pi: self.output_file = h5py.File(self.output_filename, 'w') h5io.stamp_creator_data(self.output_file) self.iter_start = self.iter_range.iter_start self.iter_stop = self.iter_range.iter_stop # Construct bin boundaries self.construct_bins(self.parse_binspec(self.binspec)) for idim, (binbounds, midpoints) in enumerate(zip(self.binbounds, self.midpoints)): self.output_file['binbounds_{}'.format(idim)] = binbounds self.output_file['midpoints_{}'.format(idim)] = midpoints # construct histogram self.construct_histogram() # Record iteration range iter_range = self.iter_range.iter_range() self.output_file['n_iter'] = iter_range self.iter_range.record_data_iter_range(self.output_file['histograms']) self.output_file.close() @staticmethod def parse_binspec(binspec): namespace = {'numpy': np, 'np': np, 'inf': float('inf')} try: binspec_compiled = eval(binspec, namespace) except Exception as e: raise ValueError('invalid bin specification: {!r}'.format(e)) else: if log.isEnabledFor(logging.DEBUG): log.debug('bin specs: {!r}'.format(binspec_compiled)) return binspec_compiled def construct_bins(self, bins): ''' Construct bins according to ``bins``, which may be: 1) A scalar integer (for that number of bins in each dimension) 2) A sequence of integers (specifying number of bins for each dimension) 3) A sequence of sequences of bin boundaries (specifying boundaries for each dimension) Sets ``self.binbounds`` to a list of arrays of bin boundaries appropriate for passing to fasthist.histnd, along with ``self.midpoints`` to the midpoints of the bins. ''' if not isiterable(bins): self._construct_bins_from_scalar(bins) elif not isiterable(bins[0]): self._construct_bins_from_int_seq(bins) else: self._construct_bins_from_bound_seqs(bins) if log.isEnabledFor(logging.DEBUG): log.debug('binbounds: {!r}'.format(self.binbounds)) def scan_data_shape(self): if self.ndim is None: dset = self.dsspec.get_iter_data(self.iter_start) self.ntimepoints = dset.shape[1] self.ndim = dset.shape[2] self.dset_dtype = dset.dtype def scan_data_range(self): '''Scan input data for range in each dimension. The number of dimensions is determined from the shape of the progress coordinate as of self.iter_start.''' self.progress.indicator.new_operation('Scanning for data range', self.iter_stop - self.iter_start) self.scan_data_shape() dset_dtype = self.dset_dtype ndim = self.ndim dsspec = self.dsspec try: minval = np.finfo(dset_dtype).min maxval = np.finfo(dset_dtype).max except ValueError: minval = np.iinfo(dset_dtype).min maxval = np.iinfo(dset_dtype).max data_range = self.data_range = [(maxval, minval) for _i in range(self.ndim)] # futures = [] # for n_iter in xrange(self.iter_start, self.iter_stop): # _remote_min_max(ndim, dset_dtype, n_iter, dsspec) # futures.append(self.work_manager.submit(_remote_min_max, args=(ndim, dset_dtype, n_iter, dsspec))) # for future in self.work_manager.as_completed(futures): for future in self.work_manager.submit_as_completed( ((_remote_min_max, (ndim, dset_dtype, n_iter, dsspec), {}) for n_iter in range(self.iter_start, self.iter_stop)), self.max_queue_len, ): bounds = future.get_result(discard=True) for idim in range(ndim): current_min, current_max = data_range[idim] current_min = min(current_min, bounds[idim][0]) current_max = max(current_max, bounds[idim][1]) data_range[idim] = (current_min, current_max) self.progress.indicator.progress += 1 def _construct_bins_from_scalar(self, bins): if self.data_range is None: self.scan_data_range() self.binbounds = [] self.midpoints = [] for idim in range(self.ndim): lb, ub = self.data_range[idim] # Advance just beyond the upper bound of the range, so that we catch # the maximum in the histogram ub *= 1.01 boundset = np.linspace(lb, ub, bins + 1) midpoints = (boundset[:-1] + boundset[1:]) / 2.0 self.binbounds.append(boundset) self.midpoints.append(midpoints) def _construct_bins_from_int_seq(self, bins): if self.data_range is None: self.scan_data_range() self.binbounds = [] self.midpoints = [] for idim in range(self.ndim): lb, ub = self.data_range[idim] # Advance just beyond the upper bound of the range, so that we catch # the maximum in the histogram ub *= 1.01 boundset = np.linspace(lb, ub, bins[idim] + 1) midpoints = (boundset[:-1] + boundset[1:]) / 2.0 self.binbounds.append(boundset) self.midpoints.append(midpoints) def _construct_bins_from_bound_seqs(self, bins): self.binbounds = [] self.midpoints = [] for boundset in bins: boundset = np.asarray(boundset) if (np.diff(boundset) <= 0).any(): raise ValueError('boundary set {!r} is not strictly monotonically increasing'.format(boundset)) self.binbounds.append(boundset) self.midpoints.append((boundset[:-1] + boundset[1:]) / 2.0) def construct_histogram(self): '''Construct a histogram using bins previously constructed with ``construct_bins()``. The time series of histogram values is stored in ``histograms``. Each histogram in the time series is normalized.''' self.scan_data_shape() iter_count = self.iter_stop - self.iter_start histograms_ds = self.output_file.create_dataset( 'histograms', dtype=np.float64, shape=((iter_count,) + tuple(len(bounds) - 1 for bounds in self.binbounds)), compression=9 if self.compress_output else None, ) binbounds = [np.require(boundset, self.dset_dtype, 'C') for boundset in self.binbounds] self.progress.indicator.new_operation('Constructing histograms', self.iter_stop - self.iter_start) task_gen = ( ( _remote_bin_iter, (iiter, n_iter, self.dsspec, self.wt_dsspec, 1 if iiter > 0 else 0, binbounds, self.ignore_out_of_range), {}, ) for (iiter, n_iter) in enumerate(range(self.iter_start, self.iter_stop)) ) # futures = set() # for iiter, n_iter in enumerate(xrange(self.iter_start, self.iter_stop)): # initpoint = 1 if iiter > 0 else 0 # futures.add(self.work_manager.submit(_remote_bin_iter, # args=(iiter, n_iter, self.dsspec, self.wt_dsspec, initpoint, binbounds))) # for future in self.work_manager.as_completed(futures): # future = self.work_manager.wait_any(futures) # for future in self.work_manager.submit_as_completed(task_gen, self.queue_size): log.debug('max queue length: {!r}'.format(self.max_queue_len)) for future in self.work_manager.submit_as_completed(task_gen, self.max_queue_len): iiter, n_iter, iter_hist = future.get_result(discard=True) self.progress.indicator.progress += 1 # store histogram histograms_ds[iiter] = iter_hist del iter_hist, future def entry_point(): WPDist().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_red.rst

Manual

w_red command

MIT License

westpa__westpa/doc/documentation/cli/w_red.rst

w_red usage: w_red [-h] [-r RCFILE] [--quiet] [--verbose] [--version] [--max-queue-length MAX_QUEUE_LENGTH] [--debug] [--terminal] [--serial | --parallel | --work-manager WORK_MANAGER] [--n-workers N_WORKERS] [--zmq-mode MODE] [--zmq-comm-mode COMM_MODE] [--zmq-write-host-info INFO_FILE] [--zmq-read-host-info INFO_FILE] [--zmq-upstream-rr-endpoint ENDPOINT] [--zmq-upstream-ann-endpoint ENDPOINT] [--zmq-downstream-rr-endpoint ENDPOINT] [--zmq-downstream-ann-endpoint ENDPOINT] [--zmq-master-heartbeat MASTER_HEARTBEAT] [--zmq-worker-heartbeat WORKER_HEARTBEAT] [--zmq-timeout-factor FACTOR] [--zmq-startup-timeout STARTUP_TIMEOUT] [--zmq-shutdown-timeout SHUTDOWN_TIMEOUT] optional arguments: -h, --help show this help message and exit general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --version show program's version number and exit parallelization options: --max-queue-length MAX_QUEUE_LENGTH Maximum number of tasks that can be queued. Useful to limit RAM use for tasks that have very large requests/response. Default: no limit. parallelization options: --serial run in serial mode --parallel run in parallel mode (using processes) --work-manager WORK_MANAGER

from h5py import File as H5File import numpy as np from westpa import rc from westpa.tools import WESTParallelTool class DurationCorrector(object): @staticmethod def from_kinetics_file(directh5, istate, fstate, dtau, n_iters=None): iter_slice = slice(n_iters) if isinstance(directh5, H5File): dataset = directh5['durations'][iter_slice] else: with H5File(directh5, 'r') as directh5: dataset = directh5['durations'][iter_slice] torf = np.logical_and(dataset['istate'] == istate, dataset['fstate'] == fstate) torf = np.logical_and(torf, dataset['weight'] > 0) durations = dataset['duration'] weights = dataset['weight'] weights[~torf] = 0.0 # mask off irrelevant flux return DurationCorrector(durations, weights, dtau) def __init__(self, durations, weights, dtau, maxduration=None): self.weights = np.array(weights) self.durations = np.array(durations) self.dtau = dtau self._f_tilde = None self._f_int1 = None if maxduration is None: self.maxduration = self.durations.shape[0] else: self.maxduration = maxduration if dtau is None: all_durations = [] all_durations.extend(durations) all_durations.extend(np.arange(maxduration)) uniq_durations = np.unique(all_durations) # unique sorts automatically self.dtau = np.min(np.diff(uniq_durations)) self._build_map() @property def event_duration_histogram(self): return self._f_tilde @property def cumulative_event_duration_histogram(self): return self._f_int1 def _build_map(self): weights = self.weights durations = self.durations maxduration = self.maxduration dtau = self.dtau taugrid = np.arange(0, maxduration, dtau, dtype=float) f_map = np.zeros(weights.shape, dtype=int) - 1 for i, tau in enumerate(taugrid): matches = np.logical_and(durations >= tau, durations < tau + dtau) f_map[matches] = i self.taugrid = taugrid self.f_map = f_map def correction(self, iters, freqs=None): r""" Return the correction factor __ __ -1 | t=theta tau=t | | |\ |\ | | | | ~ | | | | f(tau) dtau dt | * maxduration | \| \| | | t=0 tau=0 | |_ _| where ~` ^ f(tau) is proportional to f(tau)/(theta-tau), and is normalized to ^ integrate to 1, and f(tau) is sum of the weights of walkers with duration time tau. --------- Arguments --------- maxduration: the maximum duration time that could have been observed in the simulation, which is usually equal to the length of the simulation. This should be in units of tau. """ if iters is None: iters = np.arange(len(self.weights)) if freqs is None: freqs = np.ones(len(iters), dtype=float) maxduration = np.max(iters) + 1 f_map = self.f_map[iters] weights = self.weights[iters] taugrid = self.taugrid # [self.taugrid < maxduration] weights *= freqs[:, None] dtau = self.dtau f_tilde = np.zeros(len(taugrid), dtype=float) for i, tau in enumerate(taugrid): if tau < maxduration: f_tilde[i] = weights[f_map == i].sum() / (maxduration - tau + 1) if f_tilde.sum()!= 0: f_tilde /= f_tilde.sum() * dtau self._f_tilde = f_tilde # now integrate f_tilde twice # integral1[t/dtau] gives the integral of f_tilde(tau) dtau from 0 to t self._f_int1 = integral1 = np.zeros(f_tilde.shape) for i, tau in enumerate(taugrid): if i > 0 and tau < maxduration: integral1[i] = np.trapz(f_tilde[: i + 1], taugrid[: i + 1]) integral2 = np.trapz(integral1, taugrid) if integral2 == 0: return 0.0 return maxduration / integral2 def get_raw_rates(directh5, istate, fstate, n_iters=None): rate_evol = directh5['rate_evolution'][slice(n_iters), istate, fstate] avg = rate_evol['expected'] return avg def calc_avg_rate(directh5_path, istate, fstate, **kwargs): """ Return the raw or RED-corrected rate constant with the confidence interval. --------- Arguments --------- dt: timestep (ps) nstiter: duration of each iteration (number of steps) ntpr: report inteval (number of steps) """ n_iters = kwargs.pop("n_iters", None) ntpr = kwargs.pop("report_interval", 20) nstiter = kwargs.pop("n_steps_iter", 1000) callback = kwargs.pop("callback", None) red = kwargs.pop("red", False) if len(kwargs) > 0: raise ValueError("unparsed kwargs") dtau = float(ntpr) / nstiter dc = None with H5File(directh5_path, 'r') as directh5: if n_iters is None: n_iters = directh5['rate_evolution'].shape[0] rate_evol = directh5['rate_evolution'][n_iters - 1, istate, fstate] rate = rate_evol['expected'] if red: dc = DurationCorrector.from_kinetics_file(directh5, istate, fstate, dtau, n_iters) if callback is not None: kw = {"correction": dc} callback(**kw) iters = np.arange(n_iters) correction = dc.correction(iters) if dc else 1.0 rate *= correction return rate def calc_rates(directh5_path, istate, fstate, **kwargs): """ Return the raw and RED-corrected rate constants vs. iterations. This code is faster than calling calc_rate() iteratively --------- Arguments --------- dt: timestep (ps) nstiter: duration of each iteration (number of steps) ntpr: report inteval (number of steps) """ n_iters = kwargs.pop("n_iters", None) ntpr = kwargs.pop("report_interval", 20) nstiter = kwargs.pop("n_steps_iter", 1000) callback = kwargs.pop("callback", None) red = kwargs.pop("red", False) if len(kwargs) > 0: raise ValueError("unparsed kwargs") dtau = float(ntpr) / nstiter dc = None with H5File(directh5_path, 'r') as directh5: rate_evol, cilb, ciub = get_raw_rates(directh5, istate, fstate, n_iters) if n_iters is None: n_iters = len(rate_evol) if red: dc = DurationCorrector.from_kinetics_file(directh5, istate, fstate, dtau, n_iters) if callback is not None: kw = {"correction": dc} callback(**kw) raw_rates = np.zeros(n_iters) rates = np.zeros(n_iters) for i in range(n_iters): i_iter = i + 1 print("\riter %d/%d (%3.0f%%)" % (i_iter, n_iters, i_iter * 100.0 / n_iters), end="") r = rate_evol[i] iters = np.arange(i_iter) correction = dc.correction(iters) if dc else 1.0 raw_rates[i] = r rates[i] = raw_rates[i] * correction print("\n") return rates class RateCalculator: def __init__(self, directh5, istate, fstate, assignh5=None, **kwargs): n_iters = kwargs.pop("n_iters", None) ntpr = kwargs.pop("report_interval", 20) nstiter = kwargs.pop("n_steps_iter", 1000) if len(kwargs) > 0: for k in kwargs: print(k) raise ValueError("unparsed kwargs") dtau = float(ntpr) / nstiter with H5File(directh5, 'r') as f: state_labels = {} for i, raw_label in enumerate(f['state_labels']): label = raw_label.decode() if isinstance(raw_label, bytes) else raw_label state_labels[label] = i if istate not in state_labels: raise ValueError(f"istate not found: {istate}, available options are {list(state_labels.keys())}") if fstate not in state_labels: raise ValueError(f"istate not found: {fstate}, available options are {list(state_labels.keys())}") istate = state_labels[istate] fstate = state_labels[fstate] cond_fluxes = f['conditional_fluxes'][slice(n_iters), istate, fstate] if assignh5 is not None: with H5File(assignh5, 'r') as f: pops = f['labeled_populations'][slice(n_iters)] pops = pops.sum(axis=2) else: pops = None self._dc = None self._pops = pops self._cond_fluxes = cond_fluxes self._dtau = dtau self._directh5 = directh5 self._assignh5 = assignh5 self._istate = istate self._fstate = fstate @property def conditional_fluxes(self): return self._cond_fluxes @property def populations(self): return self._pops @property def tau(self): return self._tau @property def dtau(self): return self._dtau @property def istate(self): return self._istate @property def fstate(self): return self._fstate @property def n_iters(self): return len(self.conditional_fluxes) def _get_corrector(self): if self._dc is None: with H5File(self._directh5, 'r') as f: self._dc = DurationCorrector.from_kinetics_file(f, self.istate, self.fstate, self.dtau, self.n_iters) return self._dc def calc_rate(self, i_iter=None, red=False, **kwargs): if i_iter is None: i_iter = self.n_iters dc = self._get_corrector() if red else None found = False with H5File(self._directh5, 'r') as f: for i in range(f['rate_evolution'].shape[0]): rate_evol = f['rate_evolution'][i, self.istate, self.fstate] start = rate_evol['iter_start'] stop = rate_evol['iter_stop'] if i_iter >= start and i_iter < stop: rate = rate_evol['expected'] found = True break if not found: self.log.error("Can't find rate evolution data for iteration %d!" % i_iter) if dc: iters = np.arange(i_iter) correction = dc.correction(iters) rate *= correction return rate def calc_rates(self, n_iters=None, **kwargs): if n_iters is None: n_iters = self.n_iters rates = np.zeros(n_iters) for i in range(n_iters): i_iter = i + 1 print("\riter %d/%d (%3.0f%%)" % (i_iter, n_iters, i_iter * 100.0 / n_iters), end="") r = self.calc_rate(i_iter, **kwargs) rates[i] = r print("\n") return rates class WRed(WESTParallelTool): prog = 'w_red' description = '''\ Apply the RED scheme to estimate steady-state WE fluxes from shorter trajectories. ----------------------------------------------------------------------------- Source data ----------------------------------------------------------------------------- Source data is provided as a w_ipa "scheme" which is typically defined in the west.cfg file. For instance, if a user wishes to estimate RED fluxes for a scheme named "DEFAULT" that argument would be provided to w_red and WRed would estimate RED fluxes based off of the data contained in the assign.h5 and direct.h5 files in ANALYSIS/DEFAULT. ''' def __init__(self): super().__init__() def go(self): try: rc.config['west']['analysis']['red'] except Exception: raise ValueError('No RED parameters are specified in west.cfg.') try: rc.config['west']['analysis']['red']['scheme'] except Exception: raise ValueError('No scheme specified for RED calculation in west.cfg.') try: rc.config['west']['analysis']['red']['istate_label'] except Exception: raise ValueError('No intial state label specified for RED calculation in west.cfg.') try: rc.config['west']['analysis']['red']['fstate_label'] except Exception: raise ValueError('No final state label specified for RED calculation in west.cfg.') try: rc.config['west']['analysis']['red']['nstiter'] except Exception: raise ValueError('Time step not specified in west.cfg.') try: rc.config['west']['analysis']['red']['nstrep'] except Exception: raise ValueError('Time step not specified in west.cfg.') if rc.config['west']['analysis']['kinetics']['evolution'] == "cumulative": pass else: print("Only RED estimates with cumulative averaging are supported at this time.") exit() config = rc.config adir = config.get(['west', 'analysis', 'directory']) name = config.get(['west', 'analysis','red','scheme']) istate = config.get(['west', 'analysis','red', 'istate_label']) fstate = config.get(['west', 'analysis','red', 'fstate_label']) n_steps_per_iter = config.get(['west', 'analysis','red', 'nstiter']) n_steps_per_report = config.get(['west', 'analysis','red', 'nstrep']) directh5path = '%s/%s/direct.h5' % (adir, name) assignh5path = '%s/%s/assign.h5' % (adir, name) print('\nConfig successfully read from west.cfg:') print('--------------------------------------') print('scheme: %s' % name) print('states: %s -> %s' % (istate, fstate)) print('nstiter: %s' % n_steps_per_iter) print('nstrep: %s' % n_steps_per_report) print('--------------------------------------') print('\nEstimating RED fluxes...') rater = RateCalculator( directh5path, istate, fstate, n_steps_iter=n_steps_per_iter, report_interval=n_steps_per_report, assignh5=assignh5path, ) rates = rater.calc_rates(red=True, callback=None) with H5File(directh5path, "r+") as dest_file: try: dest_file.create_dataset('red_flux_evolution', data=rates) print('saved RED fluxes to red_flux_evolution in ANALYSIS/%s/direct.h5' % name) except Exception: warning = input('Dataset already exists! Overwrite? (y/n)') if warning == "y": dest_file['red_flux_evolution'][...] = rates print('saved RED fluxes to red_flux_evolution in ANALYSIS/%s/direct.h5' % name) elif warning == "n": np.save('ANALYSIS/%s/red.npy' % name, rates) print('saved RED fluxes to red_flux_evolution.npy in ANALYSIS/%s' % name) else: print('red rates not saved. Exiting...') exit def entry_point(): WRed().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_run.rst

Manual

w_run command

MIT License

westpa__westpa/doc/documentation/cli/w_run.rst

w_run w_run starts or continues a weighted ensemble simualtion. Overview Usage: w_run [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version]              [--oneseg ] [--wm-work-manager WORK_MANAGER]              [--wm-n-workers N_WORKERS] [--wm-zmq-mode MODE]              [--wm-zmq-info INFO_FILE] [--wm-zmq-task-endpoint TASK_ENDPOINT]              [--wm-zmq-result-endpoint RESULT_ENDPOINT]              [--wm-zmq-announce-endpoint ANNOUNCE_ENDPOINT]              [--wm-zmq-heartbeat-interval INTERVAL]              [--wm-zmq-task-timeout TIMEOUT] [--wm-zmq-client-comm-mode MODE] Command-Line Options See the command-line tool index <command_line_tool_index> for more information on the general options. Segment Options --oneseg Only propagate one segment (useful for debugging propagators) Example A simple example for using w_run (mostly taken from odld example that is available in the main WESTPA distribution): w_run &> west.log This commands starts up a serial weighted ensemble run and pipes the results into the west.log file. As a side note --debug option is very useful for debugging the code if something goes wrong.

import argparse import logging import traceback import westpa import westpa.work_managers as work_managers from westpa.work_managers import make_work_manager log = logging.getLogger('w_run') def entry_point(): parser = argparse.ArgumentParser('w_run','start/continue a WEST simulation') westpa.rc.add_args(parser) parser.add_argument( '--oneseg', dest='only_one_segment', action='store_true', help='only propagate one segment (useful for debugging propagators)', ) work_managers.environment.add_wm_args(parser) args = parser.parse_args() westpa.rc.process_args(args) work_managers.environment.process_wm_args(args) run_simulation() def run_simulation(): work_manager = westpa.rc.work_manager = make_work_manager() # Load the sim manager and other drivers sim_manager = westpa.rc.get_sim_manager() system = westpa.rc.get_system_driver() data_manager = westpa.rc.get_data_manager() we_driver = westpa.rc.get_we_driver() propagator = westpa.rc.get_propagator() propagator.system = system data_manager.system = system we_driver.system = system sim_manager.data_manager = data_manager sim_manager.system = system sim_manager.propagator = propagator sim_manager.we_driver = we_driver with work_manager: if work_manager.is_master: work_manager.install_sigint_handler() sim_manager.load_plugins() log.debug('preparing run') sim_manager.prepare_run() try: log.debug('beginning run') sim_manager.run() log.debug('finalizing run') sim_manager.finalize_run() except KeyboardInterrupt: westpa.rc.pstatus('interrupted; shutting down') except Exception as e: westpa.rc.pstatus('exception caught; shutting down') if str(e)!= '': log.error(f'error message: {e}') log.error(traceback.format_exc()) else: work_manager.run() if __name__ == '__main__': entry_point()

westpa__westpa

w_select.rst

Manual

w_select command

MIT License

westpa__westpa/doc/documentation/cli/w_select.rst

w_select usage: w_select [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version] [--max-queue-length MAX_QUEUE_LENGTH] [-W WEST_H5FILE] [--first-iter N_ITER] [--last-iter N_ITER] [-p MODULE.FUNCTION] [-v] [-a] [-o OUTPUT] [--serial | --parallel | --work-manager WORK_MANAGER] [--n-workers N_WORKERS] [--zmq-mode MODE] [--zmq-comm-mode COMM_MODE] [--zmq-write-host-info INFO_FILE] [--zmq-read-host-info INFO_FILE] [--zmq-upstream-rr-endpoint ENDPOINT] [--zmq-upstream-ann-endpoint ENDPOINT] [--zmq-downstream-rr-endpoint ENDPOINT] [--zmq-downstream-ann-endpoint ENDPOINT] [--zmq-master-heartbeat MASTER_HEARTBEAT] [--zmq-worker-heartbeat WORKER_HEARTBEAT] [--zmq-timeout-factor FACTOR] [--zmq-startup-timeout STARTUP_TIMEOUT] [--zmq-shutdown-timeout SHUTDOWN_TIMEOUT] Select dynamics segments matching various criteria. This requires a user-provided prediate function. By default, only matching segments are stored. If the -a/--include-ancestors option is given, then matching segments and their ancestors will be stored. Predicate function Segments are selected based on a predicate function, which must be callable as predicate(n_iter, iter_group) and return a collection of segment IDs matching the predicate in that iteration. The predicate may be inverted by specifying the -v/--invert command-line argument. Output format The output file (-o/--output, by default "select.h5") contains the following datasets: ``/n_iter`` [iteration] *(Integer)* Iteration numbers for each entry in other datasets. ``/n_segs`` [iteration] *(Integer)* Number of segment IDs matching the predicate (or inverted predicate, if -v/--invert is specified) in the given iteration. ``/seg_ids`` [iteration][segment] *(Integer)* Matching segments in each iteration. For an iteration ``n_iter``, only the first ``n_iter`` entries are valid. For example, the full list of matching seg_ids in the first stored iteration is ``seg_ids[0][:n_segs[0]]``. ``/weights`` [iteration][segment] *(Floating-point)* Weights for each matching segment in ``/seg_ids``. Command-line arguments optional arguments: -h, --help show this help message and exit general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --debug enable extra checks and emit copious information --version show program's version number and exit parallelization options: --max-queue-length MAX_QUEUE_LENGTH Maximum number of tasks that can be queued. Useful to limit RAM use for tasks that have very large requests/response. Default: no limit. WEST input data options: -W WEST_H5FILE, --west-data WEST_H5FILE Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in west.cfg). iteration range: --first-iter N_ITER Begin analysis at iteration N_ITER (default: 1). --last-iter N_ITER Conclude analysis with N_ITER, inclusive (default: last completed iteration). selection options: -p MODULE.FUNCTION, --predicate-function MODULE.FUNCTION Use the given predicate function to match segments. This function should take an iteration number and the HDF5 group corresponding to that iteration and return a sequence of seg_ids matching the predicate, as in ``match_predicate(n_iter, iter_group)``. -v, --invert Invert the match predicate. -a, --include-ancestors Include ancestors of matched segments in output. output options: -o OUTPUT, --output OUTPUT Write output to OUTPUT (default: select.h5). parallelization options: --serial run in serial mode --parallel run in parallel mode (using processes) --work-manager WORK_MANAGER use the given work manager for parallel task distribution. Available work managers are ('serial', 'threads', 'processes', 'zmq'); default is 'serial' --n-workers N_WORKERS Use up to N_WORKERS on this host, for work managers which support this option. Use 0 for a dedicated server. (Ignored by work managers which do not support this option.) options for ZeroMQ ("zmq") work manager (master or node): --zmq-mode MODE Operate as a master (server) or a node (workers/client). "server" is a deprecated synonym for "master" and "client" is a deprecated synonym for "node". --zmq-comm-mode COMM_MODE Use the given communication mode -- TCP or IPC (Unix-domain) -- sockets for communication within a node. IPC (the default) may be more efficient but is not available on (exceptionally rare) systems without node-local storage (e.g. /tmp); on such systems, TCP may be used instead. --zmq-write-host-info INFO_FILE Store hostname and port information needed to connect to this instance in INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly. Downstream nodes read this file with --zmq-read-host-info and know where how to connect. --zmq-read-host-info INFO_FILE Read hostname and port information needed to connect to the master (or other coordinating node) from INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly, writing that information with --zmq-write-host-info for this instance to read. --zmq-upstream-rr-endpoint ENDPOINT ZeroMQ endpoint to which to send request/response (task and result) traffic toward the master. --zmq-upstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to receive announcement (heartbeat and shutdown notification) traffic from the master. --zmq-downstream-rr-endpoint ENDPOINT ZeroMQ endpoint on which to listen for request/response (task and result) traffic from subsidiary workers. --zmq-downstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to send announcement (heartbeat and shutdown notification) traffic toward workers. --zmq-master-heartbeat MASTER_HEARTBEAT Every MASTER_HEARTBEAT seconds, the master announces its presence to workers. --zmq-worker-heartbeat WORKER_HEARTBEAT Every WORKER_HEARTBEAT seconds, workers announce their presence to the master. --zmq-timeout-factor FACTOR Scaling factor for heartbeat timeouts. If the master doesn't hear from a worker in WORKER_HEARTBEAT*FACTOR, the worker is assumed to have crashed. If a worker doesn't hear from the master in MASTER_HEARTBEAT*FACTOR seconds, the master is assumed to have crashed. Both cases result in shutdown. --zmq-startup-timeout STARTUP_TIMEOUT Amount of time (in seconds) to wait for communication between the master and at least one worker. This may need to be changed on very large, heavily-loaded computer systems that start all processes simultaneously. --zmq-shutdown-timeout SHUTDOWN_TIMEOUT Amount of time (in seconds) to wait for workers to shut down

from westpa.tools import WESTParallelTool, WESTDataReader, IterRangeSelection, ProgressIndicatorComponent import numpy as np from westpa.core import h5io from westpa.core.data_manager import seg_id_dtype, n_iter_dtype, weight_dtype from westpa.core.extloader import get_object def _find_matching_segments(west_datafile_name, n_iter, predicate, invert=False): '''Find all segments in iteration ``n_iter`` that match (or do not match, if ``invert`` is true) the given ``predicate``. Returns a sequence of matching seg_ids.''' with h5io.WESTPAH5File(west_datafile_name, 'r') as west_datafile: iter_group = west_datafile.get_iter_group(n_iter) nsegs = iter_group['seg_index'].shape[0] matching_ids = set(map(int, predicate(n_iter, iter_group))) if invert: matching_ids = set(range(nsegs)) - matching_ids matchvec = sorted(np.fromiter(matching_ids, dtype=seg_id_dtype, count=len(matching_ids))) return n_iter, matchvec class WSelectTool(WESTParallelTool): prog = 'w_select' description = '''\ Select dynamics segments matching various criteria. This requires a user-provided prediate function. By default, only matching segments are stored. If the -a/--include-ancestors option is given, then matching segments and their ancestors will be stored. ----------------------------------------------------------------------------- Predicate function ----------------------------------------------------------------------------- Segments are selected based on a predicate function, which must be callable as ``predicate(n_iter, iter_group)`` and return a collection of segment IDs matching the predicate in that iteration. The predicate may be inverted by specifying the -v/--invert command-line argument. ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file (-o/--output, by default "select.h5") contains the following datasets: ``/n_iter`` [iteration] *(Integer)* Iteration numbers for each entry in other datasets. ``/n_segs`` [iteration] *(Integer)* Number of segment IDs matching the predicate (or inverted predicate, if -v/--invert is specified) in the given iteration. ``/seg_ids`` [iteration][segment] *(Integer)* Matching segments in each iteration. For an iteration ``n_iter``, only the first ``n_iter`` entries are valid. For example, the full list of matching seg_ids in the first stored iteration is ``seg_ids[0][:n_segs[0]]``. ``/weights`` [iteration][segment] *(Floating-point)* Weights for each matching segment in ``/seg_ids``. ----------------------------------------------------------------------------- Command-line arguments ----------------------------------------------------------------------------- ''' def __init__(self): super().__init__() self.data_reader = WESTDataReader() self.iter_range = IterRangeSelection() self.progress = ProgressIndicatorComponent() self.output_file = None self.output_filename = None self.predicate = None self.invert = False self.include_ancestors = False def add_args(self, parser): self.data_reader.add_args(parser) self.iter_range.add_args(parser) sgroup = parser.add_argument_group('selection options') sgroup.add_argument( '-p', '--predicate-function', metavar='MODULE.FUNCTION', help='''Use the given predicate function to match segments. This function should take an iteration number and the HDF5 group corresponding to that iteration and return a sequence of seg_ids matching the predicate, as in ``match_predicate(n_iter, iter_group)``.''', ) sgroup.add_argument('-v', '--invert', dest='invert', action='store_true', help='''Invert the match predicate.''') sgroup.add_argument( '-a', '--include-ancestors', action='store_true', help='''Include ancestors of matched segments in output.''' ) ogroup = parser.add_argument_group('output options') ogroup.add_argument('-o', '--output', default='select.h5', help='''Write output to OUTPUT (default: %(default)s).''') self.progress.add_args(parser) def process_args(self, args): self.progress.process_args(args) self.data_reader.process_args(args) with self.data_reader: self.iter_range.process_args(args) predicate = get_object(args.predicate_function, path=['.']) if not callable(predicate): raise TypeError('predicate object {!r} is not callable'.format(predicate)) self.predicate = predicate self.invert = bool(args.invert) self.include_ancestors = bool(args.include_ancestors) self.output_filename = args.output def go(self): self.data_reader.open('r') output_file = h5io.WESTPAH5File(self.output_filename, mode='w') pi = self.progress.indicator iter_start, iter_stop = self.iter_range.iter_start, self.iter_range.iter_stop iter_count = iter_stop - iter_start output_file.create_dataset('n_iter', dtype=n_iter_dtype, data=list(range(iter_start, iter_stop))) current_seg_count = 0 seg_count_ds = output_file.create_dataset('n_segs', dtype=np.uint, shape=(iter_count,)) matching_segs_ds = output_file.create_dataset( 'seg_ids', shape=(iter_count, 0), maxshape=(iter_count, None), dtype=seg_id_dtype, chunks=h5io.calc_chunksize((iter_count, 1000000), seg_id_dtype), shuffle=True, compression=9, ) weights_ds = output_file.create_dataset( 'weights', shape=(iter_count, 0), maxshape=(iter_count, None), dtype=weight_dtype, chunks=h5io.calc_chunksize((iter_count, 1000000), weight_dtype), shuffle=True, compression=9, ) with pi: pi.new_operation('Finding matching segments', extent=iter_count) # futures = set() # for n_iter in xrange(iter_start,iter_stop): # futures.add(self.work_manager.submit(_find_matching_segments, # args=(self.data_reader.we_h5filename,n_iter,self.predicate,self.invert))) # for future in self.work_manager.as_completed(futures): for future in self.work_manager.submit_as_completed( ( (_find_matching_segments, (self.data_reader.we_h5filename, n_iter, self.predicate, self.invert), {}) for n_iter in range(iter_start, iter_stop) ), self.max_queue_len, ): n_iter, matching_ids = future.get_result() n_matches = len(matching_ids) if n_matches: if n_matches > current_seg_count: current_seg_count = len(matching_ids) matching_segs_ds.resize((iter_count, n_matches)) weights_ds.resize((iter_count, n_matches)) current_seg_count = n_matches seg_count_ds[n_iter - iter_start] = n_matches matching_segs_ds[n_iter - iter_start, :n_matches] = matching_ids weights_ds[n_iter - iter_start, :n_matches] = self.data_reader.get_iter_group(n_iter)['seg_index']['weight'][ sorted(matching_ids) ] del matching_ids pi.progress += 1 if self.include_ancestors: pi.new_operation('Tracing ancestors of matching segments', extent=iter_count) from_previous = set() current_seg_count = matching_segs_ds.shape[1] for n_iter in range(iter_stop - 1, iter_start - 1, -1): iiter = n_iter - iter_start n_matches = seg_count_ds[iiter] matching_ids = set(from_previous) if n_matches: matching_ids.update(matching_segs_ds[iiter, : seg_count_ds[iiter]]) from_previous.clear() n_matches = len(matching_ids) if n_matches > current_seg_count: matching_segs_ds.resize((iter_count, n_matches)) weights_ds.resize((iter_count, n_matches)) current_seg_count = n_matches if n_matches > 0: seg_count_ds[iiter] = n_matches matching_ids = sorted(matching_ids) matching_segs_ds[iiter, :n_matches] = matching_ids weights_ds[iiter, :n_matches] = self.data_reader.get_iter_group(n_iter)['seg_index']['weight'][ sorted(matching_ids) ] parent_ids = self.data_reader.get_iter_group(n_iter)['seg_index']['parent_id'][sorted(matching_ids)] from_previous.update(parent_id for parent_id in parent_ids if parent_id >= 0) # filter initial states del parent_ids del matching_ids pi.progress += 1 def entry_point(): WSelectTool().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_stateprobs.rst

Manual

w_stateprobs command

MIT License

westpa__westpa/doc/documentation/cli/deprecated/w_stateprobs.rst

w_stateprobs WARNING: w_stateprobs is being deprecated. Please use w_direct instead. usage: w_stateprobs trace [-h] [-W WEST_H5FILE] [--first-iter N_ITER] [--last-iter N_ITER] [--step-iter STEP] [-a ASSIGNMENTS] [-o OUTPUT] [-k KINETICS] [--disable-bootstrap] [--disable-correl] [--alpha ALPHA] [--autocorrel-alpha ACALPHA] [--nsets NSETS] [-e {cumulative,blocked,none}] [--window-frac WINDOW_FRAC] [--disable-averages] Calculate average populations and associated errors in state populations from weighted ensemble data. Bin assignments, including macrostate definitions, are required. (See "w_assign --help" for more information). Output format The output file (-o/--output, usually "direct.h5") contains the following dataset: /avg_state_probs [state] (Structured -- see below) Population of each state across entire range specified. /avg_color_probs [state] (Structured -- see below) Population of each ensemble across entire range specified. If --evolution-mode is specified, then the following additional datasets are available: /state_pop_evolution [window][state] (Structured -- see below). State populations based on windows of iterations of varying width. If --evolution-mode=cumulative, then these windows all begin at the iteration specified with --start-iter and grow in length by --step-iter for each successive element. If --evolution-mode=blocked, then these windows are all of width --step-iter (excluding the last, which may be shorter), the first of which begins at iteration --start-iter. /color_prob_evolution [window][state] (Structured -- see below). Ensemble populations based on windows of iterations of varying width. If --evolution-mode=cumulative, then these windows all begin at the iteration specified with --start-iter and grow in length by --step-iter for each successive element. If --evolution-mode=blocked, then these windows are all of width --step-iter (excluding the last, which may be shorter), the first of which begins at iteration --start-iter. The structure of these datasets is as follows: iter_start (Integer) Iteration at which the averaging window begins (inclusive). iter_stop (Integer) Iteration at which the averaging window ends (exclusive). expected (Floating-point) Expected (mean) value of the observable as evaluated within this window, in units of inverse tau. ci_lbound (Floating-point) Lower bound of the confidence interval of the observable within this window, in units of inverse tau. ci_ubound (Floating-point) Upper bound of the confidence interval of the observable within this window, in units of inverse tau. stderr (Floating-point) The standard error of the mean of the observable within this window, in units of inverse tau. corr_len (Integer) Correlation length of the observable within this window, in units of tau. Each of these datasets is also stamped with a number of attributes: mcbs_alpha (Floating-point) Alpha value of confidence intervals. (For example, *alpha=0.05* corresponds to a 95% confidence interval.) mcbs_nsets (Integer) Number of bootstrap data sets used in generating confidence intervals. mcbs_acalpha (Floating-point) Alpha value for determining correlation lengths. Command-line options optional arguments: -h, --help show this help message and exit WEST input data options: -W WEST_H5FILE, --west-data WEST_H5FILE Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in west.cfg). iteration range: --first-iter N_ITER Begin analysis at iteration N_ITER (default: 1). --last-iter N_ITER Conclude analysis with N_ITER, inclusive (default: last completed iteration). --step-iter STEP Analyze/report in blocks of STEP iterations. input/output options: -a ASSIGNMENTS, --assignments ASSIGNMENTS Bin assignments and macrostate definitions are in ASSIGNMENTS (default: assign.h5). -o OUTPUT, --output OUTPUT Store results in OUTPUT (default: stateprobs.h5). input/output options: -k KINETICS, --kinetics KINETICS Populations and transition rates are stored in KINETICS (default: assign.h5). confidence interval calculation options: --disable-bootstrap, -db Enable the use of Monte Carlo Block Bootstrapping. --disable-correl, -dc Disable the correlation analysis. --alpha ALPHA Calculate a (1-ALPHA) confidence interval' (default: 0.05) --autocorrel-alpha ACALPHA Evaluate autocorrelation to (1-ACALPHA) significance. Note that too small an ACALPHA will result in failure to detect autocorrelation in a noisy flux signal. (Default: same as ALPHA.) --nsets NSETS Use NSETS samples for bootstrapping (default: chosen based on ALPHA) calculation options: -e {cumulative,blocked,none}, --evolution-mode {cumulative,blocked,none} How to calculate time evolution of rate estimates. ``cumulative`` evaluates rates over windows starting with --start-iter and getting progressively wider to --stop- iter by steps of --step-iter. ``blocked`` evaluates rates over windows of width --step-iter, the first of which begins at --start-iter. ``none`` (the default) disables calculation of the time evolution of rate estimates. --window-frac WINDOW_FRAC Fraction of iterations to use in each window when running in ``cumulative`` mode. The (1 - frac) fraction of iterations will be discarded from the start of each window. misc options: --disable-averages, -da Whether or not the averages should be printed to the console (set to FALSE if flag is used).

from westpa.tools import WESTMasterCommand, WESTParallelTool from warnings import warn from westpa.cli.tools.w_direct import DStateProbs # Just a shim to make sure everything works and is backwards compatible. # We're making sure it has the appropriate functions so that it can be called # as a regular tool, and not a subcommand. class WStateProbs(DStateProbs): subcommand = 'trace' help_text = 'averages and CIs for path-tracing kinetics analysis' default_output_file ='stateprobs.h5' # This isn't strictly necessary, but for the moment, here it is. # We really need to modify the underlying class so that we don't pull this sort of stuff if it isn't necessary. # That'll take some case handling, which is fine. default_kinetics_file = 'assign.h5' class WDirect(WESTMasterCommand, WESTParallelTool): prog = 'w_stateprobs' subcommands = [WStateProbs] subparsers_title = 'calculate state-to-state kinetics by tracing trajectories' description = '''\ Calculate average populations and associated errors in state populations from weighted ensemble data. Bin assignments, including macrostate definitions, are required. (See "w_assign --help" for more information). ----------------------------------------------------------------------------- Output format ----------------------------------------------------------------------------- The output file (-o/--output, usually "stateprobs.h5") contains the following dataset: /avg_state_pops [state] (Structured -- see below) Population of each state across entire range specified. If --evolution-mode is specified, then the following additional dataset is available: /state_pop_evolution [window][state] (Structured -- see below). State populations based on windows of iterations of varying width. If --evolution-mode=cumulative, then these windows all begin at the iteration specified with --start-iter and grow in length by --step-iter for each successive element. If --evolution-mode=blocked, then these windows are all of width --step-iter (excluding the last, which may be shorter), the first of which begins at iteration --start-iter. The structure of these datasets is as follows: iter_start (Integer) Iteration at which the averaging window begins (inclusive). iter_stop (Integer) Iteration at which the averaging window ends (exclusive). expected (Floating-point) Expected (mean) value of the rate as evaluated within this window, in units of inverse tau. ci_lbound (Floating-point) Lower bound of the confidence interval on the rate within this window, in units of inverse tau. ci_ubound (Floating-point) Upper bound of the confidence interval on the rate within this window, in units of inverse tau. corr_len (Integer) Correlation length of the rate within this window, in units of tau. Each of these datasets is also stamped with a number of attributes: mcbs_alpha (Floating-point) Alpha value of confidence intervals. (For example, *alpha=0.05* corresponds to a 95% confidence interval.) mcbs_nsets (Integer) Number of bootstrap data sets used in generating confidence intervals. mcbs_acalpha (Floating-point) Alpha value for determining correlation lengths. ----------------------------------------------------------------------------- Command-line options ----------------------------------------------------------------------------- ''' def entry_point(): warn('{} is being deprecated. Please use w_direct instead.'.format(WDirect.prog)) # If we're not really supporting subcommands... import sys try: if sys.argv[1]!= 'trace': sys.argv.insert(1, 'trace') except Exception: sys.argv.insert(1, 'trace') WDirect().main() if __name__ == '__main__': entry_point()

westpa__westpa

w_states.rst

Manual

w_states command

MIT License

westpa__westpa/doc/documentation/cli/w_states.rst

w_states usage: w_states [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version] [--show | --append | --replace] [--bstate-file BSTATE_FILE] [--bstate BSTATES] [--tstate-file TSTATE_FILE] [--tstate TSTATES] [--serial | --parallel | --work-manager WORK_MANAGER] [--n-workers N_WORKERS] [--zmq-mode MODE] [--zmq-comm-mode COMM_MODE] [--zmq-write-host-info INFO_FILE] [--zmq-read-host-info INFO_FILE] [--zmq-upstream-rr-endpoint ENDPOINT] [--zmq-upstream-ann-endpoint ENDPOINT] [--zmq-downstream-rr-endpoint ENDPOINT] [--zmq-downstream-ann-endpoint ENDPOINT] [--zmq-master-heartbeat MASTER_HEARTBEAT] [--zmq-worker-heartbeat WORKER_HEARTBEAT] [--zmq-timeout-factor FACTOR] [--zmq-startup-timeout STARTUP_TIMEOUT] [--zmq-shutdown-timeout SHUTDOWN_TIMEOUT] Display or manipulate basis (initial) or target (recycling) states for a WEST simulation. By default, states are displayed (or dumped to files). If --replace is specified, all basis/target states are replaced for the next iteration. If --append is specified, the given target state(s) are appended to the list for the next iteration. Appending basis states is not permitted, as this would require renormalizing basis state probabilities in ways that may be error-prone. Instead, use w_states --show --bstate-file=bstates.txt and then edit the resulting bstates.txt file to include the new desired basis states, then use w_states --replace --bstate-file=bstates.txt to update the WEST HDF5 file appropriately. optional arguments: -h, --help show this help message and exit --bstate-file BSTATE_FILE Read (--append/--replace) or write (--show) basis state names, probabilities, and data references from/to BSTATE_FILE. --bstate BSTATES Add the given basis state (specified as a string 'label,probability[,auxref]') to the list of basis states (after those specified in --bstate-file, if any). This argument may be specified more than once, in which case the given states are appended in the order they are given on the command line. --tstate-file TSTATE_FILE Read (--append/--replace) or write (--show) target state names and representative progress coordinates from/to TSTATE_FILE --tstate TSTATES Add the given target state (specified as a string 'label,pcoord0[,pcoord1[,...]]') to the list of target states (after those specified in the file given by --tstates-from, if any). This argument may be specified more than once, in which case the given states are appended in the order they appear on the command line. general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --debug enable extra checks and emit copious information --version show program's version number and exit modes of operation: --show Display current basis/target states (or dump to files). --append Append the given basis/target states to those currently in use. --replace Replace current basis/target states with those specified. parallelization options: --serial run in serial mode --parallel run in parallel mode (using processes) --work-manager WORK_MANAGER use the given work manager for parallel task distribution. Available work managers are ('serial', 'threads', 'processes', 'zmq'); default is 'serial' --n-workers N_WORKERS Use up to N_WORKERS on this host, for work managers which support this option. Use 0 for a dedicated server. (Ignored by work managers which do not support this option.) options for ZeroMQ ("zmq") work manager (master or node): --zmq-mode MODE Operate as a master (server) or a node (workers/client). "server" is a deprecated synonym for "master" and "client" is a deprecated synonym for "node". --zmq-comm-mode COMM_MODE Use the given communication mode -- TCP or IPC (Unix-domain) -- sockets for communication within a node. IPC (the default) may be more efficient but is not available on (exceptionally rare) systems without node-local storage (e.g. /tmp); on such systems, TCP may be used instead. --zmq-write-host-info INFO_FILE Store hostname and port information needed to connect to this instance in INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly. Downstream nodes read this file with --zmq-read-host-info and know where how to connect. --zmq-read-host-info INFO_FILE Read hostname and port information needed to connect to the master (or other coordinating node) from INFO_FILE. This allows the master and nodes assisting in coordinating the communication of other nodes to choose ports randomly, writing that information with --zmq-write-host-info for this instance to read. --zmq-upstream-rr-endpoint ENDPOINT ZeroMQ endpoint to which to send request/response (task and result) traffic toward the master. --zmq-upstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to receive announcement (heartbeat and shutdown notification) traffic from the master. --zmq-downstream-rr-endpoint ENDPOINT ZeroMQ endpoint on which to listen for request/response (task and result) traffic from subsidiary workers. --zmq-downstream-ann-endpoint ENDPOINT ZeroMQ endpoint on which to send announcement (heartbeat and shutdown notification) traffic toward workers. --zmq-master-heartbeat MASTER_HEARTBEAT Every MASTER_HEARTBEAT seconds, the master announces its presence to workers. --zmq-worker-heartbeat WORKER_HEARTBEAT Every WORKER_HEARTBEAT seconds, workers announce their presence to the master. --zmq-timeout-factor FACTOR Scaling factor for heartbeat timeouts. If the master doesn't hear from a worker in WORKER_HEARTBEAT*FACTOR, the worker is assumed to have crashed. If a worker doesn't hear from the master in MASTER_HEARTBEAT*FACTOR seconds, the master is assumed to have crashed. Both cases result in shutdown. --zmq-startup-timeout STARTUP_TIMEOUT Amount of time (in seconds) to wait for communication between the master and at least one worker. This may need to be changed on very large, heavily-loaded computer systems that start all processes simultaneously. --zmq-shutdown-timeout SHUTDOWN_TIMEOUT Amount of time (in seconds) to wait for workers to shut down.

import argparse import io import logging import sys import numpy as np import westpa.work_managers as work_managers from westpa.work_managers import make_work_manager import westpa from westpa.core.segment import Segment from westpa.core.states import BasisState, TargetState log = logging.getLogger('w_states') EPS = np.finfo(np.float64).eps def entry_point(): parser = argparse.ArgumentParser( 'w_states', description='''\ Display or manipulate basis (initial) or target (recycling) states for a WEST simulation. By default, states are displayed (or dumped to files). If ``--replace`` is specified, all basis/target states are replaced for the next iteration. If ``--append`` is specified, the given target state(s) are appended to the list for the next iteration. Appending basis states is not permitted, as this would require renormalizing basis state probabilities in ways that may be error-prone. Instead, use ``w_states --show --bstate-file=bstates.txt`` and then edit the resulting ``bstates.txt`` file to include the new desired basis states, then use ``w_states --replace --bstate-file=bstates.txt`` to update the WEST HDF5 file appropriately. ''', ) westpa.rc.add_args(parser) smgroup = parser.add_argument_group('modes of operation') mode_group = smgroup.add_mutually_exclusive_group() mode_group.add_argument( '--show', dest='mode', action='store_const', const='show', help='Display current basis/target states (or dump to files).' ) mode_group.add_argument( '--append', dest='mode', action='store_const', const='append', help='Append the given basis/target states to those currently in use.', ) mode_group.add_argument( '--replace', dest='mode', action='store_const', const='replace', help='Replace current basis/target states with those specified.', ) parser.add_argument( '--bstate-file', metavar='BSTATE_FILE', help='''Read (--append/--replace) or write (--show) basis state names, probabilities, and data references from/to BSTATE_FILE.''', ) parser.add_argument( '--bstate', action='append', dest='bstates', help='''Add the given basis state (specified as a string 'label,probability[,auxref]') to the list of basis states (after those specified in --bstate-file, if any). This argument may be specified more than once, in which case the given states are appended in the order they are given on the command line.''', ) parser.add_argument( '--tstate-file', metavar='TSTATE_FILE', help='''Read (--append/--replace) or write (--show) target state names and representative progress coordinates from/to TSTATE_FILE''', ) parser.add_argument( '--tstate', action='append', dest='tstates', help='''Add the given target state (specified as a string 'label,pcoord0[,pcoord1[,...]]') to the list of target states (after those specified in the file given by --tstates-from, if any). This argument may be specified more than once, in which case the given states are appended in the order they appear on the command line.''', ) parser.set_defaults(mode='show') work_managers.environment.add_wm_args(parser) args = parser.parse_args() westpa.rc.process_args(args) work_managers.environment.process_wm_args(args) # Need to have something to pass to initialize if not hasattr(args, 'bstates'): args.bstates = None if not hasattr(args, 'tstates'): args.tstates = None if not hasattr(args, 'tstate_file'): args.tstate_file = None initialize(args.mode, args.bstates, args.bstate_file, args.tstates, args.tstate_file) # TODO: This would benefit from a refactor to set default args to None, and replace some of those "if <argument>" clauses def initialize(mode, bstates, _bstate_file, tstates, _tstate_file): work_manager = make_work_manager() system = westpa.rc.get_system_driver() with work_manager: if work_manager.is_master: data_manager = westpa.rc.get_data_manager() data_manager.open_backing(mode='a') sim_manager = westpa.rc.get_sim_manager() n_iter = data_manager.current_iteration assert mode in ('show','replace', 'append') if mode =='show': basis_states = data_manager.get_basis_states(n_iter) if basis_states: bstate_file = sys.stdout if not _bstate_file else open(_bstate_file, 'wt') bstate_file.write('# Basis states for iteration {:d}\n'.format(n_iter)) BasisState.states_to_file(basis_states, bstate_file) target_states = data_manager.get_target_states(n_iter) if target_states: tstate_file = sys.stdout if not _tstate_file else open(_tstate_file, 'wt') tstate_file.write('# Target states for iteration {:d}\n'.format(n_iter)) TargetState.states_to_file(target_states, tstate_file) elif mode =='replace': seg_index = data_manager.get_seg_index(n_iter) if (seg_index['status'] == Segment.SEG_STATUS_COMPLETE).any(): print('Iteration {:d} has completed segments; applying new states to iteration {:d}'.format(n_iter, n_iter + 1)) n_iter += 1 basis_states = [] if _bstate_file: basis_states.extend(BasisState.states_from_file(_bstate_file)) if bstates: for bstate_str in bstates: fields = bstate_str.split(',') label = fields[0] probability = float(fields[1]) try: auxref = fields[2] except IndexError: auxref = None basis_states.append(BasisState(label=label, probability=probability, auxref=auxref)) if basis_states: # Check that the total probability of basis states adds to one tprob = sum(bstate.probability for bstate in basis_states) if abs(1.0 - tprob) > len(basis_states) * EPS: pscale = 1 / tprob log.warning('Basis state probabilities do not add to unity; rescaling by {:g}'.format(pscale)) for bstate in basis_states: bstate.probability *= pscale # Assign progress coordinates to basis states sim_manager.get_bstate_pcoords(basis_states, n_iter) data_manager.create_ibstate_group(basis_states, n_iter) sim_manager.report_basis_states(basis_states) # Now handle target states target_states = [] if _tstate_file: target_states.extend(TargetState.states_from_file(_tstate_file, system.pcoord_dtype)) if tstates: tstates_strio = io.StringIO('\n'.join(tstates).replace(',','')) target_states.extend(TargetState.states_from_file(tstates_strio, system.pcoord_dtype)) del tstates_strio if not target_states: westpa.rc.pstatus('No target states specified.') else: data_manager.save_target_states(target_states, n_iter) sim_manager.report_target_states(target_states) data_manager.update_iter_group_links(n_iter) else: # args.mode == 'append' if _bstate_file or bstates: sys.stderr.write('refusing to append basis states; use --show followed by --replace instead\n') sys.exit(2) target_states = data_manager.get_target_states(n_iter) seg_index = data_manager.get_seg_index(n_iter) if (seg_index['status'] == Segment.SEG_STATUS_COMPLETE).any(): print('Iteration {:d} has completed segments; applying new states to iteration {:d}'.format(n_iter, n_iter + 1)) n_iter += 1 if _tstate_file: target_states.extend(TargetState.states_from_file(_tstate_file, system.pcoord_dtype)) if tstates: tstates_strio = io.StringIO('\n'.join(tstates).replace(',','')) target_states.extend(TargetState.states_from_file(tstates_strio, system.pcoord_dtype)) del tstates_strio if not target_states: westpa.rc.pstatus('No target states specified.') else: data_manager.save_target_states(target_states, n_iter) sim_manager.report_target_states(target_states) data_manager.update_iter_group_links(n_iter) else: work_manager.run() if __name__ == '__main__': entry_point()

westpa__westpa

w_succ.rst

Manual

w_succ command

MIT License

westpa__westpa/doc/documentation/cli/w_succ.rst

w_succ usage: w_succ [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version] [-A H5FILE] [-W WEST_H5FILE] [-o OUTPUT_FILE] List segments which successfully reach a target state. optional arguments: -h, --help show this help message and exit -o OUTPUT_FILE, --output OUTPUT_FILE Store output in OUTPUT_FILE (default: write to standard output). general options: -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --debug enable extra checks and emit copious information --version show program's version number and exit general analysis options: -A H5FILE, --analysis-file H5FILE Store intermediate and final results in H5FILE (default: analysis.h5). WEST input data options: -W WEST_H5FILE, --west-data WEST_H5FILE Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in west.cfg).

import argparse import sys import numpy as np import westpa from westpa.core.segment import Segment from westpa.oldtools.aframe import WESTAnalysisTool, WESTDataReaderMixin, CommonOutputMixin import logging log = logging.getLogger('w_succ') class WSucc(CommonOutputMixin, WESTDataReaderMixin, WESTAnalysisTool): def __init__(self): super().__init__() self.include_args['CommonOutputMixin']['print_bin_labels'] = False self.output_file = sys.stdout def find_successful_trajs(self): pcoord_formats = { 'u8': '%20d', 'i8': '%20d', 'u4': '%10d', 'i4': '%11d', 'u2': '%5d', 'i2': '%6d', 'f4': '%14.7g', 'f8': '%23.15g', } if not self.output_suppress_headers: self.output_file.write( '''\ # successful (recycled) segments # column 0: iteration # column 1: seg_id # column 2: weight # column>2: final progress coordinate value ''' ) for n_iter in range(1, self.data_manager.current_iteration): seg_index = self.get_seg_index(n_iter) all_seg_ids = np.arange(len(seg_index), dtype=np.int_) recycled_seg_ids = all_seg_ids[seg_index[:]['endpoint_type'] == Segment.SEG_ENDPOINT_RECYCLED] if len(recycled_seg_ids) == 0: # Attemping to retrieve a 0-length selection from HDF5 (the pcoords below) fails continue pcoord_ds = self.get_pcoord_dataset(n_iter) pcoord_len = pcoord_ds.shape[1] pcoord_ndim = pcoord_ds.shape[2] final_pcoords = self.get_pcoord_dataset(n_iter)[recycled_seg_ids, pcoord_len - 1, :] # The above HDF5 selection always returns a vector; we want a 2-d array final_pcoords.shape = (len(recycled_seg_ids), pcoord_ndim) for ipc, seg_id in enumerate(recycled_seg_ids): self.output_file.write('%8d %8d %20.14g' % (n_iter, seg_id, seg_index[seg_id]['weight'])) fields = [''] for field in final_pcoords[ipc]: fields.append(pcoord_formats.get(field.dtype.str[1:], '%s') % field) self.output_file.write(' '.join(fields)) self.output_file.write('\n') def entry_point(): wsucc = WSucc() parser = argparse.ArgumentParser( 'w_succ', description='''\ List segments which successfully reach a target state''', ) westpa.rc.add_args(parser) wsucc.add_args(parser) parser.add_argument( '-o', '--output', dest='output_file', help='Store output in OUTPUT_FILE (default: write to standard output).', type=argparse.FileType('wt'), default=sys.stdout, ) args = parser.parse_args() westpa.rc.process_args(args, config_required=False) wsucc.process_args(args) wsucc.output_file = args.output_file wsucc.find_successful_trajs() if __name__ == '__main__': entry_point()

westpa__westpa

w_trace.rst

Manual

w_trace command

MIT License

westpa__westpa/doc/documentation/cli/w_trace.rst

w_trace usage: w_trace [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version] [-W WEST_H5FILE] [-d DSNAME] [--output-pattern OUTPUT_PATTERN] [-o OUTPUT] N_ITER:SEG_ID [N_ITER:SEG_ID ...] Trace individual WEST trajectories and emit (or calculate) quantities along the trajectory. Trajectories are specified as N_ITER:SEG_ID pairs. Each segment is traced back to its initial point, and then various quantities (notably n_iter and seg_id) are printed in order from initial point up until the given segment in the given iteration. Output is stored in several files, all named according to the pattern given by the -o/--output-pattern parameter. The default output pattern is "traj%d_%d", where the printf-style format codes are replaced by the iteration number and segment ID of the terminal segment of the trajectory being traced. Individual datasets can be selected for writing using the -d/--dataset option (which may be specified more than once). The simplest form is -d dsname, which causes data from dataset dsname along the trace to be stored to HDF5. The dataset is assumed to be stored on a per-iteration basis, with the first dimension corresponding to seg_id and the second dimension corresponding to time within the segment. Further options are specified as comma-separated key=value pairs after the data set name, as in: -d dsname,alias=newname,index=idsname,file=otherfile.h5,slice=[100,...] The following options for datasets are supported: alias=newname When writing this data to HDF5 or text files, use ``newname`` instead of ``dsname`` to identify the dataset. This is mostly of use in conjunction with the ``slice`` option in order, e.g., to retrieve two different slices of a dataset and store then with different names for future use. index=idsname The dataset is not stored on a per-iteration basis for all segments, but instead is stored as a single dataset whose first dimension indexes n_iter/seg_id pairs. The index to these n_iter/seg_id pairs is ``idsname``. file=otherfile.h5 Instead of reading data from the main WEST HDF5 file (usually ``west.h5``), read data from ``otherfile.h5``. slice=[100,...] Retrieve only the given slice from the dataset. This can be used to pick a subset of interest to minimize I/O. positional arguments N_ITER:SEG_ID Trace trajectory ending (or at least alive at) N_ITER:SEG_ID. optional arguments -h, --help show this help message and exit -d DSNAME, --dataset DSNAME Include the dataset named DSNAME in trace output. An extended form like DSNAME[,alias=ALIAS][,index=INDEX][,file=FILE][,slice=SLICE] will obtain the dataset from the given FILE instead of the main WEST HDF5 file, slice it by SLICE, call it ALIAS in output, and/or access per-segment data by a n_iter,seg_id INDEX instead of a seg_id indexed dataset in the group for n_iter. general options -r RCFILE, --rcfile RCFILE use RCFILE as the WEST run-time configuration file (default: west.cfg) --quiet emit only essential information --verbose emit extra information --debug enable extra checks and emit copious information --version show program's version number and exit WEST input data options -W WEST_H5FILE, --west-data WEST_H5FILE Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in west.cfg). output options --output-pattern OUTPUT_PATTERN Write per-trajectory data to output files/HDF5 groups whose names begin with OUTPUT_PATTERN, which must contain two printf-style format flags which will be replaced with the iteration number and segment ID of the terminal segment of the trajectory being traced. (Default: traj_%d_%d.) -o OUTPUT, --output OUTPUT Store intermediate data and analysis results to OUTPUT (default: trajs.h5).

import re import h5py import numpy as np from westpa.tools import WESTTool, WESTDataReader import westpa from westpa.core import h5io from westpa.core.segment import Segment from westpa.core.states import InitialState from westpa.core.data_manager import weight_dtype, n_iter_dtype, seg_id_dtype, utime_dtype class Trace: '''A class representing a trace of a certain trajectory segment back to its origin.''' def __init__(self, summary, endpoint_type, basis_state, initial_state, data_manager=None): self.summary = summary self.endpoint_type = endpoint_type self.basis_state = basis_state self.initial_state = initial_state self.data_manager = data_manager or westpa.rc.get_data_manager() # A mapping from aux file names to open h5py.File objects, to minimize time self._auxfiles = {} def __len__(self): try: return len(self.summary) except TypeError: return 0 def __getitem__(self, sl): return self.summary[sl] def __iter__(self): return iter(self.summary) @classmethod def from_data_manager(cls, n_iter, seg_id, data_manager=None): '''Construct and return a trajectory trace whose last segment is identified by ``seg_id`` in the iteration number ``n_iter``.''' data_manager = data_manager or westpa.rc.get_data_manager() # These values are used later on endpoint_type = None pcoord_dtype = None pcoord_pt_shape = None seginfo = [] parent_id = seg_id while n_iter > 0 and parent_id >= 0: seg_id = parent_id iter_group = data_manager.get_iter_group(n_iter) pcoord_ds = iter_group['pcoord'] seg_index = iter_group['seg_index'] n_segs = pcoord_ds.shape[0] pcoord_len = pcoord_ds.shape[1] assert seg_id < n_segs indexrow = seg_index[seg_id] final_pcoord = pcoord_ds[seg_id, pcoord_len - 1] weight = indexrow['weight'] cputime = indexrow['cputime'] walltime = indexrow['walltime'] try: parent_id = int(indexrow['parent_id']) except IndexError: # old HDF5 version parent_id = int(iter_group['parents'][indexrow['parents_offset']]) if endpoint_type is None: endpoint_type = indexrow['endpoint_type'] pcoord_pt_shape = pcoord_ds.shape[2:] pcoord_dtype = pcoord_ds.dtype seginfo.append((n_iter, seg_id, weight, walltime, cputime, final_pcoord)) del iter_group, pcoord_ds, seg_index n_iter -= 1 # loop terminates with parent_id set to the identifier of the initial state, # seg_id set to the identifier of the first segment in the trajectory, and # n_iter set to one less than the iteration of the first segment first_iter = n_iter + 1 first_seg_id = seg_id first_parent_id = parent_id # Initial segment (for fetching initial state) first_segment = Segment(n_iter=first_iter, seg_id=first_seg_id, parent_id=first_parent_id) seginfo.reverse() summary_dtype = np.dtype( [ ('n_iter', n_iter_dtype), ('seg_id', seg_id_dtype), ('weight', weight_dtype), ('walltime', utime_dtype), ('cputime', utime_dtype), ('final_pcoord', pcoord_dtype, pcoord_pt_shape), ] ) summary = np.array(seginfo, dtype=summary_dtype) try: initial_state = data_manager.get_segment_initial_states([first_segment], first_iter)[0] except KeyError: # old HDF5 version assert parent_id < 0 istate_pcoord = data_manager.get_iter_group(first_iter)['pcoord'][first_seg_id, 0] istate_id = -(first_parent_id + 1) basis_state = None initial_state = InitialState(istate_id, None, iter_created=0, pcoord=istate_pcoord) else: basis_state = data_manager.get_basis_states(first_iter)[initial_state.basis_state_id] return cls(summary, endpoint_type, basis_state, initial_state, data_manager) def get_segment_data_slice(self, datafile, dsname, n_iter, seg_id, slice_=None, index_data=None, iter_prec=None): '''Return the data from the dataset named ``dsname`` within the given ``datafile`` (an open h5py.File object) for the given iteration and segment. By default, it is assumed that the dataset is stored in the iteration group for iteration ``n_iter``, but if ``index_data`` is provided, it must be an iterable (preferably a simple array) of (n_iter,seg_id) pairs, and the index in the ``index_data`` iterable of the matching n_iter/seg_id pair is used as the index of the data to retrieve. If an optional ``slice_`` is provided, then the given slicing tuple is appended to that used to retrieve the segment-specific data (i.e. it can be used to pluck a subset of the data that would otherwise be returned). ''' if slice_ is None: slice_ = np.s_[...] if index_data is not None: dataset = datafile[dsname] for i, (i_n_iter, i_seg_id) in enumerate(index_data): if (i_n_iter, i_seg_id) == (n_iter, seg_id): break else: raise KeyError((n_iter, seg_id)) itpl = (i,) + slice_ return dataset[itpl] else: if not iter_prec: iter_prec = datafile.attrs.get('west_iter_prec', self.data_manager.default_iter_prec) igname_tail = 'iter_{:0{iter_prec:d}d}'.format(int(n_iter), iter_prec=int(iter_prec)) try: iter_group = datafile['/iterations/' + igname_tail] except KeyError: iter_group = datafile[igname_tail] dataset = iter_group[dsname] itpl = (seg_id,) + slice_ return dataset[itpl] def trace_timepoint_dataset(self, dsname, slice_=None, auxfile=None, index_ds=None): '''Return a trace along this trajectory over a dataset which is layed out as [seg_id][timepoint][...]. Overlapping values at segment boundaries are accounted for. Returns (data_trace, weight), where data_trace is a time series of the dataset along this trajectory, and weight is the corresponding trajectory weight at each time point. If ``auxfile`` is given, then load the dataset from the given HDF5 file, which must be layed out the same way as the main HDF5 file (e.g. iterations arranged as iterations/iter_*). If index_ds is given, instead of reading data per-iteration from iter_* groups, then the given index_ds is used as an index of n_iter,seg_id pairs into ``dsname``. In this case, the target data set need not exist on a per-iteration basis inside iter_* groups. If ``slice_`` is given, then *further* slice the data returned from the HDF5 dataset. This can minimize I/O if it is known (and specified) that only a subset of the data along the trajectory is needed. ''' # Figure out where to look for the dataset if isinstance(auxfile, str): datafile = h5py.File(auxfile, 'r') close_datafile = True elif auxfile is not None: datafile = auxfile close_datafile = False else: datafile = self.data_manager.we_h5file close_datafile = False iter_prec = self.data_manager.iter_prec get_data_slice = self.get_segment_data_slice # Load the index if we use it if index_ds is not None: if isinstance(index_ds, str): index_ds = datafile[index_ds] index_data = index_ds[...] else: index_data = None # Be sure to retrieve the time series if not slice_: first_sl = np.index_exp[:,...] other_sl = np.index_exp[1:,...] else: first_sl = np.index_exp[:] + slice_ other_sl = np.index_exp[1:] + slice_ # Retrieve the first segment's data first_n_iter, first_seg_id = self.summary[0]['n_iter'], self.summary[0]['seg_id'] first_iter_data = get_data_slice(datafile, dsname, first_n_iter, first_seg_id, first_sl, index_data, iter_prec) n_segs = len(self) n_points_per_seg = len(first_iter_data) length = n_points_per_seg + (n_segs - 1) * (n_points_per_seg - 1) tracedata = np.empty((length,) + first_iter_data.shape[1:], dtype=first_iter_data.dtype) traceweight = np.empty((length,), weight_dtype) # Store first segment data tracedata[0:n_points_per_seg] = first_iter_data traceweight[0:n_points_per_seg] = self.summary[0]['weight'] del first_iter_data # Store remainder of data for iseg, summary_item in enumerate(self.summary[1:]): n_iter = summary_item['n_iter'] seg_id = summary_item['seg_id'] weight = summary_item['weight'] offset = n_points_per_seg + iseg * (n_points_per_seg - 1) length = n_points_per_seg - 1 seg_data = get_data_slice(datafile, dsname, n_iter, seg_id, other_sl, index_data, iter_prec) tracedata[offset : offset + length] = seg_data traceweight[offset : offset + length] = weight del seg_data if close_datafile: datafile.close() return tracedata, traceweight """ # This is disabled until there is a real use for it; the following code is # outdated def trace_perseg_dataset(self, dsname): '''Return a trace along this trajectory over a dataset which is layed out as [seg_id][...]. Returns (data_trace, weight), where data_trace is a time series of the dataset along this trajectory, and weight is the corresponding trajectory weight at each time point.''' first_n_iter, first_seg_id = self.summary[0]['n_iter'], self.summary[0]['seg_id'] first_iter_group = self.data_manager.get_iter_group(first_n_iter) first_iter_ds = first_iter_group[dsname] n_segs = len(self) tracedata = np.empty((n_segs,) + first_iter_ds.shape[1:], dtype=first_iter_ds.dtype) traceweight = np.empty((n_segs,), weight_dtype) tracedata[0] = first_iter_ds[first_seg_id] traceweight[0] = self.summary[0]['weight'] for isegm1, summary_item in enumerate(self.summary[1:]): iseg = isegm1 + 1 n_iter = summary_item['n_iter'] seg_id = summary_item['seg_id'] iter_group = self.data_manager.get_iter_group(n_iter) seg_data = iter_group[dsname][seg_id] tracedata[iseg] = seg_data traceweight[iseg] = summary_item['weight'] del seg_data return tracedata, traceweight """ class WTraceTool(WESTTool): prog = 'w_trace' description = '''\ Trace individual WEST trajectories and emit (or calculate) quantities along the trajectory. Trajectories are specified as N_ITER:SEG_ID pairs. Each segment is traced back to its initial point, and then various quantities (notably n_iter and seg_id) are printed in order from initial point up until the given segment in the given iteration. Output is stored in several files, all named according to the pattern given by the -o/--output-pattern parameter. The default output pattern is "traj_%d_%d", where the printf-style format codes are replaced by the iteration number and segment ID of the terminal segment of the trajectory being traced. Individual datasets can be selected for writing using the -d/--dataset option (which may be specified more than once). The simplest form is ``-d dsname``, which causes data from dataset ``dsname`` along the trace to be stored to HDF5. The dataset is assumed to be stored on a per-iteration basis, with the first dimension corresponding to seg_id and the second dimension corresponding to time within the segment. Further options are specified as comma-separated key=value pairs after the data set name, as in -d dsname,alias=newname,index=idsname,file=otherfile.h5,slice=[100,...] The following options for datasets are supported: alias=newname When writing this data to HDF5 or text files, use ``newname`` instead of ``dsname`` to identify the dataset. This is mostly of use in conjunction with the ``slice`` option in order, e.g., to retrieve two different slices of a dataset and store then with different names for future use. index=idsname The dataset is not stored on a per-iteration basis for all segments, but instead is stored as a single dataset whose first dimension indexes n_iter/seg_id pairs. The index to these n_iter/seg_id pairs is ``idsname``. file=otherfile.h5 Instead of reading data from the main WEST HDF5 file (usually ``west.h5``), read data from ``otherfile.h5``. slice=[100,...] Retrieve only the given slice from the dataset. This can be used to pick a subset of interest to minimize I/O. ------------------------------------------------------------------------------- ''' pcoord_formats = { 'u8': '%20d', 'i8': '%20d', 'u4': '%10d', 'i4': '%11d', 'u2': '%5d', 'i2': '%6d', 'f4': '%14.7g', 'f8': '%023.15g', } def __init__(self): super().__init__() self.data_reader = WESTDataReader() # self.h5storage = HDF5Storage() self.output_file = None self.output_pattern = None self.endpoints = None self.datasets = [] # Interface for command-line tools def add_args(self, parser): self.data_reader.add_args(parser) # self.h5storage.add_args(parser) parser.add_argument( '-d', '--dataset', dest='datasets', # this breaks argparse (see http://bugs.python.org/issue11874) # metavar='DSNAME[,alias=ALIAS][,index=INDEX][,file=FILE][,slice=SLICE]', metavar='DSNAME', action='append', help='''Include the dataset named DSNAME in trace output. An extended form like DSNAME[,alias=ALIAS][,index=INDEX][,file=FILE][,slice=SLICE] will obtain the dataset from the given FILE instead of the main WEST HDF5 file, slice it by SLICE, call it ALIAS in output, and/or access per-segment data by a n_iter,seg_id INDEX instead of a seg_id indexed dataset in the group for n_iter.''', ) parser.add_argument( 'endpoints', metavar='N_ITER:SEG_ID', nargs='+', help='''Trace trajectory ending (or at least alive at) N_ITER:SEG_ID.''', ) # tgroup = parser.add_argument_group('trace options') ogroup = parser.add_argument_group('output options') ogroup.add_argument( '--output-pattern', default='traj_%d_%d', help='''Write per-trajectory data to output files/HDF5 groups whose names begin with OUTPUT_PATTERN, which must contain two printf-style format flags which will be replaced with the iteration number and segment ID of the terminal segment of the trajectory being traced. (Default: %(default)s.)''', ) ogroup.add_argument( '-o', '--output', default='trajs.h5', help='Store intermediate data and analysis results to OUTPUT (default: %(default)s).', ) def process_args(self, args): self.data_reader.process_args(args) # self.h5storage.process_args(args) self.endpoints = [list(map(int, endpoint.split(':'))) for endpoint in args.endpoints] self.output_pattern = args.output_pattern for dsstr in args.datasets or []: self.datasets.append(self.parse_dataset_string(dsstr)) # self.h5storage.open_analysis_h5file() self.output_file = h5py.File(args.output, 'a') def parse_dataset_string(self, dsstr): dsinfo = {} r = re.compile(r',(?=[^\]]*(?:\[|$))') fields = r.split(dsstr) dsinfo['dsname'] = fields[0] for field in (field.strip() for field in fields[1:]): k, v = field.split('=') k = k.lower() if k in ('alias', 'file', 'index'): dsinfo[k] = v elif k =='slice': try: dsinfo['slice'] = eval('np.index_exp' + v) except SyntaxError: raise SyntaxError('invalid index expression {!r}'.format(v)) else: raise ValueError('invalid dataset option {!r}'.format(k)) return dsinfo def go(self): self.data_reader.open('r') # Create a new 'trajectories' group if this is the first trace try: trajs_group = h5io.create_hdf5_group(self.output_file, 'trajectories', replace=False, creating_program=self.prog) except ValueError: trajs_group = self.output_file['trajectories'] for n_iter, seg_id in self.endpoints: trajname = self.output_pattern % (n_iter, seg_id) trajgroup = trajs_group.create_group(trajname) trace = Trace.from_data_manager(n_iter, seg_id, self.data_reader.data_manager) with open(trajname + '_trace.txt', 'wt') as trace_output: self.emit_trace_text(trace, trace_output) self.emit_trace_h5(trace, trajgroup) aux_h5files = {} for dsinfo in self.datasets: dsname = dsinfo['dsname'] filename = dsinfo.get('file') if filename: try: aux_h5file = aux_h5files[filename] except KeyError: aux_h5file = aux_h5files[filename] = h5py.File(filename, 'r') else: aux_h5file = None slice_ = dsinfo.get('slice') alias = dsinfo.get('alias', dsname) index = dsinfo.get('index') data, weights = trace.trace_timepoint_dataset(dsname, auxfile=aux_h5file, slice_=slice_, index_ds=index) # Save data to HDF5 try: del trajgroup[alias] except KeyError: pass trajgroup[alias] = data # All weight vectors will be the same length, so only store in HDF5 once if not ('weights' in trajgroup and trajgroup['weights'].shape == weights.shape): try: del trajgroup['weights'] except KeyError: pass trajgroup['weights'] = weights def emit_trace_h5(self, trace, output_group): for dsname in ('basis_state', 'initial_state','segments'): try: del output_group[dsname] except KeyError: pass if trace.basis_state: output_group['basis_state'] = trace.basis_state.as_numpy_record() output_group['initial_state'] = trace.initial_state.as_numpy_record() output_group['segments'] = trace.summary def emit_trace_text(self, trace, output_file): '''Dump summary information about each segment in the given trace to the given output_file, which must be opened for writing in text mode. Output columns are separated by at least one space.''' if not trace: return pcoord_ndim = trace[0]['final_pcoord'].shape[0] lastseg = trace[-1] len_n_iter = max(6, len(str(lastseg['n_iter']))) len_seg_id = max(6, max(len(str(seg_id)) for seg_id in trace['seg_id'])) seg_pattern = ( ' '.join( [ '{n_iter:{len_n_iter}d}', '{seg_id:{len_seg_id}d}', '{weight:22.17e}', '{walltime:10.6g}', '{cputime:10.6g}', '{pcoord_str:s}', ] ) + '\n' ) output_file.write( '''\ # Trace of trajectory ending in n_iter:seg_id {n_iter:d}:{seg_id:d} (endpoint type {endpoint_type_text:s}) # column 0: iteration (0 => initial state) # column 1: seg_id (or initial state ID) # column 2: weight # column 3: wallclock time (s) # column 4: CPU time (s) '''.format( n_iter=int(lastseg['n_iter']), seg_id=int(lastseg['seg_id']), endpoint_type_text=Segment.endpoint_type_names[trace.endpoint_type], ) ) if pcoord_ndim == 1: output_file.write( '''\ # column 5: final progress coordinate value ''' ) else: fpcbegin = 5 fpcend = fpcbegin + pcoord_ndim - 1 output_file.write( '''\ # columns {fpcbegin:d} -- {fpcend:d}: final progress coordinate value '''.format( fpcbegin=fpcbegin, fpcend=fpcend ) ) pcoord_formats = self.pcoord_formats # Output row for initial state initial_state = trace.initial_state pcoord_str =' '.join(pcoord_formats.get(pcfield.dtype.str[1:], '%s') % pcfield for pcfield in initial_state.pcoord) output_file.write( seg_pattern.format( n_iter=0, seg_id=initial_state.state_id, weight=0.0, walltime=0, cputime=0, pcoord_str=pcoord_str, len_n_iter=len_n_iter, len_seg_id=len_seg_id, ) ) # Output rows for segments for segment in trace: pcoord_str =' '.join( pcoord_formats.get(pcfield.dtype.str[1:], '%s') % pcfield for pcfield in segment['final_pcoord'] ) output_file.write( seg_pattern.format( n_iter=int(segment['n_iter']), seg_id=int(segment['seg_id']), weight=float(segment['weight']), walltime=float(segment['walltime']), cputime=float(segment['cputime']), pcoord_str=pcoord_str, len_n_iter=len_n_iter, len_seg_id=len_seg_id, ) ) def entry_point(): WTraceTool().main() if __name__ == '__main__': entry_point()

tortoise__tortoise-orm

fields.rst

Module doc / Tutorial

Examples and usage

Apache License 2.0

tortoise__tortoise-orm/docs/fields.rst

Fields Usage Fields are defined as properties of a Model class object: from tortoise.models import Model from tortoise import fields class Tournament(Model): id = fields.IntField(pk=True) name = fields.CharField(max_length=255) emphasize-children Reference Here is the list of fields available with custom options of these fields: Base Field tortoise.fields.base Data Fields tortoise.fields.data Relational Fields tortoise.fields.relational Extending A Field It is possible to subclass fields allowing use of arbitrary types as long as they can be represented in a database compatible format. An example of this would be a simple wrapper around the ~tortoise.fields.CharField to store and query Enum types. from enum import Enum from typing import Type from tortoise import ConfigurationError from tortoise.fields import CharField class EnumField(CharField): """ An example extension to CharField that serializes Enums to and from a str representation in the DB. """ def __init__(self, enum_type: Type[Enum], **kwargs): super().__init__(128, **kwargs) if not issubclass(enum_type, Enum): raise ConfigurationError("{} is not a subclass of Enum!".format(enum_type)) self._enum_type = enum_type def to_db_value(self, value: Enum, instance) -> str: return value.value def to_python_value(self, value: str) -> Enum: try: return self._enum_type(value) except Exception: raise ValueError( "Database value {} does not exist on Enum {}.".format(value, self._enum_type) ) When subclassing, make sure that the to_db_value returns the same type as the superclass (in the case of CharField, that is a str) and that, naturally, to_python_value accepts the same type in the value parameter (also str).

from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Type, Union from pypika.terms import Term from tortoise.exceptions import ConfigurationError if TYPE_CHECKING: # pragma: nocoverage from tortoise.models import Model # TODO: Replace this with an enum CASCADE = "CASCADE" RESTRICT = "RESTRICT" SET_NULL = "SET NULL" SET_DEFAULT = "SET DEFAULT" class _FieldMeta(type): # TODO: Require functions to return field instances instead of this hack def __new__(mcs, name: str, bases: Tuple[Type,...], attrs: dict): if len(bases) > 1 and bases[0] is Field: # Instantiate class with only the 1st base class (should be Field) cls = type.__new__(mcs, name, (bases[0],), attrs) # type: Type[Field] # All other base classes are our meta types, we store them in class attributes cls.field_type = bases[1] if len(bases) == 2 else Union[bases[1:]] return cls return type.__new__(mcs, name, bases, attrs) class Field(metaclass=_FieldMeta): """ Base Field type. :param source_field: Provide a source_field name if the DB column name needs to be something specific instead of enerated off the field name. :param generated: Is this field DB-generated? :param pk: Is this field a Primary Key? Can only have a single such field on the Model, and if none is specified it will autogenerate a default primary key called ``id``. :param null: Is this field nullable? :param default: A default value for the field if not specified on Model creation. This can also be a callable for dynamic defaults in which case we will call it. The default value will not be part of the schema. :param unique: Is this field unique? :param index: Should this field be indexed by itself? :param description: Field description. Will also appear in ``Tortoise.describe_model()`` and as DB comments in the generated DDL. **Class Attributes:** These attributes needs to be defined when defining an actual field type. .. attribute:: field_type :annotation: Type[Any] The Python type the field is. If adding a type as a mixin, _FieldMeta will automatically set this to that. .. attribute:: indexable :annotation: bool = True Is the field indexable? Set to False if this field can't be indexed reliably. .. attribute:: has_db_field :annotation: bool = True Does this field have a direct corresponding DB column? Or is the field virtualized? .. attribute:: skip_to_python_if_native :annotation: bool = False If the DB driver natively supports this Python type, should we skip it? This is for optimization purposes only, where we don't need to force type conversion to and fro between Python and the DB. .. attribute:: allows_generated :annotation: bool = False Is this field able to be DB-generated? .. attribute:: function_cast :annotation: Optional[pypika.Term] = None A casting term that we need to apply in case the DB needs emulation help. .. attribute:: SQL_TYPE :annotation: str The SQL type as a string that the DB will use. .. attribute:: GENERATED_SQL :annotation: str The SQL that instructs the DB to auto-generate this field. Required if ``allows_generated`` is ``True``. **Per-DB overrides:** One can specify per-DB overrides of any of the class attributes, or the ``to_db_value`` or ``to_python_value`` methods. To do so, specify a inner class in the form of :samp:`class _db__{SQL_DIALECT}:` like so: .. code-block:: py3 class _db_sqlite: SQL_TYPE = "VARCHAR(40)" skip_to_python_if_native = False def function_cast(self, term: Term) -> Term: return functions.Cast(term, SqlTypes.NUMERIC) Tortoise will then use the overridden attributes/functions for that dialect. If you need a dynamic attribute, you can use a property. """ # Field_type is a readonly property for the instance, it is set by _FieldMeta field_type: Type[Any] = None # type: ignore indexable: bool = True has_db_field: bool = True skip_to_python_if_native: bool = False allows_generated: bool = False function_cast: Optional[Callable[[Term], Term]] = None SQL_TYPE: str = None # type: ignore GENERATED_SQL: str = None # type: ignore # This method is just to make IDE/Linters happy def __new__(cls, *args: Any, **kwargs: Any) -> "Field": return super().__new__(cls) def __init__( self, source_field: Optional[str] = None, generated: bool = False, pk: bool = False, null: bool = False, default: Any = None, unique: bool = False, index: bool = False, description: Optional[str] = None, model: "Optional[Model]" = None, **kwargs: Any, ) -> None: # TODO: Rename pk to primary_key, alias pk, deprecate # TODO: Rename index to db_index, alias index, deprecate if not self.indexable and (unique or index): raise ConfigurationError(f"{self.__class__.__name__} can't be indexed") if pk and null: raise ConfigurationError( f"{self.__class__.__name__} can't be both null=True and pk=True" ) if pk: index = True unique = True self.source_field = source_field self.generated = generated self.pk = pk self.default = default self.null = null self.unique = unique self.index = index self.model_field_name = "" self.description = description self.docstring: Optional[str] = None # TODO: consider making this not be set from constructor self.model: Type["Model"] = model # type: ignore self.reference: "Optional[Field]" = None def to_db_value(self, value: Any, instance: "Union[Type[Model], Model]") -> Any: """ Converts from the Python type to the DB type. :param value: Current python value in model. :param instance: Model class or Model instance provided to look up. Due to metacoding, to determine if this is an instance reliably, please do a: .. code-block:: py3 if hasattr(instance, "_saved_in_db"): """ if value is None or isinstance(value, self.field_type): return value return self.field_type(value) # pylint: disable=E1102 def to_python_value(self, value: Any) -> Any: """ Converts from the DB type to the Python type. :param value: Value from DB """ if value is None or isinstance(value, self.field_type): return value return self.field_type(value) # pylint: disable=E1102 @property def required(self) -> bool: """ Returns ``True`` if the field is required to be provided. It needs to be non-nullable and not have a default or be DB-generated to be required. """ return self.default is None and not self.null and not self.generated @property def constraints(self) -> dict: """ Returns a dict with constraints defined in the Pydantic/JSONSchema format. """ return {} def _get_dialects(self) -> Dict[str, dict]: return { dialect[4:]: { key: val for key, val in getattr(self, dialect).__dict__.items() if not key.startswith("_") } for dialect in [key for key in dir(self) if key.startswith("_db_")] } def get_db_field_types(self) -> Optional[Dict[str, str]]: """ Returns the DB types for this field. :return: A dictionary that is keyed by dialect. A blank dialect `""` means it is the default DB field type. """ if not self.has_db_field: # pragma: nocoverage return None return { "": getattr(self, "SQL_TYPE"), **{ dialect: _db["SQL_TYPE"] for dialect, _db in self._get_dialects().items() if "SQL_TYPE" in _db }, } def get_for_dialect(self, dialect: str, key: str) -> Any: """ Returns a field by dialect override. :param dialect: The requested SQL Dialect. :param key: The attribute/method name. """ dialect_data = self._get_dialects().get(dialect, {}) return dialect_data.get(key, getattr(self, key, None)) def describe(self, serializable: bool) -> dict: """ Describes the field. :param serializable: ``False`` if you want raw python objects, ``True`` for JSON-serialisable data. (Defaults to ``True``) :return: A dictionary containing the field description. (This assumes ``serializable=True``, which is the default): .. code-block:: python3 { "name": str # Field name "field_type": str # Field type "db_column": str # Name of DB column # Optional: Only for pk/data fields "raw_field": str # Name of raw field of the Foreign Key # Optional: Only for Foreign Keys "db_field_types": dict # DB Field types for default and DB overrides "python_type": str # Python type "generated": bool # Is the field generated by the DB? "nullable": bool # Is the column nullable? "unique": bool # Is the field unique? "indexed": bool # Is the field indexed? "default": ... # The default value (coerced to int/float/str/bool/null) "description": str # Description of the field (nullable) "docstring": str # Field docstring (nullable) } When ``serializable=False`` is specified some fields are not coerced to valid JSON types. The changes are: .. code-block:: python3 { "field_type": Field # The Field class used "python_type": Type # The actual Python type "default": ... # The default value as native type OR a callable } """ def _type_name(typ: Type) -> str: if typ.__module__ == "builtins": return typ.__name__ if typ.__module__ == "typing": return str(typ).replace("typing.", "") return f"{typ.__module__}.{typ.__name__}" def type_name(typ: Any) -> Union[str, List[str]]: try: return typ._meta.full_name except (AttributeError, TypeError): pass try: return _type_name(typ) except AttributeError: try: return [_type_name(_typ) for _typ in typ] # pragma: nobranch except TypeError: return str(typ) def default_name(default: Any) -> Optional[Union[int, float, str, bool]]: if isinstance(default, (int, float, str, bool, type(None))): return default if callable(default): return f"<function {default.__module__}.{default.__name__}>" return str(default) field_type = getattr(self, "related_model", self.field_type) desc = { "name": self.model_field_name, "field_type": self.__class__.__name__ if serializable else self.__class__, "db_column": self.source_field or self.model_field_name, "python_type": type_name(field_type) if serializable else field_type, "generated": self.generated, "nullable": self.null, "unique": self.unique, "indexed": self.index or self.unique, "default": default_name(self.default) if serializable else self.default, "description": self.description, "docstring": self.docstring, "constraints": self.constraints, } if self.has_db_field: desc["db_field_types"] = self.get_db_field_types() return desc import datetime import functools import json import warnings from decimal import Decimal from enum import Enum, IntEnum from typing import TYPE_CHECKING, Any, Callable, Optional, Type, TypeVar, Union from uuid import UUID, uuid4 from pypika import functions from pypika.enums import SqlTypes from pypika.terms import Term from tortoise.exceptions import ConfigurationError from tortoise.fields.base import Field try: from ciso8601 import parse_datetime except ImportError: # pragma: nocoverage from iso8601 import parse_date parse_datetime = functools.partial(parse_date, default_timezone=None) if TYPE_CHECKING: # pragma: nocoverage from tortoise.models import Model __all__ = ( "BigIntField", "BinaryField", "BooleanField", "CharEnumField", "CharField", "DateField", "DatetimeField", "DecimalField", "FloatField", "IntEnumField", "IntField", "JSONField", "SmallIntField", "TextField", "TimeDeltaField", "UUIDField", ) # Doing this we can replace json dumps/loads with different implementations JsonDumpsFunc = Callable[[Any], str] JsonLoadsFunc = Callable[[str], Any] JSON_DUMPS: JsonDumpsFunc = functools.partial(json.dumps, separators=(",", ":")) JSON_LOADS: JsonLoadsFunc = json.loads try: # Use python-rapidjson as an optional accelerator import rapidjson JSON_DUMPS = rapidjson.dumps JSON_LOADS = rapidjson.loads except ImportError: # pragma: nocoverage pass class IntField(Field, int): """ Integer field. (32-bit signed) ``pk`` (bool): True if field is Primary Key. """ SQL_TYPE = "INT" allows_generated = True def __init__(self, pk: bool = False, **kwargs: Any) -> None: if pk: kwargs["generated"] = bool(kwargs.get("generated", True)) super().__init__(pk=pk, **kwargs) @property def constraints(self) -> dict: return { "ge": 1 if self.generated or self.reference else -2147483648, "le": 2147483647, } class _db_postgres: GENERATED_SQL = "SERIAL NOT NULL PRIMARY KEY" class _db_sqlite: GENERATED_SQL = "INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL" class _db_mysql: GENERATED_SQL = "INT NOT NULL PRIMARY KEY AUTO_INCREMENT" class BigIntField(Field, int): """ Big integer field. (64-bit signed) ``pk`` (bool): True if field is Primary Key. """ SQL_TYPE = "BIGINT" allows_generated = True def __init__(self, pk: bool = False, **kwargs: Any) -> None: if pk: kwargs["generated"] = bool(kwargs.get("generated", True)) super().__init__(pk=pk, **kwargs) @property def constraints(self) -> dict: return { "ge": 1 if self.generated or self.reference else -9223372036854775808, "le": 9223372036854775807, } class _db_postgres: GENERATED_SQL = "BIGSERIAL NOT NULL PRIMARY KEY" class _db_sqlite: GENERATED_SQL = "INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL" class _db_mysql: GENERATED_SQL = "BIGINT NOT NULL PRIMARY KEY AUTO_INCREMENT" class SmallIntField(Field, int): """ Small integer field. (16-bit signed) ``pk`` (bool): True if field is Primary Key. """ SQL_TYPE = "SMALLINT" allows_generated = True def __init__(self, pk: bool = False, **kwargs: Any) -> None: if pk: kwargs["generated"] = bool(kwargs.get("generated", True)) super().__init__(pk=pk, **kwargs) @property def constraints(self) -> dict: return { "ge": 1 if self.generated or self.reference else -32768, "le": 32767, } class _db_postgres: GENERATED_SQL = "SMALLSERIAL NOT NULL PRIMARY KEY" class _db_sqlite: GENERATED_SQL = "INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL" class _db_mysql: GENERATED_SQL = "SMALLINT NOT NULL PRIMARY KEY AUTO_INCREMENT" class CharField(Field, str): # type: ignore """ Character field. You must provide the following: ``max_length`` (int): Maximum length of the field in characters. """ def __init__(self, max_length: int, **kwargs: Any) -> None: if int(max_length) < 1: raise ConfigurationError("'max_length' must be >= 1") self.max_length = int(max_length) super().__init__(**kwargs) @property def constraints(self) -> dict: return { "max_length": self.max_length, } @property def SQL_TYPE(self) -> str: # type: ignore return f"VARCHAR({self.max_length})" class TextField(Field, str): # type: ignore """ Large Text field. """ indexable = False SQL_TYPE = "TEXT" def __init__( self, pk: bool = False, unique: bool = False, index: bool = False, **kwargs: Any ) -> None: if pk: warnings.warn( "TextField as a PrimaryKey is Deprecated, use CharField instead", DeprecationWarning, stacklevel=2, ) if unique: raise ConfigurationError( f"TextField doesn't support unique indexes, consider CharField or another strategy" ) if index: raise ConfigurationError(f"TextField can't be indexed, consider CharField") super().__init__(pk=pk, **kwargs) class _db_mysql: SQL_TYPE = "LONGTEXT" class BooleanField(Field): """ Boolean field. """ # Bool is not subclassable, so we specify type here field_type = bool SQL_TYPE = "BOOL" class _db_sqlite: SQL_TYPE = "INT" class DecimalField(Field, Decimal): """ Accurate decimal field. You must provide the following: ``max_digits`` (int): Max digits of significance of the decimal field. ``decimal_places`` (int): How many of those signifigant digits is after the decimal point. """ skip_to_python_if_native = True def __init__(self, max_digits: int, decimal_places: int, **kwargs: Any) -> None: if int(max_digits) < 1: raise ConfigurationError("'max_digits' must be >= 1") if int(decimal_places) < 0: raise ConfigurationError("'decimal_places' must be >= 0") super().__init__(**kwargs) self.max_digits = max_digits self.decimal_places = decimal_places self.quant = Decimal("1" if decimal_places == 0 else f"1.{('0' * decimal_places)}") def to_python_value(self, value: Any) -> Optional[Decimal]: if value is None: return None return Decimal(value).quantize(self.quant).normalize() @property def SQL_TYPE(self) -> str: # type: ignore return f"DECIMAL({self.max_digits},{self.decimal_places})" class _db_sqlite: SQL_TYPE = "VARCHAR(40)" def function_cast(self, term: Term) -> Term: return functions.Cast(term, SqlTypes.NUMERIC) class DatetimeField(Field, datetime.datetime): """ Datetime field. ``auto_now`` and ``auto_now_add`` is exclusive. You can opt to set neither or only ONE of them. ``auto_now`` (bool): Always set to ``datetime.utcnow()`` on save. ``auto_now_add`` (bool): Set to ``datetime.utcnow()`` on first save only. """ skip_to_python_if_native = True SQL_TYPE = "TIMESTAMP" class _db_mysql: SQL_TYPE = "DATETIME(6)" def __init__(self, auto_now: bool = False, auto_now_add: bool = False, **kwargs: Any) -> None: if auto_now_add and auto_now: raise ConfigurationError("You can choose only 'auto_now' or 'auto_now_add'") super().__init__(**kwargs) self.auto_now = auto_now self.auto_now_add = auto_now | auto_now_add def to_python_value(self, value: Any) -> Optional[datetime.datetime]: if value is None or isinstance(value, datetime.datetime): return value return parse_datetime(value) def to_db_value( self, value: Optional[datetime.datetime], instance: "Union[Type[Model], Model]" ) -> Optional[datetime.datetime]: # Only do this if it is a Model instance, not class. Test for guaranteed instance var if hasattr(instance, "_saved_in_db") and ( self.auto_now or (self.auto_now_add and getattr(instance, self.model_field_name) is None) ): value = datetime.datetime.utcnow() setattr(instance, self.model_field_name, value) return value return value @property def constraints(self) -> dict: data = {} if self.auto_now_add: data["readOnly"] = True return data class DateField(Field, datetime.date): """ Date field. """ skip_to_python_if_native = True SQL_TYPE = "DATE" def to_python_value(self, value: Any) -> Optional[datetime.date]: if value is None or isinstance(value, datetime.date): return value return parse_datetime(value).date() class TimeDeltaField(Field, datetime.timedelta): """ A field for storing time differences. """ SQL_TYPE = "BIGINT" def to_python_value(self, value: Any) -> Optional[datetime.timedelta]: if value is None or isinstance(value, datetime.timedelta): return value return datetime.timedelta(microseconds=value) def to_db_value( self, value: Optional[datetime.timedelta], instance: "Union[Type[Model], Model]" ) -> Optional[int]: if value is None: return None return (value.days * 86400000000) + (value.seconds * 1000000) + value.microseconds class FloatField(Field, float): """ Float (double) field. """ SQL_TYPE = "DOUBLE PRECISION" class _db_sqlite: SQL_TYPE = "REAL" class _db_mysql: SQL_TYPE = "DOUBLE" class JSONField(Field, dict, list): # type: ignore """ JSON field. This field can store dictionaries or lists of any JSON-compliant structure. You can specify your own custom JSON encoder/decoder, leaving at the default should work well. If you have ``python-rapidjson`` installed, we default to using that, else the default ``json`` module will be used. ``encoder``: The custom JSON encoder. ``decoder``: The custom JSON decoder. """ SQL_TYPE = "TEXT" indexable = False class _db_postgres: SQL_TYPE = "JSONB" def __init__( self, encoder: JsonDumpsFunc = JSON_DUMPS, decoder: JsonLoadsFunc = JSON_LOADS, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.encoder = encoder self.decoder = decoder def to_db_value( self, value: Optional[Union[dict, list]], instance: "Union[Type[Model], Model]" ) -> Optional[str]: return None if value is None else self.encoder(value) def to_python_value( self, value: Optional[Union[str, dict, list]] ) -> Optional[Union[dict, list]]: return self.decoder(value) if isinstance(value, str) else value class UUIDField(Field, UUID): """ UUID Field This field can store uuid value. If used as a primary key, it will auto-generate a UUID4 by default. """ SQL_TYPE = "CHAR(36)" class _db_postgres: SQL_TYPE = "UUID" def __init__(self, **kwargs: Any) -> None: if kwargs.get("pk", False) and "default" not in kwargs: kwargs["default"] = uuid4 super().__init__(**kwargs) def to_db_value(self, value: Any, instance: "Union[Type[Model], Model]") -> Optional[str]: return value and str(value) def to_python_value(self, value: Any) -> Optional[UUID]: if value is None or isinstance(value, UUID): return value return UUID(value) class BinaryField(Field, bytes): # type: ignore """ Binary field. This is for storing ``bytes`` objects. Note that filter or queryset-update operations are not supported. """ indexable = False SQL_TYPE = "BLOB" class _db_postgres: SQL_TYPE = "BYTEA" class _db_mysql: SQL_TYPE = "LONGBLOB" class IntEnumFieldInstance(SmallIntField): def __init__( self, enum_type: Type[IntEnum], description: Optional[str] = None, **kwargs: Any ) -> None: # Validate values for item in enum_type: try: value = int(item.value) except ValueError: raise ConfigurationError("IntEnumField only supports integer enums!") if not 0 <= value < 32768: raise ConfigurationError("The valid range of IntEnumField's values is 0..32767!") # Automatic description for the field if not specified by the user if description is None: description = "\n".join([f"{e.name}: {int(e.value)}" for e in enum_type])[:2048] super().__init__(description=description, **kwargs) self.enum_type = enum_type def to_python_value(self, value: Union[int, None]) -> Union[IntEnum, None]: return self.enum_type(value) if value is not None else None def to_db_value( self, value: Union[IntEnum, None, int], instance: "Union[Type[Model], Model]" ) -> Union[int, None]: if isinstance(value, IntEnum): return int(value.value) if isinstance(value, int): return int(self.enum_type(value)) return value IntEnumType = TypeVar("IntEnumType", bound=IntEnum) def IntEnumField( enum_type: Type[IntEnumType], description: Optional[str] = None, **kwargs: Any, ) -> IntEnumType: """ Enum Field A field representing an integer enumeration. The description of the field is set automatically if not specified to a multiline list of "name: value" pairs. **Note**: Valid int value of ``enum_type`` is acceptable. ``enum_type``: The enum class ``description``: The description of the field. It is set automatically if not specified to a multiline list of "name: value" pairs. """ return IntEnumFieldInstance(enum_type, description, **kwargs) # type: ignore class CharEnumFieldInstance(CharField): def __init__( self, enum_type: Type[Enum], description: Optional[str] = None, max_length: int = 0, **kwargs: Any, ) -> None: # Automatic description for the field if not specified by the user if description is None: description = "\n".join([f"{e.name}: {str(e.value)}" for e in enum_type])[:2048] # Automatic CharField max_length if max_length == 0: for item in enum_type: item_len = len(str(item.value)) if item_len > max_length: max_length = item_len super().__init__(description=description, max_length=max_length, **kwargs) self.enum_type = enum_type def to_python_value(self, value: Union[str, None]) -> Union[Enum, None]: return self.enum_type(value) if value is not None else None def to_db_value( self, value: Union[Enum, None, str], instance: "Union[Type[Model], Model]" ) -> Union[str, None]: if isinstance(value, Enum): return str(value.value) if isinstance(value, str): return str(self.enum_type(value).value) return value CharEnumType = TypeVar("CharEnumType", bound=Enum) def CharEnumField( enum_type: Type[CharEnumType], description: Optional[str] = None, max_length: int = 0, **kwargs: Any, ) -> CharEnumType: """ Char Enum Field A field representing a character enumeration. **Warning**: If ``max_length`` is not specified or equals to zero, the size of represented char fields is automatically detected. So if later you update the enum, you need to update your table schema as well. **Note**: Valid str value of ``enum_type`` is acceptable. ``enum_type``: The enum class ``description``: The description of the field. It is set automatically if not specified to a multiline list of "name: value" pairs. ``max_length``: The length of the created CharField. If it is zero it is automatically detected from enum_type. """ return CharEnumFieldInstance(enum_type, description, max_length, **kwargs) # type: ignore from typing import ( TYPE_CHECKING, Any, AsyncGenerator, Awaitable, Generator, Generic, Iterator, List, Optional, Type, TypeVar, Union, ) from pypika import Table from typing_extensions import Literal from tortoise.exceptions import ConfigurationError, NoValuesFetched, OperationalError from tortoise.fields.base import CASCADE, RESTRICT, SET_NULL, Field if TYPE_CHECKING: # pragma: nocoverage from tortoise.models import Model from tortoise.queryset import QuerySet, Q from tortoise.backends.base.client import BaseDBAsyncClient MODEL = TypeVar("MODEL", bound="Model") OneToOneNullableRelation = Union[Awaitable[Optional[MODEL]], Optional[MODEL]] """ Type hint for the result of accessing the :func:`.OneToOneField` field in the model when obtained model can be nullable. """ OneToOneRelation = Union[Awaitable[MODEL], MODEL] """ Type hint for the result of accessing the :func:`.OneToOneField` field in the model. """ ForeignKeyNullableRelation = Union[Awaitable[Optional[MODEL]], Optional[MODEL]] """ Type hint for the result of accessing the :func:`.ForeignKeyField` field in the model when obtained model can be nullable. """ ForeignKeyRelation = Union[Awaitable[MODEL], MODEL] """ Type hint for the result of accessing the :func:`.ForeignKeyField` field in the model. """ class _NoneAwaitable: __slots__ = () def __await__(self) -> Generator[None, None, None]: yield None def __bool__(self) -> bool: return False NoneAwaitable = _NoneAwaitable() class ReverseRelation(Generic[MODEL]): """ Relation container for :func:`.ForeignKeyField`. """ def __init__( self, remote_model: Type[MODEL], relation_field: str, instance: "Model", from_field: str, ) -> None: self.remote_model = remote_model self.relation_field = relation_field self.instance = instance self.from_field = from_field self._fetched = False self._custom_query = False self.related_objects: List[MODEL] = [] @property def _query(self) -> "QuerySet[MODEL]": if not self.instance._saved_in_db: raise OperationalError( "This objects hasn't been instanced, call.save() before calling related queries" ) return self.remote_model.filter( **{self.relation_field: getattr(self.instance, self.from_field)} ) def __contains__(self, item: Any) -> bool: self._raise_if_not_fetched() return item in self.related_objects def __iter__(self) -> "Iterator[MODEL]": self._raise_if_not_fetched() return self.related_objects.__iter__() def __len__(self) -> int: self._raise_if_not_fetched() return len(self.related_objects) def __bool__(self) -> bool: self._raise_if_not_fetched() return bool(self.related_objects) def __getitem__(self, item: int) -> MODEL: self._raise_if_not_fetched() return self.related_objects[item] def __await__(self) -> Generator[Any, None, List[MODEL]]: return self._query.__await__() async def __aiter__(self) -> AsyncGenerator[Any, MODEL]: if not self._fetched: self._set_result_for_query(await self) for val in self.related_objects: yield val def filter(self, *args: "Q", **kwargs: Any) -> "QuerySet[MODEL]": """ Returns a QuerySet with related elements filtered by args/kwargs. """ return self._query.filter(*args, **kwargs) def all(self) -> "QuerySet[MODEL]": """ Returns a QuerySet with all related elements. """ return self._query def order_by(self, *orderings: str) -> "QuerySet[MODEL]": """ Returns a QuerySet related elements in order. """ return self._query.order_by(*orderings) def limit(self, limit: int) -> "QuerySet[MODEL]": """ Returns a QuerySet with at most «limit» related elements. """ return self._query.limit(limit) def offset(self, offset: int) -> "QuerySet[MODEL]": """ Returns a QuerySet with all related elements offset by «offset». """ return self._query.offset(offset) def _set_result_for_query(self, sequence: List[MODEL]) -> None: self._fetched = True self.related_objects = sequence def _raise_if_not_fetched(self) -> None: if not self._fetched: raise NoValuesFetched( "No values were fetched for this relation, first use.fetch_related()" ) class ManyToManyRelation(ReverseRelation[MODEL]): """ Many to many relation container for :func:`.ManyToManyField`. """ def __init__(self, instance: "Model", m2m_field: "ManyToManyFieldInstance") -> None: super().__init__(m2m_field.related_model, m2m_field.related_name, instance, "pk") # type: ignore self.field = m2m_field self.instance = instance async def add(self, *instances: MODEL, using_db: "Optional[BaseDBAsyncClient]" = None) -> None: """ Adds one or more of ``instances`` to the relation. If it is already added, it will be silently ignored. :raises OperationalError: If Object to add is not saved. """ if not instances: return if not self.instance._saved_in_db: raise OperationalError(f"You should first call.save() on {self.instance}") db = using_db if using_db else self.remote_model._meta.db pk_formatting_func = type(self.instance)._meta.pk.to_db_value related_pk_formatting_func = type(instances[0])._meta.pk.to_db_value through_table = Table(self.field.through) select_query = ( db.query_class.from_(through_table) .where( through_table[self.field.backward_key] == pk_formatting_func(self.instance.pk, self.instance) ) .select(self.field.backward_key, self.field.forward_key) ) query = db.query_class.into(through_table).columns( through_table[self.field.forward_key], through_table[self.field.backward_key], ) if len(instances) == 1: criterion = through_table[self.field.forward_key] == related_pk_formatting_func( instances[0].pk, instances[0] ) else: criterion = through_table[self.field.forward_key].isin( [related_pk_formatting_func(i.pk, i) for i in instances] ) select_query = select_query.where(criterion) # TODO: This is highly inefficient. Should use UNIQUE index by default. # And optionally allow duplicates. _, already_existing_relations_raw = await db.execute_query(str(select_query)) already_existing_relations = { ( pk_formatting_func(r[self.field.backward_key], self.instance), related_pk_formatting_func(r[self.field.forward_key], self.instance), ) for r in already_existing_relations_raw } insert_is_required = False for instance_to_add in instances: if not instance_to_add._saved_in_db: raise OperationalError(f"You should first call.save() on {instance_to_add}") pk_f = related_pk_formatting_func(instance_to_add.pk, instance_to_add) pk_b = pk_formatting_func(self.instance.pk, self.instance) if (pk_b, pk_f) in already_existing_relations: continue query = query.insert(pk_f, pk_b) insert_is_required = True if insert_is_required: await db.execute_query(str(query)) async def clear(self, using_db: "Optional[BaseDBAsyncClient]" = None) -> None: """ Clears ALL relations. """ db = using_db if using_db else self.remote_model._meta.db through_table = Table(self.field.through) pk_formatting_func = type(self.instance)._meta.pk.to_db_value query = ( db.query_class.from_(through_table) .where( through_table[self.field.backward_key] == pk_formatting_func(self.instance.pk, self.instance) ) .delete() ) await db.execute_query(str(query)) async def remove( self, *instances: MODEL, using_db: "Optional[BaseDBAsyncClient]" = None ) -> None: """ Removes one or more of ``instances`` from the relation. :raises OperationalError: remove() was called with no instances. """ db = using_db if using_db else self.remote_model._meta.db if not instances: raise OperationalError("remove() called on no instances") through_table = Table(self.field.through) pk_formatting_func = type(self.instance)._meta.pk.to_db_value related_pk_formatting_func = type(instances[0])._meta.pk.to_db_value if len(instances) == 1: condition = ( through_table[self.field.forward_key] == related_pk_formatting_func(instances[0].pk, instances[0]) ) & ( through_table[self.field.backward_key] == pk_formatting_func(self.instance.pk, self.instance) ) else: condition = ( through_table[self.field.backward_key] == pk_formatting_func(self.instance.pk, self.instance) ) & ( through_table[self.field.forward_key].isin( [related_pk_formatting_func(i.pk, i) for i in instances] ) ) query = db.query_class.from_(through_table).where(condition).delete() await db.execute_query(str(query)) class RelationalField(Field): has_db_field = False def __init__( self, related_model: "Type[Model]", to_field: Optional[str] = None, **kwargs: Any ) -> None: super().__init__(**kwargs) self.related_model: "Type[Model]" = related_model self.to_field: str = to_field # type: ignore self.to_field_instance: Field = None # type: ignore def describe(self, serializable: bool) -> dict: desc = super().describe(serializable) del desc["db_column"] return desc class ForeignKeyFieldInstance(RelationalField): def __init__( self, model_name: str, related_name: Union[Optional[str], Literal[False]] = None, on_delete: str = CASCADE, **kwargs: Any, ) -> None: super().__init__(None, **kwargs) # type: ignore if len(model_name.split("."))!= 2: raise ConfigurationError('Foreign key accepts model name in format "app.Model"') self.model_name = model_name self.related_name = related_name if on_delete not in {CASCADE, RESTRICT, SET_NULL}: raise ConfigurationError("on_delete can only be CASCADE, RESTRICT or SET_NULL") if on_delete == SET_NULL and not bool(kwargs.get("null")): raise ConfigurationError("If on_delete is SET_NULL, then field must have null=True set") self.on_delete = on_delete def describe(self, serializable: bool) -> dict: desc = super().describe(serializable) desc["raw_field"] = self.source_field return desc class BackwardFKRelation(RelationalField): def __init__( self, field_type: "Type[Model]", relation_field: str, relation_source_field: str, null: bool, description: Optional[str], **kwargs: Any, ) -> None: super().__init__(field_type, null=null, **kwargs) self.relation_field: str = relation_field self.relation_source_field: str = relation_source_field self.description: Optional[str] = description class OneToOneFieldInstance(ForeignKeyFieldInstance): def __init__( self, model_name: str, related_name: Union[Optional[str], Literal[False]] = None, on_delete: str = CASCADE, **kwargs: Any, ) -> None: if len(model_name.split("."))!= 2: raise ConfigurationError('OneToOneField accepts model name in format "app.Model"') super().__init__(model_name, related_name, on_delete, unique=True, **kwargs) class BackwardOneToOneRelation(BackwardFKRelation): pass class ManyToManyFieldInstance(RelationalField): field_type = ManyToManyRelation def __init__( self, model_name: str, through: Optional[str] = None, forward_key: Optional[str] = None, backward_key: str = "", related_name: str = "", field_type: "Type[Model]" = None, # type: ignore **kwargs: Any, ) -> None: # TODO: rename through to through_table # TODO: add through to use a Model super().__init__(field_type, **kwargs) if len(model_name.split("."))!= 2: raise ConfigurationError('Foreign key accepts model name in format "app.Model"') self.model_name: str = model_name self.related_name: str = related_name self.forward_key: str = forward_key or f"{model_name.split('.')[1].lower()}_id" self.backward_key: str = backward_key self.through: str = through # type: ignore self._generated: bool = False def OneToOneField( model_name: str, related_name: Union[Optional[str], Literal[False]] = None, on_delete: str = CASCADE, **kwargs: Any, ) -> OneToOneRelation: """ OneToOne relation field. This field represents a foreign key relation to another model. See :ref:`one_to_one` for usage information. You must provide the following: ``model_name``: The name of the related model in a :samp:`'{app}.{model}'` format. The following is optional: ``related_name``: The attribute name on the related model to reverse resolve the foreign key. ``on_delete``: One of: ``field.CASCADE``: Indicate that the model should be cascade deleted if related model gets deleted. ``field.RESTRICT``: Indicate that the related model delete will be restricted as long as a foreign key points to it. ``field.SET_NULL``: Resets the field to NULL in case the related model gets deleted. Can only be set if field has ``null=True`` set. ``field.SET_DEFAULT``: Resets the field to ``default`` value in case the related model gets deleted. Can only be set is field has a ``default`` set. ``to_field``: The attribute name on the related model to establish foreign key relationship. If not set, pk is used """ return OneToOneFieldInstance(model_name, related_name, on_delete, **kwargs) def ForeignKeyField( model_name: str, related_name: Union[Optional[str], Literal[False]] = None, on_delete: str = CASCADE, **kwargs: Any, ) -> ForeignKeyRelation: """ ForeignKey relation field. This field represents a foreign key relation to another model. See :ref:`foreign_key` for usage information. You must provide the following: ``model_name``: The name of the related model in a :samp:`'{app}.{model}'` format. The following is optional: ``related_name``: The attribute name on the related model to reverse resolve the foreign key. ``on_delete``: One of: ``field.CASCADE``: Indicate that the model should be cascade deleted if related model gets deleted. ``field.RESTRICT``: Indicate that the related model delete will be restricted as long as a foreign key points to it. ``field.SET_NULL``: Resets the field to NULL in case the related model gets deleted. Can only be set if field has ``null=True`` set. ``field.SET_DEFAULT``: Resets the field to ``default`` value in case the related model gets deleted. Can only be set is field has a ``default`` set. ``to_field``: The attribute name on the related model to establish foreign key relationship. If not set, pk is used """ return ForeignKeyFieldInstance(model_name, related_name, on_delete, **kwargs) def ManyToManyField( model_name: str, through: Optional[str] = None, forward_key: Optional[str] = None, backward_key: str = "", related_name: str = "", **kwargs: Any, ) -> "ManyToManyRelation": """ ManyToMany relation field. This field represents a many-to-many between this model and another model. See :ref:`many_to_many` for usage information. You must provide the following: ``model_name``: The name of the related model in a :samp:`'{app}.{model}'` format. The following is optional: ``through``: The DB table that represents the trough table. The default is normally safe. ``forward_key``: The forward lookup key on the through table. The default is normally safe. ``backward_key``: The backward lookup key on the through table. The default is normally safe. ``related_name``: The attribute name on the related model to reverse resolve the many to many. """ return ManyToManyFieldInstance( # type: ignore model_name, through, forward_key, backward_key, related_name, **kwargs )

tortoise__tortoise-orm

models.rst

Module doc / Tutorial

Model usage

Apache License 2.0

tortoise__tortoise-orm/docs/models.rst

Models Usage To get working with models, first you should import them from tortoise.models import Model With that you can start describing your own models like that class Tournament(Model): id = fields.IntField(pk=True) name = fields.TextField() created = fields.DatetimeField(auto_now_add=True) def __str__(self): return self.name class Event(Model): id = fields.IntField(pk=True) name = fields.TextField() tournament = fields.ForeignKeyField('models.Tournament', related_name='events') participants = fields.ManyToManyField('models.Team', related_name='events', through='event_team') modified = fields.DatetimeField(auto_now=True) prize = fields.DecimalField(max_digits=10, decimal_places=2, null=True) def __str__(self): return self.name class Team(Model): id = fields.IntField(pk=True) name = fields.TextField() def __str__(self): return self.name Let see in details what we accomplished here: class Tournament(Model): Every model should be derived from base model. You also can derive from your own model subclasses and you can make abstract models like this class AbstractTournament(Model): id = fields.IntField(pk=True) name = fields.TextField() created = fields.DatetimeField(auto_now_add=True) class Meta: abstract = True def __str__(self): return self.name This models won't be created in schema generation and won't create relations to other models. Further we have field fields.DatetimeField(auto_now=True). Options auto_now and auto_now_add work like Django's options. Use of __models__ If you define the variable __models__ in the module which you load your models from, generate_schema will use that list, rather than automatically finding models for you. Primary Keys In Tortoise ORM we require that a model has a primary key. That primary key will be accessible through a reserved field pk which will be an alias of whichever field has been nominated as a primary key. That alias field can be used as a field name when doing filtering e.g. .filter(pk=...) etc… Note We currently support single (non-composite) primary keys of any indexable field type, but only these field types are recommended: IntField BigIntField CharField UUIDField One must define a primary key by setting a pk parameter to True. If you don't define a primary key, we will create a primary key of type IntField with name of id for you. Note If this is used on an Integer Field, generated will be set to True unless you explicitly pass generated=False as well. Any of these are valid primary key definitions in a Model: id = fields.IntField(pk=True) checksum = fields.CharField(pk=True) guid = fields.UUIDField(pk=True) Inheritance When defining models in Tortoise ORM, you can save a lot of repetitive work by leveraging from inheritance. You can define fields in more generic classes and they are automatically available in derived classes. Base classes are not limited to Model classes. Any class will work. This way you are able to define your models in a natural and easy to maintain way. Let's have a look at some examples. from tortoise import fields from tortoise.models import Model class TimestampMixin(): created_at = fields.DatetimeField(null=True, auto_now_add=True) modified_at = fields.DatetimeField(null=True, auto_now=True) class NameMixin(): name = fields.CharField(40, unique=True) class MyAbstractBaseModel(Model): id = fields.IntField(pk=True) class Meta: abstract = True class UserModel(TimestampMixin, MyAbstractBaseModel): # Overriding the id definition # from MyAbstractBaseModel id = fields.UUIDField(pk=True) # Adding additional fields first_name = fields.CharField(20, null=True) class Meta: table = "user" class RoleModel(TimestampMixin, NameMixin, MyAbstractBaseModel): class Meta: table = "role" Using the Meta class is not necessary. But it is a good habit, to give your table an explicit name. This way you can change the model name without breaking the schema. So the following definition is valid. class RoleModel(TimestampMixin, NameMixin, MyAbstractBaseModel): pass ForeignKeyField tournament = fields.ForeignKeyField('models.Tournament', related_name='events') participants = fields.ManyToManyField('models.Team', related_name='events') modified = fields.DatetimeField(auto_now=True) prize = fields.DecimalField(max_digits=10, decimal_places=2, null=True) In event model we got some more fields, that could be interesting for us. fields.ForeignKeyField('models.Tournament', related_name='events') Here we create foreign key reference to tournament. We create it by referring to model by it's literal, consisting of app name and model name. models is default app name, but you can change it in class Meta with app = 'other'. related_name Is keyword argument, that defines field for related query on referenced models, so with that you could fetch all tournaments's events with like this: The DB-backing field Note A ForeignKeyField is a virtual field, meaning it has no direct DB backing. Instead it has a field (by default called {FKNAME}_id (that is, just an _id is appended) that is the actual DB-backing field. It will just contain the Key value of the related table. This is an important detail as it would allow one to assign/read the actual value directly, which could be considered an optimization if the entire foreign object isn't needed. Specifying an FK can be done via either passing the object: await SomeModel.create(tournament=the_tournament) # or somemodel.tournament=the_tournament or by directly accessing the DB-backing field: await SomeModel.create(tournament_id=the_tournament.pk) # or somemodel.tournament_id=the_tournament.pk Querying a relationship is typicall done by appending a double underscore, and then the foreign object's field. Then a normal query attr can be appended. This can be chained if the next key is also a foreign object: {FKNAME}__{FOREIGNFIELD}__gt=3 or {FKNAME}__{FOREIGNFK}__{VERYFOREIGNFIELD}__gt=3 There is however one major limiatation. We don't want to restrict foreign column names, or have ambiguity (e.g. a foreign object may have a field called isnull) Then this would be entierly ambugious: {FKNAME}__isnull To prevent that we require that direct filters be applied to the DB-backing field of the foreign key: {FKNAME}_id__isnull Fetching the foreign object Fetching foreign keys can be done with both async and sync interfaces. Async fetch: events = await tournament.events.all() You can async iterate over it like this: async for event in tournament.events: ... Sync usage requires that you call fetch_related before the time, and then you can use common functions such as: await tournament.fetch_related('events') events = list(tournament.events) eventlen = len(tournament.events) if SomeEvent in tournament.events: ... if tournament.events: ... firstevent = tournament.events[0] To get the Reverse-FK, e.g. an event.tournament we currently only support the sync interface. await event.fetch_related('tournament') tournament = event.tournament ManyToManyField Next field is fields.ManyToManyField('models.Team', related_name='events'). It describes many to many relation to model Team. To add to a ManyToManyField both the models need to be saved, else you will get an OperationalError raised. Resolving many to many fields can be done with both async and sync interfaces. Async fetch: participants = await tournament.participants.all() You can async iterate over it like this: async for participant in tournament.participants: ... Sync usage requires that you call fetch_related before the time, and then you can use common functions such as: await tournament.fetch_related('participants') participants = list(tournament.participants) participantlen = len(tournament.participants) if SomeParticipant in tournament.participants: ... if tournament.participants: ... firstparticipant = tournament.participants[0] The reverse lookup of team.event_team works exactly the same way. Improving relational type hinting Since Tortoise ORM is still a young project, it does not have such widespread support by various editors who help you writing code using good autocomplete for models and different relations between them. However, you can get such autocomplete by doing a little work yourself. All you need to do is add a few annotations to your models for fields that are responsible for the relations. Here is an updated example from getting_started, that will add autocomplete for all models including fields for the relations between models. from tortoise.models import Model from tortoise import fields class Tournament(Model): id = fields.IntField(pk=True) name = fields.CharField(max_length=255) events: fields.ReverseRelation["Event"] def __str__(self): return self.name class Event(Model): id = fields.IntField(pk=True) name = fields.CharField(max_length=255) tournament: fields.ForeignKeyRelation[Tournament] = fields.ForeignKeyField( "models.Tournament", related_name="events" ) participants: fields.ManyToManyRelation["Team"] = fields.ManyToManyField( "models.Team", related_name="events", through="event_team" ) def __str__(self): return self.name class Team(Model): id = fields.IntField(pk=True) name = fields.CharField(max_length=255) events: fields.ManyToManyRelation[Event] def __str__(self): return self.name

import asyncio import inspect import re from copy import copy, deepcopy from functools import partial from typing import ( Any, Awaitable, Callable, Dict, Generator, List, Optional, Set, Tuple, Type, TypeVar, ) from pypika import Order, Query, Table from tortoise.backends.base.client import BaseDBAsyncClient from tortoise.exceptions import ( ConfigurationError, IncompleteInstanceError, IntegrityError, OperationalError, TransactionManagementError, ) from tortoise.fields.base import Field from tortoise.fields.data import IntField from tortoise.fields.relational import ( BackwardFKRelation, BackwardOneToOneRelation, ForeignKeyFieldInstance, ManyToManyFieldInstance, ManyToManyRelation, NoneAwaitable, OneToOneFieldInstance, ReverseRelation, ) from tortoise.filters import get_filters_for_field from tortoise.functions import Function from tortoise.queryset import Q, QuerySet, QuerySetSingle from tortoise.signals import Signals from tortoise.transactions import current_transaction_map, in_transaction MODEL = TypeVar("MODEL", bound="Model") # TODO: Define Filter type object. Possibly tuple? def get_together(meta: "Model.Meta", together: str) -> Tuple[Tuple[str,...],...]: _together = getattr(meta, together, ()) if _together and isinstance(_together, (list, tuple)) and isinstance(_together[0], str): _together = (_together,) # return without validation, validation will be done further in the code return _together def prepare_default_ordering(meta: "Model.Meta") -> Tuple[Tuple[str, Order],...]: ordering_list = getattr(meta, "ordering", ()) parsed_ordering = tuple( QuerySet._resolve_ordering_string(ordering) for ordering in ordering_list ) return parsed_ordering def _fk_setter( self: "Model", value: "Optional[Model]", _key: str, relation_field: str, to_field: str ) -> None: setattr(self, relation_field, getattr(value, to_field) if value else None) setattr(self, _key, value) def _fk_getter( self: "Model", _key: str, ftype: "Type[Model]", relation_field: str, to_field: str ) -> Awaitable: try: return getattr(self, _key) except AttributeError: value = getattr(self, relation_field) if value: return ftype.filter(**{to_field: value}).first() return NoneAwaitable def _rfk_getter( self: "Model", _key: str, ftype: "Type[Model]", frelfield: str, from_field: str ) -> ReverseRelation: val = getattr(self, _key, None) if val is None: val = ReverseRelation(ftype, frelfield, self, from_field) setattr(self, _key, val) return val def _ro2o_getter( self: "Model", _key: str, ftype: "Type[Model]", frelfield: str, from_field: str ) -> "QuerySetSingle[Optional[Model]]": if hasattr(self, _key): return getattr(self, _key) val = ftype.filter(**{frelfield: getattr(self, from_field)}).first() setattr(self, _key, val) return val def _m2m_getter( self: "Model", _key: str, field_object: ManyToManyFieldInstance ) -> ManyToManyRelation: val = getattr(self, _key, None) if val is None: val = ManyToManyRelation(self, field_object) setattr(self, _key, val) return val def _get_comments(cls: "Type[Model]") -> Dict[str, str]: """ Get comments exactly before attributes It can be multiline comment. The placeholder "{model}" will be replaced with the name of the model class. We require that the comments are in #: (with a colon) format, so you can differentiate between private and public comments. :param cls: The class we need to extract comments from its source. :return: The dictionary of comments by field name """ try: source = inspect.getsource(cls) except TypeError: # pragma: nocoverage return {} comments = {} for cls_ in reversed(cls.__mro__): if cls_ is object: continue matches = re.findall(rf"((?:(?!\n|^)[^\w\n]*#:.*?\n)+?)[^\w\n]*(\w+)\s*[:=]", source) for match in matches: field_name = match[1] # Extract text comment = re.sub(r"(^\s*#:\s*|\s*$)", "", match[0], flags=re.MULTILINE) # Class name template comments[field_name] = comment.replace("{model}", cls_.__name__) return comments class MetaInfo: __slots__ = ( "abstract", "db_table", "app", "fields", "db_fields", "m2m_fields", "o2o_fields", "backward_o2o_fields", "fk_fields", "backward_fk_fields", "fetch_fields", "fields_db_projection", "_inited", "fields_db_projection_reverse", "filters", "fields_map", "default_connection", "basequery", "basequery_all_fields", "basetable", "_filters", "unique_together", "indexes", "pk_attr", "generated_db_fields", "_model", "table_description", "pk", "db_pk_column", "db_native_fields", "db_default_fields", "db_complex_fields", "_default_ordering", "_ordering_validated", ) def __init__(self, meta: "Model.Meta") -> None: self.abstract: bool = getattr(meta, "abstract", False) self.db_table: str = getattr(meta, "table", "") self.app: Optional[str] = getattr(meta, "app", None) self.unique_together: Tuple[Tuple[str,...],...] = get_together(meta, "unique_together") self.indexes: Tuple[Tuple[str,...],...] = get_together(meta, "indexes") self._default_ordering: Tuple[Tuple[str, Order],...] = prepare_default_ordering(meta) self._ordering_validated: bool = False self.fields: Set[str] = set() self.db_fields: Set[str] = set() self.m2m_fields: Set[str] = set() self.fk_fields: Set[str] = set() self.o2o_fields: Set[str] = set() self.backward_fk_fields: Set[str] = set() self.backward_o2o_fields: Set[str] = set() self.fetch_fields: Set[str] = set() self.fields_db_projection: Dict[str, str] = {} self.fields_db_projection_reverse: Dict[str, str] = {} self._filters: Dict[str, Dict[str, dict]] = {} self.filters: Dict[str, dict] = {} self.fields_map: Dict[str, Field] = {} self._inited: bool = False self.default_connection: Optional[str] = None self.basequery: Query = Query() self.basequery_all_fields: Query = Query() self.basetable: Table = Table("") self.pk_attr: str = getattr(meta, "pk_attr", "") self.generated_db_fields: Tuple[str] = None # type: ignore self._model: Type["Model"] = None # type: ignore self.table_description: str = getattr(meta, "table_description", "") self.pk: Field = None # type: ignore self.db_pk_column: str = "" self.db_native_fields: List[Tuple[str, str, Field]] = [] self.db_default_fields: List[Tuple[str, str, Field]] = [] self.db_complex_fields: List[Tuple[str, str, Field]] = [] @property def full_name(self) -> str: return f"{self.app}.{self._model.__name__}" def add_field(self, name: str, value: Field) -> None: if name in self.fields_map: raise ConfigurationError(f"Field {name} already present in meta") value.model = self._model self.fields_map[name] = value value.model_field_name = name if value.has_db_field: self.fields_db_projection[name] = value.source_field or name if isinstance(value, ManyToManyFieldInstance): self.m2m_fields.add(name) elif isinstance(value, BackwardOneToOneRelation): self.backward_o2o_fields.add(name) elif isinstance(value, BackwardFKRelation): self.backward_fk_fields.add(name) field_filters = get_filters_for_field( field_name=name, field=value, source_field=value.source_field or name ) self._filters.update(field_filters) self.finalise_fields() @property def db(self) -> BaseDBAsyncClient: try: return current_transaction_map[self.default_connection].get() except KeyError: raise ConfigurationError("No DB associated to model") @property def ordering(self) -> Tuple[Tuple[str, Order],...]: if not self._ordering_validated: unknown_fields = {f for f, _ in self._default_ordering} - self.fields raise ConfigurationError( f"Unknown fields {','.join(unknown_fields)} in " f"default ordering for model {self._model.__name__}" ) return self._default_ordering def get_filter(self, key: str) -> dict: return self.filters[key] def finalise_model(self) -> None: """ Finalise the model after it had been fully loaded. """ self.finalise_fields() self._generate_filters() self._generate_lazy_fk_m2m_fields() self._generate_db_fields() def finalise_fields(self) -> None: self.db_fields = set(self.fields_db_projection.values()) self.fields = set(self.fields_map.keys()) self.fields_db_projection_reverse = { value: key for key, value in self.fields_db_projection.items() } self.fetch_fields = ( self.m2m_fields | self.backward_fk_fields | self.fk_fields | self.backward_o2o_fields | self.o2o_fields ) generated_fields = [] for field in self.fields_map.values(): if not field.generated: continue generated_fields.append(field.source_field or field.model_field_name) self.generated_db_fields = tuple(generated_fields) # type: ignore self._ordering_validated = True for field_name, _ in self._default_ordering: if field_name.split("__")[0] not in self.fields: self._ordering_validated = False break def _generate_lazy_fk_m2m_fields(self) -> None: # Create lazy FK fields on model. for key in self.fk_fields: _key = f"_{key}" fk_field_object: ForeignKeyFieldInstance = self.fields_map[key] # type: ignore relation_field = fk_field_object.source_field to_field = fk_field_object.to_field_instance.model_field_name setattr( self._model, key, property( partial( _fk_getter, _key=_key, ftype=fk_field_object.related_model, relation_field=relation_field, to_field=to_field, ), partial( _fk_setter, _key=_key, relation_field=relation_field, to_field=to_field, ), partial( _fk_setter, value=None, _key=_key, relation_field=relation_field, to_field=to_field, ), ), ) # Create lazy reverse FK fields on model. for key in self.backward_fk_fields: _key = f"_{key}" backward_fk_field_object: BackwardFKRelation = self.fields_map[key] # type: ignore setattr( self._model, key, property( partial( _rfk_getter, _key=_key, ftype=backward_fk_field_object.related_model, frelfield=backward_fk_field_object.relation_field, from_field=backward_fk_field_object.to_field_instance.model_field_name, ) ), ) # Create lazy one to one fields on model. for key in self.o2o_fields: _key = f"_{key}" o2o_field_object: OneToOneFieldInstance = self.fields_map[key] # type: ignore relation_field = o2o_field_object.source_field to_field = o2o_field_object.to_field_instance.model_field_name setattr( self._model, key, property( partial( _fk_getter, _key=_key, ftype=o2o_field_object.related_model, relation_field=relation_field, to_field=to_field, ), partial( _fk_setter, _key=_key, relation_field=relation_field, to_field=to_field, ), partial( _fk_setter, value=None, _key=_key, relation_field=relation_field, to_field=to_field, ), ), ) # Create lazy reverse one to one fields on model. for key in self.backward_o2o_fields: _key = f"_{key}" backward_o2o_field_object: BackwardOneToOneRelation = self.fields_map[ # type: ignore key ] setattr( self._model, key, property( partial( _ro2o_getter, _key=_key, ftype=backward_o2o_field_object.related_model, frelfield=backward_o2o_field_object.relation_field, from_field=backward_o2o_field_object.to_field_instance.model_field_name, ), ), ) # Create lazy M2M fields on model. for key in self.m2m_fields: _key = f"_{key}" setattr( self._model, key, property(partial(_m2m_getter, _key=_key, field_object=self.fields_map[key])), ) def _generate_db_fields(self) -> None: self.db_default_fields.clear() self.db_complex_fields.clear() self.db_native_fields.clear() for key in self.db_fields: model_field = self.fields_db_projection_reverse[key] field = self.fields_map[model_field] default_converter = field.__class__.to_python_value is Field.to_python_value if ( field.skip_to_python_if_native and field.field_type in self.db.executor_class.DB_NATIVE ): self.db_native_fields.append((key, model_field, field)) elif not default_converter: self.db_complex_fields.append((key, model_field, field)) elif field.field_type in self.db.executor_class.DB_NATIVE: self.db_native_fields.append((key, model_field, field)) else: self.db_default_fields.append((key, model_field, field)) def _generate_filters(self) -> None: get_overridden_filter_func = self.db.executor_class.get_overridden_filter_func for key, filter_info in self._filters.items(): overridden_operator = get_overridden_filter_func( filter_func=filter_info["operator"] # type: ignore ) if overridden_operator: filter_info = copy(filter_info) filter_info["operator"] = overridden_operator # type: ignore self.filters[key] = filter_info class ModelMeta(type): __slots__ = () def __new__(mcs, name: str, bases: Tuple[Type,...], attrs: dict): fields_db_projection: Dict[str, str] = {} fields_map: Dict[str, Field] = {} filters: Dict[str, Dict[str, dict]] = {} fk_fields: Set[str] = set() m2m_fields: Set[str] = set() o2o_fields: Set[str] = set() meta_class: "Model.Meta" = attrs.get("Meta", type("Meta", (), {})) pk_attr: str = "id" # Searching for Field attributes in the class hierarchy def __search_for_field_attributes(base: Type, attrs: dict) -> None: """ Searching for class attributes of type fields.Field in the given class. If an attribute of the class is an instance of fields.Field, then it will be added to the fields dict. But only, if the key is not already in the dict. So derived classes have a higher precedence. Multiple Inheritance is supported from left to right. After checking the given class, the function will look into the classes according to the MRO (method resolution order). The MRO is 'natural' order, in which python traverses methods and fields. For more information on the magic behind check out: `The Python 2.3 Method Resolution Order <https://www.python.org/download/releases/2.3/mro/>`_. """ for parent in base.__mro__[1:]: __search_for_field_attributes(parent, attrs) meta = getattr(base, "_meta", None) if meta: # For abstract classes for key, value in meta.fields_map.items(): attrs[key] = value else: # For mixin classes for key, value in base.__dict__.items(): if isinstance(value, Field) and key not in attrs: attrs[key] = value # Start searching for fields in the base classes. inherited_attrs: dict = {} for base in bases: __search_for_field_attributes(base, inherited_attrs) if inherited_attrs: # Ensure that the inherited fields are before the defined ones. attrs = {**inherited_attrs, **attrs} if name!= "Model": custom_pk_present = False for key, value in attrs.items(): if isinstance(value, Field): if value.pk: if custom_pk_present: raise ConfigurationError( f"Can't create model {name} with two primary keys," " only single primary key is supported" ) if value.generated and not value.allows_generated: raise ConfigurationError( f"Field '{key}' ({value.__class__.__name__}) can't be DB-generated" ) custom_pk_present = True pk_attr = key if not custom_pk_present and not getattr(meta_class, "abstract", None): if "id" not in attrs: attrs = {"id": IntField(pk=True), **attrs} if not isinstance(attrs["id"], Field) or not attrs["id"].pk: raise ConfigurationError( f"Can't create model {name} without explicit primary key if field 'id'" " already present" ) for key, value in attrs.items(): if isinstance(value, Field): if getattr(meta_class, "abstract", None): value = deepcopy(value) fields_map[key] = value value.model_field_name = key if isinstance(value, OneToOneFieldInstance): o2o_fields.add(key) elif isinstance(value, ForeignKeyFieldInstance): fk_fields.add(key) elif isinstance(value, ManyToManyFieldInstance): m2m_fields.add(key) else: fields_db_projection[key] = value.source_field or key filters.update( get_filters_for_field( field_name=key, field=fields_map[key], source_field=fields_db_projection[key], ) ) if value.pk: filters.update( get_filters_for_field( field_name="pk", field=fields_map[key], source_field=fields_db_projection[key], ) ) # Clean the class attributes for slot in fields_map: attrs.pop(slot, None) attrs["_meta"] = meta = MetaInfo(meta_class) meta.fields_map = fields_map meta.fields_db_projection = fields_db_projection meta._filters = filters meta.fk_fields = fk_fields meta.backward_fk_fields = set() meta.o2o_fields = o2o_fields meta.backward_o2o_fields = set() meta.m2m_fields = m2m_fields meta.default_connection = None meta.pk_attr = pk_attr meta.pk = fields_map.get(pk_attr) # type: ignore if meta.pk: meta.db_pk_column = meta.pk.source_field or meta.pk_attr meta._inited = False if not fields_map: meta.abstract = True new_class: Type["Model"] = super().__new__(mcs, name, bases, attrs) for field in meta.fields_map.values(): field.model = new_class for fname, comment in _get_comments(new_class).items(): if fname in fields_map: fields_map[fname].docstring = comment if fields_map[fname].description is None: fields_map[fname].description = comment.split("\n")[0] if new_class.__doc__ and not meta.table_description: meta.table_description = inspect.cleandoc(new_class.__doc__).split("\n")[0] meta._model = new_class meta.finalise_fields() return new_class class Model(metaclass=ModelMeta): """ Base class for all Tortoise ORM Models. """ # I don' like this here, but it makes auto completion and static analysis much happier _meta = MetaInfo(None) # type: ignore _listeners: Dict[Signals, Dict[Type[MODEL], List[Callable]]] = { # type: ignore Signals.pre_save: {}, Signals.post_save: {}, Signals.pre_delete: {}, Signals.post_delete: {}, } def __init__(self, **kwargs: Any) -> None: # self._meta is a very common attribute lookup, lets cache it. meta = self._meta self._partial = False self._saved_in_db = False self._custom_generated_pk = False # Assign defaults for missing fields for key in meta.fields.difference(self._set_kwargs(kwargs)): field_object = meta.fields_map[key] if callable(field_object.default): setattr(self, key, field_object.default()) else: setattr(self, key, field_object.default) def _set_kwargs(self, kwargs: dict) -> Set[str]: meta = self._meta # Assign values and do type conversions passed_fields = {*kwargs.keys()} | meta.fetch_fields for key, value in kwargs.items(): if key in meta.fk_fields or key in meta.o2o_fields: if value and not value._saved_in_db: raise OperationalError( f"You should first call.save() on {value} before referring to it" ) setattr(self, key, value) passed_fields.add(meta.fields_map[key].source_field) elif key in meta.fields_db_projection: field_object = meta.fields_map[key] if field_object.generated: self._custom_generated_pk = True if value is None and not field_object.null: raise ValueError(f"{key} is non nullable field, but null was passed") setattr(self, key, field_object.to_python_value(value)) elif key in meta.backward_fk_fields: raise ConfigurationError( "You can't set backward relations through init, change related model instead" ) elif key in meta.backward_o2o_fields: raise ConfigurationError( "You can't set backward one to one relations through init," " change related model instead" ) elif key in meta.m2m_fields: raise ConfigurationError( "You can't set m2m relations through init, use m2m_manager instead" ) return passed_fields @classmethod def _init_from_db(cls: Type[MODEL], **kwargs: Any) -> MODEL: self = cls.__new__(cls) self._partial = False self._saved_in_db = True meta = self._meta try: # This is like so for performance reasons. # We want to avoid conditionals and calling.to_python_value() # Native fields are fields that are already converted to/from python to DB type # by the DB driver for key, model_field, field in meta.db_native_fields: setattr(self, model_field, kwargs[key]) # Fields that don't override.to_python_value() are converted without a call # as we already know what we will be doing. for key, model_field, field in meta.db_default_fields: value = kwargs[key] setattr(self, model_field, None if value is None else field.field_type(value)) # These fields need manual.to_python_value() for key, model_field, field in meta.db_complex_fields: setattr(self, model_field, field.to_python_value(kwargs[key])) except KeyError: self._partial = True # TODO: Apply similar perf optimisation as above for partial for key, value in kwargs.items(): setattr(self, key, meta.fields_map[key].to_python_value(value)) return self def __str__(self) -> str: return f"<{self.__class__.__name__}>" def __repr__(self) -> str: if self.pk: return f"<{self.__class__.__name__}: {self.pk}>" return f"<{self.__class__.__name__}>" def __hash__(self) -> int: if not self.pk: raise TypeError("Model instances without id are unhashable") return hash(self.pk) def __eq__(self, other: object) -> bool: return type(other) is type(self) and self.pk == other.pk # type: ignore def _get_pk_val(self) -> Any: return getattr(self, self._meta.pk_attr) def _set_pk_val(self, value: Any) -> None: setattr(self, self._meta.pk_attr, value) pk = property(_get_pk_val, _set_pk_val) """ Alias to the models Primary Key. Can be used as a field name when doing filtering e.g. ``.filter(pk=...)`` etc... """ def update_from_dict(self, data: dict) -> MODEL: """ Updates the current model with the provided dict. This can allow mass-updating a model from a dict, also ensuring that datatype conversions happen. This will ignore any extra fields, and NOT update the model with them, but will raise errors on bad types or updating Many-instance relations. :param data: The parameters you want to update in a dict format :return: The current model instance :raises ConfigurationError: When attempting to update a remote instance (e.g. a reverse ForeignKey or ManyToMany relation) :raises ValueError: When a passed parameter is not type compatible """ self._set_kwargs(data) return self # type: ignore @classmethod def register_listener(cls, signal: Signals, listener: Callable): """ Register listener to current model class for special Signal. :param signal: one of tortoise.signals.Signal :param listener: callable listener :raises ConfigurationError: When listener is not callable """ if not callable(listener): raise ConfigurationError("Signal listener must be callable!") cls_listeners = cls._listeners.get(signal).setdefault(cls, []) # type:ignore if listener not in cls_listeners: cls_listeners.append(listener) async def _pre_delete(self, using_db: Optional[BaseDBAsyncClient] = None,) -> None: listeners = [] cls_listeners = self._listeners.get(Signals.pre_delete, {}).get(self.__class__, []) for listener in cls_listeners: listeners.append(listener(self.__class__, self, using_db,)) await asyncio.gather(*listeners) async def _post_delete(self, using_db: Optional[BaseDBAsyncClient] = None,) -> None: listeners = [] cls_listeners = self._listeners.get(Signals.post_delete, {}).get(self.__class__, []) for listener in cls_listeners: listeners.append(listener(self.__class__, self, using_db,)) await asyncio.gather(*listeners) async def _pre_save( self, using_db: Optional[BaseDBAsyncClient] = None, update_fields: Optional[List[str]] = None, ) -> None: listeners = [] cls_listeners = self._listeners.get(Signals.pre_save, {}).get(self.__class__, []) for listener in cls_listeners: listeners.append(listener(self.__class__, self, using_db, update_fields)) await asyncio.gather(*listeners) async def _post_save( self, using_db: Optional[BaseDBAsyncClient] = None, created: bool = False, update_fields: Optional[List[str]] = None, ) -> None: listeners = [] cls_listeners = self._listeners.get(Signals.post_save, {}).get(self.__class__, []) for listener in cls_listeners: listeners.append(listener(self.__class__, self, created, using_db, update_fields)) await asyncio.gather(*listeners) async def save( self, using_db: Optional[BaseDBAsyncClient] = None, update_fields: Optional[List[str]] = None, ) -> None: """ Creates/Updates the current model object. :param update_fields: If provided, it should be a tuple/list of fields by name. This is the subset of fields that should be updated. If the object needs to be created ``update_fields`` will be ignored. :param using_db: Specific DB connection to use instead of default bound :raises IncompleteInstanceError: If the model is partial and the fields are not available for persistance. """ db = using_db or self._meta.db executor = db.executor_class(model=self.__class__, db=db) if self._partial: if update_fields: for field in update_fields: if not hasattr(self, self._meta.pk_attr): raise IncompleteInstanceError( f"{self.__class__.__name__} is a partial model without primary key fetchd. Partial update not available" ) if not hasattr(self, field): raise IncompleteInstanceError( f"{self.__class__.__name__} is a partial model, field '{field}' is not available" ) else: raise IncompleteInstanceError( f"{self.__class__.__name__} is a partial model, can only be saved with the relevant update_field provided" ) await self._pre_save(using_db, update_fields) if self._saved_in_db: await executor.execute_update(self, update_fields) created = False else: await executor.execute_insert(self) created = True self._saved_in_db = True await self._post_save(using_db, created, update_fields) async def delete(self, using_db: Optional[BaseDBAsyncClient] = None) -> None: """ Deletes the current model object. :param using_db: Specific DB connection to use instead of default bound :raises OperationalError: If object has never been persisted. """ db = using_db or self._meta.db if not self._saved_in_db: raise OperationalError("Can't delete unpersisted record") await self._pre_delete(using_db) await db.executor_class(model=self.__class__, db=db).execute_delete(self) await self._post_delete(using_db) async def fetch_related(self, *args: Any, using_db: Optional[BaseDBAsyncClient] = None) -> None: """ Fetch related fields. .. code-block:: python3 User.fetch_related("emails", "manager") :param args: The related fields that should be fetched. :param using_db: Specific DB connection to use instead of default bound """ db = using_db or self._meta.db await db.executor_class(model=self.__class__, db=db).fetch_for_list([self], *args) @classmethod async def get_or_create( cls: Type[MODEL], defaults: Optional[dict] = None, using_db: Optional[BaseDBAsyncClient] = None, **kwargs: Any, ) -> Tuple[MODEL, bool]: """ Fetches the object if exists (filtering on the provided parameters), else creates an instance with any unspecified parameters as default values. :param defaults: Default values to be added to a created instance if it can't be fetched. :param using_db: Specific DB connection to use instead of default bound :param kwargs: Query parameters. """ if not defaults: defaults = {} db = using_db if using_db else cls._meta.db async with in_transaction(connection_name=db.connection_name): instance = await cls.filter(**kwargs).first() if instance: return instance, False try: return await cls.create(**defaults, **kwargs), True except (IntegrityError, TransactionManagementError): # Let transaction close pass # Try after transaction in case transaction error return await cls.get(**kwargs), False @classmethod async def create(cls: Type[MODEL], **kwargs: Any) -> MODEL: """ Create a record in the DB and returns the object. .. code-block:: python3 user = await User.create(name="...", email="...") Equivalent to: .. code-block:: python3 user = User(name="...", email="...") await user.save() :param kwargs: Model parameters. """ instance = cls(**kwargs) instance._saved_in_db = False db = kwargs.get("using_db") or cls._meta.db await instance.save(using_db=db) return instance @classmethod async def bulk_create( cls: Type[MODEL], objects: List[MODEL], using_db: Optional[BaseDBAsyncClient] = None, ) -> None: """ Bulk insert operation: .. note:: The bulk insert operation will do the minimum to ensure that the object created in the DB has all the defaults and generated fields set, but may be incomplete reference in Python. e.g. ``IntField`` primary keys will not be populated. This is recommend only for throw away inserts where you want to ensure optimal insert performance. .. code-block:: python3 User.bulk_create([ User(name="...", email="..."), User(name="...", email="...") ]) :param objects: List of objects to bulk create :param using_db: Specific DB connection to use instead of default bound """ db = using_db or cls._meta.db await db.executor_class(model=cls, db=db).execute_bulk_insert(objects) # type: ignore @classmethod def first(cls: Type[MODEL]) -> QuerySetSingle[Optional[MODEL]]: """ Generates a QuerySet that returns the first record. """ return QuerySet(cls).first() @classmethod def filter(cls: Type[MODEL], *args: Q, **kwargs: Any) -> QuerySet[MODEL]: """ Generates a QuerySet with the filter applied. :param args: Q funtions containing constraints. Will be AND'ed. :param kwargs: Simple filter constraints. """ return QuerySet(cls).filter(*args, **kwargs) @classmethod def exclude(cls: Type[MODEL], *args: Q, **kwargs: Any) -> QuerySet[MODEL]: """ Generates a QuerySet with the exclude applied. :param args: Q funtions containing constraints. Will be AND'ed. :param kwargs: Simple filter constraints. """ return QuerySet(cls).exclude(*args, **kwargs) @classmethod def annotate(cls: Type[MODEL], **kwargs: Function) -> QuerySet[MODEL]: """ Annotates the result set with extra Functions/Aggregations. :param kwargs: Parameter name and the Function/Aggregation to annotate with. """ return QuerySet(cls).annotate(**kwargs) @classmethod def all(cls: Type[MODEL]) -> QuerySet[MODEL]: """ Returns the complete QuerySet. """ return QuerySet(cls) @classmethod def get(cls: Type[MODEL], *args: Q, **kwargs: Any) -> QuerySetSingle[MODEL]: """ Fetches a single record for a Model type using the provided filter parameters. .. code-block:: python3 user = await User.get(username="foo") :param args: Q funtions containing constraints. Will be AND'ed. :param kwargs: Simple filter constraints. :raises MultipleObjectsReturned: If provided search returned more than one object. :raises DoesNotExist: If object can not be found. """ return QuerySet(cls).get(*args, **kwargs) @classmethod def get_or_none(cls: Type[MODEL], *args: Q, **kwargs: Any) -> QuerySetSingle[Optional[MODEL]]: """ Fetches a single record for a Model type using the provided filter parameters or None. .. code-block:: python3 user = await User.get(username="foo") :param args: Q funtions containing constraints. Will be AND'ed. :param kwargs: Simple filter constraints. """ return QuerySet(cls).get_or_none(*args, **kwargs) @classmethod async def fetch_for_list( cls, instance_list: "List[Model]", *args: Any, using_db: Optional[BaseDBAsyncClient] = None, ) -> None: """ Fetches related models for provided list of Model objects. :param instance_list: List of Model objects to fetch relations for. :param args: Relation names to fetch. :param using_db: DO NOT USE """ db = using_db or cls._meta.db await db.executor_class(model=cls, db=db).fetch_for_list(instance_list, *args) @classmethod def check(cls) -> None: """ Calls various checks to validate the model. :raises ConfigurationError: If the model has not been configured correctly. """ cls._check_together("unique_together") cls._check_together("indexes") @classmethod def _check_together(cls, together: str) -> None: """ Check the value of "unique_together" option. :raises ConfigurationError: If the model has not been configured correctly. """ _together = getattr(cls._meta, together) if not isinstance(_together, (tuple, list)): raise ConfigurationError(f"'{cls.__name__}.{together}' must be a list or tuple.") if any(not isinstance(unique_fields, (tuple, list)) for unique_fields in _together): raise ConfigurationError( f"All '{cls.__name__}.{together}' elements must be lists or tuples." ) for fields_tuple in _together: for field_name in fields_tuple: field = cls._meta.fields_map.get(field_name) if not field: raise ConfigurationError( f"'{cls.__name__}.{together}' has no '{field_name}' field." ) if isinstance(field, ManyToManyFieldInstance): raise ConfigurationError( f"'{cls.__name__}.{together}' '{field_name}' field refers" " to ManyToMany field." ) @classmethod def describe(cls, serializable: bool = True) -> dict: """ Describes the given list of models or ALL registered models. :param serializable: ``False`` if you want raw python objects, ``True`` for JSON-serialisable data. (Defaults to ``True``) :return: A dictionary containing the model description. The base dict has a fixed set of keys that reference a list of fields (or a single field in the case of the primary key): .. code-block:: python3 { "name": str # Qualified model name "app": str # 'App' namespace "table": str # DB table name "abstract": bool # Is the model Abstract? "description": str # Description of table (nullable) "docstring": str # Model docstring (nullable) "unique_together": [...] # List of List containing field names that # are unique together "pk_field": {...} # Primary key field "data_fields": [...] # Data fields "fk_fields": [...] # Foreign Key fields FROM this model "backward_fk_fields": [...] # Foreign Key fields TO this model "o2o_fields": [...] # OneToOne fields FROM this model "backward_o2o_fields": [...] # OneToOne fields TO this model "m2m_fields": [...] # Many-to-Many fields } Each field is specified as defined in :meth:`tortoise.fields.base.Field.describe` """ return { "name": cls._meta.full_name, "app": cls._meta.app, "table": cls._meta.db_table, "abstract": cls._meta.abstract, "description": cls._meta.table_description or None, "docstring": inspect.cleandoc(cls.__doc__ or "") or None, "unique_together": cls._meta.unique_together or [], "pk_field": cls._meta.fields_map[cls._meta.pk_attr].describe(serializable), "data_fields": [ field.describe(serializable) for name, field in cls._meta.fields_map.items() if name!= cls._meta.pk_attr and name in (cls._meta.fields - cls._meta.fetch_fields) ], "fk_fields": [ field.describe(serializable) for name, field in cls._meta.fields_map.items() if name in cls._meta.fk_fields ], "backward_fk_fields": [ field.describe(serializable) for name, field in cls._meta.fields_map.items() if name in cls._meta.backward_fk_fields ], "o2o_fields": [ field.describe(serializable) for name, field in cls._meta.fields_map.items() if name in cls._meta.o2o_fields ], "backward_o2o_fields": [ field.describe(serializable) for name, field in cls._meta.fields_map.items() if name in cls._meta.backward_o2o_fields ], "m2m_fields": [ field.describe(serializable) for name, field in cls._meta.fields_map.items() if name in cls._meta.m2m_fields ], } def __await__(self: MODEL) -> Generator[Any, None, MODEL]: async def _self() -> MODEL: return self return _self().__await__() class Meta: """ The ``Meta`` class is used to configure metadata for the Model. Usage: .. code-block:: python3 class Foo(Model): ... class Meta: table="custom_table" unique_together=(("field_a", "field_b"), ) """

tortoise__tortoise-orm

pydantic.rst

Tutorial

How to generate Pydantic Models from Tortoise Models

Apache License 2.0

tortoise__tortoise-orm/docs/contrib/pydantic.rst

Pydantic serialisation Tortoise ORM has a Pydantic plugin that will generate Pydantic Models from Tortoise Models, and then provides helper functions to serialise that model and its related objects. We currently only support generating Pydantic objects for serialisation, and no deserialisation at this stage. Tutorial 1: Basic usage Here we introduce: - Creating a Pydantic model from a Tortoise model - Docstrings & doc-comments are used - Evaluating the generated schema - Simple serialisation with both .dict() and .json() Lets start with a basic Tortoise Model: from tortoise import fields from tortoise.models import Model class Tournament(Model): """ This references a Tournament """ id = fields.IntField(pk=True) name = fields.CharField(max_length=100) #: The date-time the Tournament record was created at created_at = fields.DatetimeField(auto_now_add=True) To create a Pydantic model from that one would call: tortoise.contrib.pydantic.creator.pydantic_model_creator from tortoise.contrib.pydantic import pydantic_model_creator Tournament_Pydantic = pydantic_model_creator(Tournament) And now have a Pydantic Model that can be used for representing schema and serialisation. The JSON-Schema of Tournament_Pydantic is now: >>> print(Tournament_Pydantic.schema()) { 'title': 'Tournament', 'description': 'This references a Tournament', 'type': 'object', 'properties': { 'id': { 'title': 'Id', 'type': 'integer' }, 'name': { 'title': 'Name', 'type': 'string' }, 'created_at': { 'title': 'Created At', 'description': 'The date-time the Tournament record was created at', 'type': 'string', 'format': 'date-time' } } } Note how the class docstring and doc-comment #: is included as descriptions in the Schema. To serialise an object it is simply (in an async context): tournament = await Tournament.create(name="New Tournament") tourpy = await Tournament_Pydantic.from_tortoise_orm(tournament) And one could get the contents by using regular Pydantic-object methods, such as .dict() or .json() >>> print(tourpy.dict()) { 'id': 1, 'name': 'New Tournament', 'created_at': datetime.datetime(2020, 3, 1, 20, 28, 9, 346808) } >>> print(tourpy.json()) { "id": 1, "name": "New Tournament", "created_at": "2020-03-01T20:28:09.346808" } 2: Querysets & Lists Here we introduce: - Creating a list-model to serialise a queryset - Default sorting is honoured from tortoise import fields from tortoise.models import Model class Tournament(Model): """ This references a Tournament """ id = fields.IntField(pk=True) name = fields.CharField(max_length=100) #: The date-time the Tournament record was created at created_at = fields.DatetimeField(auto_now_add=True) class Meta: # Define the default ordering # the pydantic serialiser will use this to order the results ordering = ["name"] To create a Pydantic list-model from that one would call: tortoise.contrib.pydantic.creator.pydantic_queryset_creator from tortoise.contrib.pydantic import pydantic_model_creator Tournament_Pydantic_List = pydantic_queryset_creator(Tournament) And now have a Pydantic Model that can be used for representing schema and serialisation. The JSON-Schema of Tournament_Pydantic_List is now: >>> print(Tournament_Pydantic_List.schema()) { 'title': 'Tournaments', 'description': 'This references a Tournament', 'type': 'array', 'items': { '$ref': '#/definitions/Tournament' }, 'definitions': { 'Tournament': { 'title': 'Tournament', 'description': 'This references a Tournament', 'type': 'object', 'properties': { 'id': { 'title': 'Id', 'type': 'integer' }, 'name': { 'title': 'Name', 'type': 'string' }, 'created_at': { 'title': 'Created At', 'description': 'The date-time the Tournament record was created at', 'type': 'string', 'format': 'date-time' } } } } } Note that the Tournament is now not the root. A simple list is. To serialise an object it is simply (in an async context): # Create objects await Tournament.create(name="New Tournament") await Tournament.create(name="Another") await Tournament.create(name="Last Tournament") tourpy = await Tournament_Pydantic_List.from_queryset(Tournament.all()) And one could get the contents by using regular Pydantic-object methods, such as .dict() or .json() >>> print(tourpy.dict()) { '__root__': [ { 'id': 2, 'name': 'Another', 'created_at': datetime.datetime(2020, 3, 2, 6, 53, 39, 776504) }, { 'id': 3, 'name': 'Last Tournament', 'created_at': datetime.datetime(2020, 3, 2, 6, 53, 39, 776848) }, { 'id': 1, 'name': 'New Tournament', 'created_at': datetime.datetime(2020, 3, 2, 6, 53, 39, 776211) } ] } >>> print(tourpy.json()) [ { "id": 2, "name": "Another", "created_at": "2020-03-02T06:53:39.776504" }, { "id": 3, "name": "Last Tournament", "created_at": "2020-03-02T06:53:39.776848" }, { "id": 1, "name": "New Tournament", "created_at": "2020-03-02T06:53:39.776211" } ] Note how .dict() has a _root__ element with the list, but the .json() has the list as root. Also note how the results are sorted alphabetically by name. 3: Relations & Early-init Here we introduce: - Relationships - Early model init Note The part of this tutorial about early-init is only required if you need to generate the pydantic models before you have initialised Tortoise ORM. Look at example_pydantic_basic (in function run) to see where the *_creator is only called after we initialised Tortoise ORM properly, in that case an early init is not needed. Source to example: example_pydantic_tut3 We define our models with a relationship: from tortoise import fields from tortoise.models import Model class Tournament(Model): """ This references a Tournament """ id = fields.IntField(pk=True) name = fields.CharField(max_length=100) #: The date-time the Tournament record was created at created_at = fields.DatetimeField(auto_now_add=True) class Event(Model): """ This references an Event in a Tournament """ id = fields.IntField(pk=True) name = fields.CharField(max_length=100) created_at = fields.DatetimeField(auto_now_add=True) tournament = fields.ForeignKeyField( "models.Tournament", related_name="events", description="The Tournement this happens in" ) Next we create our Pydantic Model using pydantic_model_creator: from tortoise.contrib.pydantic import pydantic_model_creator Tournament_Pydantic = pydantic_model_creator(Tournament) The JSON-Schema of Tournament_Pydantic is now: >>> print(Tournament_Pydantic.schema()) { 'title': 'Tournament', 'description': 'This references a Tournament', 'type': 'object', 'properties': { 'id': { 'title': 'Id', 'type': 'integer' }, 'name': { 'title': 'Name', 'type': 'string' }, 'created_at': { 'title': 'Created At', 'description': 'The date-time the Tournament record was created at', 'type': 'string', 'format': 'date-time' } } } Oh no! Where is the relation? Because the models have not fully initialised, it doesn't know about the relations at this stage. We need to initialise our model relationships early using tortoise.Tortoise.init_models from tortoise import Tortoise Tortoise.init_models(["__main__"], "models") # Now lets try again Tournament_Pydantic = pydantic_model_creator(Tournament) The JSON-Schema of Tournament_Pydantic is now: >>> print(Tournament_Pydantic.schema()) { 'title': 'Tournament', 'description': 'This references a Tournament', 'type': 'object', 'properties': { 'id': { 'title': 'Id', 'type': 'integer' }, 'name': { 'title': 'Name', 'type': 'string' }, 'created_at': { 'title': 'Created At', 'description': 'The date-time the Tournament record was created at', 'type': 'string', 'format': 'date-time' }, 'events': { 'title': 'Events', 'description': 'The Tournement this happens in', 'type': 'array', 'items': { '$ref': '#/definitions/Event' } } }, 'definitions': { 'Event': { 'title': 'Event', 'description': 'This references an Event in a Tournament', 'type': 'object', 'properties': { 'id': { 'title': 'Id', 'type': 'integer' }, 'name': { 'title': 'Name', 'type': 'string' }, 'created_at': { 'title': 'Created At', 'type': 'string', 'format': 'date-time' } } } } } Aha! that's much better. Note we can also create a model for Event the same way, and it should just work: Event_Pydantic = pydantic_model_creator(Event) >>> print(Event_Pydantic.schema()) { 'title': 'Event', 'description': 'This references an Event in a Tournament', 'type': 'object', 'properties': { 'id': { 'title': 'Id', 'type': 'integer' }, 'name': { 'title': 'Name', 'type': 'string' }, 'created_at': { 'title': 'Created At', 'type': 'string', 'format': 'date-time' }, 'tournament': { 'title': 'Tournament', 'description': 'The Tournement this happens in', 'allOf': [ { '$ref': '#/definitions/Tournament' } ] } }, 'definitions': { 'Tournament': { 'title': 'Tournament', 'description': 'This references a Tournament', 'type': 'object', 'properties': { 'id': { 'title': 'Id', 'type': 'integer' }, 'name': { 'title': 'Name', 'type': 'string' }, 'created_at': { 'title': 'Created At', 'description': 'The date-time the Tournament record was created at', 'type': 'string', 'format': 'date-time' } } } } } And that also has the relation defined! Note how both schema's don't follow relations back. This is on by default, and in a later tutorial we will show the options. Lets create and serialise the objects and see what they look like (in an async context): # Create objects tournament = await Tournament.create(name="New Tournament") event = await Event.create(name="The Event", tournament=tournament) # Serialise Tournament tourpy = await Tournament_Pydantic.from_tortoise_orm(tournament) >>> print(tourpy.json()) { "id": 1, "name": "New Tournament", "created_at": "2020-03-02T07:23:27.731656", "events": [ { "id": 1, "name": "The Event", "created_at": "2020-03-02T07:23:27.732492" } ] } And serialising the event (in an async context): eventpy = await Event_Pydantic.from_tortoise_orm(event) >>> print(eventpy.json()) { "id": 1, "name": "The Event", "created_at": "2020-03-02T07:23:27.732492", "tournament": { "id": 1, "name": "New Tournament", "created_at": "2020-03-02T07:23:27.731656" } } html-toggle 4: PydanticMeta & Callables Here we introduce: - Configuring model creator via PydanticMeta class. - Using callable functions to annotate extra data. Source to example: example_pydantic_tut4 Let's add some methods that calculate data, and tell the creators to use them: class Tournament(Model): """ This references a Tournament """ id = fields.IntField(pk=True) name = fields.CharField(max_length=100) created_at = fields.DatetimeField(auto_now_add=True) # It is useful to define the reverse relations manually so that type checking # and auto completion work events: fields.ReverseRelation["Event"] def name_length(self) -> int: """ Computed length of name """ return len(self.name) def events_num(self) -> int: """ Computed team size """ try: return len(self.events) except NoValuesFetched: return -1 class PydanticMeta: # Let's exclude the created timestamp exclude = ("created_at",) # Let's include two callables as computed columns computed = ("name_length", "events_num") class Event(Model): """ This references an Event in a Tournament """ id = fields.IntField(pk=True) name = fields.CharField(max_length=100) created_at = fields.DatetimeField(auto_now_add=True) tournament = fields.ForeignKeyField( "models.Tournament", related_name="events", description="The Tournement this happens in" ) class Meta: ordering = ["name"] class PydanticMeta: exclude = ("created_at",) There is much to unpack here. Firstly, we defined a PydanticMeta block, and in there is configuration options for the pydantic model creator. See tortoise.contrib.pydantic.creator.PydanticMeta for the available options. Secondly, we excluded created_at in both models, as we decided it provided no benefit. Thirly, we added two callables: name_length and events_num. We want these as part of the result set. Note that callables/computed fields require manual specification of return type, as without this we can't determine the record type which is needed to create a valid Pydantic schema. This is not needed for standard Tortoise ORM fields, as the fields already define a valid type. Note that the Pydantic serializer can't call async methods, but since the tortoise helpers pre-fetch relational data, it is available before serialization. So we don't need to await the relation. We should however protect against the case where no prefetching was done, hence catching and handling the tortoise.exceptions.NoValuesFetched exception. Next we create our Pydantic Model using pydantic_model_creator: from tortoise import Tortoise Tortoise.init_models(["__main__"], "models") Tournament_Pydantic = pydantic_model_creator(Tournament) The JSON-Schema of Tournament_Pydantic is now: { "title": "Tournament", "description": "This references a Tournament", "type": "object", "properties": { "id": { "title": "Id", "type": "integer" }, "name": { "title": "Name", "type": "string" }, "events": { "title": "Events", "description": "The Tournement this happens in", "type": "array", "items": { "$ref": "#/definitions/Event" } }, "name_length": { "title": "Name Length", "description": "Computes length of name", "type": "integer" }, "events_num": { "title": "Events Num", "description": "Computes team size.", "type": "integer" } }, "definitions": { "Event": { "title": "Event", "description": "This references an Event in a Tournament", "type": "object", "properties": { "id": { "title": "Id", "type": "integer" }, "name": { "title": "Name", "type": "string" } } } } } Note that created_at is removed, and name_length & events_num is added. Lets create and serialise the objects and see what they look like (in an async context): # Create objects tournament = await Tournament.create(name="New Tournament") await Event.create(name="Event 1", tournament=tournament) await Event.create(name="Event 2", tournament=tournament) # Serialise Tournament tourpy = await Tournament_Pydantic.from_tortoise_orm(tournament) >>> print(tourpy.json()) { "id": 1, "name": "New Tournament", "events": [ { "id": 1, "name": "Event 1" }, { "id": 2, "name": "Event 2" } ], "name_length": 14, "events_num": 2 }

import inspect from base64 import b32encode from hashlib import sha3_224 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, cast import pydantic from tortoise import fields from tortoise.contrib.pydantic.base import PydanticListModel, PydanticModel from tortoise.contrib.pydantic.utils import get_annotations if TYPE_CHECKING: # pragma: nocoverage from tortoise.models import Model _MODEL_INDEX: Dict[str, Type[PydanticModel]] = {} class PydanticMeta: """ The ``PydanticMeta`` class is used to configure metadata for generating the pydantic Model. Usage: .. code-block:: python3 class Foo(Model): ... class PydanticMeta: exclude = ("foo", "baa") computed = ("count_peanuts", ) """ #: If not empty, only fields this property contains will be in the pydantic model include: Tuple[str,...] = () #: Fields listed in this property will be excluded from pydantic model exclude: Tuple[str,...] = () #: Computed fields can be listed here to use in pydantic model computed: Tuple[str,...] = () #: Use backward relations without annotations - not recommended, it can be huge data #: without control backward_relations: bool = True #: Maximum recursion level allowed max_recursion: int = 3 #: Allow cycles in recursion - This can result in HUGE data - Be careful! #: Please use this with ``exclude``/``include`` and sane ``max_recursion`` allow_cycles: bool = False #: If we should exclude raw fields (the ones have _id suffixes) of relations exclude_raw_fields: bool = True #: Sort fields alphabetically. #: If not set (or ``False``) then leave fields in declaration order sort_alphabetically: bool = False def _br_it(val: str) -> str: return val.replace("\n", "<br/>").strip() def _cleandoc(obj: Any) -> str: return _br_it(inspect.cleandoc(obj.__doc__ or "")) def _pydantic_recursion_protector( cls: "Type[Model]", *, stack: tuple, exclude: Tuple[str,...] = (), include: Tuple[str,...] = (), computed: Tuple[str,...] = (), name=None, allow_cycles: bool = False, sort_alphabetically: Optional[bool] = None, ) -> Optional[Type[PydanticModel]]: """ It is an inner function to protect pydantic model creator against cyclic recursion """ if not allow_cycles and cls in (c[0] for c in stack[:-1]): return None caller_fname = stack[0][1] prop_path = [caller_fname] # It stores the fields in the hierarchy level = 1 for _, parent_fname, parent_max_recursion in stack[1:]: # Check recursion level prop_path.insert(0, parent_fname) if level >= parent_max_recursion: # This is too verbose, Do we even need a way of reporting truncated models? # tortoise.logger.warning( # "Recursion level %i has reached for model %s", # level, # parent_cls.__qualname__ + "." + ".".join(prop_path), # ) return None level += 1 return pydantic_model_creator( cls, exclude=exclude, include=include, computed=computed, name=name, _stack=stack, allow_cycles=allow_cycles, sort_alphabetically=sort_alphabetically, ) def pydantic_model_creator( cls: "Type[Model]", *, name=None, exclude: Tuple[str,...] = (), include: Tuple[str,...] = (), computed: Tuple[str,...] = (), allow_cycles: Optional[bool] = None, sort_alphabetically: Optional[bool] = None, _stack: tuple = (), exclude_readonly: bool = False, ) -> Type[PydanticModel]: """ Function to build `Pydantic Model <https://pydantic-docs.helpmanual.io/usage/models/>`__ off Tortoise Model. :param cls: The Tortoise Model :param name: Specify a custom name explicitly, instead of a generated name. :param exclude: Extra fields to exclude from the provided model. :param include: Extra fields to include from the provided model. :param computed: Extra computed fields to include from the provided model. :param allow_cycles: Do we allow any cycles in the generated model? This is only useful for recursive/self-referential models. A value of ``False`` (the default) will prevent any and all backtracking. :param sort_alphabetically: Sort the parameters alphabetically instead of Field-definition order. The default order would be: * Field definition order + * order of reverse relations (as discovered) + * order of computed functions (as provided). :param exclude_readonly: Build a subset model that excludes any readonly fields """ # Fully qualified class name fqname = cls.__module__ + "." + cls.__qualname__ postfix = "" def get_name() -> str: # If arguments are specified (different from the defaults), we append a hash to the # class name, to make it unique # We don't check by stack, as cycles get explicitly renamed. # When called later, include is explicitly set, so fence passes. nonlocal postfix is_default = ( exclude == () and include == () and computed == () and sort_alphabetically is None and allow_cycles is None ) hashval = ( f"{fqname};{exclude};{include};{computed};{_stack}:{sort_alphabetically}:{allow_cycles}" ) postfix = ( "." + b32encode(sha3_224(hashval.encode("utf-8")).digest()).decode("utf-8").lower()[:6] if not is_default else "" ) return fqname + postfix # We need separate model class for different exclude, include and computed parameters _name = name or get_name() has_submodel = False # Get settings and defaults meta = getattr(cls, "PydanticMeta", PydanticMeta) default_include: Tuple[str,...] = tuple(getattr(meta, "include", PydanticMeta.include)) default_exclude: Tuple[str,...] = tuple(getattr(meta, "exclude", PydanticMeta.exclude)) default_computed: Tuple[str,...] = tuple(getattr(meta, "computed", PydanticMeta.computed)) max_recursion: int = int(getattr(meta, "max_recursion", PydanticMeta.max_recursion)) exclude_raw_fields: bool = bool( getattr(meta, "exclude_raw_fields", PydanticMeta.exclude_raw_fields) ) _sort_fields: bool = bool( getattr(meta, "sort_alphabetically", PydanticMeta.sort_alphabetically) ) if sort_alphabetically is None else sort_alphabetically _allow_cycles: bool = bool( getattr(meta, "allow_cycles", PydanticMeta.allow_cycles) if allow_cycles is None else allow_cycles ) # Update parameters with defaults include = tuple(include) + default_include exclude = tuple(exclude) + default_exclude computed = tuple(computed) + default_computed # Get all annotations annotations = get_annotations(cls) # Properties and their annotations` store pconfig: Type[pydantic.main.BaseConfig] = type( "Config", (PydanticModel.Config,), {"title": name or cls.__name__, "extra": pydantic.main.Extra.forbid, "fields": {}}, ) pannotations: Dict[str, Optional[Type]] = {} properties: Dict[str, Any] = {"__annotations__": pannotations, "Config": pconfig} # Get model description model_description = cls.describe(serializable=False) # Field map we use field_map: Dict[str, dict] = {} pk_raw_field: str = "" def field_map_update(keys: tuple, is_relation=True) -> None: nonlocal pk_raw_field for key in keys: fds = model_description[key] if isinstance(fds, dict): fds = [fds] for fd in fds: n = fd["name"] if key == "pk_field": pk_raw_field = n # Include or exclude field if (include and n not in include) or n in exclude: continue # Remove raw fields raw_field = fd.get("raw_field", None) if raw_field is not None and exclude_raw_fields and raw_field!= pk_raw_field: del field_map[raw_field] field_map[n] = fd # Update field definitions from description if not exclude_readonly: field_map_update(("pk_field",), is_relation=False) field_map_update(("data_fields",), is_relation=False) if not exclude_readonly: field_map_update( ("fk_fields", "o2o_fields", "m2m_fields", "backward_fk_fields", "backward_o2o_fields") ) # Add possible computed fields field_map.update( { k: {"field_type": callable, "function": getattr(cls, k), "description": None} for k in computed } ) # Sort field map (Python 3.7+ has guaranteed ordered dictionary keys) if _sort_fields: # Sort Alphabetically field_map = {k: field_map[k] for k in sorted(field_map)} else: # Sort to definition order field_map = { k: field_map[k] for k in tuple(cls._meta.fields_map.keys()) + computed if k in field_map } # Process fields for fname, fdesc in field_map.items(): comment = "" fconfig: Dict[str, Any] = {} field_type = fdesc["field_type"] def get_submodel(_model: "Type[Model]") -> Optional[Type[PydanticModel]]: """ Get Pydantic model for the submodel """ nonlocal exclude, _name, has_submodel if _model: new_stack = _stack + ((cls, fname, max_recursion),) # Get pydantic schema for the submodel prefix_len = len(fname) + 1 pmodel = _pydantic_recursion_protector( _model, exclude=tuple( str(v[prefix_len:]) for v in exclude if v.startswith(fname + ".") ), include=tuple( str(v[prefix_len:]) for v in include if v.startswith(fname + ".") ), computed=tuple( str(v[prefix_len:]) for v in computed if v.startswith(fname + ".") ), stack=new_stack, allow_cycles=_allow_cycles, sort_alphabetically=sort_alphabetically, ) else: pmodel = None # If the result is None it has been exluded and we need to exclude the field if pmodel is None: exclude += (fname,) else: has_submodel = True # We need to rename if there are duplicate instances of this model if cls in (c[0] for c in _stack): _name = name or get_name() return pmodel # Foreign keys and OneToOne fields are embedded schemas if ( field_type is fields.relational.ForeignKeyFieldInstance or field_type is fields.relational.OneToOneFieldInstance or field_type is fields.relational.BackwardOneToOneRelation ): model = get_submodel(fdesc["python_type"]) if model: if fdesc.get("nullable"): fconfig["nullable"] = True if fdesc.get("nullable") or fdesc.get("default"): model = Optional[model] pannotations[fname] = model # Backward FK and ManyToMany fields are list of embedded schemas elif ( field_type is fields.relational.BackwardFKRelation or field_type is fields.relational.ManyToManyFieldInstance ): model = get_submodel(fdesc["python_type"]) if model: pannotations[fname] = List[model] # type: ignore # Computed fields as methods elif field_type is callable: func = fdesc["function"] annotation = get_annotations(cls, func).get("return", None) comment = _cleandoc(func) if annotation is not None: pannotations[fname] = annotation # Any other tortoise fields else: annotation = annotations.get(fname, None) fconfig.update(fdesc["constraints"]) ptype = fdesc["python_type"] if fdesc.get("nullable"): fconfig["nullable"] = True if fdesc.get("nullable") or fdesc.get("default"): ptype = Optional[ptype] if not (exclude_readonly and fdesc["constraints"].get("readOnly") is True): pannotations[fname] = annotation or ptype # Create a schema for the field if fname in pannotations: # Use comment if we have and enabled or use the field description if specified description = comment or _br_it(fdesc.get("docstring") or fdesc["description"] or "") fconfig["description"] = description fconfig["title"] = fname.replace("_", " ").title() pconfig.fields[fname] = fconfig # Here we endure that the name is unique, but complete objects are still labeled verbatim if not has_submodel and exclude: _name = name or f"{fqname}.leaf" elif has_submodel: _name = name or get_name() # Here we de-dup to ensure that a uniquely named object is a unique object # This fixes some Pydantic constraints. if _name in _MODEL_INDEX: return _MODEL_INDEX[_name] # Creating Pydantic class for the properties generated before model = cast(Type[PydanticModel], type(_name, (PydanticModel,), properties)) # Copy the Model docstring over model.__doc__ = _cleandoc(cls) # Store the base class setattr(model.__config__, "orig_model", cls) # Store model reference so we can de-dup it later on if needed. _MODEL_INDEX[_name] = model return model def pydantic_queryset_creator( cls: "Type[Model]", *, name=None, exclude: Tuple[str,...] = (), include: Tuple[str,...] = (), computed: Tuple[str,...] = (), allow_cycles: Optional[bool] = None, sort_alphabetically: Optional[bool] = None, ) -> Type[PydanticListModel]: """ Function to build a `Pydantic Model <https://pydantic-docs.helpmanual.io/usage/models/>`__ list off Tortoise Model. :param cls: The Tortoise Model to put in a list. :param name: Specify a custom name explicitly, instead of a generated name. The list generated name is currently naive and merely adds a "s" to the end of the singular name. :param exclude: Extra fields to exclude from the provided model. :param include: Extra fields to include from the provided model. :param computed: Extra computed fields to include from the provided model. :param allow_cycles: Do we allow any cycles in the generated model? This is only useful for recursive/self-referential models. A value of ``False`` (the default) will prevent any and all backtracking. :param sort_alphabetically: Sort the parameters alphabetically instead of Field-definition order. The default order would be: * Field definition order + * order of reverse relations (as discovered) + * order of computed functions (as provided). """ submodel = pydantic_model_creator( cls, exclude=exclude, include=include, computed=computed, allow_cycles=allow_cycles, sort_alphabetically=sort_alphabetically, ) lname = name or f"{submodel.__name__}_list" properties = {"__annotations__": {"__root__": List[submodel]}} # type: ignore # Creating Pydantic class for the properties generated before model = cast(Type[PydanticListModel], type(lname, (PydanticListModel,), properties)) # Copy the Model docstring over model.__doc__ = _cleandoc(cls) # The title of the model to hide the hash postfix setattr(model.__config__, "title", name or f"{getattr(submodel.__config__,'title')}_list") # Store the base class & submodel setattr(model.__config__, "submodel", submodel) return model from typing import TYPE_CHECKING, List, Type, Union import pydantic from pydantic import BaseModel # pylint: disable=E0611 from tortoise import fields if TYPE_CHECKING: # pragma: nocoverage from tortoise.models import Model from tortoise.queryset import QuerySet, QuerySetSingle def _get_fetch_fields( pydantic_class: "Type[PydanticModel]", model_class: "Type[Model]" ) -> List[str]: """ Recursively collect fields needed to fetch :param pydantic_class: The pydantic model class :param model_class: The tortoise model class :return: The list of fields to be fetched """ fetch_fields = [] for field_name, field_type in pydantic_class.__annotations__.items(): origin = getattr(field_type, "__origin__", None) if origin in (list, List, Union): field_type = field_type.__args__[0] # noinspection PyProtectedMember if field_name in model_class._meta.fetch_fields and issubclass(field_type, PydanticModel): subclass_fetch_fields = _get_fetch_fields( field_type, getattr(field_type.__config__, "orig_model") ) if subclass_fetch_fields: fetch_fields.extend([field_name + "__" + f for f in subclass_fetch_fields]) else: fetch_fields.append(field_name) return fetch_fields class PydanticModel(BaseModel): """ Pydantic BaseModel for Tortoise objects. This provides an extra method above the usual Pydantic `model properties <https://pydantic-docs.helpmanual.io/usage/models/#model-properties>`__ """ class Config: orm_mode = True # It should be in ORM mode to convert tortoise data to pydantic # noinspection PyMethodParameters @pydantic.validator("*", pre=True, each_item=False) # It is a classmethod! def _tortoise_convert(cls, value): # pylint: disable=E0213 # Computed fields if callable(value): return value() # Convert ManyToManyRelation to list if isinstance(value, (fields.ManyToManyRelation, fields.ReverseRelation)): return list(value) return value @classmethod async def from_tortoise_orm(cls, obj: "Model") -> "PydanticModel": """ Returns a serializable pydantic model instance built from the provided model instance. .. note:: This will prefetch all the relations automatically. It is probably what you want. If you don't want this, or require a ``sync`` method, look to using ``.from_orm()``. In that case you'd have to manage prefetching yourself, or exclude relational fields from being part of the model using :class:`tortoise.contrib.pydantic.creator.PydanticMeta`, or you would be getting ``OperationalError`` exceptions. This is due to how the ``asyncio`` framework forces I/O to happen in explicit ``await`` statements. Hence we can only do lazy-fetching during an awaited method. :param obj: The Model instance you want serialized. """ # Get fields needed to fetch fetch_fields = _get_fetch_fields(cls, getattr(cls.__config__, "orig_model")) # Fetch fields await obj.fetch_related(*fetch_fields) # Convert to pydantic object values = super().from_orm(obj) return values @classmethod async def from_queryset_single(cls, queryset: "QuerySetSingle") -> "PydanticModel": """ Returns a serializable pydantic model instance for a single model from the provided queryset. This will prefetch all the relations automatically. :param queryset: a queryset on the model this PydanticModel is based on. """ fetch_fields = _get_fetch_fields(cls, getattr(cls.__config__, "orig_model")) return cls.from_orm(await queryset.prefetch_related(*fetch_fields)) @classmethod async def from_queryset(cls, queryset: "QuerySet") -> "List[PydanticModel]": """ Returns a serializable pydantic model instance that contains a list of models, from the provided queryset. This will prefetch all the relations automatically. :param queryset: a queryset on the model this PydanticModel is based on. """ fetch_fields = _get_fetch_fields(cls, getattr(cls.__config__, "orig_model")) return [cls.from_orm(e) for e in await queryset.prefetch_related(*fetch_fields)] class PydanticListModel(BaseModel): """ Pydantic BaseModel for List of Tortoise Models This provides an extra method above the usual Pydantic `model properties <https://pydantic-docs.helpmanual.io/usage/models/#model-properties>`__ """ @classmethod async def from_queryset(cls, queryset: "QuerySet") -> "PydanticListModel": """ Returns a serializable pydantic model instance that contains a list of models, from the provided queryset. This will prefetch all the relations automatically. :param queryset: a queryset on the model this PydanticListModel is based on. """ submodel = getattr(cls.__config__, "submodel") fetch_fields = _get_fetch_fields(submodel, getattr(submodel.__config__, "orig_model")) values = cls( __root__=[submodel.from_orm(e) for e in await queryset.prefetch_related(*fetch_fields)] ) return values

tortoise__tortoise-orm

query.rst

Tutorial

How to use QuerySet to build your queries

Apache License 2.0

tortoise__tortoise-orm/docs/query.rst

Query API This document describes how to use QuerySet to build your queries Be sure to check examples for better understanding You start your query from your model class: Event.filter(id=1) There are several method on model itself to start query: - filter(*args, **kwargs) - create QuerySet with given filters - exclude(*args, **kwargs) - create QuerySet with given excluding filters - all() - create QuerySet without filters - first() - create QuerySet limited to one object and returning instance instead of list - annotate() - create QuerySet with given annotation This method returns QuerySet object, that allows further filtering and some more complex operations Also model class have this methods to create object: - create(**kwargs) - creates object with given kwargs - get_or_create(defaults, **kwargs) - gets object for given kwargs, if not found create it with additional kwargs from defaults dict Also instance of model itself has these methods: - save() - update instance, or insert it, if it was never saved before - delete() - delete instance from db - fetch_related(*args) - fetches objects related to instance. It can fetch FK relation, Backward-FK relations and M2M relations. It also can fetch variable depth of related objects like this: await team.fetch_related('events__tournament') - this will fetch all events for team, and for each of this events their tournament will be prefetched too. After fetching objects they should be available normally like this: team.events[0].tournament.name Another approach to work with related objects on instance is to query them explicitly in async for: async for team in event.participants: print(team.name) You also can filter related objects like this: await team.events.filter(name='First') which will return you a QuerySet object with predefined filter QuerySet QuerySet could be constructed, filtered and passed around without actually hitting database. Only after you await QuerySet, it will generate query and run it against database. Here are some common usage scenarios with QuerySet (we are using models defined in getting_started): Regular select into model instances: await Event.filter(name__startswith='FIFA') This query will get you all events with name starting with FIFA, where name is fields defined on model, and startswith is filter modifier. Take note, that modifiers should be separated by double underscore. You can read more on filter modifiers in Filtering section of this document. It's also possible to filter your queries with .exclude(): await Team.exclude(name__icontains='junior') As more interesting case, when you are working with related data, you could also build your query around related entities: # getting all events, which tournament name is "World Cup" await Event.filter(tournament__name='World Cup') # Gets all teams participating in events with ids 1, 2, 3 await Team.filter(events__id__in=[1,2,3]) # Gets all tournaments where teams with "junior" in their name are participating await Tournament.filter(event__participants__name__icontains='junior').distinct() Usually you not only want to filter by related data, but also get that related data as well. You could do it using .prefetch_related(): # This will fetch events, and for each of events ``.tournament`` field will be populated with # corresponding ``Tournament`` instance await Event.all().prefetch_related('tournament') # This will fetch tournament with their events and teams for each event tournament_list = await Tournament.all().prefetch_related('events__participants') # Fetched result for m2m and backward fk relations are stored in list-like container for tournament in tournament_list: print([e.name for e in tournament.events]) General rule about how prefetch_related() works is that each level of depth of related models produces 1 additional query, so .prefetch_related('events__participants') will produce two additional queries to fetch your data. Sometimes, when performance is crucial, you don't want to make additional queries like this. In cases like this you could use values() or values_list() to produce more efficient query # This will return list of dicts with keys 'id', 'name', 'tournament_name' and # 'tournament_name' will be populated by name of related tournament. # And it will be done in one query events = await Event.filter(id__in=[1,2,3]).values('id', 'name', tournament_name='tournament__name') QuerySet also supports aggregation and database functions through .annotate() method from tortoise.functions import Count, Trim, Lower, Upper, Coalesce # This query will fetch all tournaments with 10 or more events, and will # populate filed `.events_count` on instances with corresponding value await Tournament.annotate(events_count=Count('events')).filter(events_count__gte=10) await Tournament.annotate(clean_name=Trim('name'))).filter(clean_name='tournament') await Tournament.annotate(name_upper=Upper('name'))).filter(name_upper='TOURNAMENT') await Tournament.annotate(name_lower=Lower('name'))).filter(name_lower='tournament') await Tournament.annotate(desc_clean=Coalesce('desc', ''))).filter(desc_clean='') Q objects Sometimes you need to do more complicated queries than the simple AND <model>.filter() provides. Luckily we have Q objects to spice things up and help you find what you need. These Q-objects can then be used as argument to <model>.filter() instead. Q objects are extremely versatile, some example use cases: - creating an OR filter - nested filters - inverted filters - combining any of the above to simply write complicated multilayer filters Q objects can take any (special) kwargs for filtering that <model>.filter() accepts, see those docs for a full list of filter options in that regard. They can also be combined by using bitwise operators (| is OR and & is AND for those unfamiliar with bitwise operators) For example to find the events with as name Event 1 or Event 2: found_events = await Event.filter( Q(name='Event 1') | Q(name='Event 2') ) Q objects can be nested as well, the above for example is equivalent to: found_events = await Event.filter( Q(Q(name='Event 1'), Q(name='Event 2'), join_type="OR") ) If join type is omitted it defaults to AND. Note Q objects without filter arguments are considered NOP and will be ignored for the final query (regardless on if they are used as AND or OR param) Also, Q objects support negated to generate NOT (~ operator) clause in your query not_third_events = await Event.filter(~Q(name='3')) tortoise.query_utils Filtering When using .filter() method you can use number of modifiers to field names to specify desired operation teams = await Team.filter(name__icontains='CON') - not - in - checks if value of field is in passed list - not_in - gte - greater or equals than passed value - gt - greater than passed value - lte - lower or equals than passed value - lt - lower than passed value - range - between and given two values - isnull - field is null - not_isnull - field is not null - contains - field contains specified substring - icontains - case insensitive contains - startswith - if field starts with value - istartswith - case insensitive startswith - endswith - if field ends with value - iendswith - case insensitive endswith - iequals - case insensitive equals Complex prefetch Sometimes it is required to fetch only certain related records. You can achieve it with Prefetch object: tournament_with_filtered = await Tournament.all().prefetch_related( Prefetch('events', queryset=Event.filter(name='First')) ).first() F expression An F() object represents the value of a model field. It makes it possible to refer to model field values and perform database operations using them without actually having to pull them out of the database into Python memory. For example to use F to update user balance atomic: from tortoise.expressions import F await User.filter(id=1).update(balance = F('balance') - 10) await User.filter(id=1).update(balance = F('balance') + F('award'), award = 0) # or use .save() user = await User.get(id=1) user.balance = F('balance') - 10 await user.save(update_fields=['balance'])

import types from copy import copy from typing import ( TYPE_CHECKING, Any, AsyncIterator, Callable, Dict, Generator, Generic, Iterable, List, Optional, Set, Tuple, Type, TypeVar, Union, cast, ) from pypika import JoinType, Order, Table from pypika.functions import Count from pypika.queries import QueryBuilder from pypika.terms import Term from typing_extensions import Protocol from tortoise.backends.base.client import BaseDBAsyncClient, Capabilities from tortoise.exceptions import ( DoesNotExist, FieldError, IntegrityError, MultipleObjectsReturned, ParamsError, ) from tortoise.expressions import F from tortoise.fields.relational import ( ForeignKeyFieldInstance, OneToOneFieldInstance, RelationalField, ) from tortoise.functions import Function from tortoise.query_utils import Prefetch, Q, QueryModifier, _get_joins_for_related_field # Empty placeholder - Should never be edited. QUERY: QueryBuilder = QueryBuilder() if TYPE_CHECKING: # pragma: nocoverage from tortoise.models import Model MODEL = TypeVar("MODEL", bound="Model") T_co = TypeVar("T_co", covariant=True) class QuerySetSingle(Protocol[T_co]): """ Awaiting on this will resolve a single instance of the Model object, and not a sequence. """ # pylint: disable=W0104 def __await__(self) -> Generator[Any, None, T_co]: ... # pragma: nocoverage def prefetch_related(self, *args: Union[str, Prefetch]) -> "QuerySetSingle[MODEL]": ... # pragma: nocoverage def annotate(self, **kwargs: Function) -> "QuerySetSingle[MODEL]": ... # pragma: nocoverage def only(self, *fields_for_select: str) -> "QuerySetSingle[MODEL]": ... # pragma: nocoverage def values_list(self, *fields_: str, flat: bool = False) -> "ValuesListQuery": ... # pragma: nocoverage def values(self, *args: str, **kwargs: str) -> "ValuesQuery": ... # pragma: nocoverage class AwaitableQuery(Generic[MODEL]): __slots__ = ("_joined_tables", "query", "model", "_db", "capabilities", "_annotations") def __init__(self, model: Type[MODEL]) -> None: self._joined_tables: List[Table] = [] self.model: "Type[Model]" = model self.query: QueryBuilder = QUERY self._db: BaseDBAsyncClient = None # type: ignore self.capabilities: Capabilities = model._meta.db.capabilities self._annotations: Dict[str, Function] = {} def resolve_filters( self, model: "Type[Model]", q_objects: List[Q], annotations: Dict[str, Any], custom_filters: Dict[str, Dict[str, Any]], ) -> None: """ Builds the common filters for a QuerySet. :param model: The Model this querysit is based on. :param q_objects: The Q expressions to apply. :param annotations: Extra annotations to add. :param custom_filters: Pre-resolved filters to be passed though. """ has_aggregate = self._resolve_annotate() modifier = QueryModifier() for node in q_objects: modifier &= node.resolve(model, annotations, custom_filters, model._meta.basetable) where_criterion, joins, having_criterion = modifier.get_query_modifiers() for join in joins: if join[0] not in self._joined_tables: self.query = self.query.join(join[0], how=JoinType.left_outer).on(join[1]) self._joined_tables.append(join[0]) self.query._wheres = where_criterion self.query._havings = having_criterion if has_aggregate and (self._joined_tables or having_criterion or self.query._orderbys): self.query = self.query.groupby( self.model._meta.basetable[self.model._meta.db_pk_column] ) def _join_table_by_field( self, table: Table, related_field_name: str, related_field: RelationalField ) -> Table: joins = _get_joins_for_related_field(table, related_field, related_field_name) for join in joins: if join[0] not in self._joined_tables: self.query = self.query.join(join[0], how=JoinType.left_outer).on(join[1]) self._joined_tables.append(join[0]) return joins[-1][0] @staticmethod def _resolve_ordering_string(ordering: str) -> Tuple[str, Order]: order_type = Order.asc if ordering[0] == "-": field_name = ordering[1:] order_type = Order.desc else: field_name = ordering return field_name, order_type def resolve_ordering( self, model: "Type[Model]", table: Table, orderings: Iterable[Tuple[str, str]], annotations: Dict[str, Any], ) -> None: """ Applies standard ordering to QuerySet. :param model: The Model this querysit is based on. :param table: ``pypika.Table`` to keep track of the virtual SQL table (to allow self referential joins) :param orderings: What columns/order to order by :param annotations: Annotations that may be ordered on :raises FieldError: If a field provided does not exist in model. """ # Do not apply default ordering for annotated queries to not mess them up if not orderings and self.model._meta.ordering and not annotations: orderings = self.model._meta.ordering for ordering in orderings: field_name = ordering[0] if field_name in model._meta.fetch_fields: raise FieldError( "Filtering by relation is not possible. Filter by nested field of related model" ) if field_name.split("__")[0] in model._meta.fetch_fields: related_field_name = field_name.split("__")[0] related_field = cast(RelationalField, model._meta.fields_map[related_field_name]) related_table = self._join_table_by_field(table, related_field_name, related_field) self.resolve_ordering( related_field.related_model, related_table, [("__".join(field_name.split("__")[1:]), ordering[1])], {}, ) elif field_name in annotations: annotation = annotations[field_name] annotation_info = annotation.resolve(self.model, table) self.query = self.query.orderby(annotation_info["field"], order=ordering[1]) else: field_object = model._meta.fields_map.get(field_name) if not field_object: raise FieldError(f"Unknown field {field_name} for model {model.__name__}") field_name = field_object.source_field or field_name field = table[field_name] func = field_object.get_for_dialect( model._meta.db.capabilities.dialect, "function_cast" ) if func: field = func(field_object, field) self.query = self.query.orderby(field, order=ordering[1]) def _resolve_annotate(self) -> bool: if not self._annotations: return False table = self.model._meta.basetable annotation_info = {} for key, annotation in self._annotations.items(): annotation_info[key] = annotation.resolve(self.model, table) for key, info in annotation_info.items(): for join in info["joins"]: self._join_table_by_field(*join) self.query._select_other(info["field"].as_(key)) return any(info["field"].is_aggregate for info in annotation_info.values()) def _make_query(self) -> None: raise NotImplementedError() # pragma: nocoverage async def _execute(self) -> Any: raise NotImplementedError() # pragma: nocoverage class QuerySet(AwaitableQuery[MODEL]): __slots__ = ( "fields", "_prefetch_map", "_prefetch_queries", "_single", "_raise_does_not_exist", "_db", "_limit", "_offset", "_fields_for_select", "_filter_kwargs", "_orderings", "_q_objects", "_distinct", "_having", "_custom_filters", "_group_bys", ) def __init__(self, model: Type[MODEL]) -> None: super().__init__(model) self.fields: Set[str] = model._meta.db_fields self._prefetch_map: Dict[str, Set[Union[str, Prefetch]]] = {} self._prefetch_queries: Dict[str, QuerySet] = {} self._single: bool = False self._raise_does_not_exist: bool = False self._limit: Optional[int] = None self._offset: Optional[int] = None self._filter_kwargs: Dict[str, Any] = {} self._orderings: List[Tuple[str, Any]] = [] self._q_objects: List[Q] = [] self._distinct: bool = False self._having: Dict[str, Any] = {} self._custom_filters: Dict[str, dict] = {} self._fields_for_select: Tuple[str,...] = () self._group_bys: Tuple[str,...] = () def _clone(self) -> "QuerySet[MODEL]": queryset = QuerySet.__new__(QuerySet) queryset.fields = self.fields queryset.model = self.model queryset.query = self.query queryset.capabilities = self.capabilities queryset._prefetch_map = copy(self._prefetch_map) queryset._prefetch_queries = copy(self._prefetch_queries) queryset._single = self._single queryset._raise_does_not_exist = self._raise_does_not_exist queryset._db = self._db queryset._limit = self._limit queryset._offset = self._offset queryset._fields_for_select = self._fields_for_select queryset._filter_kwargs = copy(self._filter_kwargs) queryset._orderings = copy(self._orderings) queryset._joined_tables = copy(self._joined_tables) queryset._q_objects = copy(self._q_objects) queryset._distinct = self._distinct queryset._annotations = copy(self._annotations) queryset._having = copy(self._having) queryset._custom_filters = copy(self._custom_filters) queryset._group_bys = copy(self._group_bys) return queryset def _filter_or_exclude(self, *args: Q, negate: bool, **kwargs: Any) -> "QuerySet[MODEL]": queryset = self._clone() for arg in args: if not isinstance(arg, Q): raise TypeError("expected Q objects as args") if negate: queryset._q_objects.append(~arg) else: queryset._q_objects.append(arg) for key, value in kwargs.items(): if negate: queryset._q_objects.append(~Q(**{key: value})) else: queryset._q_objects.append(Q(**{key: value})) return queryset def filter(self, *args: Q, **kwargs: Any) -> "QuerySet[MODEL]": """ Filters QuerySet by given kwargs. You can filter by related objects like this: .. code-block:: python3 Team.filter(events__tournament__name='Test') You can also pass Q objects to filters as args. """ return self._filter_or_exclude(negate=False, *args, **kwargs) def exclude(self, *args: Q, **kwargs: Any) -> "QuerySet[MODEL]": """ Same as.filter(), but with appends all args with NOT """ return self._filter_or_exclude(negate=True, *args, **kwargs) def order_by(self, *orderings: str) -> "QuerySet[MODEL]": """ Accept args to filter by in format like this: .. code-block:: python3 .order_by('name', '-tournament__name') Supports ordering by related models too. :raises FieldError: If unknown field has been provided. """ queryset = self._clone() new_ordering = [] for ordering in orderings: field_name, order_type = self._resolve_ordering_string(ordering) if not ( field_name.split("__")[0] in self.model._meta.fields or field_name in self._annotations ): raise FieldError(f"Unknown field {field_name} for model {self.model.__name__}") new_ordering.append((field_name, order_type)) queryset._orderings = new_ordering return queryset def limit(self, limit: int) -> "QuerySet[MODEL]": """ Limits QuerySet to given length. :raises ParamsError: Limit should be non-negative number. """ if limit < 0: raise ParamsError("Limit should be non-negative number") queryset = self._clone() queryset._limit = limit return queryset def offset(self, offset: int) -> "QuerySet[MODEL]": """ Query offset for QuerySet. :raises ParamsError: Offset should be non-negative number. """ if offset < 0: raise ParamsError("Offset should be non-negative number") queryset = self._clone() queryset._offset = offset if self.capabilities.requires_limit and queryset._limit is None: queryset._limit = 1000000 return queryset def distinct(self) -> "QuerySet[MODEL]": """ Make QuerySet distinct. Only makes sense in combination with a ``.values()`` or ``.values_list()`` as it precedes all the fetched fields with a distinct. """ queryset = self._clone() queryset._distinct = True return queryset def annotate(self, **kwargs: Function) -> "QuerySet[MODEL]": """ Annotate result with aggregation or function result. :raises TypeError: Value of kwarg is expected to be a ``Function`` instance. """ queryset = self._clone() for key, annotation in kwargs.items(): if not isinstance(annotation, Function): raise TypeError("value is expected to be Function instance") queryset._annotations[key] = annotation from tortoise.models import get_filters_for_field queryset._custom_filters.update(get_filters_for_field(key, None, key)) return queryset def group_by(self, *fields: str) -> "QuerySet[MODEL]": """ Make QuerySet returns list of dict or tuple with group by. Must call before.values() or.values_list() """ queryset = self._clone() queryset._group_bys = fields return queryset def values_list(self, *fields_: str, flat: bool = False) -> "ValuesListQuery": """ Make QuerySet returns list of tuples for given args instead of objects. If ```flat=True`` and only one arg is passed can return flat list. If no arguments are passed it will default to a tuple containing all fields in order of declaration. """ return ValuesListQuery( db=self._db, model=self.model, q_objects=self._q_objects, flat=flat, fields_for_select_list=fields_ # type: ignore or [ field for field in self.model._meta.fields_map.keys() if field in self.model._meta.db_fields ] + list(self._annotations.keys()), distinct=self._distinct, limit=self._limit, offset=self._offset, orderings=self._orderings, annotations=self._annotations, custom_filters=self._custom_filters, group_bys=self._group_bys, ) def values(self, *args: str, **kwargs: str) -> "ValuesQuery": """ Make QuerySet return dicts instead of objects. Can pass names of fields to fetch, or as a ``field_name='name_in_dict'`` kwarg. If no arguments are passed it will default to a dict containing all fields. :raises FieldError: If duplicate key has been provided. """ if args or kwargs: fields_for_select: Dict[str, str] = {} for field in args: if field in fields_for_select: raise FieldError(f"Duplicate key {field}") fields_for_select[field] = field for return_as, field in kwargs.items(): if return_as in fields_for_select: raise FieldError(f"Duplicate key {return_as}") fields_for_select[return_as] = field else: _fields = [ field for field in self.model._meta.fields_map.keys() if field in self.model._meta.db_fields ] + list(self._annotations.keys()) fields_for_select = {field: field for field in _fields} return ValuesQuery( db=self._db, model=self.model, q_objects=self._q_objects, fields_for_select=fields_for_select, distinct=self._distinct, limit=self._limit, offset=self._offset, orderings=self._orderings, annotations=self._annotations, custom_filters=self._custom_filters, group_bys=self._group_bys, ) def delete(self) -> "DeleteQuery": """ Delete all objects in QuerySet. """ return DeleteQuery( db=self._db, model=self.model, q_objects=self._q_objects, annotations=self._annotations, custom_filters=self._custom_filters, ) def update(self, **kwargs: Any) -> "UpdateQuery": """ Update all objects in QuerySet with given kwargs. .. admonition: Example: .. code-block:: py3 await Employee.filter(occupation='developer').update(salary=5000) Will instead of returning a resultset, update the data in the DB itself. """ return UpdateQuery( db=self._db, model=self.model, update_kwargs=kwargs, q_objects=self._q_objects, annotations=self._annotations, custom_filters=self._custom_filters, ) def count(self) -> "CountQuery": """ Return count of objects in queryset instead of objects. """ return CountQuery( db=self._db, model=self.model, q_objects=self._q_objects, annotations=self._annotations, custom_filters=self._custom_filters, limit=self._limit, offset=self._offset, ) def all(self) -> "QuerySet[MODEL]": """ Return the whole QuerySet. Essentially a no-op except as the only operation. """ return self._clone() def first(self) -> QuerySetSingle[Optional[MODEL]]: """ Limit queryset to one object and return one object instead of list. """ queryset = self._clone() queryset._limit = 1 queryset._single = True return queryset # type: ignore def get(self, *args: Q, **kwargs: Any) -> QuerySetSingle[MODEL]: """ Fetch exactly one object matching the parameters. """ queryset = self.filter(*args, **kwargs) queryset._limit = 2 queryset._single = True queryset._raise_does_not_exist = True return queryset # type: ignore def get_or_none(self, *args: Q, **kwargs: Any) -> QuerySetSingle[Optional[MODEL]]: """ Fetch exactly one object matching the parameters. """ queryset = self.filter(*args, **kwargs) queryset._limit = 2 queryset._single = True return queryset # type: ignore def only(self, *fields_for_select: str) -> "QuerySet[MODEL]": """ Fetch ONLY the specified fields to create a partial model. Persisting changes on the model is allowed only when: * All the fields you want to update is specified in ``<model>.save(update_fields=[...])`` * You included the Model primary key in the `.only(...)`` To protect against common mistakes we ensure that errors get raised: * If you access a field that is not specified, you will get an ``AttributeError``. * If you do a ``<model>.save()`` a ``IncompleteInstanceError`` will be raised as the model is, as requested, incomplete. * If you do a ``<model>.save(update_fields=[...])`` and you didn't include the primary key in the ``.only(...)``, then ``IncompleteInstanceError`` will be raised indicating that updates can't be done without the primary key being known. * If you do a ``<model>.save(update_fields=[...])`` and one of the fields in ``update_fields`` was not in the ``.only(...)``, then ``IncompleteInstanceError`` as that field is not available to be updated. """ queryset = self._clone() queryset._fields_for_select = fields_for_select return queryset def prefetch_related(self, *args: Union[str, Prefetch]) -> "QuerySet[MODEL]": """ Like ``.fetch_related()`` on instance, but works on all objects in QuerySet. :raises FieldError: If the field to prefetch on is not a relation, or not found. """ queryset = self._clone() queryset._prefetch_map = {} for relation in args: if isinstance(relation, Prefetch): relation.resolve_for_queryset(queryset) continue relation_split = relation.split("__") first_level_field = relation_split[0] if first_level_field not in self.model._meta.fetch_fields: if first_level_field in self.model._meta.fields: raise FieldError( f"Field {first_level_field} on {self.model._meta.full_name} is not a relation" ) raise FieldError( f"Relation {first_level_field} for {self.model._meta.full_name} not found" ) if first_level_field not in queryset._prefetch_map.keys(): queryset._prefetch_map[first_level_field] = set() forwarded_prefetch = "__".join(relation_split[1:]) if forwarded_prefetch: queryset._prefetch_map[first_level_field].add(forwarded_prefetch) return queryset async def explain(self) -> Any: """Fetch and return information about the query execution plan. This is done by executing an ``EXPLAIN`` query whose exact prefix depends on the database backend, as documented below. - PostgreSQL: ``EXPLAIN (FORMAT JSON, VERBOSE)...`` - SQLite: ``EXPLAIN QUERY PLAN...`` - MySQL: ``EXPLAIN FORMAT=JSON...`` .. note:: This is only meant to be used in an interactive environment for debugging and query optimization. **The output format may (and will) vary greatly depending on the database backend.** """ if self._db is None: self._db = self.model._meta.db # type: ignore self._make_query() return await self._db.executor_class(model=self.model, db=self._db).execute_explain( self.query ) def using_db(self, _db: BaseDBAsyncClient) -> "QuerySet[MODEL]": """ Executes query in provided db client. Useful for transactions workaround. """ queryset = self._clone() queryset._db = _db return queryset def _make_query(self) -> None: if self._fields_for_select: table = self.model._meta.basetable db_fields_for_select = [ table[self.model._meta.fields_db_projection[field]].as_(field) for field in self._fields_for_select ] self.query = copy(self.model._meta.basequery).select(*db_fields_for_select) else: self.query = copy(self.model._meta.basequery_all_fields) self.resolve_ordering( self.model, self.model._meta.basetable, self._orderings, self._annotations ) self.resolve_filters( model=self.model, q_objects=self._q_objects, annotations=self._annotations, custom_filters=self._custom_filters, ) if self._limit: self.query._limit = self._limit if self._offset: self.query._offset = self._offset if self._distinct: self.query._distinct = True def __await__(self) -> Generator[Any, None, List[MODEL]]: if self._db is None: self._db = self.model._meta.db # type: ignore self._make_query() return self._execute().__await__() async def __aiter__(self) -> AsyncIterator[MODEL]: for val in await self: yield val async def _execute(self) -> List[MODEL]: instance_list = await self._db.executor_class( model=self.model, db=self._db, prefetch_map=self._prefetch_map, prefetch_queries=self._prefetch_queries, ).execute_select(self.query, custom_fields=list(self._annotations.keys())) if self._single: if len(instance_list) == 1: return instance_list[0] if not instance_list: if self._raise_does_not_exist: raise DoesNotExist("Object does not exist") return None # type: ignore raise MultipleObjectsReturned("Multiple objects returned, expected exactly one") return instance_list class UpdateQuery(AwaitableQuery): __slots__ = ("update_kwargs", "q_objects", "annotations", "custom_filters") def __init__( self, model: Type[MODEL], update_kwargs: Dict[str, Any], db: BaseDBAsyncClient, q_objects: List[Q], annotations: Dict[str, Any], custom_filters: Dict[str, Dict[str, Any]], ) -> None: super().__init__(model) self.update_kwargs = update_kwargs self.q_objects = q_objects self.annotations = annotations self.custom_filters = custom_filters self._db = db def _make_query(self) -> None: table = self.model._meta.basetable self.query = self._db.query_class.update(table) self.resolve_filters( model=self.model, q_objects=self.q_objects, annotations=self.annotations, custom_filters=self.custom_filters, ) # Need to get executor to get correct column_map executor = self._db.executor_class(model=self.model, db=self._db) for key, value in self.update_kwargs.items(): field_object = self.model._meta.fields_map.get(key) if not field_object: raise FieldError(f"Unknown keyword argument {key} for model {self.model}") if field_object.pk: raise IntegrityError(f"Field {key} is PK and can not be updated") if isinstance(field_object, (ForeignKeyFieldInstance, OneToOneFieldInstance)): fk_field: str = field_object.source_field # type: ignore db_field = self.model._meta.fields_map[fk_field].source_field value = executor.column_map[fk_field]( getattr(value, field_object.to_field_instance.model_field_name), None ) else: try: db_field = self.model._meta.fields_db_projection[key] except KeyError: raise FieldError(f"Field {key} is virtual and can not be updated") if isinstance(value, Term): value = F.resolver_arithmetic_expression(self.model, value)[0] elif isinstance(value, Function): value = value.resolve(self.model, table)["field"] else: value = executor.column_map[key](value, None) self.query = self.query.set(db_field, value) def __await__(self) -> Generator[Any, None, int]: if self._db is None: self._db = self.model._meta.db # type: ignore self._make_query() return self._execute().__await__() async def _execute(self) -> int: return (await self._db.execute_query(str(self.query)))[0] class DeleteQuery(AwaitableQuery): __slots__ = ("q_objects", "annotations", "custom_filters") def __init__( self, model: Type[MODEL], db: BaseDBAsyncClient, q_objects: List[Q], annotations: Dict[str, Any], custom_filters: Dict[str, Dict[str, Any]], ) -> None: super().__init__(model) self.q_objects = q_objects self.annotations = annotations self.custom_filters = custom_filters self._db = db def _make_query(self) -> None: self.query = copy(self.model._meta.basequery) self.resolve_filters( model=self.model, q_objects=self.q_objects, annotations=self.annotations, custom_filters=self.custom_filters, ) self.query._delete_from = True def __await__(self) -> Generator[Any, None, int]: if self._db is None: self._db = self.model._meta.db # type: ignore self._make_query() return self._execute().__await__() async def _execute(self) -> int: return (await self._db.execute_query(str(self.query)))[0] class CountQuery(AwaitableQuery): __slots__ = ("q_objects", "annotations", "custom_filters", "limit", "offset") def __init__( self, model: Type[MODEL], db: BaseDBAsyncClient, q_objects: List[Q], annotations: Dict[str, Any], custom_filters: Dict[str, Dict[str, Any]], limit: Optional[int], offset: Optional[int], ) -> None: super().__init__(model) self.q_objects = q_objects self.annotations = annotations self.custom_filters = custom_filters self.limit = limit self.offset = offset or 0 self._db = db def _make_query(self) -> None: self.query = copy(self.model._meta.basequery) self.resolve_filters( model=self.model, q_objects=self.q_objects, annotations=self.annotations, custom_filters=self.custom_filters, ) self.query._select_other(Count("*")) def __await__(self) -> Generator[Any, None, int]: if self._db is None: self._db = self.model._meta.db # type: ignore self._make_query() return self._execute().__await__() async def _execute(self) -> int: _, result = await self._db.execute_query(str(self.query)) count = list(dict(result[0]).values())[0] - self.offset if self.limit and count > self.limit: return self.limit return count class FieldSelectQuery(AwaitableQuery): # pylint: disable=W0223 __slots__ = ("annotations",) def __init__(self, model: Type[MODEL], annotations: Dict[str, Any]) -> None: super().__init__(model) self.annotations = annotations def _join_table_with_forwarded_fields( self, model: Type[MODEL], table: Table, field: str, forwarded_fields: str ) -> Tuple[Table, str]: if field in model._meta.fields_db_projection and not forwarded_fields: return table, model._meta.fields_db_projection[field] if field in model._meta.fields_db_projection and forwarded_fields: raise FieldError(f'Field "{field}" for model "{model.__name__}" is not relation') if field in self.model._meta.fetch_fields and not forwarded_fields: raise ValueError( 'Selecting relation "{}" is not possible, select concrete ' "field on related model".format(field) ) field_object = cast(RelationalField, model._meta.fields_map.get(field)) if not field_object: raise FieldError(f'Unknown field "{field}" for model "{model.__name__}"') table = self._join_table_by_field(table, field, field_object) forwarded_fields_split = forwarded_fields.split("__") return self._join_table_with_forwarded_fields( model=field_object.related_model, table=table, field=forwarded_fields_split[0], forwarded_fields="__".join(forwarded_fields_split[1:]), ) def add_field_to_select_query(self, field: str, return_as: str) -> None: table = self.model._meta.basetable if field in self.model._meta.fields_db_projection: db_field = self.model._meta.fields_db_projection[field] self.query._select_field(table[db_field].as_(return_as)) return if field in self.model._meta.fetch_fields: raise ValueError( 'Selecting relation "{}" is not possible, select ' "concrete field on related model".format(field) ) if field in self.annotations: self._annotations[return_as] = self.annotations[field] return field_split = field.split("__") if field_split[0] in self.model._meta.fetch_fields: related_table, related_db_field = self._join_table_with_forwarded_fields( model=self.model, table=table, field=field_split[0], forwarded_fields="__".join(field_split[1:]), ) self.query._select_field(related_table[related_db_field].as_(return_as)) return raise FieldError(f'Unknown field "{field}" for model "{self.model.__name__}"') def resolve_to_python_value(self, model: Type[MODEL], field: str) -> Callable: if field in model._meta.fetch_fields: # return as is to get whole model objects return lambda x: x if field in (x[1] for x in model._meta.db_native_fields): return lambda x: x if field in self.annotations: field_object = self.annotations[field].field_object if field_object: return field_object.to_python_value return lambda x: x if field in model._meta.fields_map: return model._meta.fields_map[field].to_python_value field_split = field.split("__") if field_split[0] in model._meta.fetch_fields: new_model = model._meta.fields_map[field_split[0]].related_model # type: ignore return self.resolve_to_python_value(new_model, "__".join(field_split[1:])) raise FieldError(f'Unknown field "{field}" for model "{model}"') def _resolve_group_bys(self, *field_names: str): group_bys = [] for field_name in field_names: field_split = field_name.split("__") related_table, related_db_field = self._join_table_with_forwarded_fields( model=self.model, table=self.model._meta.basetable, field=field_split[0], forwarded_fields="__".join(field_split[1:]) if len(field_split) > 1 else "", ) field = related_table[related_db_field].as_(field_name) group_bys.append(field) return group_bys class ValuesListQuery(FieldSelectQuery): __slots__ = ( "flat", "fields", "limit", "offset", "distinct", "orderings", "annotations", "custom_filters", "q_objects", "fields_for_select_list", "group_bys", ) def __init__( self, model: Type[MODEL], db: BaseDBAsyncClient, q_objects: List[Q], fields_for_select_list: List[str], limit: Optional[int], offset: Optional[int], distinct: bool, orderings: List[Tuple[str, str]], flat: bool, annotations: Dict[str, Any], custom_filters: Dict[str, Dict[str, Any]], group_bys: Tuple[str,...], ) -> None: super().__init__(model, annotations) if flat and (len(fields_for_select_list)!= 1): raise TypeError("You can flat value_list only if contains one field") fields_for_select = {str(i): field for i, field in enumerate(fields_for_select_list)} self.fields = fields_for_select self.limit = limit self.offset = offset self.distinct = distinct self.orderings = orderings self.custom_filters = custom_filters self.q_objects = q_objects self.fields_for_select_list = fields_for_select_list self.flat = flat self._db = db self.group_bys = group_bys def _make_query(self) -> None: self.query = copy(self.model._meta.basequery) for positional_number, field in self.fields.items(): self.add_field_to_select_query(field, positional_number) self.resolve_ordering( self.model, self.model._meta.basetable, self.orderings, self.annotations ) self.resolve_filters( model=self.model, q_objects=self.q_objects, annotations=self.annotations, custom_filters=self.custom_filters, ) if self.limit: self.query._limit = self.limit if self.offset: self.query._offset = self.offset if self.distinct: self.query._distinct = True if self.group_bys: self.query._groupbys = self._resolve_group_bys(*self.group_bys) def __await__(self) -> Generator[Any, None, List[Any]]: if self._db is None: self._db = self.model._meta.db # type: ignore self._make_query() return self._execute().__await__() # pylint: disable=E1101 async def __aiter__(self) -> AsyncIterator[Any]: for val in await self: yield val async def _execute(self) -> List[Any]: _, result = await self._db.execute_query(str(self.query)) columns = [ (key, self.resolve_to_python_value(self.model, name)) for key, name in sorted(self.fields.items()) ] if self.flat: func = columns[0][1] flatmap = lambda entry: func(entry["0"]) # noqa return list(map(flatmap, result)) listmap = lambda entry: tuple(func(entry[column]) for column, func in columns) # noqa return list(map(listmap, result)) class ValuesQuery(FieldSelectQuery): __slots__ = ( "fields_for_select", "limit", "offset", "distinct", "orderings", "annotations", "custom_filters", "q_objects", "group_bys", ) def __init__( self, model: Type[MODEL], db: BaseDBAsyncClient, q_objects: List[Q], fields_for_select: Dict[str, str], limit: Optional[int], offset: Optional[int], distinct: bool, orderings: List[Tuple[str, str]], annotations: Dict[str, Any], custom_filters: Dict[str, Dict[str, Any]], group_bys: Tuple[str,...], ) -> None: super().__init__(model, annotations) self.fields_for_select = fields_for_select self.limit = limit self.offset = offset self.distinct = distinct self.orderings = orderings self.custom_filters = custom_filters self.q_objects = q_objects self._db = db self.group_bys = group_bys def _make_query(self) -> None: self.query = copy(self.model._meta.basequery) for return_as, field in self.fields_for_select.items(): self.add_field_to_select_query(field, return_as) self.resolve_ordering( self.model, self.model._meta.basetable, self.orderings, self.annotations ) self.resolve_filters( model=self.model, q_objects=self.q_objects, annotations=self.annotations, custom_filters=self.custom_filters, ) if self.limit: self.query._limit = self.limit if self.offset: self.query._offset = self.offset if self.distinct: self.query._distinct = True if self.group_bys: self.query._groupbys = self._resolve_group_bys(*self.group_bys) def __await__(self) -> Generator[Any, None, List[dict]]: if self._db is None: self._db = self.model._meta.db # type: ignore self._make_query() return self._execute().__await__() # pylint: disable=E1101 async def __aiter__(self) -> AsyncIterator[dict]: for val in await self: yield val async def _execute(self) -> List[dict]: result = await self._db.execute_query_dict(str(self.query)) columns = [ val for val in [ (alias, self.resolve_to_python_value(self.model, field_name)) for alias, field_name in self.fields_for_select.items() ] if not isinstance(val[1], types.LambdaType) ] if columns: for row in result: for col, func in columns: row[col] = func(row[col]) return result from copy import copy from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, cast from pypika import Table from pypika.terms import Criterion from tortoise.exceptions import FieldError, OperationalError from tortoise.fields.relational import BackwardFKRelation, ManyToManyFieldInstance, RelationalField if TYPE_CHECKING: # pragma: nocoverage from tortoise.models import Model from tortoise.queryset import QuerySet def _process_filter_kwarg( model: "Type[Model]", key: str, value: Any, table: Table ) -> Tuple[Criterion, Optional[Tuple[Table, Criterion]]]: join = None if value is None and f"{key}__isnull" in model._meta.filters: param = model._meta.get_filter(f"{key}__isnull") value = True else: param = model._meta.get_filter(key) pk_db_field = model._meta.db_pk_column if param.get("table"): join = ( param["table"], table[pk_db_field] == param["table"][param["backward_key"]], ) if param.get("value_encoder"): value = param["value_encoder"](value, model) criterion = param["operator"](param["table"][param["field"]], value) else: field_object = model._meta.fields_map[param["field"]] encoded_value = ( param["value_encoder"](value, model, field_object) if param.get("value_encoder") else model._meta.db.executor_class._field_to_db(field_object, value, model) ) criterion = param["operator"](table[param["source_field"]], encoded_value) return criterion, join def _get_joins_for_related_field( table: Table, related_field: RelationalField, related_field_name: str ) -> List[Tuple[Table, Criterion]]: required_joins = [] related_table: Table = related_field.related_model._meta.basetable if isinstance(related_field, ManyToManyFieldInstance): through_table = Table(related_field.through) required_joins.append( ( through_table, table[related_field.model._meta.db_pk_column] == through_table[related_field.backward_key], ) ) required_joins.append( ( related_table, through_table[related_field.forward_key] == related_table[related_field.related_model._meta.db_pk_column], ) ) elif isinstance(related_field, BackwardFKRelation): to_field_source_field = ( related_field.to_field_instance.source_field or related_field.to_field_instance.model_field_name ) if table == related_table: related_table = related_table.as_(f"{table.get_table_name()}__{related_field_name}") required_joins.append( ( related_table, table[to_field_source_field] == related_table[related_field.relation_source_field], ) ) else: to_field_source_field = ( related_field.to_field_instance.source_field or related_field.to_field_instance.model_field_name ) from_field = related_field.model._meta.fields_map[related_field.source_field] # type: ignore from_field_source_field = from_field.source_field or from_field.model_field_name related_table = related_table.as_(f"{table.get_table_name()}__{related_field_name}") required_joins.append( (related_table, related_table[to_field_source_field] == table[from_field_source_field],) ) return required_joins class EmptyCriterion(Criterion): # type: ignore def __or__(self, other: Criterion) -> Criterion: return other def __and__(self, other: Criterion) -> Criterion: return other def __bool__(self) -> bool: return False def _and(left: Criterion, right: Criterion) -> Criterion: if left and not right: return left return left & right def _or(left: Criterion, right: Criterion) -> Criterion: if left and not right: return left return left | right class QueryModifier: """ Internal structure used to generate SQL Queries. """ def __init__( self, where_criterion: Optional[Criterion] = None, joins: Optional[List[Tuple[Table, Criterion]]] = None, having_criterion: Optional[Criterion] = None, ) -> None: self.where_criterion: Criterion = where_criterion or EmptyCriterion() self.joins = joins if joins else [] self.having_criterion: Criterion = having_criterion or EmptyCriterion() def __and__(self, other: "QueryModifier") -> "QueryModifier": return QueryModifier( where_criterion=_and(self.where_criterion, other.where_criterion), joins=self.joins + other.joins, having_criterion=_and(self.having_criterion, other.having_criterion), ) def __or__(self, other: "QueryModifier") -> "QueryModifier": if self.having_criterion or other.having_criterion: # TODO: This could be optimized? result_having_criterion = _or( _and(self.where_criterion, self.having_criterion), _and(other.where_criterion, other.having_criterion), ) return QueryModifier( joins=self.joins + other.joins, having_criterion=result_having_criterion ) if self.where_criterion and other.where_criterion: return QueryModifier( where_criterion=self.where_criterion | other.where_criterion, joins=self.joins + other.joins, ) return QueryModifier( where_criterion=self.where_criterion or other.where_criterion, joins=self.joins + other.joins, ) def __invert__(self) -> "QueryModifier": if not self.where_criterion and not self.having_criterion: return QueryModifier(joins=self.joins) if self.having_criterion: # TODO: This could be optimized? return QueryModifier( joins=self.joins, having_criterion=_and(self.where_criterion, self.having_criterion).negate(), ) return QueryModifier(where_criterion=self.where_criterion.negate(), joins=self.joins) def get_query_modifiers(self) -> Tuple[Criterion, List[Tuple[Table, Criterion]], Criterion]: """ Returns a tuple of the query criterion. """ return self.where_criterion, self.joins, self.having_criterion class Q: """ Q Expression container. Q Expressions are a useful tool to compose a query from many small parts. :param join_type: Is the join an AND or OR join type? :param args: Inner ``Q`` expressions that you want to wrap. :param kwargs: Filter statements that this Q object should encapsulate. """ __slots__ = ( "children", "filters", "join_type", "_is_negated", "_annotations", "_custom_filters", ) AND = "AND" OR = "OR" def __init__(self, *args: "Q", join_type: str = AND, **kwargs: Any) -> None: if args and kwargs: newarg = Q(join_type=join_type, **kwargs) args = (newarg,) + args kwargs = {} if not all(isinstance(node, Q) for node in args): raise OperationalError("All ordered arguments must be Q nodes") #: Contains the sub-Q's that this Q is made up of self.children: Tuple[Q,...] = args #: Contains the filters applied to this Q self.filters: Dict[str, Any] = kwargs if join_type not in {self.AND, self.OR}: raise OperationalError("join_type must be AND or OR") #: Specifies if this Q does an AND or OR on its children self.join_type = join_type self._is_negated = False self._annotations: Dict[str, Any] = {} self._custom_filters: Dict[str, Dict[str, Any]] = {} def __and__(self, other: "Q") -> "Q": """ Returns a binary AND of Q objects, use ``AND`` operator. :raises OperationalError: AND operation requires a Q node """ if not isinstance(other, Q): raise OperationalError("AND operation requires a Q node") return Q(self, other, join_type=self.AND) def __or__(self, other: "Q") -> "Q": """ Returns a binary OR of Q objects, use ``OR`` operator. :raises OperationalError: OR operation requires a Q node """ if not isinstance(other, Q): raise OperationalError("OR operation requires a Q node") return Q(self, other, join_type=self.OR) def __invert__(self) -> "Q": """ Returns a negated instance of the Q object, use ``~`` operator. """ q = Q(*self.children, join_type=self.join_type, **self.filters) q.negate() return q def negate(self) -> None: """ Negates the curent Q object. (mutation) """ self._is_negated = not self._is_negated def _resolve_nested_filter( self, model: "Type[Model]", key: str, value: Any, table: Table ) -> QueryModifier: related_field_name = key.split("__")[0] related_field = cast(RelationalField, model._meta.fields_map[related_field_name]) required_joins = _get_joins_for_related_field(table, related_field, related_field_name) modifier = Q(**{"__".join(key.split("__")[1:]): value}).resolve( model=related_field.related_model, annotations=self._annotations, custom_filters=self._custom_filters, table=required_joins[-1][0], ) return QueryModifier(joins=required_joins) & modifier def _resolve_custom_kwarg( self, model: "Type[Model]", key: str, value: Any, table: Table ) -> QueryModifier: having_info = self._custom_filters[key] annotation = self._annotations[having_info["field"]] annotation_info = annotation.resolve(model, table) operator = having_info["operator"] overridden_operator = model._meta.db.executor_class.get_overridden_filter_func( filter_func=operator ) if overridden_operator: operator = overridden_operator if annotation_info["field"].is_aggregate: modifier = QueryModifier(having_criterion=operator(annotation_info["field"], value)) else: modifier = QueryModifier(where_criterion=operator(annotation_info["field"], value)) return modifier def _resolve_regular_kwarg( self, model: "Type[Model]", key: str, value: Any, table: Table ) -> QueryModifier: if key not in model._meta.filters and key.split("__")[0] in model._meta.fetch_fields: modifier = self._resolve_nested_filter(model, key, value, table) else: criterion, join = _process_filter_kwarg(model, key, value, table) joins = [join] if join else [] modifier = QueryModifier(where_criterion=criterion, joins=joins) return modifier def _get_actual_filter_params( self, model: "Type[Model]", key: str, value: Table ) -> Tuple[str, Any]: filter_key = key if key in model._meta.fk_fields or key in model._meta.o2o_fields: field_object = model._meta.fields_map[key] if hasattr(value, "pk"): filter_value = value.pk else: filter_value = value filter_key = cast(str, field_object.source_field) elif key in model._meta.m2m_fields: if hasattr(value, "pk"): filter_value = value.pk else: filter_value = value elif ( key.split("__")[0] in model._meta.fetch_fields or key in self._custom_filters or key in model._meta.filters ): filter_value = value else: allowed = sorted( model._meta.fields | model._meta.fetch_fields | set(self._custom_filters) ) raise FieldError(f"Unknown filter param '{key}'. Allowed base values are {allowed}") return filter_key, filter_value def _resolve_kwargs(self, model: "Type[Model]", table: Table) -> QueryModifier: modifier = QueryModifier() for raw_key, raw_value in self.filters.items(): key, value = self._get_actual_filter_params(model, raw_key, raw_value) if key in self._custom_filters: filter_modifier = self._resolve_custom_kwarg(model, key, value, table) else: filter_modifier = self._resolve_regular_kwarg(model, key, value, table) if self.join_type == self.AND: modifier &= filter_modifier else: modifier |= filter_modifier if self._is_negated: modifier = ~modifier return modifier def _resolve_children(self, model: "Type[Model]", table: Table) -> QueryModifier: modifier = QueryModifier() for node in self.children: node_modifier = node.resolve(model, self._annotations, self._custom_filters, table) if self.join_type == self.AND: modifier &= node_modifier else: modifier |= node_modifier if self._is_negated: modifier = ~modifier return modifier def resolve( self, model: "Type[Model]", annotations: Dict[str, Any], custom_filters: Dict[str, Dict[str, Any]], table: Table, ) -> QueryModifier: """ Resolves the logical Q chain into the parts of a SQL statement. :param model: The Model this Q Expression should be resolved on. :param annotations: Extra annotations one wants to inject into the resultset. :param custom_filters: Pre-resolved filters to be passed though. :param table: ``pypika.Table`` to keep track of the virtual SQL table (to allow self referential joins) """ self._annotations = annotations self._custom_filters = custom_filters if self.filters: return self._resolve_kwargs(model, table) return self._resolve_children(model, table) class Prefetch: """ Prefetcher container. One would directly use this when wanting to attach a custom QuerySet for specialised prefetching. :param relation: Related field name. :param queryset: Custom QuerySet to use for prefetching. """ __slots__ = ("relation", "queryset") def __init__(self, relation: str, queryset: "QuerySet") -> None: self.relation = relation self.queryset = queryset self.queryset.query = copy(self.queryset.model._meta.basequery) def resolve_for_queryset(self, queryset: "QuerySet") -> None: """ Called internally to generate prefetching query. :param queryset: Custom QuerySet to use for prefetching. :raises OperationalError: If field does not exist in model. """ relation_split = self.relation.split("__") first_level_field = relation_split[0] if first_level_field not in queryset.model._meta.fetch_fields: raise OperationalError( f"relation {first_level_field} for {queryset.model._meta.db_table} not found" ) forwarded_prefetch = "__".join(relation_split[1:]) if forwarded_prefetch: if first_level_field not in queryset._prefetch_map.keys(): queryset._prefetch_map[first_level_field] = set() queryset._prefetch_map[first_level_field].add( Prefetch(forwarded_prefetch, self.queryset) ) else: queryset._prefetch_queries[first_level_field] = self.queryset

tortoise__tortoise-orm

schema.rst

Tutorial

How to generate schema

Apache License 2.0

tortoise__tortoise-orm/docs/schema.rst

Schema Creation Here we create connection to SQLite database client and then we discover & initialize models. tortoise.Tortoise.generate_schema generates schema on empty database. There is also the default option when generating the schemas to set the safe parameter to True which will only insert the tables if they don't already exist.

import logging from typing import TYPE_CHECKING logger = logging.getLogger("tortoise") if TYPE_CHECKING: # pragma: nocoverage from tortoise.backends.base.client import BaseDBAsyncClient def get_schema_sql(client: "BaseDBAsyncClient", safe: bool) -> str: """ Generates the SQL schema for the given client. :param client: The DB client to generate Schema SQL for :param safe: When set to true, creates the table only when it does not already exist. """ generator = client.schema_generator(client) return generator.get_create_schema_sql(safe) async def generate_schema_for_client(client: "BaseDBAsyncClient", safe: bool) -> None: """ Generates and applies the SQL schema directly to the given client. :param client: The DB client to generate Schema SQL for :param safe: When set to true, creates the table only when it does not already exist. """ generator = client.schema_generator(client) schema = get_schema_sql(client, safe) logger.debug("Creating schema: %s", schema) if schema: # pragma: nobranch await generator.generate_from_string(schema)

teskalabs__asab

config.rst

Module doc / Tutorial

Config usage

BSD 3-Clause New or Revised License

teskalabs__asab/old_docs/asab/config.rst

teskalabs__asab/asab

Configuration The configuration is provided by Config object which is a singleton. It means that you can access Config from any place of your code, without need of explicit initialisation. import asab # Initialize application object and hence the configuration app = asab.Application() # Access configuration values anywhere my_conf_value = asab.Config['section_name']['key1'] Based on ConfigParser The Config is inherited from Python Standard Library configparser.ConfigParser class. which implements a basic configuration language which provides a structure similar to what’s found in Microsoft Windows INI files. Example of the configuration file: [bitbucket.org] User = hg [topsecret.server.com] Port = 50022 ForwardX11 = no And this is how you access configuration values: >>> asab.Config['topsecret.server.com']['ForwardX11'] 'no' Multiline configuration entry A multiline configuration entries are supported. An example: [section] key= line1 line2 line3 another_key=foo Automatic load of configuration If a configuration file name is specified, the configuration is automatically loaded from a configuration file during initialiation time of Application. The configuration file name can be specified by one of -c command-line argument (1), ASAB_CONFIG environment variable (2) or config [general] config_file default value (3). ./sample_app.py -c ./etc/sample.conf Including other configuration files You can specify one or more additional configuration files that are loaded and merged from an main configuration file. It is done by [general] include configuration value. Multiple paths are separated by os.pathsep (: on Unix). The path can be specified as a glob (e.g. use of * and ? wildcard characters), it will be expanded by glob module from Python Standard Library. Included configuration files may not exists, this situation is silently ignored. [general] include=./etc/site.conf:./etc/site.d/*.conf You can also use a multiline configuration entry: [general] include= ./etc/site.conf ./etc/site.d/*.conf Configuration default values This is how you can extend configuration default values: asab.Config.add_defaults( { 'section_name': { 'key1': 'value', 'key2': 'another value' }, 'other_section': { 'key3': 'value', }, } ) Only simple types (string, int and float) are allowed in the configuration values. Don't use complex types such as lists, dictionaries or objects because these are impossible to provide via configuration files etc. Environment variables in configration Environment variables found in values are automatically expanded. [section_name] persistent_dir=${HOME}/.myapp/ >>> asab.Config['section_name']['persistent_dir'] '/home/user/.myapp/' There is a special environment variable ${THIS_DIR} that is expanded to a directory that contains a current configuration file. It is useful in complex configurations that utilizes included configuration files etc. [section_name] my_file=${THIS_DIR}/my_file.txt Another environment variable ${HOSTNAME} contains the application hostname to be used f. e. in logging file path. [section_name] my_file=${THIS_DIR}/${HOSTNAME}/my_file.txt Passwords in configration [passwords] section in the configuration serves to securely store passwords, which are then not shown publicly in the default API config endpoint's output. It is convenient for the user to store passwords at one place, so that they are not repeated in many sections of the config file(s). Usage is as follows: [connection:KafkaConnection] password=${passwords:kafka_password} [passwords] kafka_password=<MY_SECRET_PASSWORD> Obtaining seconds The seconds can be obtained using getseconds() method for values with different time units specified in the configuration: [sleep] sleep_time=5.2s another_sleep_time=10d The available units are: - y ... years - M ... months - w ... weeks - d ... days - h ... hours - m ... minutes - s ... seconds - ms .. miliseconds If no unit is specified, float of seconds is expected. The obtainment of the second value in the code can be achieved in two ways: self.SleepTime = asab.Config["sleep"].getseconds("sleep_time") self.AnotherSleepTime = asab.Config.getseconds("sleep", "another_sleep_time") Obtaining URLs A URL can be obtained using a geturl() method that takes the URL from the config and removes leading and trailing whitespaces and trailing backslashes. There is an optional parameter called scheme that can have any URL scheme like http, https, mongodb etc. Setting it to None, scheme validation gets bypassed. Setting the scheme parameter to the same scheme as in the config, it will return the URL. If it's not the same it will raise an error. There are two ways of obtaining the URL: asab.Config["urls"].geturl("teskalabs", scheme="https") asab.Config.geturl("urls", "github", scheme=None) Example: >>> asab.Config["urls"].geturl("teskalabs", scheme="https") 'https://www.teskalabs.com' For reference this would be the configuration file: [urls] teskalabs=https://www.teskalabs.com/ github=github.com

import os import sys import re import glob import logging import inspect import platform import configparser import urllib.parse import collections.abc import typing from. import utils L = logging.getLogger(__name__) class ConfigParser(configparser.ConfigParser): """ ConfigParser enhanced with new features such as adding default configuration, URL validation, automatic reading from Zookeeper etc. """ _syslog_sockets = { 'Darwin': '/var/run/syslog' } _syslog_format = { 'Darwin':'m' } _default_values = { 'general': { 'config_file': os.environ.get('ASAB_CONFIG', ''), 'tick_period': 1, # In seconds 'var_dir': os.path.expanduser('~/.' + os.path.splitext(os.path.basename(sys.argv[0]))[0]), 'changelog': '', 'manifest': '', # Daemonization 'pidfile': '!', # '!' has a special meaning => it transforms into platform specific location of pid file 'working_dir': '.', 'uid': '', 'gid': '', }, "asab:metrics": { "native_metrics": "true", "web_requests_metrics": False, # False is a default, web_requests_metrics won't be generated. "expiration": 60, }, "asab:doc": { "default_route_tag": "module_name" }, "logging": { 'verbose': os.environ.get('ASAB_VERBOSE', False), "app_name": os.path.basename(sys.argv[0]), "sd_id": "sd", # Structured data id, see RFC5424 "level": "NOTICE", "levels": "", }, "logging:console": { "format": "%(asctime)s %(levelname)s %(name)s %(struct_data)s%(message)s", "datefmt": "%d-%b-%Y %H:%M:%S.%f", }, "logging:syslog": { "enabled": "false", # TODO: "facility": 'local1', "address": _syslog_sockets.get(platform.system(), "/dev/log"), "format": _syslog_format.get(platform.system(), "3"), }, "logging:file": { "path": "", "format": "%(asctime)s %(levelname)s %(name)s %(struct_data)s%(message)s", "datefmt": "%d-%b-%Y %H:%M:%S.%f", "backup_count": 3, "backup_max_bytes": 0, "rotate_every": "", }, "library": { "azure_cache": "false", # true or the actual path of where the cache should be located }, # "passwords" section serves to securely store passwords # in the configuration file; the passwords are not # shown in the default API # # Usage in the configuration file: # # [connection:KafkaConnection] # password=${passwords:kafka_password} # # [passwords] # kafka_password=<MY_SECRET_PASSWORD> "passwords": { }, "housekeeping": { "at": "03:00", "limit": "05:00", "run_at_startup": "no", }, } if 'ASAB_ZOOKEEPER_SERVERS' in os.environ: # If `ASAB_ZOOKEEPER_SERVERS` are specified, use that as a default value _default_values['zookeeper'] = {'servers': os.environ['ASAB_ZOOKEEPER_SERVERS']} def add_defaults(self, dictionary: dict) -> None: """Add defaults to a current configuration. Args: dictionary: Arguments to be added to the default configuration. """ for section, keys in dictionary.items(): section = str(section) if section not in self._sections: try: self.add_section(section) except ValueError: if self._strict: raise for key, value in keys.items(): key = self.optionxform(str(key)) if key in self._sections[section]: # Value exists, no default needed continue if value is not None: value = str(value) if value is not None and "$" in value: self.set(section, key, os.path.expandvars(value)) else: self.set(section, key, value) def _traverse_includes(self, includes: str, this_dir: str) -> None: """ Read included config files. Nested including is supported. """ if '\n' in includes: sep = '\n' else: sep = " " for include_glob in includes.split(sep): include_glob = include_glob.strip() if len(include_glob) == 0: continue if include_glob.startswith("zookeeper"): self._include_from_zookeeper(include_glob) include_glob = os.path.expandvars(include_glob.strip()) for include in glob.glob(include_glob): include = os.path.abspath(include) if include in self._included: # Preventing infinite dependency looping L.warn("Config file '{}' can be included only once.".format(include)) continue self._included.add(include) self.set('general', 'include', '') self._load_dir_stack.append(os.path.dirname(include)) try: self.read(include) finally: self._load_dir_stack.pop() includes = self.get('general', 'include', fallback='') self._traverse_includes(includes, os.path.dirname(include_glob)) def _load(self): """ This method should be called only once, any subsequent call will lead to undefined behaviour. """ self._load_dir_stack = [] self.config_contents_list = [] self.config_name_list = [] config_fname = ConfigParser._default_values['general']['config_file'] if config_fname!= '': if not os.path.isfile(config_fname): print("Config file '{}' not found".format(config_fname), file=sys.stderr) sys.exit(1) self._load_dir_stack.append(os.path.dirname(config_fname)) try: self.read(config_fname) finally: self._load_dir_stack.pop() self.add_defaults(ConfigParser._default_values) includes = self.get('general', 'include', fallback='') self._included = set() self._traverse_includes(includes, this_dir=os.path.dirname(config_fname)) del self._load_dir_stack def _include_from_zookeeper(self, zkurl): """ Load the configuration from a ZooKeeper server and append it to the `self.config_contents_list` attribute. The method establishes a connection to the ZooKeeper server specified in the configuration file mentioned above. It retrieves the configuration by accessing the path specified in the `general` section, using the key `includes`. The server URL is provided as a list of server names: server1, server2, server3. The path to the configuration file follows this format: 'zookeeper-1:2181,zookeeper-2:2181,zookeeper-3:2181/asab/etc/zk-site.conf.' The loaded configuration is then appended to the `self.config_contents_list` attribute, allowing further processing or usage. This method supports loading configuration files in various formats, such as.json,.yaml, and.conf. Example: ```ini [asab:zookeeper] url=server1 server2 server3 [general] include=zookeeper://zookeeper-1:2181,zookeeper-2:2181,zookeeper-3:2181/asab/etc/zk-site.conf. ``` """ # parse include value into hostname and path url_pieces = urllib.parse.urlparse(zkurl) url_path = url_pieces.path url_netloc = url_pieces.netloc if not url_netloc: if "asab:zookeeper" in self: # Backward compatibility url_netloc = self["asab:zookeeper"]["servers"] else: url_netloc = self["zookeeper"]["servers"] if url_path.startswith("./"): if "asab:zookeeper" in self: # Backward compatibility url_path = self["asab:zookeeper"]["path"] + url_path[1:] else: url_path = self["zookeeper"]["path"] + url_path[1:] head, tail = os.path.split(url_path) self.config_name_list.append(tail) try: # Delayed import to minimize a hard dependency footprint import kazoo.client import json import yaml zk = kazoo.client.KazooClient(url_netloc) zk.start() data = zk.get(url_path)[0] if url_path.endswith(".json"): config = json.loads(data) self.read_dict(config) elif url_path.endswith(".yaml"): config = yaml.safe_load(data) self.read_dict(config) elif url_path.endswith(".conf"): config = data.decode("utf-8") self.read_string(config) else: raise NotImplementedError("Unknown configuration format '{}'".format(url_path)) zk.stop() zk.close() # Include in the list of config file contents self.config_contents_list.append(config) except Exception as e: L.error("Failed to obtain configuration from Zookeeper server(s): '{}'.".format(e)) sys.exit(1) def get_config_contents_list(self): return self.config_contents_list, self.config_name_list def getseconds(self, section, option, *, raw=False, vars=None, fallback=None, **kwargs) -> float: """ Get time data from config and convert time string into seconds with `convert_to_seconds()` method. The available units are: - `y` - years - `M` - months - `w` - weeks - `d` - days - `h` - hours - `m` - minutes - `s` - seconds - `ms` - milliseconds Returns: float: Time in seconds. Examples: ```python self.SleepTime = asab.Config["sleep"].getseconds("sleep_time") self.AnotherSleepTime = asab.Config.getseconds("sleep", "another_sleep_time") ``` """ if fallback is None: fallback = configparser._UNSET return self._get_conv(section, option, utils.convert_to_seconds, raw=raw, vars=vars, fallback=fallback, **kwargs) def geturl(self, section, option, *, raw=False, vars=None, fallback=None, scheme=None, **kwargs): """ Get URL from config and remove all leading and trailing whitespaces and trailing slashes. Args: scheme (str | tuple): URL scheme(s) awaited. If `None`, scheme validation is bypassed. Returns: Validated URL. Raises: ValueError: Scheme requirements are not met if set. Examples: ```ini [urls] teskalabs=https://www.teskalabs.com/ github=github.com ``` ``` python asab.Config["urls"].geturl("teskalabs", scheme="https") asab.Config.geturl("urls", "github", scheme=None) ``` """ return utils.validate_url(self.get(section, option, raw=raw, vars=vars, fallback=fallback), scheme) def getmultiline(self, section, option, *, raw=False, vars=None, fallback=None, **kwargs) -> typing.List[str]: """ Get multiline data from config. Examples: ```ini [places] visited: Praha Brno Pardubice Plzeň unvisited: ``` ```python >>> asab.Config.getmultiline("places", "visited") ["Praha", "Brno", "Pardubice", "Plzeň"] >>> asab.Config.getmultiline("places", "unvisited") [] >>> asab.Config.getmultiline("places", "nonexisting", fallback=["Gottwaldov"]) ["Gottwaldov"] ``` """ values = self.get(section, option, raw=raw, vars=vars, fallback=fallback) if isinstance(values, str): return [item.strip() for item in re.split(r"\s+", values) if len(item) > 0] else: # fallback can be anything return values class _Interpolation(configparser.ExtendedInterpolation): """Interpolation which expands environment variables in values.""" def before_read(self, parser, section, option, value): # Expand environment variables if '$' in value: os.environ['THIS_DIR'] = os.path.abspath(parser._load_dir_stack[-1]) value = os.path.expandvars(value) return super().before_read(parser, section, option, value) Config = ConfigParser(interpolation=_Interpolation()) """ Object for accessing the configuration of the ASAB application. Examples: ```python my_conf_value = asab.Config['section_name']['key'] ``` """ class Configurable(object): """ Custom object whose attributes can be loaded from the configuration. Example: ```python class ConfigurableObject(asab.Configurable): ConfigDefaults = { 'foo': 'bar', } def __init__(self, config_section_name, config=None): super().__init__(config_section_name=config_section_name, config=config) config_foo = self.Config.get('foo') ``` """ ConfigDefaults: dict = {} def __init__(self, config_section_name: str, config: typing.Optional[dict] = None): self.Config = ConfigurableDict() for base_class in inspect.getmro(self.__class__): if not hasattr(base_class, 'ConfigDefaults'): continue if len(base_class.ConfigDefaults) == 0: continue # Merge config defaults of each base class in the 'inheritance' way for key, value in base_class.ConfigDefaults.items(): if value is None: raise ValueError("None value not allowed in ConfigDefaults. Found in %s:%s " % ( config_section_name, key)) if key not in self.Config: self.Config[key] = value if Config.has_section(config_section_name): for key, value in Config.items(config_section_name): self.Config[key] = value if config is not None: self.Config.update(config) # This is for backward compatibility ConfigObject = Configurable class ConfigurableDict(collections.abc.MutableMapping): """ A dictionary supplemented with custom methods for obtaining bools, seconds, urls etc. """ def __init__(self): self._data = {} def __getitem__(self, key): return self._data[key] def __setitem__(self, key, value): self._data[key] = value def __delitem__(self, key): del self._data[key] def __iter__(self): return iter(self._data) def __len__(self): return len(self._data) def getboolean(self, key) -> bool: """ Obtain the corresponding value of the key and convert it into bool. """ value = self._data[key] return utils.string_to_boolean(value) def getseconds(self, key) -> float: """ Obtain the corresponding value of the key and convert it into seconds via `convert_to_seconds()` method. """ value = self._data[key] return utils.convert_to_seconds(value) def getint(self, key) -> int: """ Obtain the corresponding value of the key and convert it into integer. """ value = self._data[key] return int(value) def getfloat(self, key) -> float: """ Obtain the corresponding value of the key and convert it into float. """ value = self._data[key] return float(value) def geturl(self, key, scheme): """ Obtain the corresponding value of the key and parse it via `validate_url()` method. """ value = self._data[key] return utils.validate_url(value, scheme) def __repr__(self): return "<%s %r>" % (self.__class__.__name__, self._data)

teskalabs__asab

library.rst

Module doc

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teskalabs__asab/old_docs/asab/library.rst

teskalabs__asab/asab/library

Library The ASAB Library (asab.library) is a concept of the shared data content across microservices in the cluster. The asab.library provides a read-only interface for listing and reading this content. The library can also notify the ASAB microservice about changes, eg. for automated update/reload. There is a companion microservice asab-library that can be used for management and editation of the library content. The asab.library can however operate without asab-library microservice. Library structure The library content is organized in simplified filesystem manner, with directories and files. Example of the library structure: + /folder1/ - /folder1/item1.yaml - /folder1/item2.json + /folder2/ - /folder2/item3.yaml + /folder2folder2.3/ - /folder2/folder2.3/item4.json Library path rules - Any path must start with /, including the root path (/). - The folder path must end with /. - The item path must end with extension (eg. .json). Library service LibraryService Example of the use: import asab class MyApplication(asab.Application): def __init__(self): super().__init__() # Initialize the library service self.LibraryService = asab.library.LibraryService(self, "LibraryService") self.PubSub.subscribe("Library.ready!", self.on_library_ready) async def on_library_ready(self, event_name, library): print("# Library\n") for item in await self.LibraryService.list("", recursive=True): print(" *", item) if item.type == 'item': itemio = await self.LibraryService.read(item.name) if itemio is not None: with itemio: content = itemio.read() print(" - content: {} bytes".format(len(content))) else: print(" - (DISABLED)") Providers The library can be configured to work with following "backends" (aka providers): Git repository Connection to git repositories requires pygit2 library to be installed. Example of configuration: [library] providers: git+https://github.com/john/awesome_project.git Functionality The git provider clones the repository into a temporary directory and then uses the File System Provider to read the files from it. The default path for the cloned repository is /tmp/asab.library.git/ and it can be changed manually: [library:git] repodir=path/to/repository/cache Deploy tokens in GitLab GitLab uses deploy tokens to enable authentication of deployment tasks, independent of a user account. A deploy token is an SSH key that grants access to a single repository. The public part of the key is attached directly to the repository instead of a personal account, and the private part of the key remains on the server. It is the preferred preferred way over changing local SSH settings. If you want to create a deploy token for your GitLab repository, follow these steps from the manual: 1. Go to Settings > Repository > Deploy tokens section in your repository. (Note that you have to possess "Maintainer" or "Owner" role for the repository.) 2. Expand the "Deploy tokens" section. The list of current Active Deploy Tokens will be displayed. 3. Complete the fields and scopes. We recommend to specify custom "username", as you will need it later for the url in configuration. 4. Record the deploy token's values before leaving or refreshing the page! After that, you cannot access it again. After the deploy token is created, use the URL for repository in the following format: https://<username>:<deploy_token>@gitlab.example.com/john/awesome_project.git Layers The library content can be organized into unlimmited number of layers. Each layer is represented by a provider with a specific configuration. Library configuration Example: [library] providers: provider+1://... provider+2://... provider+3://...

import os import io import typing import hashlib import logging import tempfile import dataclasses import urllib.parse import xml.dom.minidom import aiohttp from...config import Config from..item import LibraryItem from.abc import LibraryProviderABC # L = logging.getLogger(__name__) # class AzureStorageLibraryProvider(LibraryProviderABC): ''' AzureStorageLibraryProvider is a library provider that reads from an Microsoft Azure Storage container. Configure by: azure+https://ACCOUNT-NAME.blob.core.windows.net/BLOB-CONTAINER If Container Public Access Level is not set to "Public access", then "Access Policy" must be created with "Read" and "List" permissions and "Shared Access Signature" (SAS) query string must be added to a URL in a configuration: azure+https://ACCOUNT-NAME.blob.core.windows.net/BLOB-CONTAINER?sv=2020-10-02&si=XXXX&sr=c&sig=XXXXXXXXXXXXXX ''' def __init__(self, library, path, layer): super().__init__(library, layer) assert path[:6] == "azure+" self.URL = urllib.parse.urlparse(path[6:]) self.Model = None # Will be set by `_load_model` method self.Path = path self.CacheDir = Config.get("library", "azure_cache") if self.CacheDir == 'false': self.CacheDir = None elif self.CacheDir == 'true': self.CacheDir = os.path.join(tempfile.gettempdir(), "asab.library.azure.{}".format(hashlib.sha256(path.encode('utf-8')).hexdigest())) # Ensure that the case directory exists if self.CacheDir is not None: try: os.makedirs(self.CacheDir) except FileExistsError: pass # Cache directory already exists self.App.TaskService.schedule(self._start()) async def _start(self): await self._load_model() if self.Model is not None: await self._set_ready() # TODO: Call this periodically async def _load_model(self): url = urllib.parse.urlunparse(urllib.parse.ParseResult( scheme=self.URL.scheme, netloc=self.URL.netloc, path=self.URL.path, params='', query=self.URL.query + "&restype=container&comp=list", fragment='' )) async with aiohttp.ClientSession() as session: async with session.get(url) as resp: if resp.status == 200: content = await resp.text() else: err = await resp.text() L.warning("Failed to list blobs from `{}`:\n{}".format(url, err)) return model = AzureDirectory("/", sub=dict()) dom = xml.dom.minidom.parseString(content) for blob in dom.getElementsByTagName("Blob"): path = get_xml_text(blob.getElementsByTagName("Name")) path = path.split('/') curmodel = model for i in range(len(path) - 1): newmodel = curmodel.sub.get(path[i]) if newmodel is None: curmodel.sub[path[i]] = newmodel = AzureDirectory( name='/' + '/'.join(path[:i + 1]), sub=dict() ) curmodel = newmodel curmodel.sub[path[-1]] = AzureItem( name='/' + '/'.join(path) ) self.Model = model # TODO: If the cache is active, remove items from the cache that: # 1) are not in the list # 2) their etag differs L.info("is connected.", struct_data={'path': self.Path}) async def list(self, path: str) -> list: if self.Model is None: L.warning("Azure Storage library provider is not ready. Cannot list {}".format(path)) raise RuntimeError("Not ready") assert path[:1] == '/' assert '//' not in path assert len(path) == 1 or path[-1:]!= '/' if path == '/': pathparts = [] else: pathparts = path.split("/")[1:] curmodel = self.Model for p in pathparts: curmodel = curmodel.sub.get(p) if curmodel is None: raise KeyError("Not '{}' found".format(path)) if curmodel.type!= 'dir': raise KeyError("Not '{}' found".format(path)) items = [] for i in curmodel.sub.values(): items.append(LibraryItem( name=i.name, type=i.type, layer=self.Layer, providers=[self], )) return items async def read(self, path: str) -> typing.IO: assert path[:1] == '/' assert '//' not in path assert len(path) == 1 or path[-1:]!= '/' headers = {} pathhash = hashlib.sha256(path.encode('utf-8')).hexdigest() cachefname = os.path.join(self.CacheDir, pathhash) if self.CacheDir is not None: try: with open(cachefname + '.etag', "r") as etagf: etag = etagf.read() # We found a local cached file with the etag, we will use that in the request # if the request returns "304 Not Modified" then we will ship the local version of the file headers['If-None-Match'] = etag except FileNotFoundError: pass url = urllib.parse.urlunparse(urllib.parse.ParseResult( scheme=self.URL.scheme, netloc=self.URL.netloc, path=self.URL.path + path, params='', query=self.URL.query, fragment='' )) async with aiohttp.ClientSession() as session: async with session.get(url, headers=headers) as resp: if resp.status == 200: etag = resp.headers.get('ETag') if self.CacheDir is not None and etag is not None: output = open(cachefname, "w+b") with open(cachefname + '.etag', "w") as etagf: etagf.write(etag) else: # Store the response into the temporary file #... that's to avoid storing the whole (and possibly large) file in the memory output = tempfile.TemporaryFile() async for chunk in resp.content.iter_chunked(16 * io.DEFAULT_BUFFER_SIZE): output.write(chunk) elif resp.status == 304 and self.CacheDir is not None: # 304 is Not Modified # The file should be read from cache output = open(cachefname, "r+b") else: L.warning("Failed to get blob:\n{}".format(await resp.text()), struct_data={'status': resp.status}) return None # Rewind the file so the reader can start consuming from the beginning output.seek(0) return output @dataclasses.dataclass class AzureDirectory: name: str sub: dict type: str = "dir" @dataclasses.dataclass class AzureItem: name: str type: str = "item" def get_xml_text(nodelist): rc = [] for node in nodelist: for textnode in node.childNodes: if textnode.nodeType == textnode.TEXT_NODE: rc.append(textnode.data) return ''.join(rc) import io import asyncio import hashlib import typing import logging import functools import os.path import urllib.parse import kazoo.exceptions from.abc import LibraryProviderABC from..item import LibraryItem from...zookeeper import ZooKeeperContainer # L = logging.getLogger(__name__) # class ZooKeeperLibraryProvider(LibraryProviderABC): """ Configuration variant: 1) ZooKeeper provider is fully configured from [zookeeper] section .. code:: [zookeeper] servers=zookeeper-1:2181,zookeeper-2:2181,zookeeper-3:2181 path=/library [library] providers: zk:// 2) ZooKeeper provider is configured by `servers` from [zookeeper] section and path from URL Path will be `/library`. .. code:: [zookeeper] servers=zookeeper-1:2181,zookeeper-2:2181,zookeeper-3:2181 path=/else [library] providers: zk:///library 2.1) ZooKeeper provider is configured by `servers` from [zookeeper] section and path from URL Path will be `/`, this is a special case to 2) .. code:: [zookeeper] servers=zookeeper-1:2181,zookeeper-2:2181,zookeeper-3:2181 path=/else [library] providers: zk:/// 3) ZooKeeper provider is fully configured from URL .. code:: [library] providers: zk://zookeeper-1:2181,zookeeper-2:2181,zookeeper-3:2181/library 4) ZooKeeper provider is configured by `servers` from [zookeeper] section and joined `path` from [zookeeper] and path from URL Path will be `/else/library` .. code:: [zookeeper] servers=zookeeper-1:2181,zookeeper-2:2181,zookeeper-3:2181 path=/else [library] providers: zk://./library If `path` from [zookeeper] section is missing, an application class name will be used Ex. `/BSQueryApp/library` """ def __init__(self, library, path, layer): super().__init__(library, layer) url_pieces = urllib.parse.urlparse(path) self.FullPath = url_pieces.scheme + '://' self.BasePath = url_pieces.path.lstrip("/") while self.BasePath.endswith("/"): self.BasePath = self.BasePath[:-1] self.BasePath = '/' + self.BasePath if self.BasePath == '/': self.BasePath = '' if url_pieces.netloc in ["", "."]: # if netloc is not provided `zk:///path`, then use `zookeeper` section from config config_section_name = 'zookeeper' z_url = None else: config_section_name = '' z_url = path # Initialize ZooKeeper client zksvc = self.App.get_service("asab.ZooKeeperService") self.ZookeeperContainer = ZooKeeperContainer( zksvc, config_section_name=config_section_name, z_path=z_url ) self.Zookeeper = self.ZookeeperContainer.ZooKeeper if config_section_name == 'zookeeper': self.FullPath += self.ZookeeperContainer.Config['servers'] else: self.FullPath += url_pieces.netloc # Handle `zk://` configuration if z_url is None and url_pieces.netloc == "" and url_pieces.path == "" and self.ZookeeperContainer.Path!= '': self.BasePath = '/' + self.ZookeeperContainer.Path # Handle `zk://./path` configuration if z_url is None and url_pieces.netloc == "." and self.ZookeeperContainer.Path!= '': self.BasePath = '/' + self.ZookeeperContainer.Path + self.BasePath self.FullPath += self.BasePath self.VersionNodePath = self.build_path('/.version.yaml') self.Version = None # Will be read when a library become ready self.VersionWatch = None self.App.PubSub.subscribe("ZooKeeperContainer.state/CONNECTED!", self._on_zk_connected) self.App.PubSub.subscribe("ZooKeeperContainer.state/LOST!", self._on_zk_lost) self.App.PubSub.subscribe("ZooKeeperContainer.state/SUSPENDED!", self._on_zk_lost) self.App.PubSub.subscribe("Application.tick/60!", self._get_version_counter) # This will check a library for changes in subscribed folders even without version counter change. self.App.PubSub.subscribe("Application.tick/60!", self._on_library_changed) self.Subscriptions = {} async def finalize(self, app): """ The `finalize` function is called when the application is shutting down """ await self.Zookeeper._stop() async def _on_zk_connected(self, event_name, zkcontainer): """ When the Zookeeper container is connected, set the self.Zookeeper property to the Zookeeper object. """ if zkcontainer!= self.ZookeeperContainer: return L.info("is connected.", struct_data={'path': self.FullPath}) def on_version_changed(version, event): self.App.Loop.call_soon_threadsafe(self._check_version_counter, version) def install_watcher(): return kazoo.recipe.watchers.DataWatch(self.Zookeeper.Client, self.VersionNodePath, on_version_changed) self.VersionWatch = await self.Zookeeper.ProactorService.execute(install_watcher) await self._set_ready() async def _on_zk_lost(self, event_name, zkcontainer): if zkcontainer!= self.ZookeeperContainer: return await self._set_ready(ready=False) async def _get_version_counter(self, event_name=None): if self.Zookeeper is None: return version = await self.Zookeeper.get_data(self.VersionNodePath) self._check_version_counter(version) def _check_version_counter(self, version): # If version is `None` aka `/.version.yaml` doesn't exists, then assume version -1 if version is not None: try: version = int(version) except ValueError: version = 1 else: version = 1 if self.Version is None: # Initial grab of the version self.Version = version return if self.Version == version: # The version has not changed return asyncio.create_task(self._on_library_changed()) async def read(self, path: str) -> typing.IO: if self.Zookeeper is None: L.warning("Zookeeper Client has not been established (yet). Cannot read {}".format(path)) raise RuntimeError("Zookeeper Client has not been established (yet). Not ready.") node_path = self.build_path(path) try: node_data = await self.Zookeeper.get_data(node_path) except kazoo.exceptions.ConnectionClosedError: L.warning("Zookeeper library provider is not ready") raise RuntimeError("Zookeeper library provider is not ready") except kazoo.exceptions.NoNodeError: return None # Consider adding other exceptions from Kazoo to indicate common non-critical errors if node_data is not None: return io.BytesIO(initial_bytes=node_data) else: return None async def list(self, path: str) -> list: if self.Zookeeper is None: L.warning("Zookeeper Client has not been established (yet). Cannot list {}".format(path)) raise RuntimeError("Zookeeper Client has not been established (yet). Not ready.") node_path = self.build_path(path) nodes = await self.Zookeeper.get_children(node_path) if nodes is None: raise KeyError("Not '{}' found".format(node_path)) items = [] for node in nodes: # Remove any component that starts with '.' startswithdot = functools.reduce(lambda x, y: x or y.startswith('.'), node.split(os.path.sep), False) if startswithdot: continue if '.' in node: # We detect files in zookeeper by presence of the dot in the filename, fname = path + node ftype = "item" else: fname = path + node + '/' ftype = "dir" items.append(LibraryItem( name=fname, type=ftype, layer=self.Layer, providers=[self], )) return items def build_path(self, path): """ It takes a path in the library and transforms in into a path within Zookeeper. It does also series of sanity checks (asserts). IMPORTANT: If you encounter asserting failure, don't remove assert. It means that your code is incorrect. """ assert path[:1] == '/' if path!= '/': node_path = self.BasePath + path else: node_path = self.BasePath # Zookeeper path should not have forward slash at the end of path node_path = node_path.rstrip("/") assert '//' not in node_path assert node_path[0] == '/' return node_path async def subscribe(self, path): path = self.BasePath + path self.Subscriptions[path] = await self._get_directory_hash(path) async def _get_directory_hash(self, path): def recursive_traversal(path, digest): if not self.Zookeeper.Client.exists(path): return children = self.Zookeeper.Client.get_children(path) for child in children: if path!= "/": child_path = "{}/{}".format(path, child) else: child_path = "/{}".format(child) zstat = self.Zookeeper.Client.exists(child_path) digest.update("{}\n{}\n".format(child_path, zstat.version).encode('utf-8')) recursive_traversal(child_path, digest) digest = hashlib.sha1() await self.Zookeeper.ProactorService.execute(recursive_traversal, path, digest) return digest.digest() async def _on_library_changed(self, event_name=None): for path, digest in self.Subscriptions.items(): try: newdigest = await self._get_directory_hash(path) if newdigest!= digest: self.Subscriptions[path] = newdigest self.App.PubSub.publish("Library.change!", self, path) except Exception as e: L.error("Failed to process library change for path: '{}'. Reason: '{}'".format(path, e)) import io import os import os.path import stat import glob import struct import typing import logging from.abc import LibraryProviderABC from..item import LibraryItem from...timer import Timer try: from.filesystem_inotify import inotify_init, inotify_add_watch, IN_CREATE, IN_ISDIR, IN_ALL_EVENTS, EVENT_FMT, EVENT_SIZE, IN_MOVED_TO, IN_IGNORED except OSError: inotify_init = None # L = logging.getLogger(__name__) # class FileSystemLibraryProvider(LibraryProviderABC): def __init__(self, library, path, layer, *, set_ready=True): ''' `set_ready` can be used to disable/defer `self._set_ready` call. ''' super().__init__(library, layer) self.BasePath = os.path.abspath(path) while self.BasePath.endswith("/"): self.BasePath = self.BasePath[:-1] L.info("is connected.", struct_data={'path': path}) # Filesystem is always ready (or you have a serious problem) if set_ready: self.App.TaskService.schedule(self._set_ready()) # Open inotify file descriptor if inotify_init is not None: init = inotify_init() if init == -1: L.warning("Subscribing to library changes in filesystem provider is not available. Inotify was not initialized.") self.FD = None else: self.FD = init self.App.Loop.add_reader(self.FD, self._on_inotify_read) self.AggrTimer = Timer(self.App, self._on_aggr_timer) else: self.FD = None self.AggrEvents = [] self.WDs = {} async def read(self, path: str) -> typing.IO: node_path = self.BasePath + path # File path must start with '/' assert node_path[:1] == '/', "File path must start with a forward slash (/). For example: /library/Templates/file.json" # File path must end with the extension assert len(os.path.splitext(node_path)[1]) > 0, "File path must end with an extension. For example: /library/Templates/item.json" # File cannot contain '//' assert '//' not in node_path try: return io.FileIO(node_path, 'rb') except FileNotFoundError: return None except IsADirectoryError: return None async def list(self, path: str) -> list: # This list method is completely synchronous, but it should look like asynchronous to make all list methods unified among providers. return self._list(path) def _list(self, path: str): node_path = self.BasePath + path # Directory path must start with '/' assert node_path[:1] == '/', "Directory path must start with a forward slash (/). For example: /library/Templates/" # Directory path must end with '/' assert node_path[-1:] == '/', "Directory path must end with a forward slash (/). For example: /library/Templates/" # Directory cannot contain '//' assert '//' not in node_path exists = os.access(node_path, os.R_OK) and os.path.isdir(node_path) if not exists: raise KeyError(" '{}' not found".format(path)) items = [] for fname in glob.iglob(os.path.join(node_path, "*")): fstat = os.stat(fname) assert fname.startswith(self.BasePath) fname = fname[len(self.BasePath):] if stat.S_ISREG(fstat.st_mode): ftype = "item" elif stat.S_ISDIR(fstat.st_mode): ftype = "dir" fname += '/' else: ftype = "?" # Remove any component that starts with '.' if any(x.startswith('.') for x in fname.split('/')): continue items.append(LibraryItem( name=fname, type=ftype, layer=self.Layer, providers=[self], )) return items def _on_inotify_read(self): data = os.read(self.FD, 64 * 1024) pos = 0 while pos < len(data): wd, mask, cookie, namesize = struct.unpack_from(EVENT_FMT, data, pos) pos += EVENT_SIZE + namesize name = (data[pos - namesize: pos].split(b'\x00', 1)[0]).decode() if mask & IN_ISDIR == IN_ISDIR and ((mask & IN_CREATE == IN_CREATE) or (mask & IN_MOVED_TO == IN_MOVED_TO)): subscribed_path, child_path = self.WDs[wd] self._subscribe_recursive(subscribed_path, "/".join([child_path, name])) if mask & IN_IGNORED == IN_IGNORED: # cleanup del self.WDs[wd] continue self.AggrEvents.append((wd, mask, cookie, os.fsdecode(name))) self.AggrTimer.restart(0.2) async def _on_aggr_timer(self): to_advertise = set() for wd, mask, cookie, name in self.AggrEvents: # When wathed directory is being removed, more than one inotify events are being produced. # When IN_IGNORED event occurs, respective wd is removed from self.WDs, # but some other events (like IN_DELETE_SELF) get to this point, without having its reference in self.WDs. subscribed_path, _ = self.WDs.get(wd, (None, None)) to_advertise.add(subscribed_path) self.AggrEvents.clear() for path in to_advertise: if path is None: continue self.App.PubSub.publish("Library.change!", self, path) async def subscribe(self, path): if not os.path.isdir(self.BasePath + path): return if self.FD is None: L.warning("Cannot subscribe to changes in the filesystem layer of the library: '{}'".format(self.BasePath)) return self._subscribe_recursive(path, path) def _subscribe_recursive(self, subscribed_path, path_to_be_listed): binary = (self.BasePath + path_to_be_listed).encode() wd = inotify_add_watch(self.FD, binary, IN_ALL_EVENTS) if wd == -1: L.error("Error in inotify_add_watch") return self.WDs[wd] = (subscribed_path, path_to_be_listed) try: items = self._list(path_to_be_listed) except KeyError: # subscribing to non-existing directory is silent return for item in items: if item.type == "dir": self._subscribe_recursive(subscribed_path, item.name) async def finalize(self, app): if self.FD is not None: self.App.Loop.remove_reader(self.FD) os.close(self.FD) import os import tempfile import logging import hashlib import re from.filesystem import FileSystemLibraryProvider from...config import Config # L = logging.getLogger(__name__) # try: import pygit2 except ImportError: L.critical("Please install pygit2 package to enable Git Library Provider. >>> pip install pygit2") raise SystemExit("Application exiting....") class GitLibraryProvider(FileSystemLibraryProvider): """ Read-only git provider to read from remote repository. It clones a remote git repository to a temporary directory and then uses the FileSystemLibraryProvider to read the files. To read from local git repository, please use FileSystemProvider. .. code:: [library] providers=git+<URL or deploy token>#<branch name> [library:git] repodir=<optional location of the repository cache> """ def __init__(self, library, path, layer): # format: 'git+http[s]://[<username>:<deploy token>@]<url>[#<branch>]' pattern = re.compile(r"git\+(https?://)((.*):(.*)@)?([^#]*)(?:#(.*))?$") path_split = pattern.findall(path)[0] L.debug(path_split) self.URLScheme, self.UserInfo, self.User, self.DeployToken, self.URLPath, self.Branch = path_split self.URL = "".join([self.URLScheme, self.UserInfo, self.URLPath]) self.Branch = self.Branch if self.Branch!= '' else None repodir = Config.get("library:git", "repodir", fallback=None) if repodir is not None: self.RepoPath = os.path.abspath(repodir) else: tempdir = tempfile.gettempdir() self.RepoPath = os.path.join( tempdir, "asab.library.git", hashlib.sha256(path.encode('utf-8')).hexdigest() ) super().__init__(library, self.RepoPath, layer, set_ready=False) self.GitRepository = None from...proactor import Module self.App.add_module(Module) self.ProactorService = self.App.get_service("asab.ProactorService") self.PullLock = False self.SubscribedPaths = set() self.App.TaskService.schedule(self.initialize_git_repository()) self.App.PubSub.subscribe("Application.tick/60!", self._periodic_pull) async def _periodic_pull(self, event_name): """ Changes in remote repository are being pulled every minute. `PullLock` flag ensures that only if previous "pull" has finished, new one can start. """ if self.GitRepository is None: return if self.PullLock: return self.PullLock = True try: to_publish = await self.ProactorService.execute(self._do_pull) # Once reset of the head is finished, PubSub message about the change in the subscribed directory gets published. for path in to_publish: self.App.PubSub.publish("Library.change!", self, path) except pygit2.GitError: L.warning("Periodic pull from the remote repository failed.") finally: self.PullLock = False async def initialize_git_repository(self): def init_task(): if pygit2.discover_repository(self.RepoPath) is None: # For a new repository, clone the remote bit os.makedirs(self.RepoPath, mode=0o700, exist_ok=True) self.GitRepository = pygit2.clone_repository( url=self.URL, path=self.RepoPath, checkout_branch=self.Branch ) else: # For existing repository, pull the latest changes self.GitRepository = pygit2.Repository(self.RepoPath) self._do_pull() try: await self.ProactorService.execute(init_task) except KeyError as err: pygit_message = str(err).replace('\"', '') if pygit_message == "'refs/remotes/origin/{}'".format(self.Branch): # branch does not exist L.exception( "Branch does not exist.", struct_data={ "url": self.URLPath, "branch": self.Branch } ) else: L.exception("Error when initializing git repository: {}".format(pygit_message)) self.App.stop() # NOTE: raising Exception doesn't exit the app except pygit2.GitError as err: pygit_message = str(err).replace('\"', '') if pygit_message == "unexpected http status code: 404": # repository not found L.exception( "Git repository not found.", struct_data={ "url": self.URLPath } ) elif pygit_message == "remote authentication required but no callback set": # either repository not found or authentication failed L.exception( "Authentication failed when initializing git repository.\n" "Check if the 'providers' option satisfies the format: 'git+<username>:<deploy token>@<URL>#<branch name>'", struct_data={ "url": self.URLPath, "username": self.User, "deploy_token": self.DeployToken } ) elif 'cannot redirect from': # bad URL L.exception( "Git repository not found.", struct_data={ "url": self.URLPath } ) elif 'Temporary failure in name resolution' in pygit_message: # Internet connection does L.exception( "Git repository not initialized: connection failed. Check your network connection.", struct_data={ "url": self.URLPath } ) else: L.exception("Git repository not initialized: {}".format(err)) self.App.stop() except Exception as err: L.exception(err) assert self.GitRepository.remotes["origin"] is not None, "Git repository not initialized." await self._set_ready() def _do_fetch(self): """ It fetches the remote repository and returns the commit ID of the remote branch :return: The commit id of the latest commit on the remote repository. """ if self.GitRepository is None: return None self.GitRepository.remotes["origin"].fetch() if self.Branch is None: reference = self.GitRepository.lookup_reference("refs/remotes/origin/HEAD") else: reference = self.GitRepository.lookup_reference("refs/remotes/origin/{}".format(self.Branch)) commit_id = reference.peel().id return commit_id def _do_pull(self): new_commit_id = self._do_fetch() if new_commit_id == self.GitRepository.head.target: return [] # Before new head is set, check the diffs. If changes in subscribed directory occured, add path to "to_publish" list. to_publish = [] for path in self.SubscribedPaths: for i in self.GitRepository.diff(self.GitRepository.head.target, new_commit_id).deltas: if ("/" + i.old_file.path).startswith(path): to_publish.append(path) # Reset HEAD self.GitRepository.head.set_target(new_commit_id) self.GitRepository.reset(new_commit_id, pygit2.GIT_RESET_HARD) return to_publish async def subscribe(self, path): if not os.path.isdir(self.BasePath + path): return self.SubscribedPaths.add(path)

teskalabs__asab

log.rst

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teskalabs__asab/old_docs/asab/log.rst

teskalabs__asab/asab

Logging ASAB logging is built on top of a standard Python logging module. It means that it logs to stderr when running on a console and ASAB also provides file and syslog output (both RFC5424 and RFC3164) for background mode of operations. Log timestamps are captured with sub-second precision (depending on the system capabilities) and displayed including microsecond part. Recommended use We recommend to create a logger L in every module that captures all necessary logging output. Alternative logging strategies are also supported. import logging L = logging.getLogger(__name__) ... L.warning("Hello world!") Example of the output to the console: 25-Mar-2018 23:33:58.044595 WARNING myapp.mymodule Hello world! Logging Levels ASAB uses Python logging levels with the addition of LOG_NOTICE level. LOG_NOTICE level is similar to logging.INFO level but it is visible in even in non-verbose mode. L.log(asab.LOG_NOTICE, "This message will be visible regardless verbose configuration.") --------------------------------------------------------------- Level Numeric value Syslog Severity level ---------------- --------------- ------------------------------ CRITICAL 50 Critical / crit / 2 ERROR 40 Error / err / 3 WARNING 30 Warning / warning / 4 LOG_NOTICE 25 Notice / notice / 5 INFO 20 Informational / info / 6 DEBUG 10 Debug / debug / 7 NOTSET 0 --------------------------------------------------------------- Example of a custom level configuration: [logging] levels= myApp.module1 DEBUG myApp.module2 WARNING customLogger ERROR The logger name and the corresponding logging level are separated by a space, each logger is on a separate line. Verbose mode The command-line argument -v enables verbose logging. It means that log entries with levels DEBUG and INFO will be visible. It also enables asyncio debug logging. The actual verbose mode is avaiable at asab.Config["logging"]["verbose"] boolean option. L.debug("This message will be visible only in verbose mode.") Structured data ASAB supports a structured data to be added to a log entry. It follows the RFC 5424, section STRUCTURED-DATA. Structured data are a dictionary, that has to be seriazable to JSON. L.warning("Hello world!", struct_data={'key1':'value1', 'key2':2}) Example of the output to the console: 25-Mar-2018 23:33:58.044595 WARNING myapp.mymodule [sd key1="value1" key2="2"] Hello world! Logging to file The command-line argument -l on command-line enables logging to file. Also non-empty path option in the section [logging:file] of configuration file enables logging to file as well. Example of the configuration file section: [logging:file] path=/var/log/asab.log format="%%(asctime)s %%(levelname)s %%(name)s %%(struct_data)s%%(message)s", datefmt="%%d-%%b-%%Y %%H:%%M:%%S.%%f" backup_count=3 rotate_every=1d When the deployment expects more instances of the same application to be logging into the same file, it is recommended, that the variable hostname is used in the file path: [logging:file] path=/var/log/${HOSTNAME}/asab.log In this way, the applications will log to seperate log files in different folders, which is an intended behavior, since race conditions may occur when different application instances log into the same file. Logging to console ASAB will log to the console only if it detects that it runs in the foreground respectively on the terminal using os.isatty or if the environment variable ASABFORCECONSOLE is set to 1. This is useful setup for eg. PyCharm. Log rotation ASAB supports a log rotation. The log rotation is triggered by a UNIX signal SIGHUP, which can be used e.g. to integrate with logrotate utility. It is implemented using logging.handlers.RotatingFileHandler from a Python standard library. Also, a time-based log rotation can be configured using rotate_every option. backup_count specifies a number of old files to be kept prior their removal. The system will save old log files by appending the extensions ‘.1’, ‘.2’ etc., to the filename. rotate_every specifies an time interval of a log rotation. Default value is empty string, which means that the time-based log rotation is disabled. The interval is specified by an integer value and an unit, e.g. 1d (for 1 day) or 30M (30 minutes). Known units are H for hours, M for minutes, d for days and s for seconds. Logging to syslog The command-line argument -s enables logging to syslog. A configuration section [logging:syslog] can be used to specify details about desired syslog logging. Example of the configuration file section: [logging:syslog] enabled=true format=5 address=tcp://syslog.server.lan:1554/ enabled is equivalent to command-line switch -s and it enables syslog logging target. format speficies which logging format will be used. Possible values are: - 5 for (new) syslog format (RFC 5424 ) , - 3 for old BSD syslog format (RFC 3164 ), typically used by /dev/log and - m for Mac OSX syslog flavour that is based on BSD syslog format but it is not fully compatible. The default value is 3 on Linux and m on Mac OSX. address specifies the location of the Syslog server. It could be a UNIX path such as /dev/log or URL. Possible URL values: - tcp://syslog.server.lan:1554/ for Syslog over TCP - udp://syslog.server.lan:1554/ for Syslog over UDP - unix-connect:///path/to/syslog.socket for Syslog over UNIX socket (stream) - unix-sendto:///path/to/syslog.socket for Syslog over UNIX socket (datagram), equivalent to /path/to/syslog.socket, used by a /dev/log. The default value is a /dev/log on Linux or /var/run/syslog on Mac OSX. Logging of obsolete features It proved to be essential to inform operators about features that are going to be obsoleted. ASAB offers the unified "obsolete" logger. This logger can indicate that a particular feature is marked as "obsolete" thru logs. Such a log message can then be "grepped" from logs uniformly. It is recommended to include eol attribute in the struct_data of the log with a YYYY-MM-DD date/time of the planned obsoletion of the feature. Hint: We suggest automating the detection of obsolete warnings in logs so that the operations are informed well ahead of the actual removal of the feature. The string to seek in logs is " OBSOLETE ". Example of the use: asab.LogObsolete.warning("Use of the obsolete function", struct_data={'eol':'2022-31-12'}) Log example: 21-Jul-2022 14:32:40.983884 WARNING OBSOLETE [eol="2022-31-12"] Use of the obsolete function

import asyncio import datetime import logging import logging.handlers import os import pprint import queue import re import socket import sys import time import traceback import urllib.parse from.config import Config from.timer import Timer from.utils import running_in_container LOG_NOTICE = 25 """ Info log level that is visible in non-verbose mode. It should not be used for warnings and errors. """ logging.addLevelName(LOG_NOTICE, "NOTICE") L = logging.getLogger(__name__) _NAME_TO_LEVEL = { "NOTSET": logging.NOTSET, "NOT SET": logging.NOTSET, "NOT_SET": logging.NOTSET, "DEBUG": logging.DEBUG, "INFO": logging.INFO, "NOTICE": LOG_NOTICE, "LOG_NOTICE": LOG_NOTICE, "LOG NOTICE": LOG_NOTICE, "WARNING": logging.WARNING, "WARN": logging.WARNING, "ERROR": logging.ERROR, "FATAL": logging.CRITICAL, "CRITICAL": logging.CRITICAL, } class Logging(object): def __init__(self, app): self.RootLogger = logging.getLogger() self.ConsoleHandler = None self.FileHandler = None self.SyslogHandler = None if not self.RootLogger.hasHandlers(): # Add console logger if needed if os.isatty(sys.stdout.fileno()) or os.environ.get('ASABFORCECONSOLE', '0')!= '0': self._configure_console_logging() # Initialize file handler file_path = Config["logging:file"]["path"] if len(file_path) > 0: # Ensure file path directory = os.path.dirname(file_path) if not os.path.exists(directory): os.makedirs(directory) self.FileHandler = logging.handlers.RotatingFileHandler( file_path, backupCount=Config.getint("logging:file", "backup_count"), maxBytes=Config.getint("logging:file", "backup_max_bytes"), ) self.FileHandler.setLevel(logging.DEBUG) self.FileHandler.setFormatter(StructuredDataFormatter( fmt=Config["logging:file"]["format"], datefmt=Config["logging:file"]["datefmt"], sd_id=Config["logging"]["sd_id"], )) self.RootLogger.addHandler(self.FileHandler) rotate_every = Config.get("logging:file", "rotate_every") if rotate_every!= '': rotate_every = re.match(r"^([0-9]+)([dMHs])$", rotate_every) if rotate_every is not None: i, u = rotate_every.groups() i = int(i) if i <= 0: self.RootLogger.error("Invalid 'rotate_every' configuration value.") else: if u == 'H': i = i * 60 * 60 elif u == 'M': i = i * 60 elif u == 'd': i = i * 60 * 60 * 24 elif u =='s': pass # PubSub is not ready at this moment, we need to create timer in a future async def schedule(app, interval): self.LogRotatingTime = Timer(app, self._on_tick_rotate_check, autorestart=True) self.LogRotatingTime.start(i) asyncio.ensure_future(schedule(app, i)) else: self.RootLogger.error("Invalid 'rotate_every' configuration value.") # Initialize syslog if Config["logging:syslog"].getboolean("enabled"): address = Config["logging:syslog"]["address"] if address[:1] == '/': self.SyslogHandler = AsyncIOHandler(app.Loop, socket.AF_UNIX, socket.SOCK_DGRAM, address) else: url = urllib.parse.urlparse(address) if url.scheme == 'tcp': self.SyslogHandler = AsyncIOHandler(app.Loop, socket.AF_INET, socket.SOCK_STREAM, ( url.hostname if url.hostname is not None else 'localhost', url.port if url.port is not None else logging.handlers.SYSLOG_UDP_PORT )) elif url.scheme == 'udp': self.SyslogHandler = AsyncIOHandler(app.Loop, socket.AF_INET, socket.SOCK_DGRAM, ( url.hostname if url.hostname is not None else 'localhost', url.port if url.port is not None else logging.handlers.SYSLOG_UDP_PORT )) elif url.scheme == 'unix-connect': self.SyslogHandler = AsyncIOHandler(app.Loop, socket.AF_UNIX, socket.SOCK_STREAM, url.path) elif url.scheme == 'unix-sendto': self.SyslogHandler = AsyncIOHandler(app.Loop, socket.AF_UNIX, socket.SOCK_DGRAM, url.path) else: self.RootLogger.warning("Invalid logging:syslog address '{}'".format(address)) address = None if self.SyslogHandler is not None: self.SyslogHandler.setLevel(logging.DEBUG) format = Config["logging:syslog"]["format"] if format =='m': self.SyslogHandler.setFormatter(MacOSXSyslogFormatter(sd_id=Config["logging"]["sd_id"])) elif format == '5': self.SyslogHandler.setFormatter(SyslogRFC5424Formatter(sd_id=Config["logging"]["sd_id"])) elif format == '5micro': self.SyslogHandler.setFormatter(SyslogRFC5424microFormatter(sd_id=Config["logging"]["sd_id"])) else: self.SyslogHandler.setFormatter(SyslogRFC3164Formatter(sd_id=Config["logging"]["sd_id"])) self.RootLogger.addHandler(self.SyslogHandler) # No logging is configured if self.ConsoleHandler is None and self.FileHandler is None and self.SyslogHandler is None: # Let's check if we run in Docker and if so, then log on stderr if running_in_container(): self._configure_console_logging() else: self.RootLogger.warning("Logging seems to be already configured. Proceed with caution.") if Config["logging"].getboolean("verbose"): self.RootLogger.setLevel(logging.DEBUG) else: level_name = Config["logging"]["level"].upper() try: self.RootLogger.setLevel(_NAME_TO_LEVEL.get(level_name, level_name)) except ValueError: L.error("Cannot detect logging level '{}'".format(level_name)) # Fine-grained log level configurations levels = Config["logging"].get('levels') for level_line in levels.split('\n'): level_line = level_line.strip() if len(level_line) == 0 or level_line.startswith('#') or level_line.startswith(';'): # line starts with a comment continue try: logger_name, level_name = level_line.split(' ', 1) except ValueError: L.error("Cannot read line '{}' in '[logging] levels' section, expected format: 'logger_name level_name'.".format(level_line)) continue level = _NAME_TO_LEVEL.get(level_name.upper(), level_name.upper()) try: logging.getLogger(logger_name).setLevel(level) except ValueError: L.error("Cannot detect logging level '{}' for {} logger".format(level_name, logger_name)) def rotate(self): if self.FileHandler is not None: self.RootLogger.log(LOG_NOTICE, "Rotating logs") self.FileHandler.doRollover() async def _on_tick_rotate_check(self): if self.FileHandler is not None: if self.FileHandler.stream.tell() > 1000: self.rotate() def _configure_console_logging(self): self.ConsoleHandler = logging.StreamHandler(stream=sys.stderr) # Disable colors when running in container if running_in_container(): self.ConsoleHandler.setFormatter(StructuredDataFormatter( fmt=Config["logging:console"]["format"], datefmt=Config["logging:console"]["datefmt"], sd_id=Config["logging"]["sd_id"], use_color=False )) else: self.ConsoleHandler.setFormatter(StructuredDataFormatter( fmt=Config["logging:console"]["format"], datefmt=Config["logging:console"]["datefmt"], sd_id=Config["logging"]["sd_id"], use_color=True )) self.ConsoleHandler.setLevel(logging.DEBUG) self.RootLogger.addHandler(self.ConsoleHandler) class _StructuredDataLogger(logging.Logger): ''' This class extends a default python logger class, specifically by adding ``struct_data`` parameter to logging functions. It means that you can use expressions such as ``logger.info("Hello world!", struct_data={'key':'value'})``. ''' def _log(self, level, msg, args, exc_info=None, struct_data=None, extra=None, stack_info=False): if struct_data is not None: if extra is None: extra = dict() extra['_struct_data'] = struct_data super()._log(level, msg, args, exc_info=exc_info, extra=extra, stack_info=stack_info) logging.setLoggerClass(_StructuredDataLogger) class StructuredDataFormatter(logging.Formatter): ''' The logging formatter that renders log messages that includes structured data. ''' empty_sd = "" BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE = range(8) def __init__(self, facility=16, fmt=None, datefmt=None, style='%', sd_id='sd', use_color: bool = False): super().__init__(fmt, datefmt, style) self.SD_id = sd_id self.Facility = facility self.UseColor = use_color def format(self, record): ''' Format the specified record as text. ''' record.struct_data = self.render_struct_data(record.__dict__.get("_struct_data")) # The Priority value is calculated by first multiplying the Facility number by 8 and then adding the numerical value of the Severity. if record.levelno <= logging.DEBUG: severity = 7 # Debug color = self.BLUE elif record.levelno <= logging.INFO: severity = 6 # Informational color = self.GREEN elif record.levelno <= LOG_NOTICE: severity = 5 # Notice color = self.CYAN elif record.levelno <= logging.WARNING: severity = 4 # Warning color = self.YELLOW elif record.levelno <= logging.ERROR: severity = 3 # Error color = self.RED elif record.levelno <= logging.CRITICAL: severity = 2 # Critical color = self.MAGENTA else: severity = 1 # Alert color = self.WHITE if self.UseColor: levelname = record.levelname levelname_color = _COLOR_SEQ % (30 + color) + levelname + _RESET_SEQ record.levelname = levelname_color record.priority = (self.Facility << 3) + severity return super().format(record) def formatTime(self, record, datefmt=None): ''' Return the creation time of the specified LogRecord as formatted text. ''' try: ct = datetime.datetime.fromtimestamp(record.created) if datefmt is not None: s = ct.strftime(datefmt) else: t = ct.strftime("%Y-%m-%d %H:%M:%S") s = "%s.%03d" % (t, record.msecs) return s except BaseException as e: print("ERROR when logging: {}".format(e), file=sys.stderr) return str(ct) def render_struct_data(self, struct_data): ''' Return the string with structured data. ''' if struct_data is None: return self.empty_sd else: return "[{sd_id} {sd_params}] ".format( sd_id=self.SD_id, sd_params=" ".join(['{}="{}"'.format(key, val) for key, val in struct_data.items()])) def _loop_exception_handler(loop, context): ''' This is an logging exception handler for asyncio. It's purpose is to nicely log any unhandled excpetion that arises in the asyncio tasks. ''' exception = context.pop('exception', None) message = context.pop('message', '') if len(message) > 0: message += '\n' if len(context) > 0: message += pprint.pformat(context) if exception is not None: ex_traceback = exception.__traceback__ tb_lines = [line.rstrip('\n') for line in traceback.format_exception(exception.__class__, exception, ex_traceback)] message += '\n' + '\n'.join(tb_lines) logging.getLogger().error(message) class MacOSXSyslogFormatter(StructuredDataFormatter): """ It implements Syslog formatting for Mac OSX syslog (aka format ``m``). """ def __init__(self, fmt=None, datefmt=None, style='%', sd_id='sd'): fmt = '<%(priority)s>%(asctime)s {app_name}[{proc_id}]: %(levelname)s %(name)s %(struct_data)s%(message)s\000'.format( app_name=Config["logging"]["app_name"], proc_id=os.getpid(), ) # Initialize formatter super().__init__(fmt=fmt, datefmt='%b %d %H:%M:%S', style=style, sd_id=sd_id) class SyslogRFC3164Formatter(StructuredDataFormatter): """ Implementation of a legacy or BSD Syslog (RFC 3164) formatting (aka format ``3``). """ def __init__(self, fmt=None, datefmt=None, style='%', sd_id='sd'): fmt = '<%(priority)s>%(asctime)s {hostname} {app_name}[{proc_id}]:%(levelname)s %(name)s %(struct_data)s%(message)s\000'.format( app_name=Config["logging"]["app_name"], hostname=socket.gethostname(), proc_id=os.getpid(), ) # Initialize formatter super().__init__(fmt=fmt, datefmt='%b %d %H:%M:%S', style=style, sd_id=sd_id) class SyslogRFC5424Formatter(StructuredDataFormatter): """ It implements Syslog formatting for Mac OSX syslog (aka format ``5``). """ empty_sd = " " def __init__(self, fmt=None, datefmt=None, style='%', sd_id='sd'): fmt = '<%(priority)s>1 %(asctime)s.%(msecs)dZ {hostname} {app_name} {proc_id} %(name)s [log l="%(levelname)s"]%(struct_data)s%(message)s'.format( app_name=Config["logging"]["app_name"], hostname=socket.gethostname(), proc_id=os.getpid(), ) # Initialize formatter super().__init__(fmt=fmt, datefmt='%Y-%m-%dT%H:%M:%S', style=style, sd_id=sd_id) # Convert time to GMT self.converter = time.gmtime class SyslogRFC5424microFormatter(StructuredDataFormatter): """ It implements Syslog formatting for syslog (aka format ``micro``) in RFC5424micro format. """ empty_sd = "-" def __init__(self, fmt=None, datefmt=None, style='%', sd_id='sd'): fmt = '<%(priority)s>1 %(asctime)sZ {hostname} {app_name} {proc_id} %(name)s [log l="%(levelname)s"]%(struct_data)s%(message)s'.format( app_name=Config["logging"]["app_name"], hostname=socket.gethostname(), proc_id=os.getpid(), ) super().__init__(fmt=fmt, datefmt='%Y-%m-%dT%H:%M:%S.%f', style=style, sd_id=sd_id) self.converter = time.gmtime class AsyncIOHandler(logging.Handler): """ A logging handler similar to a standard `logging.handlers.SocketHandler` that utilizes `asyncio`. It implements a queue for decoupling logging from a networking. The networking is fully event-driven via `asyncio` mechanisms. """ def __init__(self, loop, family, sock_type, address, facility=logging.handlers.SysLogHandler.LOG_LOCAL1): logging.Handler.__init__(self) self._family = family self._type = sock_type self._address = address self._loop = loop self._socket = None self._reset() self._queue = queue.Queue() self._loop.call_soon(self._connect, self._loop) def _reset(self): self._write_ready = False if self._socket is not None: self._loop.remove_writer(self._socket) self._loop.remove_reader(self._socket) self._socket.close() self._socket = None def _connect(self, loop): self._reset() try: self._socket = socket.socket(self._family, self._type) self._socket.setblocking(0) self._socket.connect(self._address) except Exception as e: print("Error when opening syslog connection to '{}'".format(self._address), e, file=sys.stderr) return self._loop.add_writer(self._socket, self._on_write) self._loop.add_reader(self._socket, self._on_read) def _on_write(self): self._write_ready = True self._loop.remove_writer(self._socket) while not self._queue.empty(): msg = self._queue.get_nowait() try: self._socket.sendall(msg) except Exception as e: # Contingency dump when the socket is not ready print(msg.decode("utf-8"), file=sys.stderr) print( "Error when writing to syslog '{}': {}".format(self._address, e), traceback.format_exc(), sep="\n", file=sys.stderr ) def _on_read(self): try: _ = self._socket.recvfrom(1024) # We receive "something"... let's ignore that! return except Exception as e: print("Error on the syslog socket '{}'".format(self._address), e, file=sys.stderr) # Close a socket - there is no reason for reading or socket is actually closed self._reset() def emit(self, record): """ This is the entry point for log entries. """ try: msg = self.format(record).encode('utf-8') if self._write_ready: try: self._socket.sendall(msg) except Exception as e: print("Error when writing to syslog '{}'".format(self._address), e, file=sys.stderr) self._enqueue(msg) else: self._enqueue(msg) except Exception as e: print("Error when emit to syslog '{}'".format(self._address), e, file=sys.stderr) self.handleError(record) def _enqueue(self, record): self._queue.put(record) _RESET_SEQ = "\033[0m" _COLOR_SEQ = "\033[1;%dm" _BOLD_SEQ = "\033[1m"

teskalabs__asab

storage.rst

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teskalabs__asab/old_docs/asab/storage.rst

teskalabs__asab/asab/storage

Storage The ASAB's Storage Service supports data storage in-memory or in dedicated document databases, including MongoDB and ElasticSearch. Configuration First, specify the storage type in the configuration. The options for the storage type are: - `inmemory`: Collects data directly in memory - `mongodb`: Collects data using MongoDB database. Depends on pymongo and motor libraries. - `elasticsearch`: Collects data using ElasticSearch database. Depends on aiohttp library. Storage Service provides a unified interface for accessing and manipulating collections across multiple database technologies. [asab:storage] type=mongodb For accessing the storage, simply add asab.storage.Module` when initializing and register the service. class MyApplication(asab.Application): async def initialize(self): self.add_module(asab.storage.Module) async def main(self): storage = self.get_service("asab.StorageService") Manipulation with databases Upsertor Upsertor is an object that works like a pointer to the specified database and optionally to object id. It is used for inserting new objects, updating existing objects and deleting them. u = storage.upsertor("test-collection") The StorageService.upsertor() method creates an upsertor object associated with the specified collection. It takes collection as an argument and can have two parameters obj_id and version, which are used for getting an existing object by its ID and version. Inserting an object For inserting an object to the collection, use the Upsertor.set() method. u.set("key", "value") To execute these procedures, simply run the Upsertor.execute() coroutine method, which commits the upsertor data to the storage and returns the ID of the object. Since it is a coroutine, it must be awaited. object_id = await u.execute() The Upsertor.execute() method has optional parameters custom_data and event_type, which are used for webhook requests. object_id = await u.execute( custom_data= {"foo": "bar"}, event_type="object_created" ) Getting a single object For getting a single object, use StorageService.get() coroutine method that takes two arguments collection and obj_id and finds an object by its ID in collection. obj = await storage.get(collection="test-collection", obj_id=object_id) print(obj) When the requested object is not found in the collection, the method raises KeyError. Remember to handle this exception properly when using databases in your services and prevent them from crashing! Note MongoDB storage service in addition provides a coroutine method get_by() which is used for accessing an object by finding its key-value pair. obj = await storage.get_by(database="test-collection", key="key", value="value") Updating an object For updating an object, first obtain the upsertor specifying its obj_id and version. u = storage.upsertor( collection="test-collection", obj_id=object_id, version=obj['_v'] ) We strongly recommend to read the version from the object such as above. That creates a soft lock on the record. It means that if the object is updated by other component in meanwhile, your upsertor will fail and you should retry the whole operation. The new objects should have a version set to 0, which is done by default. After obtaining an upsertor, you can update the object via the Upsertor.set() coroutine. u.set("key", "new_value") object_id = await u.execute() Deleting an object For deleting an object from database, use the StorageService.delete() coroutine method which takes arguments collection and obj_id, deletes the object and returns its ID. deleted_id = await u.delete("test-collection", object_id) Storing data in memory If the option inmemory is set, ASAB will store data in its own memory. In particular, asab.StorageService is initialized with an attribute InMemoryCollections which is a dictionary where all the collections are stored in. Note You can go through all the databases directly by accessing InMemoryCollections attribute, although we do not recommend that. import pprint storage = self.get_service("asab.StorageService") pprint.pprint(storage.InMemoryCollections, indent=2) Storing data in MongoDB If the option mongodb is set, ASAB will store data in MongoDB database. ASAB uses motor library which provides non-blocking MongoDB driver for asyncio. You can specify the database name and URL for MongoDB in config file (the following example is the default configuration): [asab:storage] type=mongodb mongodb_uri=mongodb://localhost:27017 mongodb_database=asabdb You can use all the methods from the abstract class. MongoDB Storage class provides in addition two methods, StorageService.get_by() and StorageService.collection(). The method StorageService.get_by() is used in the same way as StorageService.get() except that it takes the arguments key and value instead of obj_id. obj = await storage.get_by(database="test-collection", key="key", value="value") The method collection() is used for accessing the database directly. It takes collection as the argument and returns motor.motor_asyncio.AsyncIOMotorCollection object, which can be used for calling MongoDB directives. collection = await storage.collection("test-collection") cursor = collection.find({}) while await cursor.fetch_next: data = cursor.next_object() pprint.pprint(data) The full list of methods suitable for this object is described in the official documentation. Storing data in ElasticSearch When using ElasticSearch, add configurations for URL, username and password. [asab:storage] type=elasticsearch elasticsearch_url=http://localhost:9200/ elasticsearch_username=JohnDoe elasticsearch_password=lorem_ipsum_dolor?sit_amet!2023 You can also specify the refreshing parameter and scroll timeout for ElasticSearch Scroll API. [asab:storage] refresh=true scroll_timeout=1m ElasticSearch Storage provides in addition other methods for creating index templates, mappings etc (see the Reference section). Encryption and decryption Data stored in the database can be encrypted using an algorithm that adheres to the Advanced Encryption Standard (AES). AES Key settings In order to use encryption, first make sure you have the cryptography package installed. Then specify the AES Key in the config file. [asab:storage] aes_key=random_key_string Note The AES Key is used as both an encryption and decryption key. It is recommended to keep it in a separate configuration file that is not exposed anywhere publicly. The actual binary AES Key is obtained from the aes_key specified in the config file by encoding and hashing it using the standard hashlib algorithms, so do not worry about the length and type of the key. Encrypting data The Upsertor.set() method has an optional boolean parameter encrypt for encrypting the data before they are stored. Only values of the type bytes can be encrypted. If you want to encrypt other values, encode them first. message = "This is a super secret message!" number = 2023 message_binary = message.encode("ascii") number_binary = number.encode("ascii") u.set("message", message_binary, encrypt=True) u.set("number", number_binary, encrypt=True) object_id = await u.execute() Decrypting data The StorageService.get() coroutine method has an optional parameter decrypt which takes an iterable object (i.e. a list, tuple, set, ...) with the names of keys whose values are to be decrypted. data = await storage.get( collection="test-collection", obj_id=object_id, decrypt=["message", "number"] ) If some of the keys to be decrypted are missing in the required document, the method will ignore them and continue. Note Data that has been encrypted can be identified by the prefix "$aes-cbc$" and are stored in a binary format. Under the hood For encrypting data, we use the certified symmetric AES-CBC algorithm. In fact, the abstract base class StorageServiceABC provides two methods aes_encrypt() and aes_decrypt() that are called automatically in Upsertor.set() and StorageService.get() methods when the parameter encrypt or decrypt is specified. AES-CBC is a mode of operation for the Advanced Encryption Standard (AES) algorithm that provides confidentiality and integrity for data. In AES-CBC, the plaintext is divided into blocks of fixed size (usually 128 bits), and each block is encrypted using the AES algorithm with a secret key. CBC stands for "Cipher Block Chaining" and it is a technique that adds an extra step to the encryption process to ensure that each ciphertext block depends on the previous one. This means that any modification to the ciphertext will produce a completely different plaintext after decryption. The algorithm is a symmetric cipher, which is suitable for encrypting large amounts of data. It requires much less computation power than asymmetric ciphers and is much more useful for bulk encrypting large amounts of data.

import datetime import typing import motor.motor_asyncio import pymongo import bson import asab from.exceptions import DuplicateError from.service import StorageServiceABC from.upsertor import UpsertorABC asab.Config.add_defaults( { 'asab:storage': { 'mongodb_uri':'mongodb://localhost:27017', 'mongodb_database': 'asabdb', } } ) class StorageService(StorageServiceABC): ''' StorageService for MongoDB. Depends on `pymongo` and `motor`. ''' def __init__(self, app, service_name, config_section_name='asab:storage'): super().__init__(app, service_name) self.Client = motor.motor_asyncio.AsyncIOMotorClient(asab.Config.get(config_section_name,'mongodb_uri')) self.Database = self.Client.get_database( asab.Config.get(config_section_name,'mongodb_database'), codec_options=bson.codec_options.CodecOptions(tz_aware=True, tzinfo=datetime.timezone.utc), ) assert self.Database is not None def upsertor(self, collection: str, obj_id=None, version=0): return MongoDBUpsertor(self, collection, obj_id, version) async def get(self, collection: str, obj_id, decrypt=None) -> dict: coll = self.Database[collection] ret = await coll.find_one({'_id': obj_id}) if ret is None: raise KeyError("NOT-FOUND") if decrypt is not None: for field in decrypt: if field in ret: ret[field] = self.aes_decrypt(ret[field]) return ret async def get_by(self, collection: str, key: str, value, decrypt=None) -> dict: coll = self.Database[collection] ret = await coll.find_one({key: value}) if ret is None: raise KeyError("NOT-FOUND") if decrypt is not None: for field in decrypt: if field in ret: ret[field] = self.aes_decrypt(ret[field]) return ret async def collection(self, collection: str) -> motor.motor_asyncio.AsyncIOMotorCollection: """ Get collection. Useful for custom operations. Args: collection: Collection to get. Returns: `AsyncIOMotorCollection` object connected to the queried database. Examples: >>> coll = await storage.collection("test-collection") >>> cursor = coll.find({}) >>> while await cursor.fetch_next: ... obj = cursor.next_object() ... pprint.pprint(obj) """ return self.Database[collection] async def delete(self, collection: str, obj_id): coll = self.Database[collection] ret = await coll.find_one_and_delete({'_id': obj_id}) if ret is None: raise KeyError("NOT-FOUND") return ret['_id'] class MongoDBUpsertor(UpsertorABC): @classmethod def generate_id(cls): return bson.objectid.ObjectId() async def execute(self, custom_data: typing.Optional[dict] = None, event_type: typing.Optional[str] = None): id_name = self.get_id_name() addobj = {} if len(self.ModSet) > 0: addobj['$set'] = self.ModSet if len(self.ModInc) > 0: addobj['$inc'] = self.ModInc if len(self.ModPush) > 0: addobj['$push'] = {k: {'$each': v} for k, v in self.ModPush.items()} if len(self.ModUnset) > 0: addobj['$unset'] = {k: "" for k in self.ModUnset} filtr = {} if self.ObjId is not None: filtr[id_name] = self.ObjId else: # We are going to insert a new object without explicit Id assert (self.Version == 0) or (self.Version is None) if self.Version is not None: filtr['_v'] = int(self.Version) # First wave (adding stuff) if len(addobj) > 0: coll = self.Storage.Database[self.Collection] try: ret = await coll.find_one_and_update( filtr, update=addobj, upsert=True if (self.Version == 0) or (self.Version is None) else False, return_document=pymongo.collection.ReturnDocument.AFTER ) except pymongo.errors.DuplicateKeyError as e: if hasattr(e, "details"): raise DuplicateError("Duplicate key error: {}".format(e), self.ObjId, key_value=e.details.get("keyValue")) else: raise DuplicateError("Duplicate key error: {}".format(e), self.ObjId) if ret is None: # Object might have been changed in the meantime raise KeyError("NOT-FOUND") self.ObjId = ret[id_name] # for k, v in self.ModPull.items(): # o = obj.pop(k, None) # if o is None: o = list() # for x in v: # try: # o.remove(x) # except ValueError: # pass # obj[k] = o if self.Storage.WebhookURIs is not None: webhook_data = { "collection": self.Collection, } if custom_data is not None: webhook_data["custom"] = custom_data if event_type is not None: webhook_data["event_type"] = event_type # Add upsetor data; do not include fields that start with "__" upsertor_data = { "id_field_name": id_name, "id": self.ObjId, "_v": int(self.Version), } if len(self.ModSet) > 0: upsertor_data["set"] = {k: v for k, v in self.ModSet.items() if not k.startswith("__")} if len(self.ModInc) > 0: upsertor_data["inc"] = {k: v for k, v in self.ModInc.items() if not k.startswith("__")} if len(self.ModPush) > 0: upsertor_data["push"] = {k: v for k, v in self.ModPush.items() if not k.startswith("__")} if len(self.ModUnset) > 0: upsertor_data["unset"] = {k: v for k, v in self.ModUnset.items() if not k.startswith("__")} webhook_data["upsertor"] = upsertor_data await self.webhook(webhook_data) return self.ObjId import abc import json import urllib.parse import uuid import hashlib import datetime import logging import asab.web.rest.json import http.client import typing # L = logging.getLogger(__name__) # class UpsertorABC(abc.ABC): def __init__(self, storage, collection, obj_id, version=None): self.Storage = storage self.Collection = collection self.ObjId = obj_id self.Version = version now = datetime.datetime.now(datetime.timezone.utc) self.ModSet = { '_m': now, # Set the modification datetime } if version == 0: self.ModSet['_c'] = now # Set the creation datetime self.ModUnset = {} self.ModInc = { '_v': 1, # Increment '_v' at every change } self.ModPush = {} self.ModPull = {} self.WebhookResponseData = {} def get_id_name(self): return "_id" @classmethod def generate_id(cls) -> bytes: """ Generate a unique ID string using a combination of a random UUID and a SHA-256 hash. Returns: A string representation of the generated ID. """ m = hashlib.sha256() m.update(uuid.uuid4().bytes) return m.digest() def set(self, objField, value, encrypt=False, encrypt_iv=None): """ Add key and value to the upsertor. Args: objField: Key of the object. value: Value of the object. encrypt: Allow encryption. encrypt_iv: Custom initialization vector. """ if encrypt: value = self.Storage.aes_encrypt(value, iv=encrypt_iv) self.ModSet[objField] = value def unset(self, obj_field): ''' Scalar unset ''' self.ModUnset[obj_field] = "" def increment(self, field_name, amount=1): ''' Scalar increment ''' self.ModInc[field_name] = amount def decrement(self, field_name, amount=1): ''' Scalar decrement ''' return self.increment(field_name, -amount) def push(self, field_name, value): ''' Push an item into a list ''' if self.ModPush.get(field_name) is None: self.ModPush[field_name] = [] self.ModPush[field_name].append(value) def pull(self, field_name, value): ''' Pull an item from a list ''' if self.ModPull.get(field_name) is None: self.ModPull[field_name] = [] self.ModPull[field_name].append(value) @abc.abstractmethod async def execute(self, custom_data: typing.Optional[dict] = None, event_type: typing.Optional[str] = None): """ Commit upsertor data to the storage. Afterwards, send a webhook request with upsertion details. Args: custom_data: Custom execution data. Included in webhook payload. event_type: Event type included in webhook payload. Raises: DuplicateError: Raised if there is a colliding object already stored in a storage. """ pass async def webhook(self, data: dict): # TODO: add docstring assert self.Storage.WebhookURIs is not None json_dump = asab.web.rest.json.JSONDumper(pretty=False)(data) for uri in self.Storage.WebhookURIs: self.WebhookResponseData[uri] = await self.Storage.ProactorService.execute( self._webhook, json_dump, uri, self.Storage.WebhookAuth) def _webhook(self, data, uri, auth=None): u = urllib.parse.urlparse(uri) if u.scheme == "https": conn = http.client.HTTPSConnection(u.netloc) else: conn = http.client.HTTPConnection(u.netloc) headers = {"Content-Type": "application/json"} if auth is not None: headers["Authorization"] = auth try: conn.request("PUT", uri, data, headers) response = conn.getresponse() if response.status // 100!= 2: text = response.read() L.error( "Webhook endpoint responded with {}: {}".format(response.status, text), struct_data={"uri": uri}) return self.WebhookResponseData = json.load(response) except ConnectionRefusedError: L.error("Webhook call failed: Connection refused.", struct_data={"uri": uri}) return except json.decoder.JSONDecodeError as e: L.error("Failed to decode JSON response from webhook: {}".format(str(e)), struct_data={"uri": uri}) except Exception as e: L.error("Webhook call failed with {}: {}".format(type(e).__name__, str(e)), struct_data={"uri": uri}) finally: conn.close() import abc import secrets import hashlib import logging import asab import re try: import cryptography.hazmat.primitives.ciphers import cryptography.hazmat.primitives.ciphers.algorithms import cryptography.hazmat.primitives.ciphers.modes except ModuleNotFoundError: cryptography = None # L = logging.getLogger(__name__) # ENCRYPTED_PREFIX = b"$aes-cbc$" class StorageServiceABC(asab.Service): """ An abstract class for the Storage Service. """ def __init__(self, app, service_name): super().__init__(app, service_name) self.WebhookURIs = asab.Config.get("asab:storage:changestream", "webhook_uri", fallback="") or None if self.WebhookURIs is not None: self.WebhookURIs = [uri for uri in re.split(r"\s+", self.WebhookURIs) if len(uri) > 0] try: self.ProactorService = app.get_service("asab.ProactorService") except KeyError as e: raise Exception("Storage webhooks require ProactorService") from e self.WebhookAuth = asab.Config.get("asab:storage:changestream", "webhook_auth", fallback="") or None # Specify a non-empty AES key to enable AES encryption of selected fields self._AESKey = asab.Config.get("asab:storage", "aes_key", fallback="") if len(self._AESKey) > 0: if cryptography is None: raise ModuleNotFoundError( "You are using storage encryption without 'cryptography' installed. " "Please run 'pip install cryptography' " "or install asab with'storage_encryption' optional dependency.") self._AESKey = hashlib.sha256(self._AESKey.encode("utf-8")).digest() else: self._AESKey = None @abc.abstractmethod def upsertor(self, collection: str, obj_id=None, version: int = 0) -> None: """ Create an upsertor object for the specified collection. If updating an existing object, please specify its `obj_id` and also `version` that you need to read from a storage upfront. If `obj_id` is None, we assume that you want to insert a new object and generate its new `obj_id`, `version` should be set to 0 (default) in that case. If you want to insert a new object with a specific `obj_id`, specify `obj_id` and set a version to 0. - If there will be a colliding object already stored in a storage, `execute()` method will fail on `DuplicateError`. Args: collection: Name of collection to work with obj_id: Primary identification of an object in the storage (e.g. primary key) version: Specify a current version of the object and hence prevent byzantine faults. \ You should always read the version from the storage upfront, prior using an upsertor. \ That creates a soft lock on the record. It means that if the object is updated by other \ component in meanwhile, your upsertor will fail and you should retry the whole operation. \ The new objects should have a `version` set to 0. """ pass @abc.abstractmethod async def get(self, collection: str, obj_id, decrypt: bool = None) -> dict: """ Get object from collection by its ID. Args: collection: Collection to get from. obj_id: Object identification. decrypt: Set of fields to decrypt. Returns: The object retrieved from a storage. Raises: KeyError: Raised if `obj_id` is not found in `collection`. """ pass @abc.abstractmethod async def get_by(self, collection: str, key: str, value, decrypt=None) -> dict: """ Get object from collection by its key and value. Args: collection: Collection to get from key: Key to filter on value: Value to filter on decrypt: Set of fields to decrypt Returns: The object retrieved from a storage. Raises: KeyError: If object {key: value} not found in `collection` """ pass @abc.abstractmethod async def delete(self, collection: str, obj_id): """ Delete object from collection. Args: collection: Collection to get from obj_id: Object identification Returns: ID of the deleted object. Raises: KeyError: Raised when obj_id cannot be found in collection. """ pass def aes_encrypt(self, raw: bytes, iv: bytes = None) -> bytes: """ Take an array of bytes and encrypt it using AES-CBC. Args: raw: The data to be encrypted. iv: AES-CBC initialization vector, 16 bytes long. If left empty, a random 16-byte array will be used. Returns: The encrypted data. Raises: TypeError: The data are not in binary format. """ block_size = cryptography.hazmat.primitives.ciphers.algorithms.AES.block_size // 8 if self._AESKey is None: raise RuntimeError( "No aes_key specified in asab:storage configuration. " "If you want to use encryption, specify a non-empty aes_key." ) if not isinstance(raw, bytes): if isinstance(raw, str): raise TypeError("String objects must be encoded before encryption") else: raise TypeError("Only 'bytes' objects can be encrypted") # Pad the text to fit the blocks pad_length = -len(raw) % block_size if pad_length!= 0: raw = raw + b"\00" * pad_length if iv is None: iv = secrets.token_bytes(block_size) algorithm = cryptography.hazmat.primitives.ciphers.algorithms.AES(self._AESKey) mode = cryptography.hazmat.primitives.ciphers.modes.CBC(iv) cipher = cryptography.hazmat.primitives.ciphers.Cipher(algorithm, mode) encryptor = cipher.encryptor() encrypted = ENCRYPTED_PREFIX + iv + (encryptor.update(raw) + encryptor.finalize()) return encrypted def aes_decrypt(self, encrypted: bytes) -> bytes: """ Decrypt encrypted data using AES-CBC. Args: encrypted: The encrypted data to decrypt. It must start with b"$aes-cbc$" prefix, followed by one-block-long initialization vector. Returns: The decrypted data. """ block_size = cryptography.hazmat.primitives.ciphers.algorithms.AES.block_size // 8 if self._AESKey is None: raise RuntimeError( "No aes_key specified in asab:storage configuration. " "If you want to use encryption, specify a non-empty aes_key." ) if not isinstance(encrypted, bytes): raise TypeError("Only values of type 'bytes' can be decrypted") # Strip the prefix if not encrypted.startswith(ENCRYPTED_PREFIX): raise ValueError("Encrypted data must start with {!r} prefix".format(ENCRYPTED_PREFIX)) encrypted = encrypted[len(ENCRYPTED_PREFIX):] # Separate the initialization vector iv, encrypted = encrypted[:block_size], encrypted[block_size:] algorithm = cryptography.hazmat.primitives.ciphers.algorithms.AES(self._AESKey) mode = cryptography.hazmat.primitives.ciphers.modes.CBC(iv) cipher = cryptography.hazmat.primitives.ciphers.Cipher(algorithm, mode) decryptor = cipher.decryptor() raw = decryptor.update(encrypted) + decryptor.finalize() # Strip padding raw = raw.rstrip(b"\x00") return raw def encryption_enabled(self) -> bool: """ Check if AESKey is not empty. Returns: True if AESKey is not empty. """ return self._AESKey is not None import typing from.service import StorageServiceABC from.upsertor import UpsertorABC from.exceptions import DuplicateError class InMemoryUpsertor(UpsertorABC): def __init__(self, storage, collection, obj_id, version=None): super().__init__(storage, collection, obj_id, version) if self.ObjId is None: # generate a random unique binary ID self.ObjId = self.generate_id() async def execute(self, custom_data: typing.Optional[dict] = None, event_type: typing.Optional[str] = None) -> typing.Union[str, bytes]: """Commit the changes prepared in upsertor. :custom_data (dict, optional): Not implemented yet. Defaults to None. :event_type (str, optional): Not implemented yet. Defaults to None. Raises: :RuntimeError: Raised if the object ID was not found in the previous version. Returns: :str | bytes: ID of the created or updated document. """ # TODO: Implement webhook call id_name = self.get_id_name() # Get the object if self.Version == 0: obj = { id_name: self.ObjId } self.Storage._set(self.Collection, self.ObjId, obj) else: obj = await self.Storage.get(self.Collection, self.ObjId) if obj is None: if self.Version is None: obj = { id_name: self.ObjId } self.Storage._set(self.Collection, self.ObjId, obj) else: raise RuntimeError("Previous version of '{}' not found".format(self.ObjId)) for k, v in self.ModSet.items(): obj[k] = v for k, v in self.ModUnset.items(): obj.pop(k, None) for k, v in self.ModInc.items(): o = obj.pop(k, 0) obj[k] = o + v for k, v in self.ModPush.items(): o = obj.pop(k, None) if o is None: o = list() o.extend(v) obj[k] = o for k, v in self.ModPull.items(): o = obj.pop(k, None) if o is None: o = list() for x in v: try: o.remove(x) except ValueError: pass obj[k] = o return self.ObjId class StorageService(StorageServiceABC): def __init__(self, app, service_name): super().__init__(app, service_name) self.InMemoryCollections = {} def upsertor(self, collection: str, obj_id=None, version=0) -> InMemoryUpsertor: """Obtain an in-memory upsertor for given collection and possibly for the specified object. :collection (str): The name of the collection. :obj_id (_type_, optional): The ID of the document to retrieve. Defaults to None. :version (int, optional): The version of the collection. Defaults to 0. Returns: :InMemoryUpsertor: Upsertor for given collection. """ return InMemoryUpsertor(self, collection, obj_id, version) async def get(self, collection: str, obj_id: typing.Union[str, bytes], decrypt=None) -> dict: """Retrieve a document from an in-memory collection by its ID. :collection (str): The name of the collection to retrieve the document from. :obj_id (str | bytes): The ID of the document to retrieve. :decrypt (_type_, optional): A list of field names to decrypt. Defaults to None. Returns: :dict: A dictionary representing the retrieved document.bIf `decrypt` is not None, the specified fields in the document are decrypted using AES decryption algorithm. """ coll = self.InMemoryCollections[collection] data = coll[obj_id] if decrypt is not None: for field in decrypt: if field in data: data[field] = self.aes_decrypt(data[field]) return data async def get_by(self, collection: str, key: str, value, decrypt=None) -> dict: """ Retrieve a document from an in-memory collection by key and value. Not implemented yet. Raises: :NotImplementedError: Not implemented on InMemoryStorage """ raise NotImplementedError() async def delete(self, collection: str, obj_id): """ Delete a document from an in-memory collection. :param collection: Collection to delete from :param obj_id: Object identification Raises: :KeyError: If `obj_id` not found in `collection` """ coll = self.InMemoryCollections[collection] del coll[obj_id] def _set(self, collection: str, obj_id, obj): try: coll = self.InMemoryCollections[collection] except KeyError: coll = {} self.InMemoryCollections[collection] = coll nobj = coll.setdefault(obj_id, obj) if nobj!= obj: raise DuplicateError("Already exists", obj_id) import time import json import aiohttp import logging import datetime import urllib.parse import typing from.service import StorageServiceABC from.upsertor import UpsertorABC from..config import Config from..tls import SSLContextBuilder import ssl # L = logging.getLogger(__name__) # Config.add_defaults( { 'asab:storage': { # You may specify multiple ElasticSearch nodes by e.g. http://es01:9200,es02:9200,es03:9200/ 'elasticsearch_url': 'http://localhost:9200/', 'elasticsearch_username': '', 'elasticsearch_password': '', 'elasticsearch_api_key': '', # make the operation visible to search directly, options: true, false, wait_for # see: https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-index_.html 'refresh': 'true', 'scroll_timeout': '1m', # For SSL options such as `cafile`, please refer to tls.py } } ) class StorageService(StorageServiceABC): """ StorageService for Elastic Search. Depends on `aiohttp` library. """ def __init__(self, app, service_name, config_section_name='asab:storage'): super().__init__(app, service_name) self.Loop = app.Loop self.URL = Config.get(config_section_name, 'elasticsearch_url') parsed_url = urllib.parse.urlparse(self.URL) self.ServerUrls = [ urllib.parse.urlunparse((parsed_url.scheme, netloc, parsed_url.path, None, None, None)) for netloc in parsed_url.netloc.split(',') ] self.Refresh = Config.get(config_section_name,'refresh') self.ScrollTimeout = Config.get(config_section_name,'scroll_timeout') # Authorization: username or API-key username = Config.get(config_section_name, 'elasticsearch_username') api_key = Config.get(config_section_name, 'elasticsearch_api_key') if username!= '' and api_key!= '': L.warning("Both username and API key specified. ES Storage service may not function properly. Please choose one option.") if username == '': self._auth = None else: password = Config.get(config_section_name, 'elasticsearch_password') self._auth = aiohttp.BasicAuth(login=username, password=password) self._ClientSession = None # Create headers for requests self.Headers = {'Content-Type': 'application/json'} if api_key!= '': self.Headers['Authorization'] = "ApiKey {}".format(api_key) self.SSLContextBuilder = SSLContextBuilder(config_section_name) async def finalize(self, app): """ Close the current client session. """ if self._ClientSession is not None and not self._ClientSession.closed: await self._ClientSession.close() self._ClientSession = None def session(self): """ Get the current client session. """ if self._ClientSession is None: self._ClientSession = aiohttp.ClientSession(auth=self._auth) elif self._ClientSession.closed: self._ClientSession = aiohttp.ClientSession(auth=self._auth) return self._ClientSession async def is_connected(self) -> bool: """Check if the service is connected to ElasticSearch cluster. Raises: ConnectionError: Connection failed. Returns: bool: True if the service is connected. """ for url in self.ServerUrls: if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None try: async with self.session().request( method="GET", url=url, ssl=ssl_context, headers=self.Headers, ) as resp: await self.session().close() if resp.status not in {200, 201}: resp = await resp.json() L.error("Failed to connect to ElasticSearch.", struct_data={ "code": resp.get("status"), "reason": resp.get("error", {}).get("reason") }) return False except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise ConnectionError("Failed to connect to '{}'.".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) L.info("Connected to ElasticSearch.", struct_data={"urls": self.ServerUrls}) return True async def get(self, index: str, obj_id: str, decrypt=None) -> dict: """Get object by its index and object ID. Args: index (str): Index for the query. obj_id (str): ID of the object. decrypt (None): Not implemented yet. Defaults to None. Raises: NotImplementedError: Encryption and decryption has not yet been implemented for ECS. ConnectionError: Connection failed. ConnectionRefusedError: Authorization required. KeyError: Object with the ID does not exist. Returns: The query result. """ if decrypt is not None: raise NotImplementedError("AES encryption for ElasticSearch not implemented") for url in self.ServerUrls: request_url = "{}{}/_doc/{}".format(url, index, obj_id) try: if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None async with self.session().request( method="GET", url=request_url, ssl=ssl_context, headers=self.Headers, ) as resp: if resp.status == 401: raise ConnectionRefusedError("Response code 401: Unauthorized. Provide authorization by specifying either user name and password or api key.") elif resp.status not in {200, 201}: resp = await resp.json() raise ConnectionError("Failed to retrieve data from ElasticSearch. Got {}: {}".format( resp.get("status"), resp.get("error", {}).get("reason") )) else: obj = await resp.json() if not obj.get("found"): raise KeyError("No existing object with ID {}".format(obj_id)) ret = obj['_source'] ret['_v'] = obj['_version'] ret['_id'] = obj['_id'] return ret except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise ConnectionError("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) async def get_by(self, collection: str, key: str, value, decrypt=None): raise NotImplementedError("get_by") async def delete(self, index: str, _id=None) -> dict: """Delete an entire index or document from that index. Args: index: Index to delete. _id: If specified, only document with the ID is deleted. Raises: ConnectionRefusedError: Authorization required (status 401) KeyError: No existing object with ID ConnectionError: Unexpected status code Exception: ClientConnectorError Returns: The deleted document or message that the entire index was deleted. """ for url in self.ServerUrls: try: if _id: request_url = "{}{}/_doc/{}?refresh={}".format(url, index, _id, self.Refresh) else: request_url = "{}{}".format(url, index) if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None async with self.session().request( method="DELETE", url=request_url, ssl=ssl_context, headers=self.Headers ) as resp: if resp.status == 401: raise ConnectionRefusedError("Response code 401: Unauthorized. Provide authorization by specifying either user name and password or api key.") elif resp.status == 404: raise KeyError("No existing object with ID {}".format(_id)) elif resp.status not in {200, 201}: raise ConnectionError("Failed to retrieve data from ElasticSearch. Got {}: {}".format( resp.get("status"), resp.get("error", {}).get("reason") )) else: json_response = await resp.json() if json_response.get("acknowledged", False): return json_response assert json_response["result"] == "deleted", "Document was not deleted" await self.session().close() return json_response except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise Exception("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) async def mapping(self, index: str) -> dict: """Retrieve mapping definitions for one index. :param index: Specified index. :type index: str :raise Exception: Connection failed. Returns: dict: Mapping definitions for the index. """ for url in self.ServerUrls: request_url = "{}{}/_mapping".format(url, index) if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None try: async with self.session().request( method="GET", url=request_url, ssl=ssl_context, headers=self.Headers ) as resp: obj = await resp.json() await self.session().close() return obj except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise ConnectionError("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) async def get_index_template(self, template_name: str) -> dict: """Retrieve ECS Index template for the given template name. :param template_name: The name of the ECS template to retrieve. :type template_name: str :raise Exception: Raised if connection to all server URLs fails. :return: ElasticSearch Index template. """ for url in self.ServerUrls: request_url = "{}_template/{}?format=json".format(url, template_name) if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None try: async with self.session().request( method="GET", url=request_url, headers=self.Headers, ssl=ssl_context, ) as resp: assert resp.status == 200, "Unexpected response code: {}".format(resp.status) content = await resp.json() await self.session().close() return content except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise Exception("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) async def put_index_template(self, template_name: str, template: dict) -> dict: """Create a new ECS index template. :param template_name: The name of ECS template. :param template: Body for the request. :return: JSON response. :raise Exception: Raised if connection to all server URLs fails. """ for url in self.ServerUrls: request_url = "{}_template/{}?include_type_name".format(url, template_name) L.warning("Posting index template into url: {}".format(request_url)) if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None try: async with self.session().request( method="POST", url=request_url, data=json.dumps(template), headers=self.Headers, ssl=ssl_context, ) as resp: assert resp.status == 200, "Unexpected response code: {}".format(resp.status) resp = await resp.json() await self.session().close() return resp except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise Exception("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) return {} async def reindex(self, previous_index, new_index): for url in self.ServerUrls: try: if url.endswith('/'): request_url = "{}_reindex".format(url) else: request_url = "{}/_reindex".format(url) if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None async with self.session().request( method="POST", url=request_url, headers=self.Headers, ssl=ssl_context, data=json.dumps({ "source": { "index": previous_index, }, "dest": { "index": new_index, } }) ) as resp: if resp.status!= 200: raise AssertionError( "Unexpected response code when reindexing: {}, {}".format( resp.status, await resp.text() ) ) resp = await resp.json() await self.session().close() return resp except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise ConnectionError("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) async def scroll(self, index: str, body: typing.Optional[dict] = None) -> dict: """Retrieve the next batch of results for a scrolling search. :param index: The index name. :type index: str :param body: Custom body for the request. Defaults to None. :type body: dict :return: JSON response. :raise Exception: Raised if connection to all server URLs fails. """ if body is None: body = { "query": {"bool": {"must": {"match_all": {}}}} } scroll_id = None while True: for url in self.ServerUrls: if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None if scroll_id is None: path = "{}/_search?scroll={}".format( index, self.ScrollTimeout ) request_body = body else: path = "_search/scroll" request_body = { "scroll": self.ScrollTimeout, "scroll_id": scroll_id, } request_url = "{}{}".format(url, path) try: async with self.session().request( method="POST", url=request_url, json=request_body, headers=self.Headers, ssl=ssl_context, ) as resp: if resp.status!= 200: data = await resp.text() L.error( "Failed to fetch data from ElasticSearch: {} from {}\n{}".format( resp.status, url, data ) ) break response_json = await resp.json() await self.session().close() except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise Exception( "Failed to connect to '{}'".format( url ) ) else: L.warning( "Failed to connect to '{}', iterating to another cluster node".format( url ) ) scroll_id = response_json.get("_scroll_id") if scroll_id is None: break return response_json def upsertor(self, index: str, obj_id=None, version: int = 0): return ElasticSearchUpsertor(self, index, obj_id, version) async def list(self, index: str, _from: int = 0, size: int = 10000, body: typing.Optional[dict] = None) -> dict: """List data matching the index. :param index: Specified index. :param _from: Starting document offset. Defaults to 0. :type _from: int :param size: The number of hits to return. Defaults to 10000. :type size: int :param body: An optional request body. Defaults to None. :type body: dict :return: The query search result. :raise Exception: Raised if connection to all server URLs fails. """ if body is None: body = { 'query': { 'bool': { 'must': { 'match_all': {} } } } } for url in self.ServerUrls: if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None try: request_url = "{}{}/_search?size={}&from={}&version=true".format(url, index, size, _from) async with self.session().request( method="GET", url=request_url, json=body, headers=self.Headers, ssl=ssl_context, ) as resp: assert resp.status == 200, "Unexpected response code: {}".format(resp.status) content = await resp.json() return content except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise Exception("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) async def count(self, index) -> int: """ Get the number of matches for a given index. :param index: The specified index. :return: The number of matches for a given index. :raise Exception: Connection failed. """ for url in self.ServerUrls: try: count_url = "{}{}/_count".format(url, index) if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None async with self.session().request( method="GET", url=count_url, ssl=ssl_context, headers=self.Headers ) as resp: assert resp.status == 200, "Unexpected response code: {}".format(resp.status) total_count = await resp.json() return total_count except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise Exception("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) async def indices(self, search_string=None): """ Return high-level information about indices in a cluster, including backing indices for data streams. :param search_string: A search string. Default to None. """ for url in self.ServerUrls: if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None try: request_url = "{}_cat/indices/{}?format=json".format(url, search_string if search_string is not None else "*") async with self.session().request( method="GET", url=request_url, ssl=ssl_context, headers=self.Headers ) as resp: assert resp.status == 200, "Unexpected response code: {}".format(resp.status) return await resp.json() except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise Exception("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) async def empty_index(self, index): ''' Create an empty ECS index. ''' # TODO: There is an option here to specify settings (e.g. shard number, replica number etc) and mappings here for url in self.ServerUrls: if url.startswith('https://'): ssl_context = self.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None try: request_url = "{}{}".format(url, index) async with self.session().request( method="PUT", url=request_url, ssl=ssl_context, headers=self.Headers ) as resp: assert resp.status == 200, "Unexpected response code: {}".format(resp.status) return await resp.json() except aiohttp.client_exceptions.ClientConnectorError: if url == self.ServerUrls[-1]: raise Exception("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) class ElasticSearchUpsertor(UpsertorABC): def __init__(self, storage, collection, obj_id, version=None): super().__init__(storage, collection, obj_id, version) now = int(time.time()) self.ModSet['_m'] = now if version == 0: self.ModSet['_c'] = now # Set the creation timestamp api_key = Config.get('asab:storage', 'elasticsearch_api_key') self.Headers = {'Content-Type': 'application/json'} if api_key!= '': self.Headers['Authorization'] = "ApiKey {}".format(api_key) @classmethod def generate_id(cls): raise NotImplementedError("generate_id") async def execute(self, custom_data: typing.Optional[dict] = None, event_type: typing.Optional[str] = None): # TODO: Implement webhook call if self.ObjId is None: return await self._insert_new_object() else: return await self._update_existing_object() async def _insert_new_object(self): upsert_data = {} if self.Version is None: self.Version = 0 if len(self.ModSet) > 0: for k, v in self.ModSet.items(): upsert_data[k] = serialize(self.ModSet[k]) if len(self.ModInc) > 0: # addobj['$inc'] = self.ModInc # raise NotImplementedError("yet") pass if len(self.ModPush) > 0: # addobj['$push'] = {k: {'$each': v} for k, v in self.ModPush.items()} raise NotImplementedError("yet") # This is insert of the new document, the ObjId is to be generated by the ElasicSearch for url in self.Storage.ServerUrls: request_url = "{}{}/_doc?refresh={}".format( url, self.Collection, self.Storage.Refresh ) if url.startswith('https://'): ssl_context = self.Storage.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None try: async with self.Storage.session().request( method="POST", url=request_url, headers=self.Headers, json=upsert_data, ssl=ssl_context ) as resp: if resp.status == 401: raise ConnectionRefusedError("Response code 401: Unauthorized. Provide authorization by specifying either user name and password or api key.") elif resp.status not in {200, 201}: raise ConnectionError("Unexpected response code: {}".format(resp.status)) else: resp_json = await resp.json() self.ObjId = resp_json['_id'] await self.Storage.session().close() return self.ObjId except aiohttp.client_exceptions.ClientConnectorError: if url == self.Storage.ServerUrls[-1]: raise Exception("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) except aiohttp.client_exceptions.ServerDisconnectedError: raise Exception("Failed to connect to '{}'".format(url)) except ValueError as err: raise ConnectionError("Both username and API key specified. Please choose one option. {}".format(err)) # except Exception: # raise Exception("Failed to connect to '{}'".format(url)) async def _update_existing_object(self): upsert_data = {"doc": {}, "doc_as_upsert": True} if len(self.ModSet) > 0: for k, v in self.ModSet.items(): upsert_data["doc"][k] = serialize(self.ModSet[k]) for url in self.Storage.ServerUrls: if url.startswith('https://'): ssl_context = self.Storage.SSLContextBuilder.build(ssl.PROTOCOL_TLS_CLIENT) else: ssl_context = None try: request_url = "{}{}/_update/{}?refresh={}".format(url, self.Collection, self.ObjId, self.Storage.Refresh) async with self.Storage.session().request( method="POST", url=request_url, json=upsert_data, headers=self.Headers, ssl=ssl_context, ) as resp: if resp.status == 401: raise ConnectionRefusedError("Response code 401: Unauthorized. Provide authorization by specifying either user name and password or api key.") elif resp.status not in {200, 201}: raise ConnectionError("Unexpected response code: {}".format(resp.status)) else: resp_json = await resp.json() assert resp_json["result"] == "updated" or resp_json[ "result"] == "created", "Creating/updating was unsuccessful" await self.Storage.session().close() return self.ObjId except aiohttp.client_exceptions.ClientConnectorError: if url == self.Storage.ServerUrls[-1]: raise Exception("Failed to connect to '{}'".format(url)) else: L.warning("Failed to connect to '{}', iterating to another cluster node".format(url)) except aiohttp.client_exceptions.ServerDisconnectedError: raise Exception("Failed to connect to '{}'".format(url)) def serialize(v): if isinstance(v, datetime.datetime): return v.timestamp() else: return v

statsmodels__statsmodels

contingency_tables.rst

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statsmodels__statsmodels/docs/source/contingency_tables.rst

Contingency tables Statsmodels supports a variety of approaches for analyzing contingency tables, including methods for assessing independence, symmetry, homogeneity, and methods for working with collections of tables from a stratified population. The methods described here are mainly for two-way tables. Multi-way tables can be analyzed using log-linear models. Statsmodels does not currently have a dedicated API for loglinear modeling, but Poisson regression in statsmodels.genmod.GLM can be used for this purpose. A contingency table is a multi-way table that describes a data set in which each observation belongs to one category for each of several variables. For example, if there are two variables, one with r levels and one with c levels, then we have a r × c contingency table. The table can be described in terms of the number of observations that fall into a given cell of the table, e.g. T_(ij) is the number of observations that have level i for the first variable and level j for the second variable. Note that each variable must have a finite number of levels (or categories), which can be either ordered or unordered. In different contexts, the variables defining the axes of a contingency table may be called categorical variables or factor variables. They may be either nominal (if their levels are unordered) or ordinal (if their levels are ordered). The underlying population for a contingency table is described by a distribution table P_(i, j). The elements of P are probabilities, and the sum of all elements in P is 1. Methods for analyzing contingency tables use the data in T to learn about properties of P. The statsmodels.stats.Table is the most basic class for working with contingency tables. We can create a Table object directly from any rectangular array-like object containing the contingency table cell counts: import numpy as np import pandas as pd import statsmodels.api as sm df = sm.datasets.get_rdataset("Arthritis", "vcd").data tab = pd.crosstab(df['Treatment'], df['Improved']) tab = tab.loc[:, ["None", "Some", "Marked"]] table = sm.stats.Table(tab) Alternatively, we can pass the raw data and let the Table class construct the array of cell counts for us: data = df[["Treatment", "Improved"]] table = sm.stats.Table.from_data(data) Independence Independence is the property that the row and column factors occur independently. Association is the lack of independence. If the joint distribution is independent, it can be written as the outer product of the row and column marginal distributions: P_(ij) = ∑_(k)P_(ij) ⋅ ∑_(k)P_(kj)  for all  i, j We can obtain the best-fitting independent distribution for our observed data, and then view residuals which identify particular cells that most strongly violate independence: print(table.table_orig) print(table.fittedvalues) print(table.resid_pearson) In this example, compared to a sample from a population in which the rows and columns are independent, we have too many observations in the placebo/no improvement and treatment/marked improvement cells, and too few observations in the placebo/marked improvement and treated/no improvement cells. This reflects the apparent benefits of the treatment. If the rows and columns of a table are unordered (i.e. are nominal factors), then the most common approach for formally assessing independence is using Pearson's χ² statistic. It's often useful to look at the cell-wise contributions to the χ² statistic to see where the evidence for dependence is coming from. rslt = table.test_nominal_association() print(rslt.pvalue) print(table.chi2_contribs) For tables with ordered row and column factors, we can us the linear by linear association test to obtain more power against alternative hypotheses that respect the ordering. The test statistic for the linear by linear association test is ∑_(k)r_(i)c_(j)T_(ij) where r_(i) and c_(j) are row and column scores. Often these scores are set to the sequences 0, 1, .... This gives the 'Cochran-Armitage trend test'. python rslt = table.test_ordinal_association() print(rslt.pvalue) We can assess the association in a r × x table by constructing a series of 2 × 2 tables and calculating their odds ratios. There are two ways to do this. The local odds ratios construct 2 × 2 tables from adjacent row and column categories. python print(table.local_oddsratios) taloc = sm.stats.Table2x2(np.asarray([[7, 29], [21, 13]])) print(taloc.oddsratio) taloc = sm.stats.Table2x2(np.asarray([[29, 7], [13, 7]])) print(taloc.oddsratio) The cumulative odds ratios construct 2 × 2 tables by dichotomizing the row and column factors at each possible point. print(table.cumulative_oddsratios) tab1 = np.asarray([[7, 29 + 7], [21, 13 + 7]]) tacum = sm.stats.Table2x2(tab1) print(tacum.oddsratio) tab1 = np.asarray([[7 + 29, 7], [21 + 13, 7]]) tacum = sm.stats.Table2x2(tab1) print(tacum.oddsratio) A mosaic plot is a graphical approach to informally assessing dependence in two-way tables. from statsmodels.graphics.mosaicplot import mosaic fig, _ = mosaic(data, index=["Treatment", "Improved"]) Symmetry and homogeneity Symmetry is the property that P_(i, j) = P_(j, i) for every i and j. Homogeneity is the property that the marginal distribution of the row factor and the column factor are identical, meaning that ∑_(j)P_(ij) = ∑_(j)P_(ji)∀i Note that for these properties to be applicable the table P (and T) must be square, and the row and column categories must be identical and must occur in the same order. To illustrate, we load a data set, create a contingency table, and calculate the row and column margins. The Table class contains methods for analyzing r × c contingency tables. The data set loaded below contains assessments of visual acuity in people's left and right eyes. We first load the data and create a contingency table. df = sm.datasets.get_rdataset("VisualAcuity", "vcd").data df = df.loc[df.gender == "female", :] tab = df.set_index(['left', 'right']) del tab["gender"] tab = tab.unstack() tab.columns = tab.columns.get_level_values(1) print(tab) Next we create a SquareTable object from the contingency table. sqtab = sm.stats.SquareTable(tab) row, col = sqtab.marginal_probabilities print(row) print(col) The summary method prints results for the symmetry and homogeneity testing procedures. print(sqtab.summary()) If we had the individual case records in a dataframe called data, we could also perform the same analysis by passing the raw data using the SquareTable.from_data class method. sqtab = sm.stats.SquareTable.from_data(data[['left', 'right']]) print(sqtab.summary()) A single 2x2 table Several methods for working with individual 2x2 tables are provided in the sm.stats.Table2x2 class. The summary method displays several measures of association between the rows and columns of the table. table = np.asarray([[35, 21], [25, 58]]) t22 = sm.stats.Table2x2(table) print(t22.summary()) Note that the risk ratio is not symmetric so different results will be obtained if the transposed table is analyzed. table = np.asarray([[35, 21], [25, 58]]) t22 = sm.stats.Table2x2(table.T) print(t22.summary()) Stratified 2x2 tables Stratification occurs when we have a collection of contingency tables defined by the same row and column factors. In the example below, we have a collection of 2x2 tables reflecting the joint distribution of smoking and lung cancer in each of several regions of China. It is possible that the tables all have a common odds ratio, even while the marginal probabilities vary among the strata. The 'Breslow-Day' procedure tests whether the data are consistent with a common odds ratio. It appears below as the Test of constant OR. The Mantel-Haenszel procedure tests whether this common odds ratio is equal to one. It appears below as the Test of OR=1. It is also possible to estimate the common odds and risk ratios and obtain confidence intervals for them. The summary method displays all of these results. Individual results can be obtained from the class methods and attributes. data = sm.datasets.china_smoking.load_pandas() mat = np.asarray(data.data) tables = [np.reshape(x.tolist(), (2, 2)) for x in mat] st = sm.stats.StratifiedTable(tables) print(st.summary())

""" Methods for analyzing two-way contingency tables (i.e. frequency tables for observations that are cross-classified with respect to two categorical variables). The main classes are: * Table : implements methods that can be applied to any two-way contingency table. * SquareTable : implements methods that can be applied to a square two-way contingency table. * Table2x2 : implements methods that can be applied to a 2x2 contingency table. * StratifiedTable : implements methods that can be applied to a collection of 2x2 contingency tables. Also contains functions for conducting McNemar's test and Cochran's q test. Note that the inference procedures may depend on how the data were sampled. In general the observed units are independent and identically distributed. """ from statsmodels.tools.decorators import cache_readonly import numpy as np from scipy import stats import pandas as pd import warnings from statsmodels import iolib from statsmodels.tools import sm_exceptions def _make_df_square(table): """ Reindex a pandas DataFrame so that it becomes square, meaning that the row and column indices contain the same values, in the same order. The row and column index are extended to achieve this. """ if not isinstance(table, pd.DataFrame): return table # If the table is not square, make it square if not table.index.equals(table.columns): ix = list(set(table.index) | set(table.columns)) ix.sort() table = table.reindex(index=ix, columns=ix, fill_value=0) # Ensures that the rows and columns are in the same order. table = table.reindex(table.columns) return table class _Bunch(object): def __repr__(self): return "<bunch containing results, print to see contents>" def __str__(self): ky = [k for k, _ in self.__dict__.items()] ky.sort() m = max([len(k) for k in ky]) tab = [] f = "{:" + str(m) + "} {}" for k in ky: tab.append(f.format(k, self.__dict__[k])) return "\n".join(tab) class Table(object): """ A two-way contingency table. Parameters ---------- table : array_like A contingency table. shift_zeros : boolean If True and any cell count is zero, add 0.5 to all values in the table. Attributes ---------- table_orig : array_like The original table is cached as `table_orig`. See Also -------- statsmodels.graphics.mosaicplot.mosaic scipy.stats.chi2_contingency Notes ----- The inference procedures used here are all based on a sampling model in which the units are independent and identically distributed, with each unit being classified with respect to two categorical variables. References ---------- Definitions of residuals: https://onlinecourses.science.psu.edu/stat504/node/86 """ def __init__(self, table, shift_zeros=True): self.table_orig = table self.table = np.asarray(table, dtype=np.float64) if shift_zeros and (self.table.min() == 0): self.table[self.table == 0] = 0.5 def __str__(self): s = ("A %dx%d contingency table with counts:\n" % tuple(self.table.shape)) s += np.array_str(self.table) return s @classmethod def from_data(cls, data, shift_zeros=True): """ Construct a Table object from data. Parameters ---------- data : array_like The raw data, from which a contingency table is constructed using the first two columns. shift_zeros : boolean If True and any cell count is zero, add 0.5 to all values in the table. Returns ------- A Table instance. """ if isinstance(data, pd.DataFrame): table = pd.crosstab(data.iloc[:, 0], data.iloc[:, 1]) else: table = pd.crosstab(data[:, 0], data[:, 1]) return cls(table, shift_zeros) def test_nominal_association(self): """ Assess independence for nominal factors. Assessment of independence between rows and columns using chi^2 testing. The rows and columns are treated as nominal (unordered) categorical variables. Returns ------- A bunch containing the following attributes: statistic : float The chi^2 test statistic. df : integer The degrees of freedom of the reference distribution pvalue : float The p-value for the test. """ statistic = np.asarray(self.chi2_contribs).sum() df = np.prod(np.asarray(self.table.shape) - 1) pvalue = 1 - stats.chi2.cdf(statistic, df) b = _Bunch() b.statistic = statistic b.df = df b.pvalue = pvalue return b def test_ordinal_association(self, row_scores=None, col_scores=None): """ Assess independence between two ordinal variables. This is the 'linear by linear' association test, which uses weights or scores to target the test to have more power against ordered alternatives. Parameters ---------- row_scores : array_like An array of numeric row scores col_scores : array_like An array of numeric column scores Returns ------- A bunch with the following attributes: statistic : float The test statistic. null_mean : float The expected value of the test statistic under the null hypothesis. null_sd : float The standard deviation of the test statistic under the null hypothesis. zscore : float The Z-score for the test statistic. pvalue : float The p-value for the test. Notes ----- The scores define the trend to which the test is most sensitive. Using the default row and column scores gives the Cochran-Armitage trend test. """ if row_scores is None: row_scores = np.arange(self.table.shape[0]) if col_scores is None: col_scores = np.arange(self.table.shape[1]) if len(row_scores)!= self.table.shape[0]: msg = ("The length of `row_scores` must match the first " + "dimension of `table`.") raise ValueError(msg) if len(col_scores)!= self.table.shape[1]: msg = ("The length of `col_scores` must match the second " + "dimension of `table`.") raise ValueError(msg) # The test statistic statistic = np.dot(row_scores, np.dot(self.table, col_scores)) # Some needed quantities n_obs = self.table.sum() rtot = self.table.sum(1) um = np.dot(row_scores, rtot) u2m = np.dot(row_scores**2, rtot) ctot = self.table.sum(0) vn = np.dot(col_scores, ctot) v2n = np.dot(col_scores**2, ctot) # The null mean and variance of the test statistic e_stat = um * vn / n_obs v_stat = (u2m - um**2 / n_obs) * (v2n - vn**2 / n_obs) / (n_obs - 1) sd_stat = np.sqrt(v_stat) zscore = (statistic - e_stat) / sd_stat pvalue = 2 * stats.norm.cdf(-np.abs(zscore)) b = _Bunch() b.statistic = statistic b.null_mean = e_stat b.null_sd = sd_stat b.zscore = zscore b.pvalue = pvalue return b @cache_readonly def marginal_probabilities(self): """ Estimate marginal probability distributions for the rows and columns. Returns ------- row : ndarray Marginal row probabilities col : ndarray Marginal column probabilities """ n = self.table.sum() row = self.table.sum(1) / n col = self.table.sum(0) / n if isinstance(self.table_orig, pd.DataFrame): row = pd.Series(row, self.table_orig.index) col = pd.Series(col, self.table_orig.columns) return row, col @cache_readonly def independence_probabilities(self): """ Returns fitted joint probabilities under independence. The returned table is outer(row, column), where row and column are the estimated marginal distributions of the rows and columns. """ row, col = self.marginal_probabilities itab = np.outer(row, col) if isinstance(self.table_orig, pd.DataFrame): itab = pd.DataFrame(itab, self.table_orig.index, self.table_orig.columns) return itab @cache_readonly def fittedvalues(self): """ Returns fitted cell counts under independence. The returned cell counts are estimates under a model where the rows and columns of the table are independent. """ probs = self.independence_probabilities fit = self.table.sum() * probs return fit @cache_readonly def resid_pearson(self): """ Returns Pearson residuals. The Pearson residuals are calculated under a model where the rows and columns of the table are independent. """ fit = self.fittedvalues resids = (self.table - fit) / np.sqrt(fit) return resids @cache_readonly def standardized_resids(self): """ Returns standardized residuals under independence. """ row, col = self.marginal_probabilities sresids = self.resid_pearson / np.sqrt(np.outer(1 - row, 1 - col)) return sresids @cache_readonly def chi2_contribs(self): """ Returns the contributions to the chi^2 statistic for independence. The returned table contains the contribution of each cell to the chi^2 test statistic for the null hypothesis that the rows and columns are independent. """ return self.resid_pearson**2 @cache_readonly def local_log_oddsratios(self): """ Returns local log odds ratios. The local log odds ratios are the log odds ratios calculated for contiguous 2x2 sub-tables. """ ta = self.table.copy() a = ta[0:-1, 0:-1] b = ta[0:-1, 1:] c = ta[1:, 0:-1] d = ta[1:, 1:] tab = np.log(a) + np.log(d) - np.log(b) - np.log(c) rslt = np.empty(self.table.shape, np.float64) rslt *= np.nan rslt[0:-1, 0:-1] = tab if isinstance(self.table_orig, pd.DataFrame): rslt = pd.DataFrame(rslt, index=self.table_orig.index, columns=self.table_orig.columns) return rslt @cache_readonly def local_oddsratios(self): """ Returns local odds ratios. See documentation for local_log_oddsratios. """ return np.exp(self.local_log_oddsratios) @cache_readonly def cumulative_log_oddsratios(self): """ Returns cumulative log odds ratios. The cumulative log odds ratios for a contingency table with ordered rows and columns are calculated by collapsing all cells to the left/right and above/below a given point, to obtain a 2x2 table from which a log odds ratio can be calculated. """ ta = self.table.cumsum(0).cumsum(1) a = ta[0:-1, 0:-1] b = ta[0:-1, -1:] - a c = ta[-1:, 0:-1] - a d = ta[-1, -1] - (a + b + c) tab = np.log(a) + np.log(d) - np.log(b) - np.log(c) rslt = np.empty(self.table.shape, np.float64) rslt *= np.nan rslt[0:-1, 0:-1] = tab if isinstance(self.table_orig, pd.DataFrame): rslt = pd.DataFrame(rslt, index=self.table_orig.index, columns=self.table_orig.columns) return rslt @cache_readonly def cumulative_oddsratios(self): """ Returns the cumulative odds ratios for a contingency table. See documentation for cumulative_log_oddsratio. """ return np.exp(self.cumulative_log_oddsratios) class SquareTable(Table): """ Methods for analyzing a square contingency table. Parameters ---------- table : array_like A square contingency table, or DataFrame that is converted to a square form. shift_zeros : boolean If True and any cell count is zero, add 0.5 to all values in the table. These methods should only be used when the rows and columns of the table have the same categories. If `table` is provided as a Pandas DataFrame, the row and column indices will be extended to create a square table, inserting zeros where a row or column is missing. Otherwise the table should be provided in a square form, with the (implicit) row and column categories appearing in the same order. """ def __init__(self, table, shift_zeros=True): table = _make_df_square(table) # Non-pandas passes through k1, k2 = table.shape if k1!= k2: raise ValueError('table must be square') super(SquareTable, self).__init__(table, shift_zeros) def symmetry(self, method="bowker"): """ Test for symmetry of a joint distribution. This procedure tests the null hypothesis that the joint distribution is symmetric around the main diagonal, that is .. math:: p_{i, j} = p_{j, i} for all i, j Returns ------- A bunch with attributes: statistic : float chisquare test statistic p-value : float p-value of the test statistic based on chisquare distribution df : int degrees of freedom of the chisquare distribution Notes ----- The implementation is based on the SAS documentation. R includes it in `mcnemar.test` if the table is not 2 by 2. However a more direct generalization of the McNemar test to larger tables is provided by the homogeneity test (TableSymmetry.homogeneity). The p-value is based on the chi-square distribution which requires that the sample size is not very small to be a good approximation of the true distribution. For 2x2 contingency tables the exact distribution can be obtained with `mcnemar` See Also -------- mcnemar homogeneity """ if method.lower()!= "bowker": raise ValueError("method for symmetry testing must be 'bowker'") k = self.table.shape[0] upp_idx = np.triu_indices(k, 1) tril = self.table.T[upp_idx] # lower triangle in column order triu = self.table[upp_idx] # upper triangle in row order statistic = ((tril - triu)**2 / (tril + triu + 1e-20)).sum() df = k * (k-1) / 2. pvalue = stats.chi2.sf(statistic, df) b = _Bunch() b.statistic = statistic b.pvalue = pvalue b.df = df return b def homogeneity(self, method="stuart_maxwell"): """ Compare row and column marginal distributions. Parameters ---------- method : string Either'stuart_maxwell' or 'bhapkar', leading to two different estimates of the covariance matrix for the estimated difference between the row margins and the column margins. Returns a bunch with attributes: statistic : float The chi^2 test statistic pvalue : float The p-value of the test statistic df : integer The degrees of freedom of the reference distribution Notes ----- For a 2x2 table this is equivalent to McNemar's test. More generally the procedure tests the null hypothesis that the marginal distribution of the row factor is equal to the marginal distribution of the column factor. For this to be meaningful, the two factors must have the same sample space (i.e. the same categories). """ if self.table.shape[0] < 1: raise ValueError('table is empty') elif self.table.shape[0] == 1: b = _Bunch() b.statistic = 0 b.pvalue = 1 b.df = 0 return b method = method.lower() if method not in ["bhapkar", "stuart_maxwell"]: raise ValueError("method '%s' for homogeneity not known" % method) n_obs = self.table.sum() pr = self.table.astype(np.float64) / n_obs # Compute margins, eliminate last row/column so there is no # degeneracy row = pr.sum(1)[0:-1] col = pr.sum(0)[0:-1] pr = pr[0:-1, 0:-1] # The estimated difference between row and column margins. d = col - row # The degrees of freedom of the chi^2 reference distribution. df = pr.shape[0] if method == "bhapkar": vmat = -(pr + pr.T) - np.outer(d, d) dv = col + row - 2*np.diag(pr) - d**2 np.fill_diagonal(vmat, dv) elif method == "stuart_maxwell": vmat = -(pr + pr.T) dv = row + col - 2*np.diag(pr) np.fill_diagonal(vmat, dv) try: statistic = n_obs * np.dot(d, np.linalg.solve(vmat, d)) except np.linalg.LinAlgError: warnings.warn("Unable to invert covariance matrix", sm_exceptions.SingularMatrixWarning) b = _Bunch() b.statistic = np.nan b.pvalue = np.nan b.df = df return b pvalue = 1 - stats.chi2.cdf(statistic, df) b = _Bunch() b.statistic = statistic b.pvalue = pvalue b.df = df return b def summary(self, alpha=0.05, float_format="%.3f"): """ Produce a summary of the analysis. Parameters ---------- alpha : float `1 - alpha` is the nominal coverage probability of the interval. float_format : str Used to format numeric values in the table. method : str The method for producing the confidence interval. Currently must be 'normal' which uses the normal approximation. """ fmt = float_format headers = ["Statistic", "P-value", "DF"] stubs = ["Symmetry", "Homogeneity"] sy = self.symmetry() hm = self.homogeneity() data = [[fmt % sy.statistic, fmt % sy.pvalue, '%d' % sy.df], [fmt % hm.statistic, fmt % hm.pvalue, '%d' % hm.df]] tab = iolib.SimpleTable(data, headers, stubs, data_aligns="r", table_dec_above='') return tab class Table2x2(SquareTable): """ Analyses that can be performed on a 2x2 contingency table. Parameters ---------- table : array_like A 2x2 contingency table shift_zeros : boolean If true, 0.5 is added to all cells of the table if any cell is equal to zero. Notes ----- The inference procedures used here are all based on a sampling model in which the units are independent and identically distributed, with each unit being classified with respect to two categorical variables. Note that for the risk ratio, the analysis is not symmetric with respect to the rows and columns of the contingency table. The two rows define population subgroups, column 0 is the number of 'events', and column 1 is the number of 'non-events'. """ def __init__(self, table, shift_zeros=True): if type(table) is list: table = np.asarray(table) if (table.ndim!= 2) or (table.shape[0]!= 2) or (table.shape[1]!= 2): raise ValueError("Table2x2 takes a 2x2 table as input.") super(Table2x2, self).__init__(table, shift_zeros) @classmethod def from_data(cls, data, shift_zeros=True): """ Construct a Table object from data. Parameters ---------- data : array_like The raw data, the first column defines the rows and the second column defines the columns. shift_zeros : boolean If True, and if there are any zeros in the contingency table, add 0.5 to all four cells of the table. """ if isinstance(data, pd.DataFrame): table = pd.crosstab(data.iloc[:, 0], data.iloc[:, 1]) else: table = pd.crosstab(data[:, 0], data[:, 1]) return cls(table, shift_zeros) @cache_readonly def log_oddsratio(self): """ Returns the log odds ratio for a 2x2 table. """ f = self.table.flatten() return np.dot(np.log(f), np.r_[1, -1, -1, 1]) @cache_readonly def oddsratio(self): """ Returns the odds ratio for a 2x2 table. """ return (self.table[0, 0] * self.table[1, 1] / (self.table[0, 1] * self.table[1, 0])) @cache_readonly def log_oddsratio_se(self): """ Returns the standard error for the log odds ratio. """ return np.sqrt(np.sum(1 / self.table)) def oddsratio_pvalue(self, null=1): """ P-value for a hypothesis test about the odds ratio. Parameters ---------- null : float The null value of the odds ratio. """ return self.log_oddsratio_pvalue(np.log(null)) def log_oddsratio_pvalue(self, null=0): """ P-value for a hypothesis test about the log odds ratio. Parameters ---------- null : float The null value of the log odds ratio. """ zscore = (self.log_oddsratio - null) / self.log_oddsratio_se pvalue = 2 * stats.norm.cdf(-np.abs(zscore)) return pvalue def log_oddsratio_confint(self, alpha=0.05, method="normal"): """ A confidence level for the log odds ratio. Parameters ---------- alpha : float `1 - alpha` is the nominal coverage probability of the confidence interval. method : string The method for producing the confidence interval. Currently must be 'normal' which uses the normal approximation. """ f = -stats.norm.ppf(alpha / 2) lor = self.log_oddsratio se = self.log_oddsratio_se lcb = lor - f * se ucb = lor + f * se return lcb, ucb def oddsratio_confint(self, alpha=0.05, method="normal"): """ A confidence interval for the odds ratio. Parameters ---------- alpha : float `1 - alpha` is the nominal coverage probability of the confidence interval. method : string The method for producing the confidence interval. Currently must be 'normal' which uses the normal approximation. """ lcb, ucb = self.log_oddsratio_confint(alpha, method=method) return np.exp(lcb), np.exp(ucb) @cache_readonly def riskratio(self): """ Returns the risk ratio for a 2x2 table. The risk ratio is calculated with respect to the rows. """ p = self.table[:, 0] / self.table.sum(1) return p[0] / p[1] @cache_readonly def log_riskratio(self): """ Returns the log of the risk ratio. """ return np.log(self.riskratio) @cache_readonly def log_riskratio_se(self): """ Returns the standard error of the log of the risk ratio. """ n = self.table.sum(1) p = self.table[:, 0] / n va = np.sum((1 - p) / (n*p)) return np.sqrt(va) def riskratio_pvalue(self, null=1): """ p-value for a hypothesis test about the risk ratio. Parameters ---------- null : float The null value of the risk ratio. """ return self.log_riskratio_pvalue(np.log(null)) def log_riskratio_pvalue(self, null=0): """ p-value for a hypothesis test about the log risk ratio. Parameters ---------- null : float The null value of the log risk ratio. """ zscore = (self.log_riskratio - null) / self.log_riskratio_se pvalue = 2 * stats.norm.cdf(-np.abs(zscore)) return pvalue def log_riskratio_confint(self, alpha=0.05, method="normal"): """ A confidence interval for the log risk ratio. Parameters ---------- alpha : float `1 - alpha` is the nominal coverage probability of the confidence interval. method : string The method for producing the confidence interval. Currently must be 'normal' which uses the normal approximation. """ f = -stats.norm.ppf(alpha / 2) lrr = self.log_riskratio se = self.log_riskratio_se lcb = lrr - f * se ucb = lrr + f * se return lcb, ucb def riskratio_confint(self, alpha=0.05, method="normal"): """ A confidence interval for the risk ratio. Parameters ---------- alpha : float `1 - alpha` is the nominal coverage probability of the confidence interval. method : string The method for producing the confidence interval. Currently must be 'normal' which uses the normal approximation. """ lcb, ucb = self.log_riskratio_confint(alpha, method=method) return np.exp(lcb), np.exp(ucb) def summary(self, alpha=0.05, float_format="%.3f", method="normal"): """ Summarizes results for a 2x2 table analysis. Parameters ---------- alpha : float `1 - alpha` is the nominal coverage probability of the confidence intervals. float_format : str Used to format the numeric values in the table. method : str The method for producing the confidence interval. Currently must be 'normal' which uses the normal approximation. """ def fmt(x): if isinstance(x, str): return x return float_format % x headers = ["Estimate", "SE", "LCB", "UCB", "p-value"] stubs = ["Odds ratio", "Log odds ratio", "Risk ratio", "Log risk ratio"] lcb1, ucb1 = self.oddsratio_confint(alpha, method) lcb2, ucb2 = self.log_oddsratio_confint(alpha, method) lcb3, ucb3 = self.riskratio_confint(alpha, method) lcb4, ucb4 = self.log_riskratio_confint(alpha, method) data = [[fmt(x) for x in [self.oddsratio, "", lcb1, ucb1, self.oddsratio_pvalue()]], [fmt(x) for x in [self.log_oddsratio, self.log_oddsratio_se, lcb2, ucb2, self.oddsratio_pvalue()]], [fmt(x) for x in [self.riskratio, "", lcb3, ucb3, self.riskratio_pvalue()]], [fmt(x) for x in [self.log_riskratio, self.log_riskratio_se, lcb4, ucb4, self.riskratio_pvalue()]]] tab = iolib.SimpleTable(data, headers, stubs, data_aligns="r", table_dec_above='') return tab class StratifiedTable(object): """ Analyses for a collection of 2x2 contingency tables. Such a collection may arise by stratifying a single 2x2 table with respect to another factor. This class implements the 'Cochran-Mantel-Haenszel' and 'Breslow-Day' procedures for analyzing collections of 2x2 contingency tables. Parameters ---------- tables : list or ndarray Either a list containing several 2x2 contingency tables, or a 2x2xk ndarray in which each slice along the third axis is a 2x2 contingency table. Notes ----- This results are based on a sampling model in which the units are independent both within and between strata. """ def __init__(self, tables, shift_zeros=False): if isinstance(tables, np.ndarray): sp = tables.shape if (len(sp)!= 3) or (sp[0]!= 2) or (sp[1]!= 2): raise ValueError("If an ndarray, argument must be 2x2xn") table = tables else: # Create a data cube table = np.dstack(tables).astype(np.float64) if shift_zeros: zx = (table == 0).sum(0).sum(0) ix = np.flatnonzero(zx > 0) if len(ix) > 0: table = table.copy() table[:, :, ix] += 0.5 self.table = table self._cache = {} # Quantities to precompute. Table entries are [[a, b], [c, # d]], 'ad' is 'a * d', 'apb' is 'a + b', 'dma' is 'd - a', # etc. self._apb = table[0, 0, :] + table[0, 1, :] self._apc = table[0, 0, :] + table[1, 0, :] self._bpd = table[0, 1, :] + table[1, 1, :] self._cpd = table[1, 0, :] + table[1, 1, :] self._ad = table[0, 0, :] * table[1, 1, :] self._bc = table[0, 1, :] * table[1, 0, :] self._apd = table[0, 0, :] + table[1, 1, :] self._dma = table[1, 1, :] - table[0, 0, :] self._n = table.sum(0).sum(0) @classmethod def from_data(cls, var1, var2, strata, data): """ Construct a StratifiedTable object from data. Parameters ---------- var1 : int or string The column index or name of `data` specifying the variable defining the rows of the contingency table. The variable must have only two distinct values. var2 : int or string The column index or name of `data` specifying the variable defining the columns of the contingency table. The variable must have only two distinct values. strata : int or string The column index or name of `data` specifying the variable defining the strata. data : array_like The raw data. A cross-table for analysis is constructed from the first two columns. Returns ------- A StratifiedTable instance. """ if not isinstance(data, pd.DataFrame): data1 = pd.DataFrame(index=np.arange(data.shape[0]), columns=[var1, var2, strata]) data1.loc[:, var1] = data[:, var1] data1.loc[:, var2] = data[:, var2] data1.loc[:, strata] = data[:, strata] else: data1 = data[[var1, var2, strata]] gb = data1.groupby(strata).groups tables = [] for g in gb: ii = gb[g] tab = pd.crosstab(data1.loc[ii, var1], data1.loc[ii, var2]) if (tab.shape!= np.r_[2, 2]).any(): msg = "Invalid table dimensions" raise ValueError(msg) tables.append(np.asarray(tab)) return cls(tables) def test_null_odds(self, correction=False): """ Test that all tables have odds ratio equal to 1. This is the 'Mantel-Haenszel' test. Parameters ---------- correction : boolean If True, use the continuity correction when calculating the test statistic. Returns ------- A bunch containing the chi^2 test statistic and p-value. """ statistic = np.sum(self.table[0, 0, :] - self._apb * self._apc / self._n) statistic = np.abs(statistic) if correction: statistic -= 0.5 statistic = statistic**2 denom = self._apb * self._apc * self._bpd * self._cpd denom /= (self._n**2 * (self._n - 1)) denom = np.sum(denom) statistic /= denom # df is always 1 pvalue = 1 - stats.chi2.cdf(statistic, 1) b = _Bunch() b.statistic = statistic b.pvalue = pvalue return b @cache_readonly def oddsratio_pooled(self): """ The pooled odds ratio. The value is an estimate of a common odds ratio across all of the stratified tables. """ odds_ratio = np.sum(self._ad / self._n) / np.sum(self._bc / self._n) return odds_ratio @cache_readonly def logodds_pooled(self): """ Returns the logarithm of the pooled odds ratio. See oddsratio_pooled for more information. """ return np.log(self.oddsratio_pooled) @cache_readonly def riskratio_pooled(self): """ Estimate of the pooled risk ratio. """ acd = self.table[0, 0, :] * self._cpd cab = self.table[1, 0, :] * self._apb rr = np.sum(acd / self._n) / np.sum(cab / self._n) return rr @cache_readonly def risk_pooled(self): # Deprecated due to name being misleading msg = "'risk_pooled' is deprecated, use 'riskratio_pooled' instead" warnings.warn(msg, DeprecationWarning) return self.riskratio_pooled @cache_readonly def logodds_pooled_se(self): """ Estimated standard error of the pooled log odds ratio References ---------- Robins, James, Norman Breslow, and Sander Greenland. "Estimators of the Mantel-Haenszel Variance Consistent in Both Sparse Data and Large-Strata Limiting Models." Biometrics 42, no. 2 (1986): 311-23. """ adns = np.sum(self._ad / self._n) bcns = np.sum(self._bc / self._n) lor_va = np.sum(self._apd * self._ad / self._n**2) / adns**2 mid = self._apd * self._bc / self._n**2 mid += (1 - self._apd / self._n) * self._ad / self._n mid = np.sum(mid) mid /= (adns * bcns) lor_va += mid lor_va += np.sum((1 - self._apd / self._n) * self._bc / self._n) / bcns**2 lor_va /= 2 lor_se = np.sqrt(lor_va) return lor_se def logodds_pooled_confint(self, alpha=0.05, method="normal"): """ A confidence interval for the pooled log odds ratio. Parameters ---------- alpha : float `1 - alpha` is the nominal coverage probability of the interval. method : string The method for producing the confidence interval. Currently must be 'normal' which uses the normal approximation. Returns ------- lcb : float The lower confidence limit. ucb : float The upper confidence limit. """ lor = np.log(self.oddsratio_pooled) lor_se = self.logodds_pooled_se f = -stats.norm.ppf(alpha / 2) lcb = lor - f * lor_se ucb = lor + f * lor_se return lcb, ucb def oddsratio_pooled_confint(self, alpha=0.05, method="normal"): """ A confidence interval for the pooled odds ratio. Parameters ---------- alpha : float `1 - alpha` is the nominal coverage probability of the interval. method : string The method for producing the confidence interval. Currently must be 'normal' which uses the normal approximation. Returns ------- lcb : float The lower confidence limit. ucb : float The upper confidence limit. """ lcb, ucb = self.logodds_pooled_confint(alpha, method=method) lcb = np.exp(lcb) ucb = np.exp(ucb) return lcb, ucb def test_equal_odds(self, adjust=False): """ Test that all odds ratios are identical. This is the 'Breslow-Day' testing procedure. Parameters ---------- adjust : boolean Use the 'Tarone' adjustment to achieve the chi^2 asymptotic distribution. Returns ------- A bunch containing the following attributes: statistic : float The chi^2 test statistic. p-value : float The p-value for the test. """ table = self.table r = self.oddsratio_pooled a = 1 - r b = r * (self._apb + self._apc) + self._dma c = -r * self._apb * self._apc # Expected value of first cell e11 = (-b + np.sqrt(b**2 - 4*a*c)) / (2*a) # Variance of the first cell v11 = (1 / e11 + 1 / (self._apc - e11) + 1 / (self._apb - e11) + 1 / (self._dma + e11)) v11 = 1 / v11 statistic = np.sum((table[0, 0, :] - e11)**2 / v11) if adjust: adj = table[0, 0, :].sum() - e11.sum() adj = adj**2 adj /= np.sum(v11) statistic -= adj pvalue = 1 - stats.chi2.cdf(statistic, table.shape[2] - 1) b = _Bunch() b.statistic = statistic b.pvalue = pvalue return b def summary(self, alpha=0.05, float_format="%.3f", method="normal"): """ A summary of all the main results. Parameters ---------- alpha : float `1 - alpha` is the nominal coverage probability of the confidence intervals. float_format : str Used for formatting numeric values in the summary. method : str The method for producing the confidence interval. Currently must be 'normal' which uses the normal approximation. """ def fmt(x): if isinstance(x, str): return x return float_format % x co_lcb, co_ucb = self.oddsratio_pooled_confint( alpha=alpha, method=method) clo_lcb, clo_ucb = self.logodds_pooled_confint( alpha=alpha, method=method) headers = ["Estimate", "LCB", "UCB"] stubs = ["Pooled odds", "Pooled log odds", "Pooled risk ratio", ""] data = [[fmt(x) for x in [self.oddsratio_pooled, co_lcb, co_ucb]], [fmt(x) for x in [self.logodds_pooled, clo_lcb, clo_ucb]], [fmt(x) for x in [self.riskratio_pooled, "", ""]], ['', '', '']] tab1 = iolib.SimpleTable(data, headers, stubs, data_aligns="r", table_dec_above='') headers = ["Statistic", "P-value", ""] stubs = ["Test of OR=1", "Test constant OR"] rslt1 = self.test_null_odds() rslt2 = self.test_equal_odds() data = [[fmt(x) for x in [rslt1.statistic, rslt1.pvalue, ""]], [fmt(x) for x in [rslt2.statistic, rslt2.pvalue, ""]]] tab2 = iolib.SimpleTable(data, headers, stubs, data_aligns="r") tab1.extend(tab2) headers = ["", "", ""] stubs = ["Number of tables", "Min n", "Max n", "Avg n", "Total n"] ss = self.table.sum(0).sum(0) data = [["%d" % self.table.shape[2], '', ''], ["%d" % min(ss), '', ''], ["%d" % max(ss), '', ''], ["%.0f" % np.mean(ss), '', ''], ["%d" % sum(ss), '', '', '']] tab3 = iolib.SimpleTable(data, headers, stubs, data_aligns="r") tab1.extend(tab3) return tab1 def mcnemar(table, exact=True, correction=True): """ McNemar test of homogeneity. Parameters ---------- table : array_like A square contingency table. exact : bool If exact is true, then the binomial distribution will be used. If exact is false, then the chisquare distribution will be used, which is the approximation to the distribution of the test statistic for large sample sizes. correction : bool If true, then a continuity correction is used for the chisquare distribution (if exact is false.) Returns ------- A bunch with attributes: statistic : float or int, array The test statistic is the chisquare statistic if exact is false. If the exact binomial distribution is used, then this contains the min(n1, n2), where n1, n2 are cases that are zero in one sample but one in the other sample. pvalue : float or array p-value of the null hypothesis of equal marginal distributions. Notes ----- This is a special case of Cochran's Q test, and of the homogeneity test. The results when the chisquare distribution is used are identical, except for continuity correction. """ table = _make_df_square(table) table = np.asarray(table, dtype=np.float64) n1, n2 = table[0, 1], table[1, 0] if exact: statistic = np.minimum(n1, n2) # binom is symmetric with p=0.5 pvalue = stats.binom.cdf(statistic, n1 + n2, 0.5) * 2 pvalue = np.minimum(pvalue, 1) # limit to 1 if n1==n2 else: corr = int(correction) # convert bool to 0 or 1 statistic = (np.abs(n1 - n2) - corr)**2 / (1. * (n1 + n2)) df = 1 pvalue = stats.chi2.sf(statistic, df) b = _Bunch() b.statistic = statistic b.pvalue = pvalue return b def cochrans_q(x, return_object=True): """ Cochran's Q test for identical binomial proportions. Parameters ---------- x : array_like, 2d (N, k) data with N cases and k variables return_object : boolean Return values as bunch instead of as individual values. Returns ------- Returns a bunch containing the following attributes, or the individual values according to the value of `return_object`. statistic : float test statistic pvalue : float pvalue from the chisquare distribution Notes ----- Cochran's Q is a k-sample extension of the McNemar test. If there are only two groups, then Cochran's Q test and the McNemar test are equivalent. The procedure tests that the probability of success is the same for every group. The alternative hypothesis is that at least two groups have a different probability of success. In Wikipedia terminology, rows are blocks and columns are treatments. The number of rows N, should be large for the chisquare distribution to be a good approximation. The Null hypothesis of the test is that all treatments have the same effect. References ---------- https://en.wikipedia.org/wiki/Cochran_test SAS Manual for NPAR TESTS """ x = np.asarray(x, dtype=np.float64) gruni = np.unique(x) N, k = x.shape count_row_success = (x == gruni[-1]).sum(1, float) count_col_success = (x == gruni[-1]).sum(0, float) count_row_ss = count_row_success.sum() count_col_ss = count_col_success.sum() assert count_row_ss == count_col_ss # just a calculation check # From the SAS manual q_stat = ((k-1) * (k * np.sum(count_col_success**2) - count_col_ss**2) / (k * count_row_ss - np.sum(count_row_success**2))) # Note: the denominator looks just like k times the variance of # the columns # Wikipedia uses a different, but equivalent expression # q_stat = (k-1) * (k * np.sum(count_row_success**2) - count_row_ss**2) # / (k * count_col_ss - np.sum(count_col_success**2)) df = k - 1 pvalue = stats.chi2.sf(q_stat, df) if return_object: b = _Bunch() b.statistic = q_stat b.df = df b.pvalue = pvalue return b return q_stat, pvalue, df

statsmodels__statsmodels

discretemod.rst

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statsmodels__statsmodels/docs/source/discretemod.rst

Regression with Discrete Dependent Variable Regression models for limited and qualitative dependent variables. The module currently allows the estimation of models with binary (Logit, Probit), nominal (MNLogit), or count (Poisson, NegativeBinomial) data. Starting with version 0.9, this also includes new count models, that are still experimental in 0.9, NegativeBinomialP, GeneralizedPoisson and zero-inflated models, ZeroInflatedPoisson, ZeroInflatedNegativeBinomialP and ZeroInflatedGeneralizedPoisson. Examples # Load the data from Spector and Mazzeo (1980) spector_data = sm.datasets.spector.load_pandas() spector_data.exog = sm.add_constant(spector_data.exog) # Logit Model logit_mod = sm.Logit(spector_data.endog, spector_data.exog) logit_res = logit_mod.fit() print(logit_res.summary()) Technical Documentation Currently all models are estimated by Maximum Likelihood and assume independently and identically distributed errors. All discrete regression models define the same methods and follow the same structure, which is similar to the regression results but with some methods specific to discrete models. Additionally some of them contain additional model specific methods and attributes. DiscreteModel is a superclass of all discrete regression models. The estimation results are returned as an instance of one of the subclasses of DiscreteResults. Each category of models, binary, count and multinomial, have their own intermediate level of model and results classes. This intermediate classes are mostly to facilitate the implementation of the methods and attributes defined by DiscreteModel and DiscreteResults.

__all__ = ["ZeroInflatedPoisson", "ZeroInflatedGeneralizedPoisson", "ZeroInflatedNegativeBinomialP"] import warnings import numpy as np import statsmodels.base.model as base import statsmodels.base.wrapper as wrap import statsmodels.regression.linear_model as lm from statsmodels.discrete.discrete_model import (DiscreteModel, CountModel, Poisson, Logit, CountResults, L1CountResults, Probit, _discrete_results_docs, _validate_l1_method, GeneralizedPoisson, NegativeBinomialP) from statsmodels.distributions import zipoisson, zigenpoisson, zinegbin from statsmodels.tools.numdiff import approx_fprime, approx_hess from statsmodels.tools.decorators import cache_readonly from statsmodels.tools.sm_exceptions import ConvergenceWarning _doc_zi_params = """ exog_infl : array_like or None Explanatory variables for the binary inflation model, i.e. for mixing probability model. If None, then a constant is used. offset : array_like Offset is added to the linear prediction with coefficient equal to 1. exposure : array_like Log(exposure) is added to the linear prediction with coefficient equal to 1. inflation : string, 'logit' or 'probit' The model for the zero inflation, either Logit (default) or Probit """ class GenericZeroInflated(CountModel): __doc__ = """ Generiz Zero Inflated model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. exog_infl: array A reference to the zero-inflated exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : _doc_zi_params + base._missing_param_doc} def __init__(self, endog, exog, exog_infl=None, offset=None, inflation='logit', exposure=None, missing='none', **kwargs): super(GenericZeroInflated, self).__init__(endog, exog, offset=offset, exposure=exposure, missing=missing, **kwargs) if exog_infl is None: self.k_inflate = 1 self.exog_infl = np.ones((endog.size, self.k_inflate), dtype=np.float64) else: self.exog_infl = exog_infl self.k_inflate = exog_infl.shape[1] if len(exog.shape) == 1: self.k_exog = 1 else: self.k_exog = exog.shape[1] self.infl = inflation if inflation == 'logit': self.model_infl = Logit(np.zeros(self.exog_infl.shape[0]), self.exog_infl) self._hessian_inflate = self._hessian_logit elif inflation == 'probit': self.model_infl = Probit(np.zeros(self.exog_infl.shape[0]), self.exog_infl) self._hessian_inflate = self._hessian_probit else: raise ValueError("inflation == %s, which is not handled" % inflation) self.inflation = inflation self.k_extra = self.k_inflate if len(self.exog)!= len(self.exog_infl): raise ValueError('exog and exog_infl have different number of' 'observation. `missing` handling is not supported') infl_names = ['inflate_%s' % i for i in self.model_infl.data.param_names] self.exog_names[:] = infl_names + list(self.exog_names) self.exog_infl = np.asarray(self.exog_infl, dtype=np.float64) self._init_keys.extend(['exog_infl', 'inflation']) self._null_drop_keys = ['exog_infl'] def loglike(self, params): """ Loglikelihood of Generic Zero Inflated model Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes -------- .. math:: \\ln L=\\sum_{y_{i}=0}\\ln(w_{i}+(1-w_{i})*P_{main\\_model})+ \\sum_{y_{i}>0}(\\ln(1-w_{i})+L_{main\\_model}) where P - pdf of main model, L - loglike function of main model. """ return np.sum(self.loglikeobs(params)) def loglikeobs(self, params): """ Loglikelihood for observations of Generic Zero Inflated model Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : ndarray The log likelihood for each observation of the model evaluated at `params`. See Notes Notes -------- .. math:: \\ln L=\\ln(w_{i}+(1-w_{i})*P_{main\\_model})+ \\ln(1-w_{i})+L_{main\\_model} where P - pdf of main model, L - loglike function of main model. for observations :math:`i=1,...,n` """ params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] y = self.endog w = self.model_infl.predict(params_infl) w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) llf_main = self.model_main.loglikeobs(params_main) zero_idx = np.nonzero(y == 0)[0] nonzero_idx = np.nonzero(y)[0] llf = np.zeros_like(y, dtype=np.float64) llf[zero_idx] = (np.log(w[zero_idx] + (1 - w[zero_idx]) * np.exp(llf_main[zero_idx]))) llf[nonzero_idx] = np.log(1 - w[nonzero_idx]) + llf_main[nonzero_idx] return llf def fit(self, start_params=None, method='bfgs', maxiter=35, full_output=1, disp=1, callback=None, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs): if start_params is None: offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0) if np.size(offset) == 1 and offset == 0: offset = None start_params = self._get_start_params() if callback is None: # work around perfect separation callback #3895 callback = lambda *x: x mlefit = super(GenericZeroInflated, self).fit(start_params=start_params, maxiter=maxiter, disp=disp, method=method, full_output=full_output, callback=callback, **kwargs) zipfit = self.result_class(self, mlefit._results) result = self.result_class_wrapper(zipfit) if cov_kwds is None: cov_kwds = {} result._get_robustcov_results(cov_type=cov_type, use_self=True, use_t=use_t, **cov_kwds) return result fit.__doc__ = DiscreteModel.fit.__doc__ def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): _validate_l1_method(method) if np.size(alpha) == 1 and alpha!= 0: k_params = self.k_exog + self.k_inflate alpha = alpha * np.ones(k_params) extra = self.k_extra - self.k_inflate alpha_p = alpha[:-(self.k_extra - extra)] if (self.k_extra and np.size(alpha) > 1) else alpha if start_params is None: offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0) if np.size(offset) == 1 and offset == 0: offset = None start_params = self.model_main.fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=0, callback=callback, alpha=alpha_p, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs).params start_params = np.append(np.ones(self.k_inflate), start_params) cntfit = super(CountModel, self).fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) discretefit = self.result_class_reg(self, cntfit) return self.result_class_reg_wrapper(discretefit) fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__ def score_obs(self, params): """ Generic Zero Inflated model score (gradient) vector of the log-likelihood Parameters ---------- params : array_like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` """ params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] y = self.endog w = self.model_infl.predict(params_infl) w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) score_main = self.model_main.score_obs(params_main) llf_main = self.model_main.loglikeobs(params_main) llf = self.loglikeobs(params) zero_idx = np.nonzero(y == 0)[0] nonzero_idx = np.nonzero(y)[0] mu = self.model_main.predict(params_main) dldp = np.zeros((self.exog.shape[0], self.k_exog), dtype=np.float64) dldw = np.zeros_like(self.exog_infl, dtype=np.float64) dldp[zero_idx,:] = (score_main[zero_idx].T * (1 - (w[zero_idx]) / np.exp(llf[zero_idx]))).T dldp[nonzero_idx,:] = score_main[nonzero_idx] if self.inflation == 'logit': dldw[zero_idx,:] = (self.exog_infl[zero_idx].T * w[zero_idx] * (1 - w[zero_idx]) * (1 - np.exp(llf_main[zero_idx])) / np.exp(llf[zero_idx])).T dldw[nonzero_idx,:] = -(self.exog_infl[nonzero_idx].T * w[nonzero_idx]).T elif self.inflation == 'probit': return approx_fprime(params, self.loglikeobs) return np.hstack((dldw, dldp)) def score(self, params): return self.score_obs(params).sum(0) def _hessian_main(self, params): pass def _hessian_logit(self, params): params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] y = self.endog w = self.model_infl.predict(params_infl) w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) score_main = self.model_main.score_obs(params_main) llf_main = self.model_main.loglikeobs(params_main) llf = self.loglikeobs(params) zero_idx = np.nonzero(y == 0)[0] nonzero_idx = np.nonzero(y)[0] hess_arr = np.zeros((self.k_inflate, self.k_exog + self.k_inflate)) pmf = np.exp(llf) #d2l/dw2 for i in range(self.k_inflate): for j in range(i, -1, -1): hess_arr[i, j] = (( self.exog_infl[zero_idx, i] * self.exog_infl[zero_idx, j] * (w[zero_idx] * (1 - w[zero_idx]) * ((1 - np.exp(llf_main[zero_idx])) * (1 - 2 * w[zero_idx]) * np.exp(llf[zero_idx]) - (w[zero_idx] - w[zero_idx]**2) * (1 - np.exp(llf_main[zero_idx]))**2) / pmf[zero_idx]**2)).sum() - (self.exog_infl[nonzero_idx, i] * self.exog_infl[nonzero_idx, j] * w[nonzero_idx] * (1 - w[nonzero_idx])).sum()) #d2l/dpdw for i in range(self.k_inflate): for j in range(self.k_exog): hess_arr[i, j + self.k_inflate] = -(score_main[zero_idx, j] * w[zero_idx] * (1 - w[zero_idx]) * self.exog_infl[zero_idx, i] / pmf[zero_idx]).sum() return hess_arr def _hessian_probit(self, params): pass def hessian(self, params): """ Generic Zero Inflated model Hessian matrix of the loglikelihood Parameters ---------- params : array_like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` Notes ----- """ hess_arr_main = self._hessian_main(params) hess_arr_infl = self._hessian_inflate(params) if hess_arr_main is None or hess_arr_infl is None: return approx_hess(params, self.loglike) dim = self.k_exog + self.k_inflate hess_arr = np.zeros((dim, dim)) hess_arr[:self.k_inflate,:] = hess_arr_infl hess_arr[self.k_inflate:,self.k_inflate:] = hess_arr_main tri_idx = np.triu_indices(self.k_exog + self.k_inflate, k=1) hess_arr[tri_idx] = hess_arr.T[tri_idx] return hess_arr def predict(self, params, exog=None, exog_infl=None, exposure=None, offset=None, which='mean'): """ Predict response variable of a count model given exogenous variables. Parameters ---------- params : array_like The parameters of the model exog : array, optional A reference to the exogenous design. If not assigned, will be used exog from fitting. exog_infl : array, optional A reference to the zero-inflated exogenous design. If not assigned, will be used exog from fitting. offset : array, optional Offset is added to the linear prediction with coefficient equal to 1. exposure : array, optional Log(exposure) is added to the linear prediction with coefficient equal to 1. If exposure is specified, then it will be logged by the method. The user does not need to log it first. which : string, optional Define values that will be predicted. 'mean','mean-main', 'linear','mean-nonzero', 'prob-zero, 'prob', 'prob-main' Default is'mean'. Notes ----- """ if exog is None: exog = self.exog if exog_infl is None: exog_infl = self.exog_infl if exposure is None: exposure = getattr(self, 'exposure', 0) else: exposure = np.log(exposure) if offset is None: offset = 0 params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] prob_main = 1 - self.model_infl.predict(params_infl, exog_infl) lin_pred = np.dot(exog, params_main[:self.exog.shape[1]]) + exposure + offset # Refactor: This is pretty hacky, # there should be an appropriate predict method in model_main # this is just prob(y=0 | model_main) tmp_exog = self.model_main.exog tmp_endog = self.model_main.endog tmp_offset = getattr(self.model_main, 'offset', ['no']) tmp_exposure = getattr(self.model_main, 'exposure', ['no']) self.model_main.exog = exog self.model_main.endog = np.zeros((exog.shape[0])) self.model_main.offset = offset self.model_main.exposure = exposure llf = self.model_main.loglikeobs(params_main) self.model_main.exog = tmp_exog self.model_main.endog = tmp_endog # tmp_offset might be an array with elementwise equality testing if len(tmp_offset) == 1 and tmp_offset[0] == 'no': del self.model_main.offset else: self.model_main.offset = tmp_offset if len(tmp_exposure) == 1 and tmp_exposure[0] == 'no': del self.model_main.exposure else: self.model_main.exposure = tmp_exposure # end hack prob_zero = (1 - prob_main) + prob_main * np.exp(llf) if which =='mean': return prob_main * np.exp(lin_pred) elif which =='mean-main': return np.exp(lin_pred) elif which == 'linear': return lin_pred elif which =='mean-nonzero': return prob_main * np.exp(lin_pred) / (1 - prob_zero) elif which == 'prob-zero': return prob_zero elif which == 'prob-main': return prob_main elif which == 'prob': return self._predict_prob(params, exog, exog_infl, exposure, offset) else: raise ValueError('which = %s is not available' % which) class ZeroInflatedPoisson(GenericZeroInflated): __doc__ = """ Poisson Zero Inflated model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. exog_infl: array A reference to the zero-inflated exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : _doc_zi_params + base._missing_param_doc} def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None, inflation='logit', missing='none', **kwargs): super(ZeroInflatedPoisson, self).__init__(endog, exog, offset=offset, inflation=inflation, exog_infl=exog_infl, exposure=exposure, missing=missing, **kwargs) self.model_main = Poisson(self.endog, self.exog, offset=offset, exposure=exposure) self.distribution = zipoisson self.result_class = ZeroInflatedPoissonResults self.result_class_wrapper = ZeroInflatedPoissonResultsWrapper self.result_class_reg = L1ZeroInflatedPoissonResults self.result_class_reg_wrapper = L1ZeroInflatedPoissonResultsWrapper def _hessian_main(self, params): params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] y = self.endog w = self.model_infl.predict(params_infl) w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) score = self.score(params) zero_idx = np.nonzero(y == 0)[0] nonzero_idx = np.nonzero(y)[0] mu = self.model_main.predict(params_main) hess_arr = np.zeros((self.k_exog, self.k_exog)) coeff = (1 + w[zero_idx] * (np.exp(mu[zero_idx]) - 1)) #d2l/dp2 for i in range(self.k_exog): for j in range(i, -1, -1): hess_arr[i, j] = (( self.exog[zero_idx, i] * self.exog[zero_idx, j] * mu[zero_idx] * (w[zero_idx] - 1) * (1 / coeff - w[zero_idx] * mu[zero_idx] * np.exp(mu[zero_idx]) / coeff**2)).sum() - (mu[nonzero_idx] * self.exog[nonzero_idx, i] * self.exog[nonzero_idx, j]).sum()) return hess_arr def _predict_prob(self, params, exog, exog_infl, exposure, offset): params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] counts = np.atleast_2d(np.arange(0, np.max(self.endog)+1)) if len(exog_infl.shape) < 2: transform = True w = np.atleast_2d( self.model_infl.predict(params_infl, exog_infl))[:, None] else: transform = False w = self.model_infl.predict(params_infl, exog_infl)[:, None] w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) mu = self.model_main.predict(params_main, exog, offset=offset)[:, None] result = self.distribution.pmf(counts, mu, w) return result[0] if transform else result def _get_start_params(self): start_params = self.model_main.fit(disp=0, method="nm").params start_params = np.append(np.ones(self.k_inflate) * 0.1, start_params) return start_params class ZeroInflatedGeneralizedPoisson(GenericZeroInflated): __doc__ = """ Zero Inflated Generalized Poisson model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. exog_infl: array A reference to the zero-inflated exogenous design. p: scalar P denotes parametrizations for ZIGP regression. """ % {'params' : base._model_params_doc, 'extra_params' : _doc_zi_params + """p : float dispersion power parameter for the GeneralizedPoisson model. p=1 for ZIGP-1 and p=2 for ZIGP-2. Default is p=2 """ + base._missing_param_doc} def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None, inflation='logit', p=2, missing='none', **kwargs): super(ZeroInflatedGeneralizedPoisson, self).__init__(endog, exog, offset=offset, inflation=inflation, exog_infl=exog_infl, exposure=exposure, missing=missing, **kwargs) self.model_main = GeneralizedPoisson(self.endog, self.exog, offset=offset, exposure=exposure, p=p) self.distribution = zigenpoisson self.k_exog += 1 self.k_extra += 1 self.exog_names.append("alpha") self.result_class = ZeroInflatedGeneralizedPoissonResults self.result_class_wrapper = ZeroInflatedGeneralizedPoissonResultsWrapper self.result_class_reg = L1ZeroInflatedGeneralizedPoissonResults self.result_class_reg_wrapper = L1ZeroInflatedGeneralizedPoissonResultsWrapper def _get_init_kwds(self): kwds = super(ZeroInflatedGeneralizedPoisson, self)._get_init_kwds() kwds['p'] = self.model_main.parameterization + 1 return kwds def _predict_prob(self, params, exog, exog_infl, exposure, offset): params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] p = self.model_main.parameterization counts = np.atleast_2d(np.arange(0, np.max(self.endog)+1)) if len(exog_infl.shape) < 2: transform = True w = np.atleast_2d( self.model_infl.predict(params_infl, exog_infl))[:, None] else: transform = False w = self.model_infl.predict(params_infl, exog_infl)[:, None] w[w == 1.] = np.nextafter(1, 0) mu = self.model_main.predict(params_main, exog, exposure=exposure, offset=offset)[:, None] result = self.distribution.pmf(counts, mu, params_main[-1], p, w) return result[0] if transform else result def _get_start_params(self): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=ConvergenceWarning) start_params = ZeroInflatedPoisson(self.endog, self.exog, exog_infl=self.exog_infl).fit(disp=0).params start_params = np.append(start_params, 0.1) return start_params class ZeroInflatedNegativeBinomialP(GenericZeroInflated): __doc__ = """ Zero Inflated Generalized Negative Binomial model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. exog_infl: array A reference to the zero-inflated exogenous design. p: scalar P denotes parametrizations for ZINB regression. p=1 for ZINB-1 and p=2 for ZINB-2. Default is p=2 """ % {'params' : base._model_params_doc, 'extra_params' : _doc_zi_params + """p : float dispersion power parameter for the NegativeBinomialP model. p=1 for ZINB-1 and p=2 for ZINM-2. Default is p=2 """ + base._missing_param_doc} def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None, inflation='logit', p=2, missing='none', **kwargs): super(ZeroInflatedNegativeBinomialP, self).__init__(endog, exog, offset=offset, inflation=inflation, exog_infl=exog_infl, exposure=exposure, missing=missing, **kwargs) self.model_main = NegativeBinomialP(self.endog, self.exog, offset=offset, exposure=exposure, p=p) self.distribution = zinegbin self.k_exog += 1 self.k_extra += 1 self.exog_names.append("alpha") self.result_class = ZeroInflatedNegativeBinomialResults self.result_class_wrapper = ZeroInflatedNegativeBinomialResultsWrapper self.result_class_reg = L1ZeroInflatedNegativeBinomialResults self.result_class_reg_wrapper = L1ZeroInflatedNegativeBinomialResultsWrapper def _get_init_kwds(self): kwds = super(ZeroInflatedNegativeBinomialP, self)._get_init_kwds() kwds['p'] = self.model_main.parameterization return kwds def _predict_prob(self, params, exog, exog_infl, exposure, offset): params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] p = self.model_main.parameterization counts = np.arange(0, np.max(self.endog)+1) if len(exog_infl.shape) < 2: transform = True w = np.atleast_2d( self.model_infl.predict(params_infl, exog_infl))[:, None] else: transform = False w = self.model_infl.predict(params_infl, exog_infl)[:, None] w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) mu = self.model_main.predict(params_main, exog, exposure=exposure, offset=offset)[:, None] result = self.distribution.pmf(counts, mu, params_main[-1], p, w) return result[0] if transform else result def _get_start_params(self): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=ConvergenceWarning) start_params = self.model_main.fit(disp=0, method='nm').params start_params = np.append(np.zeros(self.k_inflate), start_params) return start_params class ZeroInflatedPoissonResults(CountResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for Zero Inflated Poisson", "extra_attr": ""} @cache_readonly def _dispersion_factor(self): mu = self.predict(which='linear') w = 1 - self.predict() / np.exp(self.predict(which='linear')) return (1 + w * np.exp(mu)) def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): """Get marginal effects of the fitted model. Not yet implemented for Zero Inflated Models """ raise NotImplementedError("not yet implemented for zero inflation") class L1ZeroInflatedPoissonResults(L1CountResults, ZeroInflatedPoissonResults): pass class ZeroInflatedPoissonResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(ZeroInflatedPoissonResultsWrapper, ZeroInflatedPoissonResults) class L1ZeroInflatedPoissonResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1ZeroInflatedPoissonResultsWrapper, L1ZeroInflatedPoissonResults) class ZeroInflatedGeneralizedPoissonResults(CountResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for Zero Inflated Generalized Poisson", "extra_attr": ""} @cache_readonly def _dispersion_factor(self): p = self.model.model_main.parameterization alpha = self.params[self.model.k_inflate:][-1] mu = np.exp(self.predict(which='linear')) w = 1 - self.predict() / mu return ((1 + alpha * mu**p)**2 + w * mu) def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): """Get marginal effects of the fitted model. Not yet implemented for Zero Inflated Models """ raise NotImplementedError("not yet implemented for zero inflation") class L1ZeroInflatedGeneralizedPoissonResults(L1CountResults, ZeroInflatedGeneralizedPoissonResults): pass class ZeroInflatedGeneralizedPoissonResultsWrapper( lm.RegressionResultsWrapper): pass wrap.populate_wrapper(ZeroInflatedGeneralizedPoissonResultsWrapper, ZeroInflatedGeneralizedPoissonResults) class L1ZeroInflatedGeneralizedPoissonResultsWrapper( lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1ZeroInflatedGeneralizedPoissonResultsWrapper, L1ZeroInflatedGeneralizedPoissonResults) class ZeroInflatedNegativeBinomialResults(CountResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for Zero Inflated Genaralized Negative Binomial", "extra_attr": ""} @cache_readonly def _dispersion_factor(self): p = self.model.model_main.parameterization alpha = self.params[self.model.k_inflate:][-1] mu = np.exp(self.predict(which='linear')) w = 1 - self.predict() / mu return (1 + alpha * mu**(p-1) + w * mu) def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): """Get marginal effects of the fitted model. Not yet implemented for Zero Inflated Models """ raise NotImplementedError("not yet implemented for zero inflation") class L1ZeroInflatedNegativeBinomialResults(L1CountResults, ZeroInflatedNegativeBinomialResults): pass class ZeroInflatedNegativeBinomialResultsWrapper( lm.RegressionResultsWrapper): pass wrap.populate_wrapper(ZeroInflatedNegativeBinomialResultsWrapper, ZeroInflatedNegativeBinomialResults) class L1ZeroInflatedNegativeBinomialResultsWrapper( lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1ZeroInflatedNegativeBinomialResultsWrapper, L1ZeroInflatedNegativeBinomialResults) """ Limited dependent variable and qualitative variables. Includes binary outcomes, count data, (ordered) ordinal data and limited dependent variables. General References -------------------- A.C. Cameron and P.K. Trivedi. `Regression Analysis of Count Data`. Cambridge, 1998 G.S. Madalla. `Limited-Dependent and Qualitative Variables in Econometrics`. Cambridge, 1983. W. Greene. `Econometric Analysis`. Prentice Hall, 5th. edition. 2003. """ __all__ = ["Poisson", "Logit", "Probit", "MNLogit", "NegativeBinomial", "GeneralizedPoisson", "NegativeBinomialP"] from statsmodels.compat.python import range from scipy.special import loggamma import numpy as np from pandas import get_dummies from scipy.special import gammaln, digamma, polygamma from scipy import stats, special from scipy.stats import nbinom import statsmodels.tools.tools as tools from statsmodels.tools import data as data_tools from statsmodels.tools.decorators import cache_readonly from statsmodels.tools.sm_exceptions import (PerfectSeparationError, SpecificationWarning) from statsmodels.tools.numdiff import approx_fprime_cs import statsmodels.base.model as base from statsmodels.base.data import handle_data # for mnlogit import statsmodels.regression.linear_model as lm import statsmodels.base.wrapper as wrap from statsmodels.base.l1_slsqp import fit_l1_slsqp from statsmodels.distributions import genpoisson_p try: import cvxopt # noqa:F401 have_cvxopt = True except ImportError: have_cvxopt = False import warnings #TODO: When we eventually get user-settable precision, we need to change # this FLOAT_EPS = np.finfo(float).eps #TODO: add options for the parameter covariance/variance # ie., OIM, EIM, and BHHH see Green 21.4 _discrete_models_docs = """ """ _discrete_results_docs = """ %(one_line_description)s Parameters ---------- model : A DiscreteModel instance params : array_like The parameters of a fitted model. hessian : array_like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Attributes ---------- df_resid : float See model definition. df_model : float See model definition. llf : float Value of the loglikelihood %(extra_attr)s""" _l1_results_attr = """ nnz_params : Integer The number of nonzero parameters in the model. Train with trim_params == True or else numerical error will distort this. trimmed : Boolean array trimmed[i] == True if the ith parameter was trimmed from the model.""" _get_start_params_null_docs = """ Compute one-step moment estimator for null (constant-only) model This is a preliminary estimator used as start_params. Returns ------- params : ndarray parameter estimate based one one-step moment matching """ # helper for MNLogit (will be generally useful later) def _numpy_to_dummies(endog): if endog.dtype.kind in ['S', 'O']: endog_dummies, ynames = tools.categorical(endog, drop=True, dictnames=True) elif endog.ndim == 2: endog_dummies = endog ynames = range(endog.shape[1]) else: endog_dummies, ynames = tools.categorical(endog, drop=True, dictnames=True) return endog_dummies, ynames def _pandas_to_dummies(endog): if endog.ndim == 2: if endog.shape[1] == 1: yname = endog.columns[0] endog_dummies = get_dummies(endog.iloc[:, 0]) else: # series yname = 'y' endog_dummies = endog else: yname = endog.name endog_dummies = get_dummies(endog) ynames = endog_dummies.columns.tolist() return endog_dummies, ynames, yname def _validate_l1_method(method): """ As of 0.10.0, the supported values for `method` in `fit_regularized` are "l1" and "l1_cvxopt_cp". If an invalid value is passed, raise with a helpful error message Parameters ---------- method : str Raises ------ ValueError """ if method not in ['l1', 'l1_cvxopt_cp']: raise ValueError('`method` = {method} is not supported, use either ' '"l1" or "l1_cvxopt_cp"'.format(method=method)) #### Private Model Classes #### class DiscreteModel(base.LikelihoodModel): """ Abstract class for discrete choice models. This class does not do anything itself but lays out the methods and call signature expected of child classes in addition to those of statsmodels.model.LikelihoodModel. """ def __init__(self, endog, exog, **kwargs): super(DiscreteModel, self).__init__(endog, exog, **kwargs) self.raise_on_perfect_prediction = True def initialize(self): """ Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model. """ # assumes constant rank = np.linalg.matrix_rank(self.exog) self.df_model = float(rank - 1) self.df_resid = float(self.exog.shape[0] - rank) def cdf(self, X): """ The cumulative distribution function of the model. """ raise NotImplementedError def pdf(self, X): """ The probability density (mass) function of the model. """ raise NotImplementedError def _check_perfect_pred(self, params, *args): endog = self.endog fittedvalues = self.cdf(np.dot(self.exog, params[:self.exog.shape[1]])) if (self.raise_on_perfect_prediction and np.allclose(fittedvalues - endog, 0)): msg = "Perfect separation detected, results not available" raise PerfectSeparationError(msg) def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): """ Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit """ if callback is None: callback = self._check_perfect_pred else: pass # make a function factory to have multiple call-backs mlefit = super(DiscreteModel, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) return mlefit # up to subclasses to wrap results fit.__doc__ += base.LikelihoodModel.fit.__doc__ def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=True, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, qc_verbose=False, **kwargs): """ Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters ---------- start_params : array_like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : 'l1' or 'l1_cvxopt_cp' See notes for details. maxiter : Integer or 'defined_by_method' Maximum number of iterations to perform. If 'defined_by_method', then use method defaults (see notes). full_output : bool Set to True to have all available output in the Results object's mle_retvals attribute. The output is dependent on the solver. See LikelihoodModelResults notes section for more information. disp : bool Set to True to print convergence messages. fargs : tuple Extra arguments passed to the likelihood function, i.e., loglike(x,*args) callback : callable callback(xk) Called after each iteration, as callback(xk), where xk is the current parameter vector. retall : bool Set to True to return list of solutions at each iteration. Available in Results object's mle_retvals attribute. alpha : non-negative scalar or numpy array (same size as parameters) The weight multiplying the l1 penalty term trim_mode : 'auto,'size', or 'off' If not 'off', trim (set to zero) parameters that would have been zero if the solver reached the theoretical minimum. If 'auto', trim params using the Theory above. If'size', trim params if they have very small absolute value size_trim_tol : float or 'auto' (default = 'auto') For use when trim_mode =='size' auto_trim_tol : float For sue when trim_mode == 'auto'. Use qc_tol : float Print warning and don't allow auto trim when (ii) (above) is violated by this much. qc_verbose : Boolean If true, print out a full QC report upon failure Notes ----- Extra parameters are not penalized if alpha is given as a scalar. An example is the shape parameter in NegativeBinomial `nb1` and `nb2`. Optional arguments for the solvers (available in Results.mle_settings):: 'l1' acc : float (default 1e-6) Requested accuracy as used by slsqp 'l1_cvxopt_cp' abstol : float absolute accuracy (default: 1e-7). reltol : float relative accuracy (default: 1e-6). feastol : float tolerance for feasibility conditions (default: 1e-7). refinement : int number of iterative refinement steps when solving KKT equations (default: 1). Optimization methodology With :math:`L` the negative log likelihood, we solve the convex but non-smooth problem .. math:: \\min_\\beta L(\\beta) + \\sum_k\\alpha_k |\\beta_k| via the transformation to the smooth, convex, constrained problem in twice as many variables (adding the "added variables" :math:`u_k`) .. math:: \\min_{\\beta,u} L(\\beta) + \\sum_k\\alpha_k u_k, subject to .. math:: -u_k \\leq \\beta_k \\leq u_k. With :math:`\\partial_k L` the derivative of :math:`L` in the :math:`k^{th}` parameter direction, theory dictates that, at the minimum, exactly one of two conditions holds: (i) :math:`|\\partial_k L| = \\alpha_k` and :math:`\\beta_k \\neq 0` (ii) :math:`|\\partial_k L| \\leq \\alpha_k` and :math:`\\beta_k = 0` """ _validate_l1_method(method) # Set attributes based on method cov_params_func = self.cov_params_func_l1 ### Bundle up extra kwargs for the dictionary kwargs. These are ### passed through super(...).fit() as kwargs and unpacked at ### appropriate times alpha = np.array(alpha) assert alpha.min() >= 0 try: kwargs['alpha'] = alpha except TypeError: kwargs = dict(alpha=alpha) kwargs['alpha_rescaled'] = kwargs['alpha'] / float(self.endog.shape[0]) kwargs['trim_mode'] = trim_mode kwargs['size_trim_tol'] = size_trim_tol kwargs['auto_trim_tol'] = auto_trim_tol kwargs['qc_tol'] = qc_tol kwargs['qc_verbose'] = qc_verbose ### Define default keyword arguments to be passed to super(...).fit() if maxiter == 'defined_by_method': if method == 'l1': maxiter = 1000 elif method == 'l1_cvxopt_cp': maxiter = 70 ## Parameters to pass to super(...).fit() # For the 'extra' parameters, pass all that are available, # even if we know (at this point) we will only use one. extra_fit_funcs = {'l1': fit_l1_slsqp} if have_cvxopt and method == 'l1_cvxopt_cp': from statsmodels.base.l1_cvxopt import fit_l1_cvxopt_cp extra_fit_funcs['l1_cvxopt_cp'] = fit_l1_cvxopt_cp elif method.lower() == 'l1_cvxopt_cp': message = ("Attempt to use l1_cvxopt_cp failed since cvxopt " "could not be imported") if callback is None: callback = self._check_perfect_pred else: pass # make a function factory to have multiple call-backs mlefit = super(DiscreteModel, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, extra_fit_funcs=extra_fit_funcs, cov_params_func=cov_params_func, **kwargs) return mlefit # up to subclasses to wrap results def cov_params_func_l1(self, likelihood_model, xopt, retvals): """ Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero'd values set to np.nan. """ H = likelihood_model.hessian(xopt) trimmed = retvals['trimmed'] nz_idx = np.nonzero(~trimmed)[0] nnz_params = (~trimmed).sum() if nnz_params > 0: H_restricted = H[nz_idx[:, None], nz_idx] # Covariance estimate for the nonzero params H_restricted_inv = np.linalg.inv(-H_restricted) else: H_restricted_inv = np.zeros(0) cov_params = np.nan * np.ones(H.shape) cov_params[nz_idx[:, None], nz_idx] = H_restricted_inv return cov_params def predict(self, params, exog=None, linear=False): """ Predict response variable of a model given exogenous variables. """ raise NotImplementedError def _derivative_exog(self, params, exog=None, dummy_idx=None, count_idx=None): """ This should implement the derivative of the non-linear function """ raise NotImplementedError def _derivative_exog_helper(self, margeff, params, exog, dummy_idx, count_idx, transform): """ Helper for _derivative_exog to wrap results appropriately """ from.discrete_margins import _get_count_effects, _get_dummy_effects if count_idx is not None: margeff = _get_count_effects(margeff, exog, count_idx, transform, self, params) if dummy_idx is not None: margeff = _get_dummy_effects(margeff, exog, dummy_idx, transform, self, params) return margeff class BinaryModel(DiscreteModel): def __init__(self, endog, exog, **kwargs): super(BinaryModel, self).__init__(endog, exog, **kwargs) if (not issubclass(self.__class__, MultinomialModel) and not np.all((self.endog >= 0) & (self.endog <= 1))): raise ValueError("endog must be in the unit interval.") def predict(self, params, exog=None, linear=False): """ Predict response variable of a model given exogenous variables. Parameters ---------- params : array_like Fitted parameters of the model. exog : array_like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. linear : bool, optional If True, returns the linear predictor dot(exog,params). Else, returns the value of the cdf at the linear predictor. Returns ------- array Fitted values at exog. """ if exog is None: exog = self.exog if not linear: return self.cdf(np.dot(exog, params)) else: return np.dot(exog, params) def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): _validate_l1_method(method) bnryfit = super(BinaryModel, self).fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) discretefit = L1BinaryResults(self, bnryfit) return L1BinaryResultsWrapper(discretefit) fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__ def _derivative_predict(self, params, exog=None, transform='dydx'): """ For computing marginal effects standard errors. This is used only in the case of discrete and count regressors to get the variance-covariance of the marginal effects. It returns [d F / d params] where F is the predict. Transform can be 'dydx' or 'eydx'. Checking is done in margeff computations for appropriate transform. """ if exog is None: exog = self.exog dF = self.pdf(np.dot(exog, params))[:,None] * exog if 'ey' in transform: dF /= self.predict(params, exog)[:,None] return dF def _derivative_exog(self, params, exog=None, transform='dydx', dummy_idx=None, count_idx=None): """ For computing marginal effects returns dF(XB) / dX where F(.) is the predicted probabilities transform can be 'dydx', 'dyex', 'eydx', or 'eyex'. Not all of these make sense in the presence of discrete regressors, but checks are done in the results in get_margeff. """ # Note: this form should be appropriate for # group 1 probit, logit, logistic, cloglog, heckprob, xtprobit if exog is None: exog = self.exog margeff = np.dot(self.pdf(np.dot(exog, params))[:, None], params[None, :]) if 'ex' in transform: margeff *= exog if 'ey' in transform: margeff /= self.predict(params, exog)[:, None] return self._derivative_exog_helper(margeff, params, exog, dummy_idx, count_idx, transform) class MultinomialModel(BinaryModel): def _handle_data(self, endog, exog, missing, hasconst, **kwargs): if data_tools._is_using_ndarray_type(endog, None): endog_dummies, ynames = _numpy_to_dummies(endog) yname = 'y' elif data_tools._is_using_pandas(endog, None): endog_dummies, ynames, yname = _pandas_to_dummies(endog) else: endog = np.asarray(endog) endog_dummies, ynames = _numpy_to_dummies(endog) yname = 'y' if not isinstance(ynames, dict): ynames = dict(zip(range(endog_dummies.shape[1]), ynames)) self._ynames_map = ynames data = handle_data(endog_dummies, exog, missing, hasconst, **kwargs) data.ynames = yname # overwrite this to single endog name data.orig_endog = endog self.wendog = data.endog # repeating from upstream... for key in kwargs: if key in ['design_info', 'formula']: # leave attached to data continue try: setattr(self, key, data.__dict__.pop(key)) except KeyError: pass return data def initialize(self): """ Preprocesses the data for MNLogit. """ super(MultinomialModel, self).initialize() # This is also a "whiten" method in other models (eg regression) self.endog = self.endog.argmax(1) # turn it into an array of col idx self.J = self.wendog.shape[1] self.K = self.exog.shape[1] self.df_model *= (self.J-1) # for each J - 1 equation. self.df_resid = self.exog.shape[0] - self.df_model - (self.J-1) def predict(self, params, exog=None, linear=False): """ Predict response variable of a model given exogenous variables. Parameters ---------- params : array_like 2d array of fitted parameters of the model. Should be in the order returned from the model. exog : array_like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous variables. If you only have one regressor and would like to do prediction, you must provide a 2d array with shape[1] == 1. linear : bool, optional If True, returns the linear predictor dot(exog,params). Else, returns the value of the cdf at the linear predictor. Notes ----- Column 0 is the base case, the rest conform to the rows of params shifted up one for the base case. """ if exog is None: # do here to accomodate user-given exog exog = self.exog if exog.ndim == 1: exog = exog[None] pred = super(MultinomialModel, self).predict(params, exog, linear) if linear: pred = np.column_stack((np.zeros(len(exog)), pred)) return pred def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): if start_params is None: start_params = np.zeros((self.K * (self.J-1))) else: start_params = np.asarray(start_params) callback = lambda x : None # placeholder until check_perfect_pred # skip calling super to handle results from LikelihoodModel mnfit = base.LikelihoodModel.fit(self, start_params = start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) mnfit.params = mnfit.params.reshape(self.K, -1, order='F') mnfit = MultinomialResults(self, mnfit) return MultinomialResultsWrapper(mnfit) fit.__doc__ = DiscreteModel.fit.__doc__ def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): if start_params is None: start_params = np.zeros((self.K * (self.J-1))) else: start_params = np.asarray(start_params) mnfit = DiscreteModel.fit_regularized( self, start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) mnfit.params = mnfit.params.reshape(self.K, -1, order='F') mnfit = L1MultinomialResults(self, mnfit) return L1MultinomialResultsWrapper(mnfit) fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__ def _derivative_predict(self, params, exog=None, transform='dydx'): """ For computing marginal effects standard errors. This is used only in the case of discrete and count regressors to get the variance-covariance of the marginal effects. It returns [d F / d params] where F is the predicted probabilities for each choice. dFdparams is of shape nobs x (J*K) x (J-1)*K. The zero derivatives for the base category are not included. Transform can be 'dydx' or 'eydx'. Checking is done in margeff computations for appropriate transform. """ if exog is None: exog = self.exog if params.ndim == 1: # will get flatted from approx_fprime params = params.reshape(self.K, self.J-1, order='F') eXB = np.exp(np.dot(exog, params)) sum_eXB = (1 + eXB.sum(1))[:,None] J = int(self.J) K = int(self.K) repeat_eXB = np.repeat(eXB, J, axis=1) X = np.tile(exog, J-1) # this is the derivative wrt the base level F0 = -repeat_eXB * X / sum_eXB ** 2 # this is the derivative wrt the other levels when # dF_j / dParams_j (ie., own equation) #NOTE: this computes too much, any easy way to cut down? F1 = eXB.T[:,:,None]*X * (sum_eXB - repeat_eXB) / (sum_eXB**2) F1 = F1.transpose((1,0,2)) # put the nobs index first # other equation index other_idx = ~np.kron(np.eye(J-1), np.ones(K)).astype(bool) F1[:, other_idx] = (-eXB.T[:,:,None]*X*repeat_eXB / \ (sum_eXB**2)).transpose((1,0,2))[:, other_idx] dFdX = np.concatenate((F0[:, None,:], F1), axis=1) if 'ey' in transform: dFdX /= self.predict(params, exog)[:, :, None] return dFdX def _derivative_exog(self, params, exog=None, transform='dydx', dummy_idx=None, count_idx=None): """ For computing marginal effects returns dF(XB) / dX where F(.) is the predicted probabilities transform can be 'dydx', 'dyex', 'eydx', or 'eyex'. Not all of these make sense in the presence of discrete regressors, but checks are done in the results in get_margeff. For Multinomial models the marginal effects are P[j] * (params[j] - sum_k P[k]*params[k]) It is returned unshaped, so that each row contains each of the J equations. This makes it easier to take derivatives of this for standard errors. If you want average marginal effects you can do margeff.reshape(nobs, K, J, order='F).mean(0) and the marginal effects for choice J are in column J """ J = int(self.J) # number of alternative choices K = int(self.K) # number of variables # Note: this form should be appropriate for # group 1 probit, logit, logistic, cloglog, heckprob, xtprobit if exog is None: exog = self.exog if params.ndim == 1: # will get flatted from approx_fprime params = params.reshape(K, J-1, order='F') zeroparams = np.c_[np.zeros(K), params] # add base in cdf = self.cdf(np.dot(exog, params)) # TODO: meaningful interpretation for `iterm`? iterm = np.array([cdf[:, [i]] * zeroparams[:, i] for i in range(int(J))]).sum(0) margeff = np.array([cdf[:, [j]] * (zeroparams[:, j] - iterm) for j in range(J)]) # swap the axes to make sure margeff are in order nobs, K, J margeff = np.transpose(margeff, (1, 2, 0)) if 'ex' in transform: margeff *= exog if 'ey' in transform: margeff /= self.predict(params, exog)[:,None,:] margeff = self._derivative_exog_helper(margeff, params, exog, dummy_idx, count_idx, transform) return margeff.reshape(len(exog), -1, order='F') class CountModel(DiscreteModel): def __init__(self, endog, exog, offset=None, exposure=None, missing='none', **kwargs): super(CountModel, self).__init__(endog, exog, missing=missing, offset=offset, exposure=exposure, **kwargs) if exposure is not None: self.exposure = np.log(self.exposure) self._check_inputs(self.offset, self.exposure, self.endog) if offset is None: delattr(self, 'offset') if exposure is None: delattr(self, 'exposure') # promote dtype to float64 if needed dt = np.promote_types(self.endog.dtype, np.float64) self.endog = np.asarray(self.endog, dt) dt = np.promote_types(self.exog.dtype, np.float64) self.exog = np.asarray(self.exog, dt) def _check_inputs(self, offset, exposure, endog): if offset is not None and offset.shape[0]!= endog.shape[0]: raise ValueError("offset is not the same length as endog") if exposure is not None and exposure.shape[0]!= endog.shape[0]: raise ValueError("exposure is not the same length as endog") def _get_init_kwds(self): # this is a temporary fixup because exposure has been transformed # see #1609 kwds = super(CountModel, self)._get_init_kwds() if 'exposure' in kwds and kwds['exposure'] is not None: kwds['exposure'] = np.exp(kwds['exposure']) return kwds def predict(self, params, exog=None, exposure=None, offset=None, linear=False): """ Predict response variable of a count model given exogenous variables. Notes ----- If exposure is specified, then it will be logged by the method. The user does not need to log it first. """ # the following is copied from GLM predict (without family/link check) # Use fit offset if appropriate if offset is None and exog is None and hasattr(self, 'offset'): offset = self.offset elif offset is None: offset = 0. # Use fit exposure if appropriate if exposure is None and exog is None and hasattr(self, 'exposure'): # Already logged exposure = self.exposure elif exposure is None: exposure = 0. else: exposure = np.log(exposure) if exog is None: exog = self.exog fitted = np.dot(exog, params[:exog.shape[1]]) linpred = fitted + exposure + offset if not linear: return np.exp(linpred) # not cdf else: return linpred def _derivative_predict(self, params, exog=None, transform='dydx'): """ For computing marginal effects standard errors. This is used only in the case of discrete and count regressors to get the variance-covariance of the marginal effects. It returns [d F / d params] where F is the predict. Transform can be 'dydx' or 'eydx'. Checking is done in margeff computations for appropriate transform. """ if exog is None: exog = self.exog #NOTE: this handles offset and exposure dF = self.predict(params, exog)[:,None] * exog if 'ey' in transform: dF /= self.predict(params, exog)[:,None] return dF def _derivative_exog(self, params, exog=None, transform="dydx", dummy_idx=None, count_idx=None): """ For computing marginal effects. These are the marginal effects d F(XB) / dX For the Poisson model F(XB) is the predicted counts rather than the probabilities. transform can be 'dydx', 'dyex', 'eydx', or 'eyex'. Not all of these make sense in the presence of discrete regressors, but checks are done in the results in get_margeff. """ # group 3 poisson, nbreg, zip, zinb if exog is None: exog = self.exog k_extra = getattr(self, 'k_extra', 0) params_exog = params if k_extra == 0 else params[:-k_extra] margeff = self.predict(params, exog)[:,None] * params_exog[None,:] if 'ex' in transform: margeff *= exog if 'ey' in transform: margeff /= self.predict(params, exog)[:,None] return self._derivative_exog_helper(margeff, params, exog, dummy_idx, count_idx, transform) def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): cntfit = super(CountModel, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) discretefit = CountResults(self, cntfit) return CountResultsWrapper(discretefit) fit.__doc__ = DiscreteModel.fit.__doc__ def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): _validate_l1_method(method) cntfit = super(CountModel, self).fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) discretefit = L1CountResults(self, cntfit) return L1CountResultsWrapper(discretefit) fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__ class OrderedModel(DiscreteModel): pass #### Public Model Classes #### class Poisson(CountModel): __doc__ = """ Poisson model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : """offset : array_like Offset is added to the linear prediction with coefficient equal to 1. exposure : array_like Log(exposure) is added to the linear prediction with coefficient equal to 1. """ + base._missing_param_doc} @property def family(self): from statsmodels.genmod import families return families.Poisson() def cdf(self, X): """ Poisson model cumulative distribution function Parameters ---------- X : array_like `X` is the linear predictor of the model. See notes. Returns ------- The value of the Poisson CDF at each point. Notes ----- The CDF is defined as .. math:: \\exp\\left(-\\lambda\\right)\\sum_{i=0}^{y}\\frac{\\lambda^{i}}{i!} where :math:`\\lambda` assumes the loglinear model. I.e., .. math:: \\ln\\lambda_{i}=X\\beta The parameter `X` is :math:`X\\beta` in the above formula. """ y = self.endog return stats.poisson.cdf(y, np.exp(X)) def pdf(self, X): """ Poisson model probability mass function Parameters ---------- X : array_like `X` is the linear predictor of the model. See notes. Returns ------- pdf : ndarray The value of the Poisson probability mass function, PMF, for each point of X. Notes -------- The PMF is defined as .. math:: \\frac{e^{-\\lambda_{i}}\\lambda_{i}^{y_{i}}}{y_{i}!} where :math:`\\lambda` assumes the loglinear model. I.e., .. math:: \\ln\\lambda_{i}=x_{i}\\beta The parameter `X` is :math:`x_{i}\\beta` in the above formula. """ y = self.endog return np.exp(stats.poisson.logpmf(y, np.exp(X))) def loglike(self, params): """ Loglikelihood of Poisson model Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes -------- .. math:: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) XB = np.dot(self.exog, params) + offset + exposure endog = self.endog return np.sum(-np.exp(XB) + endog*XB - gammaln(endog+1)) def loglikeobs(self, params): """ Loglikelihood for observations of Poisson model Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : array_like The log likelihood for each observation of the model evaluated at `params`. See Notes Notes -------- .. math:: \\ln L_{i}=\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] for observations :math:`i=1,...,n` """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) XB = np.dot(self.exog, params) + offset + exposure endog = self.endog #np.sum(stats.poisson.logpmf(endog, np.exp(XB))) return -np.exp(XB) + endog*XB - gammaln(endog+1) def _get_start_params_null(self): offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) const = (self.endog / np.exp(offset + exposure)).mean() params = [np.log(const)] return params _get_start_params_null.__doc__ = _get_start_params_null_docs def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): if start_params is None and self.data.const_idx is not None: # k_params or k_exog not available? start_params = 0.001 * np.ones(self.exog.shape[1]) start_params[self.data.const_idx] = self._get_start_params_null()[0] cntfit = super(CountModel, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) if 'cov_type' in kwargs: cov_kwds = kwargs.get('cov_kwds', {}) kwds = {'cov_type':kwargs['cov_type'], 'cov_kwds':cov_kwds} else: kwds = {} discretefit = PoissonResults(self, cntfit, **kwds) return PoissonResultsWrapper(discretefit) fit.__doc__ = DiscreteModel.fit.__doc__ def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): _validate_l1_method(method) cntfit = super(CountModel, self).fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) discretefit = L1PoissonResults(self, cntfit) return L1PoissonResultsWrapper(discretefit) fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__ def fit_constrained(self, constraints, start_params=None, **fit_kwds): """fit the model subject to linear equality constraints The constraints are of the form `R params = q` where R is the constraint_matrix and q is the vector of constraint_values. The estimation creates a new model with transformed design matrix, exog, and converts the results back to the original parameterization. Parameters ---------- constraints : formula expression or tuple If it is a tuple, then the constraint needs to be given by two arrays (constraint_matrix, constraint_value), i.e. (R, q). Otherwise, the constraints can be given as strings or list of strings. see t_test for details start_params : None or array_like starting values for the optimization. `start_params` needs to be given in the original parameter space and are internally transformed. **fit_kwds : keyword arguments fit_kwds are used in the optimization of the transformed model. Returns ------- results : Results instance """ #constraints = (R, q) # TODO: temporary trailing underscore to not overwrite the monkey # patched version # TODO: decide whether to move the imports from patsy import DesignInfo from statsmodels.base._constraints import fit_constrained # same pattern as in base.LikelihoodModel.t_test lc = DesignInfo(self.exog_names).linear_constraint(constraints) R, q = lc.coefs, lc.constants # TODO: add start_params option, need access to tranformation # fit_constrained needs to do the transformation params, cov, res_constr = fit_constrained(self, R, q, start_params=start_params, fit_kwds=fit_kwds) #create dummy results Instance, TODO: wire up properly res = self.fit(maxiter=0, method='nm', disp=0, warn_convergence=False) # we get a wrapper back res.mle_retvals['fcall'] = res_constr.mle_retvals.get('fcall', np.nan) res.mle_retvals['iterations'] = res_constr.mle_retvals.get( 'iterations', np.nan) res.mle_retvals['converged'] = res_constr.mle_retvals['converged'] res._results.params = params res._results.cov_params_default = cov cov_type = fit_kwds.get('cov_type', 'nonrobust') if cov_type!= 'nonrobust': res._results.normalized_cov_params = cov # assume scale=1 else: res._results.normalized_cov_params = None k_constr = len(q) res._results.df_resid += k_constr res._results.df_model -= k_constr res._results.constraints = lc res._results.k_constr = k_constr res._results.results_constrained = res_constr return res def score(self, params): """ Poisson model score (gradient) vector of the log-likelihood Parameters ---------- params : array_like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial\\ln L}{\\partial\\beta}=\\sum_{i=1}^{n}\\left(y_{i}-\\lambda_{i}\\right)x_{i} where the loglinear model is assumed .. math:: \\ln\\lambda_{i}=x_{i}\\beta """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) X = self.exog L = np.exp(np.dot(X,params) + offset + exposure) return np.dot(self.endog - L, X) def score_obs(self, params): """ Poisson model Jacobian of the log-likelihood for each observation Parameters ---------- params : array_like The parameters of the model Returns ------- score : array_like The score vector (nobs, k_vars) of the model evaluated at `params` Notes ----- .. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left(y_{i}-\\lambda_{i}\\right)x_{i} for observations :math:`i=1,...,n` where the loglinear model is assumed .. math:: \\ln\\lambda_{i}=x_{i}\\beta """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) X = self.exog L = np.exp(np.dot(X,params) + offset + exposure) return (self.endog - L)[:,None] * X def score_factor(self, params): """ Poisson model score_factor for each observation Parameters ---------- params : array_like The parameters of the model Returns ------- score : array_like The score factor (nobs, ) of the model evaluated at `params` Notes ----- .. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left(y_{i}-\\lambda_{i}\\right) for observations :math:`i=1,...,n` where the loglinear model is assumed .. math:: \\ln\\lambda_{i}=x_{i}\\beta """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) X = self.exog L = np.exp(np.dot(X,params) + offset + exposure) return (self.endog - L) def hessian(self, params): """ Poisson model Hessian matrix of the loglikelihood Parameters ---------- params : array_like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\sum_{i=1}^{n}\\lambda_{i}x_{i}x_{i}^{\\prime} where the loglinear model is assumed .. math:: \\ln\\lambda_{i}=x_{i}\\beta """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) X = self.exog L = np.exp(np.dot(X,params) + exposure + offset) return -np.dot(L*X.T, X) def hessian_factor(self, params): """ Poisson model Hessian factor Parameters ---------- params : array_like The parameters of the model Returns ------- hess : ndarray, (nobs,) The Hessian factor, second derivative of loglikelihood function with respect to the linear predictor evaluated at `params` Notes ----- .. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\sum_{i=1}^{n}\\lambda_{i} where the loglinear model is assumed .. math:: \\ln\\lambda_{i}=x_{i}\\beta """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) X = self.exog L = np.exp(np.dot(X,params) + exposure + offset) return L class GeneralizedPoisson(CountModel): __doc__ = """ Generalized Poisson model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : """ p: scalar P denotes parameterizations for GP regression. p=1 for GP-1 and p=2 for GP-2. Default is p=1. offset : array_like Offset is added to the linear prediction with coefficient equal to 1. exposure : array_like Log(exposure) is added to the linear prediction with coefficient equal to 1. """ + base._missing_param_doc} def __init__(self, endog, exog, p = 1, offset=None, exposure=None, missing='none', **kwargs): super(GeneralizedPoisson, self).__init__(endog, exog, offset=offset, exposure=exposure, missing=missing, **kwargs) self.parameterization = p - 1 self.exog_names.append('alpha') self.k_extra = 1 self._transparams = False def _get_init_kwds(self): kwds = super(GeneralizedPoisson, self)._get_init_kwds() kwds['p'] = self.parameterization + 1 return kwds def loglike(self, params): """ Loglikelihood of Generalized Poisson model Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes -------- .. math:: \\ln L=\\sum_{i=1}^{n}\\left[\\mu_{i}+(y_{i}-1)*ln(\\mu_{i}+ \\alpha*\\mu_{i}^{p-1}*y_{i})-y_{i}*ln(1+\\alpha*\\mu_{i}^{p-1})- ln(y_{i}!)-\\frac{\\mu_{i}+\\alpha*\\mu_{i}^{p-1}*y_{i}}{1+\\alpha* \\mu_{i}^{p-1}}\\right] """ return np.sum(self.loglikeobs(params)) def loglikeobs(self, params): """ Loglikelihood for observations of Generalized Poisson model Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : ndarray The log likelihood for each observation of the model evaluated at `params`. See Notes Notes -------- .. math:: \\ln L=\\sum_{i=1}^{n}\\left[\\mu_{i}+(y_{i}-1)*ln(\\mu_{i}+ \\alpha*\\mu_{i}^{p-1}*y_{i})-y_{i}*ln(1+\\alpha*\\mu_{i}^{p-1})- ln(y_{i}!)-\\frac{\\mu_{i}+\\alpha*\\mu_{i}^{p-1}*y_{i}}{1+\\alpha* \\mu_{i}^{p-1}}\\right] for observations :math:`i=1,...,n` """ if self._transparams: alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] p = self.parameterization endog = self.endog mu = self.predict(params) mu_p = np.power(mu, p) a1 = 1 + alpha * mu_p a2 = mu + (a1 - 1) * endog return (np.log(mu) + (endog - 1) * np.log(a2) - endog * np.log(a1) - gammaln(endog + 1) - a2 / a1) def _get_start_params_null(self): offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) const = (self.endog / np.exp(offset + exposure)).mean() params = [np.log(const)] mu = const * np.exp(offset + exposure) resid = self.endog - mu a = self._estimate_dispersion(mu, resid, df_resid=resid.shape[0] - 1) params.append(a) return np.array(params) _get_start_params_null.__doc__ = _get_start_params_null_docs def _estimate_dispersion(self, mu, resid, df_resid=None): q = self.parameterization if df_resid is None: df_resid = resid.shape[0] a = ((np.abs(resid) / np.sqrt(mu) - 1) * mu**(-q)).sum() / df_resid return a def fit(self, start_params=None, method='bfgs', maxiter=35, full_output=1, disp=1, callback=None, use_transparams=False, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs): # TODO: Fix doc string """ use_transparams : bool This parameter enable internal transformation to impose non-negativity. True to enable. Default is False. use_transparams=True imposes the no underdispersion (alpha > 0) constaint. In case use_transparams=True and method="newton" or "ncg" transformation is ignored. """ if use_transparams and method not in ['newton', 'ncg']: self._transparams = True else: if use_transparams: warnings.warn('Parameter "use_transparams" is ignored', RuntimeWarning) self._transparams = False if start_params is None: offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0) if np.size(offset) == 1 and offset == 0: offset = None optim_kwds_prelim = {'disp': 0,'skip_hessian': True, 'warn_convergence': False} optim_kwds_prelim.update(kwargs.get('optim_kwds_prelim', {})) mod_poi = Poisson(self.endog, self.exog, offset=offset) res_poi = mod_poi.fit(**optim_kwds_prelim) start_params = res_poi.params a = self._estimate_dispersion(res_poi.predict(), res_poi.resid, df_resid=res_poi.df_resid) start_params = np.append(start_params, max(-0.1, a)) if callback is None: # work around perfect separation callback #3895 callback = lambda *x: x mlefit = super(GeneralizedPoisson, self).fit(start_params=start_params, maxiter=maxiter, method=method, disp=disp, full_output=full_output, callback=callback, **kwargs) if use_transparams and method not in ["newton", "ncg"]: self._transparams = False mlefit._results.params[-1] = np.exp(mlefit._results.params[-1]) gpfit = GeneralizedPoissonResults(self, mlefit._results) result = GeneralizedPoissonResultsWrapper(gpfit) if cov_kwds is None: cov_kwds = {} result._get_robustcov_results(cov_type=cov_type, use_self=True, use_t=use_t, **cov_kwds) return result fit.__doc__ = DiscreteModel.fit.__doc__ + fit.__doc__ def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): _validate_l1_method(method) if np.size(alpha) == 1 and alpha!= 0: k_params = self.exog.shape[1] + self.k_extra alpha = alpha * np.ones(k_params) alpha[-1] = 0 alpha_p = alpha[:-1] if (self.k_extra and np.size(alpha) > 1) else alpha self._transparams = False if start_params is None: offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0) if np.size(offset) == 1 and offset == 0: offset = None mod_poi = Poisson(self.endog, self.exog, offset=offset) start_params = mod_poi.fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=0, callback=callback, alpha=alpha_p, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs).params start_params = np.append(start_params, 0.1) cntfit = super(CountModel, self).fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) discretefit = L1GeneralizedPoissonResults(self, cntfit) return L1GeneralizedPoissonResultsWrapper(discretefit) fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__ def score_obs(self, params): if self._transparams: alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] p = self.parameterization exog = self.exog y = self.endog[:,None] mu = self.predict(params)[:,None] mu_p = np.power(mu, p) a1 = 1 + alpha * mu_p a2 = mu + alpha * mu_p * y a3 = alpha * p * mu ** (p - 1) a4 = a3 * y dmudb = mu * exog dalpha = (mu_p * (y * ((y - 1) / a2 - 2 / a1) + a2 / a1**2)) dparams = dmudb * (-a4 / a1 + a3 * a2 / (a1 ** 2) + (1 + a4) * ((y - 1) / a2 - 1 / a1) + 1 / mu) return np.concatenate((dparams, np.atleast_2d(dalpha)), axis=1) def score(self, params): score = np.sum(self.score_obs(params), axis=0) if self._transparams: score[-1] == score[-1] ** 2 return score else: return score def _score_p(self, params): """ Generalized Poisson model derivative of the log-likelihood by p-parameter Parameters ---------- params : array_like The parameters of the model Returns ------- dldp : float dldp is first derivative of the loglikelihood function, evaluated at `p-parameter`. """ if self._transparams: alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] p = self.parameterization exog = self.exog y = self.endog[:,None] mu = self.predict(params)[:,None] mu_p = np.power(mu, p) a1 = 1 + alpha * mu_p a2 = mu + alpha * mu_p * y dp = np.sum((np.log(mu) * ((a2 - mu) * ((y - 1) / a2 - 2 / a1) + (a1 - 1) * a2 / a1 ** 2))) return dp def hessian(self, params): """ Generalized Poisson model Hessian matrix of the loglikelihood Parameters ---------- params : array_like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` """ if self._transparams: alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] p = self.parameterization exog = self.exog y = self.endog[:,None] mu = self.predict(params)[:,None] mu_p = np.power(mu, p) a1 = 1 + alpha * mu_p a2 = mu + alpha * mu_p * y a3 = alpha * p * mu ** (p - 1) a4 = a3 * y a5 = p * mu ** (p - 1) dmudb = mu * exog # for dl/dparams dparams dim = exog.shape[1] hess_arr = np.empty((dim+1,dim+1)) for i in range(dim): for j in range(i + 1): hess_arr[i,j] = np.sum(mu * exog[:,i,None] * exog[:,j,None] * (mu * (a3 * a4 / a1**2 - 2 * a3**2 * a2 / a1**3 + 2 * a3 * (a4 + 1) / a1**2 - a4 * p / (mu * a1) + a3 * p * a2 / (mu * a1**2) + (y - 1) * a4 * (p - 1) / (a2 * mu) - (y - 1) * (1 + a4)**2 / a2**2 - a4 * (p - 1) / (a1 * mu)) + ((y - 1) * (1 + a4) / a2 - (1 + a4) / a1)), axis=0) tri_idx = np.triu_indices(dim, k=1) hess_arr[tri_idx] = hess_arr.T[tri_idx] # for dl/dparams dalpha dldpda = np.sum((2 * a4 * mu_p / a1**2 - 2 * a3 * mu_p * a2 / a1**3 - mu_p * y * (y - 1) * (1 + a4) / a2**2 + mu_p * (1 + a4) / a1**2 + a5 * y * (y - 1) / a2 - 2 * a5 * y / a1 + a5 * a2 / a1**2) * dmudb, axis=0) hess_arr[-1,:-1] = dldpda hess_arr[:-1,-1] = dldpda # for dl/dalpha dalpha dldada = mu_p**2 * (3 * y / a1**2 - (y / a2)**2. * (y - 1) - 2 * a2 / a1**3) hess_arr[-1,-1] = dldada.sum() return hess_arr def predict(self, params, exog=None, exposure=None, offset=None, which='mean'): """ Predict response variable of a count model given exogenous variables. Notes ----- If exposure is specified, then it will be logged by the method. The user does not need to log it first. """ if exog is None: exog = self.exog if exposure is None: exposure = getattr(self, 'exposure', 0) elif exposure!= 0: exposure = np.log(exposure) if offset is None: offset = getattr(self, 'offset', 0) fitted = np.dot(exog, params[:exog.shape[1]]) linpred = fitted + exposure + offset if which =='mean': return np.exp(linpred) elif which == 'linear': return linpred elif which =='prob': counts = np.atleast_2d(np.arange(0, np.max(self.endog)+1)) mu = self.predict(params, exog=exog, exposure=exposure, offset=offset)[:,None] return genpoisson_p.pmf(counts, mu, params[-1], self.parameterization + 1) else: raise ValueError('keyword \'which\' not recognized') class Logit(BinaryModel): __doc__ = """ Binary choice logit model %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : base._missing_param_doc} def cdf(self, X): """ The logistic cumulative distribution function Parameters ---------- X : array_like `X` is the linear predictor of the logit model. See notes. Returns ------- 1/(1 + exp(-X)) Notes ----- In the logit model, .. math:: \\Lambda\\left(x^{\\prime}\\beta\\right)= \\text{Prob}\\left(Y=1|x\\right)= \\frac{e^{x^{\\prime}\\beta}}{1+e^{x^{\\prime}\\beta}} """ X = np.asarray(X) return 1/(1+np.exp(-X)) def pdf(self, X): """ The logistic probability density function Parameters ---------- X : array_like `X` is the linear predictor of the logit model. See notes. Returns ------- pdf : ndarray The value of the Logit probability mass function, PMF, for each point of X. ``np.exp(-x)/(1+np.exp(-X))**2`` Notes ----- In the logit model, .. math:: \\lambda\\left(x^{\\prime}\\beta\\right)=\\frac{e^{-x^{\\prime}\\beta}}{\\left(1+e^{-x^{\\prime}\\beta}\\right)^{2}} """ X = np.asarray(X) return np.exp(-X)/(1+np.exp(-X))**2 def loglike(self, params): """ Log-likelihood of logit model. Parameters ---------- params : array_like The parameters of the logit model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes ----- .. math:: \\ln L=\\sum_{i}\\ln\\Lambda \\left(q_{i}x_{i}^{\\prime}\\beta\\right) Where :math:`q=2y-1`. This simplification comes from the fact that the logistic distribution is symmetric. """ q = 2*self.endog - 1 X = self.exog return np.sum(np.log(self.cdf(q*np.dot(X,params)))) def loglikeobs(self, params): """ Log-likelihood of logit model for each observation. Parameters ---------- params : array_like The parameters of the logit model. Returns ------- loglike : ndarray The log likelihood for each observation of the model evaluated at `params`. See Notes Notes ----- .. math:: \\ln L=\\sum_{i}\\ln\\Lambda \\left(q_{i}x_{i}^{\\prime}\\beta\\right) for observations :math:`i=1,...,n` where :math:`q=2y-1`. This simplification comes from the fact that the logistic distribution is symmetric. """ q = 2*self.endog - 1 X = self.exog return np.log(self.cdf(q*np.dot(X,params))) def score(self, params): """ Logit model score (gradient) vector of the log-likelihood Parameters ---------- params: array_like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial\\ln L}{\\partial\\beta}=\\sum_{i=1}^{n}\\left(y_{i}-\\Lambda_{i}\\right)x_{i} """ y = self.endog X = self.exog L = self.cdf(np.dot(X,params)) return np.dot(y - L,X) def score_obs(self, params): """ Logit model Jacobian of the log-likelihood for each observation Parameters ---------- params: array_like The parameters of the model Returns ------- jac : array_like The derivative of the loglikelihood for each observation evaluated at `params`. Notes ----- .. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left(y_{i}-\\Lambda_{i}\\right)x_{i} for observations :math:`i=1,...,n` """ y = self.endog X = self.exog L = self.cdf(np.dot(X, params)) return (y - L)[:,None] * X def hessian(self, params): """ Logit model Hessian matrix of the log-likelihood Parameters ---------- params : array_like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\sum_{i}\\Lambda_{i}\\left(1-\\Lambda_{i}\\right)x_{i}x_{i}^{\\prime} """ X = self.exog L = self.cdf(np.dot(X,params)) return -np.dot(L*(1-L)*X.T,X) def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): bnryfit = super(Logit, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) discretefit = LogitResults(self, bnryfit) return BinaryResultsWrapper(discretefit) fit.__doc__ = DiscreteModel.fit.__doc__ class Probit(BinaryModel): __doc__ = """ Binary choice Probit model %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : base._missing_param_doc} def cdf(self, X): """ Probit (Normal) cumulative distribution function Parameters ---------- X : array_like The linear predictor of the model (XB). Returns ------- cdf : ndarray The cdf evaluated at `X`. Notes ----- This function is just an alias for scipy.stats.norm.cdf """ return stats.norm._cdf(X) def pdf(self, X): """ Probit (Normal) probability density function Parameters ---------- X : array_like The linear predictor of the model (XB). Returns ------- pdf : ndarray The value of the normal density function for each point of X. Notes ----- This function is just an alias for scipy.stats.norm.pdf """ X = np.asarray(X) return stats.norm._pdf(X) def loglike(self, params): """ Log-likelihood of probit model (i.e., the normal distribution). Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes ----- .. math:: \\ln L=\\sum_{i}\\ln\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right) Where :math:`q=2y-1`. This simplification comes from the fact that the normal distribution is symmetric. """ q = 2*self.endog - 1 X = self.exog return np.sum(np.log(np.clip(self.cdf(q*np.dot(X,params)), FLOAT_EPS, 1))) def loglikeobs(self, params): """ Log-likelihood of probit model for each observation Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : array_like The log likelihood for each observation of the model evaluated at `params`. See Notes Notes ----- .. math:: \\ln L_{i}=\\ln\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right) for observations :math:`i=1,...,n` where :math:`q=2y-1`. This simplification comes from the fact that the normal distribution is symmetric. """ q = 2*self.endog - 1 X = self.exog return np.log(np.clip(self.cdf(q*np.dot(X,params)), FLOAT_EPS, 1)) def score(self, params): """ Probit model score (gradient) vector Parameters ---------- params : array_like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial\\ln L}{\\partial\\beta}=\\sum_{i=1}^{n}\\left[\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}\\right]x_{i} Where :math:`q=2y-1`. This simplification comes from the fact that the normal distribution is symmetric. """ y = self.endog X = self.exog XB = np.dot(X,params) q = 2*y - 1 # clip to get rid of invalid divide complaint L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), FLOAT_EPS, 1 - FLOAT_EPS) return np.dot(L,X) def score_obs(self, params): """ Probit model Jacobian for each observation Parameters ---------- params : array_like The parameters of the model Returns ------- jac : array_like The derivative of the loglikelihood for each observation evaluated at `params`. Notes ----- .. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left[\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}\\right]x_{i} for observations :math:`i=1,...,n` Where :math:`q=2y-1`. This simplification comes from the fact that the normal distribution is symmetric. """ y = self.endog X = self.exog XB = np.dot(X,params) q = 2*y - 1 # clip to get rid of invalid divide complaint L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), FLOAT_EPS, 1 - FLOAT_EPS) return L[:,None] * X def hessian(self, params): """ Probit model Hessian matrix of the log-likelihood Parameters ---------- params : array_like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\lambda_{i}\\left(\\lambda_{i}+x_{i}^{\\prime}\\beta\\right)x_{i}x_{i}^{\\prime} where .. math:: \\lambda_{i}=\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)} and :math:`q=2y-1` """ X = self.exog XB = np.dot(X,params) q = 2*self.endog - 1 L = q*self.pdf(q*XB)/self.cdf(q*XB) return np.dot(-L*(L+XB)*X.T,X) def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): bnryfit = super(Probit, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) discretefit = ProbitResults(self, bnryfit) return BinaryResultsWrapper(discretefit) fit.__doc__ = DiscreteModel.fit.__doc__ class MNLogit(MultinomialModel): __doc__ = """ Multinomial logit model Parameters ---------- endog : array_like `endog` is an 1-d vector of the endogenous response. `endog` can contain strings, ints, or floats. Note that if it contains strings, every distinct string will be a category. No stripping of whitespace is done. exog : array_like A nobs x k array where `nobs` is the number of observations and `k` is the number of regressors. An intercept is not included by default and should be added by the user. See `statsmodels.tools.add_constant`. %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. J : float The number of choices for the endogenous variable. Note that this is zero-indexed. K : float The actual number of parameters for the exogenous design. Includes the constant if the design has one. names : dict A dictionary mapping the column number in `wendog` to the variables in `endog`. wendog : array An n x j array where j is the number of unique categories in `endog`. Each column of j is a dummy variable indicating the category of each observation. See `names` for a dictionary mapping each column to its category. Notes ----- See developer notes for further information on `MNLogit` internals. """ % {'extra_params' : base._missing_param_doc} def pdf(self, eXB): """ NotImplemented """ raise NotImplementedError def cdf(self, X): """ Multinomial logit cumulative distribution function. Parameters ---------- X : array The linear predictor of the model XB. Returns ------- cdf : ndarray The cdf evaluated at `X`. Notes ----- In the multinomial logit model. .. math:: \\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)} """ eXB = np.column_stack((np.ones(len(X)), np.exp(X))) return eXB/eXB.sum(1)[:,None] def loglike(self, params): """ Log-likelihood of the multinomial logit model. Parameters ---------- params : array_like The parameters of the multinomial logit model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes ----- .. math:: \\ln L=\\sum_{i=1}^{n}\\sum_{j=0}^{J}d_{ij}\\ln \\left(\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)} {\\sum_{k=0}^{J} \\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right) where :math:`d_{ij}=1` if individual `i` chose alternative `j` and 0 if not. """ params = params.reshape(self.K, -1, order='F') d = self.wendog logprob = np.log(self.cdf(np.dot(self.exog,params))) return np.sum(d * logprob) def loglikeobs(self, params): """ Log-likelihood of the multinomial logit model for each observation. Parameters ---------- params : array_like The parameters of the multinomial logit model. Returns ------- loglike : array_like The log likelihood for each observation of the model evaluated at `params`. See Notes Notes ----- .. math:: \\ln L_{i}=\\sum_{j=0}^{J}d_{ij}\\ln \\left(\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)} {\\sum_{k=0}^{J} \\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right) for observations :math:`i=1,...,n` where :math:`d_{ij}=1` if individual `i` chose alternative `j` and 0 if not. """ params = params.reshape(self.K, -1, order='F') d = self.wendog logprob = np.log(self.cdf(np.dot(self.exog,params))) return d * logprob def score(self, params): """ Score matrix for multinomial logit model log-likelihood Parameters ---------- params : array The parameters of the multinomial logit model. Returns ------- score : ndarray, (K * (J-1),) The 2-d score vector, i.e. the first derivative of the loglikelihood function, of the multinomial logit model evaluated at `params`. Notes ----- .. math:: \\frac{\\partial\\ln L}{\\partial\\beta_{j}}=\\sum_{i}\\left(d_{ij}-\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right)x_{i} for :math:`j=1,...,J` In the multinomial model the score matrix is K x J-1 but is returned as a flattened array to work with the solvers. """ params = params.reshape(self.K, -1, order='F') firstterm = self.wendog[:,1:] - self.cdf(np.dot(self.exog, params))[:,1:] #NOTE: might need to switch terms if params is reshaped return np.dot(firstterm.T, self.exog).flatten() def loglike_and_score(self, params): """ Returns log likelihood and score, efficiently reusing calculations. Note that both of these returned quantities will need to be negated before being minimized by the maximum likelihood fitting machinery. """ params = params.reshape(self.K, -1, order='F') cdf_dot_exog_params = self.cdf(np.dot(self.exog, params)) loglike_value = np.sum(self.wendog * np.log(cdf_dot_exog_params)) firstterm = self.wendog[:, 1:] - cdf_dot_exog_params[:, 1:] score_array = np.dot(firstterm.T, self.exog).flatten() return loglike_value, score_array def score_obs(self, params): """ Jacobian matrix for multinomial logit model log-likelihood Parameters ---------- params : array The parameters of the multinomial logit model. Returns ------- jac : array_like The derivative of the loglikelihood for each observation evaluated at `params`. Notes ----- .. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta_{j}}=\\left(d_{ij}-\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right)x_{i} for :math:`j=1,...,J`, for observations :math:`i=1,...,n` In the multinomial model the score vector is K x (J-1) but is returned as a flattened array. The Jacobian has the observations in rows and the flatteded array of derivatives in columns. """ params = params.reshape(self.K, -1, order='F') firstterm = self.wendog[:,1:] - self.cdf(np.dot(self.exog, params))[:,1:] #NOTE: might need to switch terms if params is reshaped return (firstterm[:,:,None] * self.exog[:,None,:]).reshape(self.exog.shape[0], -1) def hessian(self, params): """ Multinomial logit Hessian matrix of the log-likelihood Parameters ---------- params : array_like The parameters of the model Returns ------- hess : ndarray, (J*K, J*K) The Hessian, second derivative of loglikelihood function with respect to the flattened parameters, evaluated at `params` Notes ----- .. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta_{j}\\partial\\beta_{l}}=-\\sum_{i=1}^{n}\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\left[\\boldsymbol{1}\\left(j=l\\right)-\\frac{\\exp\\left(\\beta_{l}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right]x_{i}x_{l}^{\\prime} where :math:`\\boldsymbol{1}\\left(j=l\\right)` equals 1 if `j` = `l` and 0 otherwise. The actual Hessian matrix has J**2 * K x K elements. Our Hessian is reshaped to be square (J*K, J*K) so that the solvers can use it. This implementation does not take advantage of the symmetry of the Hessian and could probably be refactored for speed. """ params = params.reshape(self.K, -1, order='F') X = self.exog pr = self.cdf(np.dot(X,params)) partials = [] J = self.J K = self.K for i in range(J-1): for j in range(J-1): # this loop assumes we drop the first col. if i == j: partials.append(\ -np.dot(((pr[:,i+1]*(1-pr[:,j+1]))[:,None]*X).T,X)) else: partials.append(-np.dot(((pr[:,i+1]*-pr[:,j+1])[:,None]*X).T,X)) H = np.array(partials) # the developer's notes on multinomial should clear this math up H = np.transpose(H.reshape(J-1, J-1, K, K), (0, 2, 1, 3)).reshape((J-1)*K, (J-1)*K) return H #TODO: Weibull can replaced by a survival analsysis function # like stat's streg (The cox model as well) #class Weibull(DiscreteModel): # """ # Binary choice Weibull model # # Notes # ------ # This is unfinished and untested. # """ ##TODO: add analytic hessian for Weibull # def initialize(self): # pass # # def cdf(self, X): # """ # Gumbell (Log Weibull) cumulative distribution function # """ ## return np.exp(-np.exp(-X)) # return stats.gumbel_r.cdf(X) # # these two are equivalent. # # Greene table and discussion is incorrect. # # def pdf(self, X): # """ # Gumbell (LogWeibull) probability distribution function # """ # return stats.gumbel_r.pdf(X) # # def loglike(self, params): # """ # Loglikelihood of Weibull distribution # """ # X = self.exog # cdf = self.cdf(np.dot(X,params)) # y = self.endog # return np.sum(y*np.log(cdf) + (1-y)*np.log(1-cdf)) # # def score(self, params): # y = self.endog # X = self.exog # F = self.cdf(np.dot(X,params)) # f = self.pdf(np.dot(X,params)) # term = (y*f/F + (1 - y)*-f/(1-F)) # return np.dot(term,X) # # def hessian(self, params): # hess = nd.Jacobian(self.score) # return hess(params) # # def fit(self, start_params=None, method='newton', maxiter=35, tol=1e-08): ## The example had problems with all zero start values, Hessian = 0 # if start_params is None: # start_params = OLS(self.endog, self.exog).fit().params # mlefit = super(Weibull, self).fit(start_params=start_params, # method=method, maxiter=maxiter, tol=tol) # return mlefit # class NegativeBinomial(CountModel): __doc__ = """ Negative Binomial Model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. References ---------- Greene, W. 2008. "Functional forms for the negtive binomial model for count data". Economics Letters. Volume 99, Number 3, pp.585-590. Hilbe, J.M. 2011. "Negative binomial regression". Cambridge University Press. """ % {'params': base._model_params_doc, 'extra_params': """loglike_method : string Log-likelihood type. 'nb2','nb1', or 'geometric'. Fitted value :math:`\\mu` Heterogeneity parameter :math:`\\alpha` - nb2: Variance equal to :math:`\\mu + \\alpha\\mu^2` (most common) - nb1: Variance equal to :math:`\\mu + \\alpha\\mu` - geometric: Variance equal to :math:`\\mu + \\mu^2` offset : array_like Offset is added to the linear prediction with coefficient equal to 1. exposure : array_like Log(exposure) is added to the linear prediction with coefficient equal to 1. """ + base._missing_param_doc} def __init__(self, endog, exog, loglike_method='nb2', offset=None, exposure=None, missing='none', **kwargs): super(NegativeBinomial, self).__init__(endog, exog, offset=offset, exposure=exposure, missing=missing, **kwargs) self.loglike_method = loglike_method self._initialize() if loglike_method in ['nb2', 'nb1']: self.exog_names.append('alpha') self.k_extra = 1 else: self.k_extra = 0 # store keys for extras if we need to recreate model instance # we need to append keys that don't go to super self._init_keys.append('loglike_method') def _initialize(self): if self.loglike_method == 'nb2': self.hessian = self._hessian_nb2 self.score = self._score_nbin self.loglikeobs = self._ll_nb2 self._transparams = True # transform lnalpha -> alpha in fit elif self.loglike_method == 'nb1': self.hessian = self._hessian_nb1 self.score = self._score_nb1 self.loglikeobs = self._ll_nb1 self._transparams = True # transform lnalpha -> alpha in fit elif self.loglike_method == 'geometric': self.hessian = self._hessian_geom self.score = self._score_geom self.loglikeobs = self._ll_geometric else: raise ValueError('Likelihood type must "nb1", "nb2" ' 'or "geometric"') # Workaround to pickle instance methods def __getstate__(self): odict = self.__dict__.copy() # copy the dict since we change it del odict['hessian'] del odict['score'] del odict['loglikeobs'] return odict def __setstate__(self, indict): self.__dict__.update(indict) self._initialize() def _ll_nbin(self, params, alpha, Q=0): if np.any(np.iscomplex(params)) or np.iscomplex(alpha): gamma_ln = loggamma else: gamma_ln = gammaln endog = self.endog mu = self.predict(params) size = 1/alpha * mu**Q prob = size/(size+mu) coeff = (gamma_ln(size+endog) - gamma_ln(endog+1) - gamma_ln(size)) llf = coeff + size*np.log(prob) + endog*np.log(1-prob) return llf def _ll_nb2(self, params): if self._transparams: # got lnalpha during fit alpha = np.exp(params[-1]) else: alpha = params[-1] return self._ll_nbin(params[:-1], alpha, Q=0) def _ll_nb1(self, params): if self._transparams: # got lnalpha during fit alpha = np.exp(params[-1]) else: alpha = params[-1] return self._ll_nbin(params[:-1], alpha, Q=1) def _ll_geometric(self, params): # we give alpha of 1 because it's actually log(alpha) where alpha=0 return self._ll_nbin(params, 1, 0) def loglike(self, params): r""" Loglikelihood for negative binomial model Parameters ---------- params : array_like The parameters of the model. If `loglike_method` is nb1 or nb2, then the ancillary parameter is expected to be the last element. Returns ------- llf : float The loglikelihood value at `params` Notes ----- Following notation in Greene (2008), with negative binomial heterogeneity parameter :math:`\alpha`: .. math:: \lambda_i &= exp(X\beta) \\ \theta &= 1 / \alpha \\ g_i &= \theta \lambda_i^Q \\ w_i &= g_i/(g_i + \lambda_i) \\ r_i &= \theta / (\theta+\lambda_i) \\ ln \mathcal{L}_i &= ln \Gamma(y_i+g_i) - ln \Gamma(1+y_i) + g_iln (r_i) + y_i ln(1-r_i) where :math`Q=0` for NB2 and geometric and :math:`Q=1` for NB1. For the geometric, :math:`\alpha=0` as well. """ llf = np.sum(self.loglikeobs(params)) return llf def _score_geom(self, params): exog = self.exog y = self.endog[:, None] mu = self.predict(params)[:, None] dparams = exog * (y-mu)/(mu+1) return dparams.sum(0) def _score_nbin(self, params, Q=0): """ Score vector for NB2 model """ if self._transparams: # lnalpha came in during fit alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] exog = self.exog y = self.endog[:,None] mu = self.predict(params)[:,None] a1 = 1/alpha * mu**Q prob = a1 / (a1 + mu) # a1 aka "size" in _ll_nbin if Q == 1: # nb1 # Q == 1 --> a1 = mu / alpha --> prob = 1 / (alpha + 1) dgpart = digamma(y + a1) - digamma(a1) dparams = exog * a1 * (np.log(prob) + dgpart) dalpha = ((alpha * (y - mu * np.log(prob) - mu*(dgpart + 1)) - mu * (np.log(prob) + dgpart))/ (alpha**2*(alpha + 1))).sum() elif Q == 0: # nb2 dgpart = digamma(y + a1) - digamma(a1) dparams = exog*a1 * (y-mu)/(mu+a1) da1 = -alpha**-2 dalpha = (dgpart + np.log(a1) - np.log(a1+mu) - (y-mu)/(a1+mu)).sum() * da1 #multiply above by constant outside sum to reduce rounding error if self._transparams: return np.r_[dparams.sum(0), dalpha*alpha] else: return np.r_[dparams.sum(0), dalpha] def _score_nb1(self, params): return self._score_nbin(params, Q=1) def _hessian_geom(self, params): exog = self.exog y = self.endog[:,None] mu = self.predict(params)[:,None] # for dl/dparams dparams dim = exog.shape[1] hess_arr = np.empty((dim, dim)) const_arr = mu*(1+y)/(mu+1)**2 for i in range(dim): for j in range(dim): if j > i: continue hess_arr[i,j] = np.sum(-exog[:,i,None] * exog[:,j,None] * const_arr, axis=0) tri_idx = np.triu_indices(dim, k=1) hess_arr[tri_idx] = hess_arr.T[tri_idx] return hess_arr def _hessian_nb1(self, params): """ Hessian of NB1 model. """ if self._transparams: # lnalpha came in during fit alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] exog = self.exog y = self.endog[:,None] mu = self.predict(params)[:,None] a1 = mu/alpha dgpart = digamma(y + a1) - digamma(a1) prob = 1 / (1 + alpha) # equiv: a1 / (a1 + mu) # for dl/dparams dparams dim = exog.shape[1] hess_arr = np.empty((dim+1,dim+1)) #const_arr = a1*mu*(a1+y)/(mu+a1)**2 # not all of dparams dparams = exog / alpha * (np.log(prob) + dgpart) dmudb = exog*mu xmu_alpha = exog * a1 trigamma = (special.polygamma(1, a1 + y) - special.polygamma(1, a1)) for i in range(dim): for j in range(dim): if j > i: continue hess_arr[i,j] = np.sum(dparams[:,i,None] * dmudb[:,j,None] + xmu_alpha[:,i,None] * xmu_alpha[:,j,None] * trigamma, axis=0) tri_idx = np.triu_indices(dim, k=1) hess_arr[tri_idx] = hess_arr.T[tri_idx] # for dl/dparams dalpha da1 = -alpha**-2 dldpda = np.sum(-a1 * dparams + exog * a1 * (-trigamma*mu/alpha**2 - prob), axis=0) hess_arr[-1,:-1] = dldpda hess_arr[:-1,-1] = dldpda log_alpha = np.log(prob) alpha3 = alpha**3 alpha2 = alpha**2 mu2 = mu**2 dada = ((alpha3*mu*(2*log_alpha + 2*dgpart + 3) - 2*alpha3*y + 4*alpha2*mu*(log_alpha + dgpart) + alpha2 * (2*mu - y) + 2*alpha*mu2*trigamma + mu2 * trigamma + alpha2 * mu2 * trigamma + 2*alpha*mu*(log_alpha + dgpart) )/(alpha**4*(alpha2 + 2*alpha + 1))) hess_arr[-1,-1] = dada.sum() return hess_arr def _hessian_nb2(self, params): """ Hessian of NB2 model. """ if self._transparams: # lnalpha came in during fit alpha = np.exp(params[-1]) else: alpha = params[-1] a1 = 1/alpha params = params[:-1] exog = self.exog y = self.endog[:,None] mu = self.predict(params)[:,None] prob = a1 / (a1 + mu) dgpart = digamma(a1 + y) - digamma(a1) # for dl/dparams dparams dim = exog.shape[1] hess_arr = np.empty((dim+1,dim+1)) const_arr = a1*mu*(a1+y)/(mu+a1)**2 for i in range(dim): for j in range(dim): if j > i: continue hess_arr[i,j] = np.sum(-exog[:,i,None] * exog[:,j,None] * const_arr, axis=0) tri_idx = np.triu_indices(dim, k=1) hess_arr[tri_idx] = hess_arr.T[tri_idx] # for dl/dparams dalpha da1 = -alpha**-2 dldpda = -np.sum(mu*exog*(y-mu)*a1**2/(mu+a1)**2, axis=0) hess_arr[-1,:-1] = dldpda hess_arr[:-1,-1] = dldpda # for dl/dalpha dalpha #NOTE: polygamma(1,x) is the trigamma function da2 = 2*alpha**-3 dalpha = da1 * (dgpart + np.log(prob) - (y - mu)/(a1+mu)) dada = (da2 * dalpha/da1 + da1**2 * (special.polygamma(1, a1+y) - special.polygamma(1, a1) + 1/a1 - 1/(a1 + mu) + (y - mu)/(mu + a1)**2)).sum() hess_arr[-1,-1] = dada return hess_arr #TODO: replace this with analytic where is it used? def score_obs(self, params): sc = approx_fprime_cs(params, self.loglikeobs) return sc def _get_start_params_null(self): offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) const = (self.endog / np.exp(offset + exposure)).mean() params = [np.log(const)] mu = const * np.exp(offset + exposure) resid = self.endog - mu a = self._estimate_dispersion(mu, resid, df_resid=resid.shape[0] - 1) params.append(a) return np.array(params) _get_start_params_null.__doc__ = _get_start_params_null_docs def _estimate_dispersion(self, mu, resid, df_resid=None): if df_resid is None: df_resid = resid.shape[0] if self.loglike_method == 'nb2': #params.append(np.linalg.pinv(mu[:,None]).dot(resid**2 / mu - 1)) a = ((resid**2 / mu - 1) / mu).sum() / df_resid else: #self.loglike_method == 'nb1': a = (resid**2 / mu - 1).sum() / df_resid return a def fit(self, start_params=None, method='bfgs', maxiter=35, full_output=1, disp=1, callback=None, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs): # Note: don't let super handle robust covariance because it has # transformed params self._transparams = False # always define attribute if self.loglike_method.startswith('nb') and method not in ['newton', 'ncg']: self._transparams = True # in case same Model instance is refit elif self.loglike_method.startswith('nb'): # method is newton/ncg self._transparams = False # because we need to step in alpha space if start_params is None: # Use poisson fit as first guess. #TODO, Warning: this assumes exposure is logged offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0) if np.size(offset) == 1 and offset == 0: offset = None optim_kwds_prelim = {'disp': 0,'skip_hessian': True, 'warn_convergence': False} optim_kwds_prelim.update(kwargs.get('optim_kwds_prelim', {})) mod_poi = Poisson(self.endog, self.exog, offset=offset) res_poi = mod_poi.fit(**optim_kwds_prelim) start_params = res_poi.params if self.loglike_method.startswith('nb'): a = self._estimate_dispersion(res_poi.predict(), res_poi.resid, df_resid=res_poi.df_resid) start_params = np.append(start_params, max(0.05, a)) else: if self._transparams is True: # transform user provided start_params dispersion, see #3918 start_params = np.array(start_params, copy=True) start_params[-1] = np.log(start_params[-1]) if callback is None: # work around perfect separation callback #3895 callback = lambda *x: x mlefit = super(NegativeBinomial, self).fit(start_params=start_params, maxiter=maxiter, method=method, disp=disp, full_output=full_output, callback=callback, **kwargs) # TODO: Fix NBin _check_perfect_pred if self.loglike_method.startswith('nb'): # mlefit is a wrapped counts results self._transparams = False # don't need to transform anymore now # change from lnalpha to alpha if method not in ["newton", "ncg"]: mlefit._results.params[-1] = np.exp(mlefit._results.params[-1]) nbinfit = NegativeBinomialResults(self, mlefit._results) result = NegativeBinomialResultsWrapper(nbinfit) else: result = mlefit if cov_kwds is None: cov_kwds = {} #TODO: make this unnecessary? result._get_robustcov_results(cov_type=cov_type, use_self=True, use_t=use_t, **cov_kwds) return result def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): _validate_l1_method(method) if self.loglike_method.startswith('nb') and (np.size(alpha) == 1 and alpha!= 0): # don't penalize alpha if alpha is scalar k_params = self.exog.shape[1] + self.k_extra alpha = alpha * np.ones(k_params) alpha[-1] = 0 # alpha for regularized poisson to get starting values alpha_p = alpha[:-1] if (self.k_extra and np.size(alpha) > 1) else alpha self._transparams = False if start_params is None: # Use poisson fit as first guess. #TODO, Warning: this assumes exposure is logged offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0) if np.size(offset) == 1 and offset == 0: offset = None mod_poi = Poisson(self.endog, self.exog, offset=offset) start_params = mod_poi.fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=0, callback=callback, alpha=alpha_p, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs).params if self.loglike_method.startswith('nb'): start_params = np.append(start_params, 0.1) cntfit = super(CountModel, self).fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) discretefit = L1NegativeBinomialResults(self, cntfit) return L1NegativeBinomialResultsWrapper(discretefit) class NegativeBinomialP(CountModel): __doc__ = """ Generalized Negative Binomial (NB-P) model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. p : scalar P denotes parameterizations for NB-P regression. p=1 for NB-1 and p=2 for NB-2. Default is p=1. """ % {'params' : base._model_params_doc, 'extra_params' : """p: scalar P denotes parameterizations for NB regression. p=1 for NB-1 and p=2 for NB-2. Default is p=2. offset : array_like Offset is added to the linear prediction with coefficient equal to 1. exposure : array_like Log(exposure) is added to the linear prediction with coefficient equal to 1. """ + base._missing_param_doc} def __init__(self, endog, exog, p=2, offset=None, exposure=None, missing='none', **kwargs): super(NegativeBinomialP, self).__init__(endog, exog, offset=offset, exposure=exposure, missing=missing, **kwargs) self.parameterization = p self.exog_names.append('alpha') self.k_extra = 1 self._transparams = False def _get_init_kwds(self): kwds = super(NegativeBinomialP, self)._get_init_kwds() kwds['p'] = self.parameterization return kwds def loglike(self, params): """ Loglikelihood of Generalized Negative Binomial (NB-P) model Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. """ return np.sum(self.loglikeobs(params)) def loglikeobs(self, params): """ Loglikelihood for observations of Generalized Negative Binomial (NB-P) model Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : ndarray The log likelihood for each observation of the model evaluated at `params`. See Notes """ if self._transparams: alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] p = self.parameterization y = self.endog mu = self.predict(params) mu_p = mu**(2 - p) a1 = mu_p / alpha a2 = mu + a1 llf = (gammaln(y + a1) - gammaln(y + 1) - gammaln(a1) + a1 * np.log(a1) + y * np.log(mu) - (y + a1) * np.log(a2)) return llf def score_obs(self, params): """ Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood for each observations. Parameters ---------- params : array_like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` """ if self._transparams: alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] p = 2 - self.parameterization y = self.endog mu = self.predict(params) mu_p = mu**p a1 = mu_p / alpha a2 = mu + a1 a3 = y + a1 a4 = p * a1 / mu dgpart = digamma(a3) - digamma(a1) dgterm = dgpart + np.log(a1 / a2) + 1 - a3 / a2 # TODO: better name/interpretation for dgterm? dparams = (a4 * dgterm - a3 / a2 + y / mu) dparams = (self.exog.T * mu * dparams).T dalpha = -a1 / alpha * dgterm return np.concatenate((dparams, np.atleast_2d(dalpha).T), axis=1) def score(self, params): """ Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood Parameters ---------- params : array_like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` """ score = np.sum(self.score_obs(params), axis=0) if self._transparams: score[-1] == score[-1] ** 2 return score else: return score def hessian(self, params): """ Generalized Negative Binomial (NB-P) model hessian maxtrix of the log-likelihood Parameters ---------- params : array_like The parameters of the model Returns ------- hessian : ndarray, 2-D The hessian matrix of the model. """ if self._transparams: alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] p = 2 - self.parameterization y = self.endog exog = self.exog mu = self.predict(params) mu_p = mu**p a1 = mu_p / alpha a2 = mu + a1 a3 = y + a1 a4 = p * a1 / mu prob = a1 / a2 lprob = np.log(prob) dgpart = digamma(a3) - digamma(a1) pgpart = polygamma(1, a3) - polygamma(1, a1) dim = exog.shape[1] hess_arr = np.zeros((dim + 1, dim + 1)) coeff = mu**2 * (((1 + a4)**2 * a3 / a2**2 - a3 / a2 * (p - 1) * a4 / mu - y / mu**2 - 2 * a4 * (1 + a4) / a2 + p * a4 / mu * (lprob + dgpart + 2) - a4 / mu * (lprob + dgpart + 1) + a4**2 * pgpart) + (-(1 + a4) * a3 / a2 + y / mu + a4 * (lprob + dgpart + 1)) / mu) for i in range(dim): hess_arr[i, :-1] = np.sum(self.exog[:, :].T * self.exog[:, i] * coeff, axis=1) hess_arr[-1,:-1] = (self.exog[:, :].T * mu * a1 * ((1 + a4) * (1 - a3 / a2) / a2 - p * (lprob + dgpart + 2) / mu + p / mu * (a3 + p * a1) / a2 - a4 * pgpart) / alpha).sum(axis=1) da2 = (a1 * (2 * lprob + 2 * dgpart + 3 - 2 * a3 / a2 + a1 * pgpart - 2 * prob + prob * a3 / a2) / alpha**2) hess_arr[-1, -1] = da2.sum() tri_idx = np.triu_indices(dim + 1, k=1) hess_arr[tri_idx] = hess_arr.T[tri_idx] return hess_arr def _get_start_params_null(self): offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) q = self.parameterization - 1 const = (self.endog / np.exp(offset + exposure)).mean() params = [np.log(const)] mu = const * np.exp(offset + exposure) resid = self.endog - mu a = self._estimate_dispersion(mu, resid, df_resid=resid.shape[0] - 1) params.append(a) return np.array(params) _get_start_params_null.__doc__ = _get_start_params_null_docs def _estimate_dispersion(self, mu, resid, df_resid=None): q = self.parameterization - 1 if df_resid is None: df_resid = resid.shape[0] a = ((resid**2 / mu - 1) * mu**(-q)).sum() / df_resid return a def fit(self, start_params=None, method='bfgs', maxiter=35, full_output=1, disp=1, callback=None, use_transparams=False, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs): # TODO: Fix doc string """ use_transparams : bool This parameter enable internal transformation to impose non-negativity. True to enable. Default is False. use_transparams=True imposes the no underdispersion (alpha > 0) constaint. In case use_transparams=True and method="newton" or "ncg" transformation is ignored. """ if use_transparams and method not in ['newton', 'ncg']: self._transparams = True else: if use_transparams: warnings.warn('Parameter "use_transparams" is ignored', RuntimeWarning) self._transparams = False if start_params is None: offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0) if np.size(offset) == 1 and offset == 0: offset = None optim_kwds_prelim = {'disp': 0,'skip_hessian': True, 'warn_convergence': False} optim_kwds_prelim.update(kwargs.get('optim_kwds_prelim', {})) mod_poi = Poisson(self.endog, self.exog, offset=offset) res_poi = mod_poi.fit(**optim_kwds_prelim) start_params = res_poi.params a = self._estimate_dispersion(res_poi.predict(), res_poi.resid, df_resid=res_poi.df_resid) start_params = np.append(start_params, max(0.05, a)) if callback is None: # work around perfect separation callback #3895 callback = lambda *x: x mlefit = super(NegativeBinomialP, self).fit(start_params=start_params, maxiter=maxiter, method=method, disp=disp, full_output=full_output, callback=callback, **kwargs) if use_transparams and method not in ["newton", "ncg"]: self._transparams = False mlefit._results.params[-1] = np.exp(mlefit._results.params[-1]) nbinfit = NegativeBinomialResults(self, mlefit._results) result = NegativeBinomialResultsWrapper(nbinfit) if cov_kwds is None: cov_kwds = {} result._get_robustcov_results(cov_type=cov_type, use_self=True, use_t=use_t, **cov_kwds) return result fit.__doc__ += DiscreteModel.fit.__doc__ def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): _validate_l1_method(method) if np.size(alpha) == 1 and alpha!= 0: k_params = self.exog.shape[1] + self.k_extra alpha = alpha * np.ones(k_params) alpha[-1] = 0 alpha_p = alpha[:-1] if (self.k_extra and np.size(alpha) > 1) else alpha self._transparams = False if start_params is None: offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0) if np.size(offset) == 1 and offset == 0: offset = None mod_poi = Poisson(self.endog, self.exog, offset=offset) start_params = mod_poi.fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=0, callback=callback, alpha=alpha_p, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs).params start_params = np.append(start_params, 0.1) cntfit = super(CountModel, self).fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) discretefit = L1NegativeBinomialResults(self, cntfit) return L1NegativeBinomialResultsWrapper(discretefit) fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__ def predict(self, params, exog=None, exposure=None, offset=None, which='mean'): """ Predict response variable of a model given exogenous variables. Parameters ---------- params : array_like 2d array of fitted parameters of the model. Should be in the order returned from the model. exog : array_like, optional 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous variables. If you only have one regressor and would like to do prediction, you must provide a 2d array with shape[1] == 1. linear : bool, optional If True, returns the linear predictor dot(exog,params). Else, returns the value of the cdf at the linear predictor. offset : array_like, optional Offset is added to the linear prediction with coefficient equal to 1. exposure : array_like, optional Log(exposure) is added to the linear prediction with coefficient equal to 1. which :'mean', 'linear', 'prob', optional. 'mean' returns the exp of linear predictor exp(dot(exog,params)). 'linear' returns the linear predictor dot(exog,params). 'prob' return probabilities for counts from 0 to max(endog). Default is'mean'. Notes ----- """ if exog is None: exog = self.exog if exposure is None: exposure = getattr(self, 'exposure', 0) elif exposure!= 0: exposure = np.log(exposure) if offset is None: offset = getattr(self, 'offset', 0) fitted = np.dot(exog, params[:exog.shape[1]]) linpred = fitted + exposure + offset if which =='mean': return np.exp(linpred) elif which == 'linear': return linpred elif which =='prob': counts = np.atleast_2d(np.arange(0, np.max(self.endog)+1)) mu = self.predict(params, exog, exposure, offset) size, prob = self.convert_params(params, mu) return nbinom.pmf(counts, size[:,None], prob[:,None]) else: raise ValueError('keyword "which" = %s not recognized' % which) def convert_params(self, params, mu): alpha = params[-1] p = 2 - self.parameterization size = 1. / alpha * mu**p prob = size / (size + mu) return (size, prob) ### Results Class ### class DiscreteResults(base.LikelihoodModelResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for the discrete dependent variable models.", "extra_attr" : ""} def __init__(self, model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None): #super(DiscreteResults, self).__init__(model, params, # np.linalg.inv(-hessian), scale=1.) self.model = model self.df_model = model.df_model self.df_resid = model.df_resid self._cache = {} self.nobs = model.exog.shape[0] self.__dict__.update(mlefit.__dict__) if not hasattr(self, 'cov_type'): # do this only if super, i.e. mlefit didn't already add cov_type # robust covariance if use_t is not None: self.use_t = use_t if cov_type == 'nonrobust': self.cov_type = 'nonrobust' self.cov_kwds = {'description' : 'Standard Errors assume that the'+ 'covariance matrix of the errors is correctly'+ 'specified.'} else: if cov_kwds is None: cov_kwds = {} from statsmodels.base.covtype import get_robustcov_results get_robustcov_results(self, cov_type=cov_type, use_self=True, **cov_kwds) def __getstate__(self): # remove unpicklable methods mle_settings = getattr(self,'mle_settings', None) if mle_settings is not None: if 'callback' in mle_settings: mle_settings['callback'] = None if 'cov_params_func' in mle_settings: mle_settings['cov_params_func'] = None return self.__dict__ @cache_readonly def prsquared(self): """ McFadden's pseudo-R-squared. `1 - (llf / llnull)` """ return 1 - self.llf/self.llnull @cache_readonly def llr(self): """ Likelihood ratio chi-squared statistic; `-2*(llnull - llf)` """ return -2*(self.llnull - self.llf) @cache_readonly def llr_pvalue(self): """ The chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribution with degrees of freedom `df_model`. """ return stats.distributions.chi2.sf(self.llr, self.df_model) def set_null_options(self, llnull=None, attach_results=True, **kwds): """set fit options for Null (constant-only) model This resets the cache for related attributes which is potentially fragile. This only sets the option, the null model is estimated when llnull is accessed, if llnull is not yet in cache. Parameters ---------- llnull : None or float If llnull is not None, then the value will be directly assigned to the cached attribute "llnull". attach_results : bool Sets an internal flag whether the results instance of the null model should be attached. By default without calling this method, thenull model results are not attached and only the loglikelihood value llnull is stored. kwds : keyword arguments `kwds` are directly used as fit keyword arguments for the null model, overriding any provided defaults. Returns ------- no returns, modifies attributes of this instance """ # reset cache, note we need to add here anything that depends on # llnullor the null model. If something is missing, then the attribute # might be incorrect. self._cache.pop('llnull', None) self._cache.pop('llr', None) self._cache.pop('llr_pvalue', None) self._cache.pop('prsquared', None) if hasattr(self,'res_null'): del self.res_null if llnull is not None: self._cache['llnull'] = llnull self._attach_nullmodel = attach_results self._optim_kwds_null = kwds @cache_readonly def llnull(self): """ Value of the constant-only loglikelihood """ model = self.model kwds = model._get_init_kwds().copy() for key in getattr(model, '_null_drop_keys', []): del kwds[key] # TODO: what parameters to pass to fit? mod_null = model.__class__(model.endog, np.ones(self.nobs), **kwds) # TODO: consider catching and warning on convergence failure? # in the meantime, try hard to converge. see # TestPoissonConstrained1a.test_smoke optim_kwds = getattr(self, '_optim_kwds_null', {}).copy() if'start_params' in optim_kwds: # user provided sp_null = optim_kwds.pop('start_params') elif hasattr(model, '_get_start_params_null'): # get moment estimates if available sp_null = model._get_start_params_null() else: sp_null = None opt_kwds = dict(method='bfgs', warn_convergence=False, maxiter=10000, disp=0) opt_kwds.update(optim_kwds) if optim_kwds: res_null = mod_null.fit(start_params=sp_null, **opt_kwds) else: # this should be a reasonably method case across versions res_null = mod_null.fit(start_params=sp_null, method='nm', warn_convergence=False, maxiter=10000, disp=0) res_null = mod_null.fit(start_params=res_null.params, method='bfgs', warn_convergence=False, maxiter=10000, disp=0) if getattr(self, '_attach_nullmodel', False) is not False: self.res_null = res_null return res_null.llf @cache_readonly def fittedvalues(self): """ Linear predictor XB. """ return np.dot(self.model.exog, self.params[:self.model.exog.shape[1]]) @cache_readonly def resid_response(self): """ Respnose residuals. The response residuals are defined as `endog - fittedvalues` """ return self.model.endog - self.predict() @cache_readonly def aic(self): """ Akaike information criterion. `-2*(llf - p)` where `p` is the number of regressors including the intercept. """ return -2*(self.llf - (self.df_model+1)) @cache_readonly def bic(self): """ Bayesian information criterion. `-2*llf + ln(nobs)*p` where `p` is the number of regressors including the intercept. """ return -2*self.llf + np.log(self.nobs)*(self.df_model+1) def _get_endog_name(self, yname, yname_list): if yname is None: yname = self.model.endog_names if yname_list is None: yname_list = self.model.endog_names return yname, yname_list def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): """Get marginal effects of the fitted model. Parameters ---------- at : str, optional Options are: - 'overall', The average of the marginal effects at each observation. -'mean', The marginal effects at the mean of each regressor. -'median', The marginal effects at the median of each regressor. - 'zero', The marginal effects at zero for each regressor. - 'all', The marginal effects at each observation. If `at` is all only margeff will be available from the returned object. Note that if `exog` is specified, then marginal effects for all variables not specified by `exog` are calculated using the `at` option. method : str, optional Options are: - 'dydx' - dy/dx - No transformation is made and marginal effects are returned. This is the default. - 'eyex' - estimate elasticities of variables in `exog` -- d(lny)/d(lnx) - 'dyex' - estimate semielasticity -- dy/d(lnx) - 'eydx' - estimate semeilasticity -- d(lny)/dx Note that tranformations are done after each observation is calculated. Semi-elasticities for binary variables are computed using the midpoint method. 'dyex' and 'eyex' do not make sense for discrete variables. atexog : array_like, optional Optionally, you can provide the exogenous variables over which to get the marginal effects. This should be a dictionary with the key as the zero-indexed column number and the value of the dictionary. Default is None for all independent variables less the constant. dummy : bool, optional If False, treats binary variables (if present) as continuous. This is the default. Else if True, treats binary variables as changing from 0 to 1. Note that any variable that is either 0 or 1 is treated as binary. Each binary variable is treated separately for now. count : bool, optional If False, treats count variables (if present) as continuous. This is the default. Else if True, the marginal effect is the change in probabilities when each observation is increased by one. Returns ------- DiscreteMargins : marginal effects instance Returns an object that holds the marginal effects, standard errors, confidence intervals, etc. See `statsmodels.discrete.discrete_margins.DiscreteMargins` for more information. Notes ----- When using after Poisson, returns the expected number of events per period, assuming that the model is loglinear. """ from statsmodels.discrete.discrete_margins import DiscreteMargins return DiscreteMargins(self, (at, method, atexog, dummy, count)) def summary(self, yname=None, xname=None, title=None, alpha=.05, yname_list=None): """Summarize the Regression Results Parameters ---------- yname : str, optional Default is `y` xname : list[str], optional Names for the exogenous variables, default is "var_xx". Must match the number of parameters in the model title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ top_left = [('Dep. Variable:', None), ('Model:', [self.model.__class__.__name__]), ('Method:', ['MLE']), ('Date:', None), ('Time:', None), ('converged:', ["%s" % self.mle_retvals['converged']]), ] top_right = [('No. Observations:', None), ('Df Residuals:', None), ('Df Model:', None), ('Pseudo R-squ.:', ["%#6.4g" % self.prsquared]), ('Log-Likelihood:', None), ('LL-Null:', ["%#8.5g" % self.llnull]), ('LLR p-value:', ["%#6.4g" % self.llr_pvalue]) ] if hasattr(self, 'cov_type'): top_left.append(('Covariance Type:', [self.cov_type])) if title is None: title = self.model.__class__.__name__ +'' + "Regression Results" # boiler plate from statsmodels.iolib.summary import Summary smry = Summary() yname, yname_list = self._get_endog_name(yname, yname_list) # for top of table smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) # for parameters, etc smry.add_table_params(self, yname=yname_list, xname=xname, alpha=alpha, use_t=self.use_t) if hasattr(self, 'constraints'): smry.add_extra_txt(['Model has been estimated subject to linear ' 'equality constraints.']) return smry def summary2(self, yname=None, xname=None, title=None, alpha=.05, float_format="%.4f"): """Experimental function to summarize regression results Parameters ---------- yname : str Name of the dependent variable (optional) xname : list[str], optional List of strings of length equal to the number of parameters Names of the independent variables (optional) title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format : str print format for floats in parameters summary Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary2.Summary : class to hold summary results """ from statsmodels.iolib import summary2 smry = summary2.Summary() smry.add_base(results=self, alpha=alpha, float_format=float_format, xname=xname, yname=yname, title=title) if hasattr(self, 'constraints'): smry.add_text('Model has been estimated subject to linear ' 'equality constraints.') return smry class CountResults(DiscreteResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for count data", "extra_attr": ""} @cache_readonly def resid(self): """ Residuals Notes ----- The residuals for Count models are defined as .. math:: y - p where :math:`p = \\exp(X\\beta)`. Any exposure and offset variables are also handled. """ return self.model.endog - self.predict() class NegativeBinomialResults(CountResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for NegativeBinomial 1 and 2", "extra_attr": ""} @cache_readonly def lnalpha(self): """Natural log of alpha""" return np.log(self.params[-1]) @cache_readonly def lnalpha_std_err(self): """Natural log of standardized error""" return self.bse[-1] / self.params[-1] @cache_readonly def aic(self): # + 1 because we estimate alpha k_extra = getattr(self.model, 'k_extra', 0) return -2*(self.llf - (self.df_model + self.k_constant + k_extra)) @cache_readonly def bic(self): # + 1 because we estimate alpha k_extra = getattr(self.model, 'k_extra', 0) return -2*self.llf + np.log(self.nobs)*(self.df_model + self.k_constant + k_extra) class GeneralizedPoissonResults(NegativeBinomialResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for Generalized Poisson", "extra_attr": ""} @cache_readonly def _dispersion_factor(self): p = getattr(self.model, 'parameterization', 0) mu = self.predict() return (1 + self.params[-1] * mu**p)**2 class L1CountResults(DiscreteResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for count data fit by l1 regularization", "extra_attr" : _l1_results_attr} def __init__(self, model, cntfit): super(L1CountResults, self).__init__(model, cntfit) # self.trimmed is a boolean array with T/F telling whether or not that # entry in params has been set zero'd out. self.trimmed = cntfit.mle_retvals['trimmed'] self.nnz_params = (~self.trimmed).sum() # Set degrees of freedom. In doing so, # adjust for extra parameter in NegativeBinomial nb1 and nb2 # extra parameter is not included in df_model k_extra = getattr(self.model, 'k_extra', 0) self.df_model = self.nnz_params - 1 - k_extra self.df_resid = float(self.model.endog.shape[0] - self.nnz_params) + k_extra class PoissonResults(CountResults): def predict_prob(self, n=None, exog=None, exposure=None, offset=None, transform=True): """ Return predicted probability of each count level for each observation Parameters ---------- n : array_like or int The counts for which you want the probabilities. If n is None then the probabilities for each count from 0 to max(y) are given. Returns ------- ndarray A nobs x n array where len(`n`) columns are indexed by the count n. If n is None, then column 0 is the probability that each observation is 0, column 1 is the probability that each observation is 1, etc. """ if n is not None: counts = np.atleast_2d(n) else: counts = np.atleast_2d(np.arange(0, np.max(self.model.endog)+1)) mu = self.predict(exog=exog, exposure=exposure, offset=offset, transform=transform, linear=False)[:,None] # uses broadcasting return stats.poisson.pmf(counts, mu) @property def resid_pearson(self): """ Pearson residuals Notes ----- Pearson residuals are defined to be .. math:: r_j = \\frac{(y - M_jp_j)}{\\sqrt{M_jp_j(1-p_j)}} where :math:`p_j=cdf(X\\beta)` and :math:`M_j` is the total number of observations sharing the covariate pattern :math:`j`. For now :math:`M_j` is always set to 1. """ # Pearson residuals p = self.predict() # fittedvalues is still linear return (self.model.endog - p)/np.sqrt(p) class L1PoissonResults(L1CountResults, PoissonResults): pass class L1NegativeBinomialResults(L1CountResults, NegativeBinomialResults): pass class L1GeneralizedPoissonResults(L1CountResults, GeneralizedPoissonResults): pass class OrderedResults(DiscreteResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for ordered discrete data.", "extra_attr" : ""} pass class BinaryResults(DiscreteResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for binary data", "extra_attr" : ""} def pred_table(self, threshold=.5): """ Prediction table Parameters ---------- threshold : scalar Number between 0 and 1. Threshold above which a prediction is considered 1 and below which a prediction is considered 0. Notes ----- pred_table[i,j] refers to the number of times "i" was observed and the model predicted "j". Correct predictions are along the diagonal. """ model = self.model actual = model.endog pred = np.array(self.predict() > threshold, dtype=float) bins = np.array([0, 0.5, 1]) return np.histogram2d(actual, pred, bins=bins)[0] def summary(self, yname=None, xname=None, title=None, alpha=.05, yname_list=None): smry = super(BinaryResults, self).summary(yname, xname, title, alpha, yname_list) fittedvalues = self.model.cdf(self.fittedvalues) absprederror = np.abs(self.model.endog - fittedvalues) predclose_sum = (absprederror < 1e-4).sum() predclose_frac = predclose_sum / len(fittedvalues) # add warnings/notes etext = [] if predclose_sum == len(fittedvalues): # TODO: nobs? wstr = "Complete Separation: The results show that there is" wstr += "complete separation.\n" wstr += "In this case the Maximum Likelihood Estimator does " wstr += "not exist and the parameters\n" wstr += "are not identified." etext.append(wstr) elif predclose_frac > 0.1: # TODO: get better diagnosis wstr = "Possibly complete quasi-separation: A fraction " wstr += "%4.2f of observations can be\n" % predclose_frac wstr += "perfectly predicted. This might indicate that there " wstr += "is complete\nquasi-separation. In this case some " wstr += "parameters will not be identified." etext.append(wstr) if etext: smry.add_extra_txt(etext) return smry summary.__doc__ = DiscreteResults.summary.__doc__ @cache_readonly def resid_dev(self): """ Deviance residuals Notes ----- Deviance residuals are defined .. math:: d_j = \\pm\\left(2\\left[Y_j\\ln\\left(\\frac{Y_j}{M_jp_j}\\right) + (M_j - Y_j\\ln\\left(\\frac{M_j-Y_j}{M_j(1-p_j)} \\right) \\right] \\right)^{1/2} where :math:`p_j = cdf(X\\beta)` and :math:`M_j` is the total number of observations sharing the covariate pattern :math:`j`. For now :math:`M_j` is always set to 1. """ #These are the deviance residuals #model = self.model endog = self.model.endog #exog = model.exog # M = # of individuals that share a covariate pattern # so M[i] = 2 for i = two share a covariate pattern M = 1 p = self.predict() #Y_0 = np.where(exog == 0) #Y_M = np.where(exog == M) #NOTE: Common covariate patterns are not yet handled res = -(1-endog)*np.sqrt(2*M*np.abs(np.log(1-p))) + \ endog*np.sqrt(2*M*np.abs(np.log(p))) return res @cache_readonly def resid_pearson(self): """ Pearson residuals Notes ----- Pearson residuals are defined to be .. math:: r_j = \\frac{(y - M_jp_j)}{\\sqrt{M_jp_j(1-p_j)}} where :math:`p_j=cdf(X\\beta)` and :math:`M_j` is the total number of observations sharing the covariate pattern :math:`j`. For now :math:`M_j` is always set to 1. """ # Pearson residuals #model = self.model endog = self.model.endog #exog = model.exog # M = # of individuals that share a covariate pattern # so M[i] = 2 for i = two share a covariate pattern # use unique row pattern? M = 1 p = self.predict() return (endog - M*p)/np.sqrt(M*p*(1-p)) @cache_readonly def resid_response(self): """ The response residuals Notes ----- Response residuals are defined to be .. math:: y - p where :math:`p=cdf(X\\beta)`. """ return self.model.endog - self.predict() class LogitResults(BinaryResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for Logit Model", "extra_attr": ""} @cache_readonly def resid_generalized(self): """ Generalized residuals Notes ----- The generalized residuals for the Logit model are defined .. math:: y - p where :math:`p=cdf(X\\beta)`. This is the same as the `resid_response` for the Logit model. """ # Generalized residuals return self.model.endog - self.predict() class ProbitResults(BinaryResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for Probit Model", "extra_attr": ""} @cache_readonly def resid_generalized(self): """ Generalized residuals Notes ----- The generalized residuals for the Probit model are defined .. math:: y\\frac{\\phi(X\\beta)}{\\Phi(X\\beta)}-(1-y)\\frac{\\phi(X\\beta)}{1-\\Phi(X\\beta)} """ # generalized residuals model = self.model endog = model.endog XB = self.predict(linear=True) pdf = model.pdf(XB) cdf = model.cdf(XB) return endog * pdf/cdf - (1-endog)*pdf/(1-cdf) class L1BinaryResults(BinaryResults): __doc__ = _discrete_results_docs % {"one_line_description" : "Results instance for binary data fit by l1 regularization", "extra_attr" : _l1_results_attr} def __init__(self, model, bnryfit): super(L1BinaryResults, self).__init__(model, bnryfit) # self.trimmed is a boolean array with T/F telling whether or not that # entry in params has been set zero'd out. self.trimmed = bnryfit.mle_retvals['trimmed'] self.nnz_params = (~self.trimmed).sum() self.df_model = self.nnz_params - 1 self.df_resid = float(self.model.endog.shape[0] - self.nnz_params) class MultinomialResults(DiscreteResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for multinomial data", "extra_attr" : ""} def __init__(self, model, mlefit): super(MultinomialResults, self).__init__(model, mlefit) self.J = model.J self.K = model.K def _maybe_convert_ynames_int(self, ynames): # see if they're integers issue_warning = False msg = ('endog contains values are that not int-like. Uses string ' 'representation of value. Use integer-valued endog to ' 'suppress this warning.') for i in ynames: try: if ynames[i] % 1 == 0: ynames[i] = str(int(ynames[i])) else: issue_warning = True ynames[i] = str(ynames[i]) except TypeError: ynames[i] = str(ynames[i]) if issue_warning: import warnings warnings.warn(msg, SpecificationWarning) return ynames def _get_endog_name(self, yname, yname_list, all=False): """ If all is False, the first variable name is dropped """ model = self.model if yname is None: yname = model.endog_names if yname_list is None: ynames = model._ynames_map ynames = self._maybe_convert_ynames_int(ynames) # use range below to ensure sortedness ynames = [ynames[key] for key in range(int(model.J))] ynames = ['='.join([yname, name]) for name in ynames] if not all: yname_list = ynames[1:] # assumes first variable is dropped else: yname_list = ynames return yname, yname_list def pred_table(self): """ Returns the J x J prediction table. Notes ----- pred_table[i,j] refers to the number of times "i" was observed and the model predicted "j". Correct predictions are along the diagonal. """ ju = self.model.J - 1 # highest index # these are the actual, predicted indices #idx = lzip(self.model.endog, self.predict().argmax(1)) bins = np.concatenate(([0], np.linspace(0.5, ju - 0.5, ju), [ju])) return np.histogram2d(self.model.endog, self.predict().argmax(1), bins=bins)[0] @cache_readonly def bse(self): bse = np.sqrt(np.diag(self.cov_params())) return bse.reshape(self.params.shape, order='F') @cache_readonly def aic(self): return -2*(self.llf - (self.df_model+self.model.J-1)) @cache_readonly def bic(self): return -2*self.llf + np.log(self.nobs)*(self.df_model+self.model.J-1) def conf_int(self, alpha=.05, cols=None): confint = super(DiscreteResults, self).conf_int(alpha=alpha, cols=cols) return confint.transpose(2,0,1) def margeff(self): raise NotImplementedError("Use get_margeff instead") @cache_readonly def resid_misclassified(self): """ Residuals indicating which observations are misclassified. Notes ----- The residuals for the multinomial model are defined as .. math:: argmax(y_i) \\neq argmax(p_i) where :math:`argmax(y_i)` is the index of the category for the endogenous variable and :math:`argmax(p_i)` is the index of the predicted probabilities for each category. That is, the residual is a binary indicator that is 0 if the category with the highest predicted probability is the same as that of the observed variable and 1 otherwise. """ # it's 0 or 1 - 0 for correct prediction and 1 for a missed one return (self.model.wendog.argmax(1)!= self.predict().argmax(1)).astype(float) def summary2(self, alpha=0.05, float_format="%.4f"): """Experimental function to summarize regression results Parameters ---------- alpha : float significance level for the confidence intervals float_format : str print format for floats in parameters summary Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary2.Summary : class to hold summary results """ from statsmodels.iolib import summary2 smry = summary2.Summary() smry.add_dict(summary2.summary_model(self)) # One data frame per value of endog eqn = self.params.shape[1] confint = self.conf_int(alpha) for i in range(eqn): coefs = summary2.summary_params((self, self.params[:, i], self.bse[:, i], self.tvalues[:, i], self.pvalues[:, i], confint[i]), alpha=alpha) # Header must show value of endog level_str = self.model.endog_names +'='+ str(i) coefs[level_str] = coefs.index coefs = coefs.iloc[:, [-1, 0, 1, 2, 3, 4, 5]] smry.add_df(coefs, index=False, header=True, float_format=float_format) smry.add_title(results=self) return smry class L1MultinomialResults(MultinomialResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for multinomial data fit by l1 regularization", "extra_attr" : _l1_results_attr} def __init__(self, model, mlefit): super(L1MultinomialResults, self).__init__(model, mlefit) # self.trimmed is a boolean array with T/F telling whether or not that # entry in params has been set zero'd out. self.trimmed = mlefit.mle_retvals['trimmed'] self.nnz_params = (~self.trimmed).sum() # Note: J-1 constants self.df_model = self.nnz_params - (self.model.J - 1) self.df_resid = float(self.model.endog.shape[0] - self.nnz_params) #### Results Wrappers #### class OrderedResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(OrderedResultsWrapper, OrderedResults) class CountResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(CountResultsWrapper, CountResults) class NegativeBinomialResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(NegativeBinomialResultsWrapper, NegativeBinomialResults) class GeneralizedPoissonResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(GeneralizedPoissonResultsWrapper, GeneralizedPoissonResults) class PoissonResultsWrapper(lm.RegressionResultsWrapper): pass #_methods = { # "predict_prob" : "rows", # } #_wrap_methods = lm.wrap.union_dicts( # lm.RegressionResultsWrapper._wrap_methods, # _methods) wrap.populate_wrapper(PoissonResultsWrapper, PoissonResults) class L1CountResultsWrapper(lm.RegressionResultsWrapper): pass class L1PoissonResultsWrapper(lm.RegressionResultsWrapper): pass #_methods = { # "predict_prob" : "rows", # } #_wrap_methods = lm.wrap.union_dicts( # lm.RegressionResultsWrapper._wrap_methods, # _methods) wrap.populate_wrapper(L1PoissonResultsWrapper, L1PoissonResults) class L1NegativeBinomialResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1NegativeBinomialResultsWrapper, L1NegativeBinomialResults) class L1GeneralizedPoissonResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1GeneralizedPoissonResultsWrapper, L1GeneralizedPoissonResults) class BinaryResultsWrapper(lm.RegressionResultsWrapper): _attrs = {"resid_dev" : "rows", "resid_generalized" : "rows", "resid_pearson" : "rows", "resid_response" : "rows" } _wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs, _attrs) wrap.populate_wrapper(BinaryResultsWrapper, BinaryResults) class L1BinaryResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1BinaryResultsWrapper, L1BinaryResults) class MultinomialResultsWrapper(lm.RegressionResultsWrapper): _attrs = {"resid_misclassified" : "rows"} _wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs, _attrs) wrap.populate_wrapper(MultinomialResultsWrapper, MultinomialResults) class L1MultinomialResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1MultinomialResultsWrapper, L1MultinomialResults)

statsmodels__statsmodels

duration.rst

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statsmodels__statsmodels/docs/source/duration.rst

statsmodels__statsmodels/statsmodels/duration

Methods for Survival and Duration Analysis statsmodels.duration implements several standard methods for working with censored data. These methods are most commonly used when the data consist of durations between an origin time point and the time at which some event of interest occurred. A typical example is a medical study in which the origin is the time at which a subject is diagnosed with some condition, and the event of interest is death (or disease progression, recovery, etc.). Currently only right-censoring is handled. Right censoring occurs when we know that an event occurred after a given time t, but we do not know the exact event time. Survival function estimation and inference The statsmodels.api.SurvfuncRight class can be used to estimate a survival function using data that may be right censored. SurvfuncRight implements several inference procedures including confidence intervals for survival distribution quantiles, pointwise and simultaneous confidence bands for the survival function, and plotting procedures. The duration.survdiff function provides testing procedures for comparing survival distributions. Examples Here we create a SurvfuncRight object using data from the flchain study, which is available through the R datasets repository. We fit the survival distribution only for the female subjects. import statsmodels.api as sm data = sm.datasets.get_rdataset("flchain", "survival").data df = data.loc[data.sex == "F", :] sf = sm.SurvfuncRight(df["futime"], df["death"]) The main features of the fitted survival distribution can be seen by calling the summary method: sf.summary().head() We can obtain point estimates and confidence intervals for quantiles of the survival distribution. Since only around 30% of the subjects died during this study, we can only estimate quantiles below the 0.3 probability point: sf.quantile(0.25) sf.quantile_ci(0.25) To plot a single survival function, call the plot method: sf.plot() Since this is a large dataset with a lot of censoring, we may wish to not plot the censoring symbols: fig = sf.plot() ax = fig.get_axes()[0] pt = ax.get_lines()[1] pt.set_visible(False) We can also add a 95% simultaneous confidence band to the plot. Typically these bands only plotted for central part of the distribution. fig = sf.plot() lcb, ucb = sf.simultaneous_cb() ax = fig.get_axes()[0] ax.fill_between(sf.surv_times, lcb, ucb, color='lightgrey') ax.set_xlim(365, 365*10) ax.set_ylim(0.7, 1) ax.set_ylabel("Proportion alive") ax.set_xlabel("Days since enrollment") Here we plot survival functions for two groups (females and males) on the same axes: gb = data.groupby("sex") ax = plt.axes() sexes = [] for g in gb: sexes.append(g[0]) sf = sm.SurvfuncRight(g[1]["futime"], g[1]["death"]) sf.plot(ax) li = ax.get_lines() li[1].set_visible(False) li[3].set_visible(False) plt.figlegend((li[0], li[2]), sexes, "center right") plt.ylim(0.6, 1) ax.set_ylabel("Proportion alive") ax.set_xlabel("Days since enrollment") We can formally compare two survival distributions with survdiff, which implements several standard nonparametric procedures. The default procedure is the logrank test: stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex) Here are some of the other testing procedures implemented by survdiff: # Fleming-Harrington with p=1, i.e. weight by pooled survival time stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='fh', fh_p=1) # Gehan-Breslow, weight by number at risk stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='gb') # Tarone-Ware, weight by the square root of the number at risk stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='tw') Regression methods Proportional hazard regression models ("Cox models") are a regression technique for censored data. They allow variation in the time to an event to be explained in terms of covariates, similar to what is done in a linear or generalized linear regression model. These models express the covariate effects in terms of "hazard ratios", meaning the the hazard (instantaneous event rate) is multiplied by a given factor depending on the value of the covariates. Examples import statsmodels.api as sm import statsmodels.formula.api as smf data = sm.datasets.get_rdataset("flchain", "survival").data del data["chapter"] data = data.dropna() data["lam"] = data["lambda"] data["female"] = (data["sex"] == "F").astype(int) data["year"] = data["sample.yr"] - min(data["sample.yr"]) status = data["death"].values mod = smf.phreg("futime ~ 0 + age + female + creatinine + " "np.sqrt(kappa) + np.sqrt(lam) + year + mgus", data, status=status, ties="efron") rslt = mod.fit() print(rslt.summary()) See statsmodels-examples for more detailed examples. There are some notebook examples on the Wiki: Wiki notebooks for PHReg and Survival Analysis

import numpy as np import pandas as pd from scipy.stats.distributions import chi2, norm from statsmodels.graphics import utils def _calc_survfunc_right(time, status, weights=None, entry=None, compress=True, retall=True): """ Calculate the survival function and its standard error for a single group. """ # Convert the unique times to ranks (0, 1, 2,...) if entry is None: utime, rtime = np.unique(time, return_inverse=True) else: tx = np.concatenate((time, entry)) utime, rtime = np.unique(tx, return_inverse=True) rtime = rtime[0:len(time)] # Number of deaths at each unique time. ml = len(utime) if weights is None: d = np.bincount(rtime, weights=status, minlength=ml) else: d = np.bincount(rtime, weights=status*weights, minlength=ml) # Size of risk set just prior to each event time. if weights is None: n = np.bincount(rtime, minlength=ml) else: n = np.bincount(rtime, weights=weights, minlength=ml) if entry is not None: n = np.cumsum(n) - n rentry = np.searchsorted(utime, entry, side='left') if weights is None: n0 = np.bincount(rentry, minlength=ml) else: n0 = np.bincount(rentry, weights=weights, minlength=ml) n0 = np.cumsum(n0) - n0 n = n0 - n else: n = np.cumsum(n[::-1])[::-1] # Only retain times where an event occured. if compress: ii = np.flatnonzero(d > 0) d = d[ii] n = n[ii] utime = utime[ii] # The survival function probabilities. sp = 1 - d / n.astype(np.float64) ii = sp < 1e-16 sp[ii] = 1e-16 sp = np.log(sp) sp = np.cumsum(sp) sp = np.exp(sp) sp[ii] = 0 if not retall: return sp, utime, rtime, n, d # Standard errors if weights is None: # Greenwood's formula denom = n * (n - d) denom = np.clip(denom, 1e-12, np.inf) se = d / denom.astype(np.float64) se[(n == d) | (n == 0)] = np.nan se = np.cumsum(se) se = np.sqrt(se) locs = np.isfinite(se) | (sp!= 0) se[locs] *= sp[locs] se[~locs] = np.nan else: # Tsiatis' (1981) formula se = d / (n * n).astype(np.float64) se = np.cumsum(se) se = np.sqrt(se) return sp, se, utime, rtime, n, d def _calc_incidence_right(time, status, weights=None): """ Calculate the cumulative incidence function and its standard error. """ # Calculate the all-cause survival function. status0 = (status >= 1).astype(np.float64) sp, utime, rtime, n, d = _calc_survfunc_right(time, status0, weights, compress=False, retall=False) ngrp = int(status.max()) # Number of cause-specific deaths at each unique time. d = [] for k in range(ngrp): status0 = (status == k + 1).astype(np.float64) if weights is None: d0 = np.bincount(rtime, weights=status0, minlength=len(utime)) else: d0 = np.bincount(rtime, weights=status0*weights, minlength=len(utime)) d.append(d0) # The cumulative incidence function probabilities. ip = [] sp0 = np.r_[1, sp[:-1]] / n for k in range(ngrp): ip0 = np.cumsum(sp0 * d[k]) ip.append(ip0) # The standard error of the cumulative incidence function. if weights is not None: return ip, None, utime se = [] da = sum(d) for k in range(ngrp): ra = da / (n * (n - da)) v = ip[k]**2 * np.cumsum(ra) v -= 2 * ip[k] * np.cumsum(ip[k] * ra) v += np.cumsum(ip[k]**2 * ra) ra = (n - d[k]) * d[k] / n v += np.cumsum(sp0**2 * ra) ra = sp0 * d[k] / n v -= 2 * ip[k] * np.cumsum(ra) v += 2 * np.cumsum(ip[k] * ra) se.append(np.sqrt(v)) return ip, se, utime def _checkargs(time, status, entry, freq_weights, exog): if len(time)!= len(status): raise ValueError("time and status must have the same length") if entry is not None and (len(entry)!= len(time)): msg = "entry times and event times must have the same length" raise ValueError(msg) if entry is not None and np.any(entry >= time): msg = "Entry times must not occur on or after event times" raise ValueError(msg) if freq_weights is not None and (len(freq_weights)!= len(time)): raise ValueError("weights, time and status must have the same length") if exog is not None and (exog.shape[0]!= len(time)): raise ValueError("the rows of exog should align with time") class CumIncidenceRight(object): """ Estimation and inference for a cumulative incidence function. If J = 1, 2,... indicates the event type, the cumulative incidence function for cause j is: I(t, j) = P(T <= t and J=j) Only right censoring is supported. If frequency weights are provided, the point estimate is returned without a standard error. Parameters ---------- time : array_like An array of times (censoring times or event times) status : array_like If status >= 1 indicates which event occured at time t. If status = 0, the subject was censored at time t. title : string Optional title used for plots and summary output. freq_weights : array_like Optional frequency weights exog : array_like Optional, if present used to account for violation of independent censoring. bw_factor : float Band-width multiplier for kernel-based estimation. Only used if exog is provided. dimred : boolean If True, proportional hazards regression models are used to reduce exog to two columns by predicting overall events and censoring in two separate models. If False, exog is used directly for calculating kernel weights without dimension reduction. Attributes ---------- times : array_like The distinct times at which the incidence rates are estimated cinc : list of arrays cinc[k-1] contains the estimated cumulative incidence rates for outcome k=1,2,... cinc_se : list of arrays The standard errors for the values in `cinc`. Not available when exog and/or frequency weights are provided. Notes ----- When exog is provided, a local estimate of the cumulative incidence rate around each point is provided, and these are averaged to produce an estimate of the marginal cumulative incidence functions. The procedure is analogous to that described in Zeng (2004) for estimation of the marginal survival function. The approach removes bias resulting from dependent censoring when the censoring becomes independent conditioned on the columns of exog. References ---------- The Stata stcompet procedure: http://www.stata-journal.com/sjpdf.html?articlenum=st0059 Dinse, G. E. and M. G. Larson. 1986. A note on semi-Markov models for partially censored data. Biometrika 73: 379-386. Marubini, E. and M. G. Valsecchi. 1995. Analysing Survival Data from Clinical Trials and Observational Studies. Chichester, UK: John Wiley & Sons. D. Zeng (2004). Estimating marginal survival function by adjusting for dependent censoring using many covariates. Annals of Statistics 32:4. https://arxiv.org/pdf/math/0409180.pdf """ def __init__(self, time, status, title=None, freq_weights=None, exog=None, bw_factor=1., dimred=True): _checkargs(time, status, None, freq_weights, None) time = self.time = np.asarray(time) status = self.status = np.asarray(status) if freq_weights is not None: freq_weights = self.freq_weights = np.asarray(freq_weights) if exog is not None: from._kernel_estimates import _kernel_cumincidence exog = self.exog = np.asarray(exog) nobs = exog.shape[0] kw = nobs**(-1/3.0) * bw_factor kfunc = lambda x: np.exp(-x**2 / kw**2).sum(1) x = _kernel_cumincidence(time, status, exog, kfunc, freq_weights, dimred) self.times = x[0] self.cinc = x[1] return x = _calc_incidence_right(time, status, freq_weights) self.cinc = x[0] self.cinc_se = x[1] self.times = x[2] self.title = "" if not title else title class SurvfuncRight(object): """ Estimation and inference for a survival function. The survival function S(t) = P(T > t) is the probability that an event time T is greater than t. This class currently only supports right censoring. Parameters ---------- time : array_like An array of times (censoring times or event times) status : array_like Status at the event time, status==1 is the 'event' (e.g. death, failure), meaning that the event occurs at the given value in `time`; status==0 indicates that censoring has occured, meaning that the event occurs after the given value in `time`. entry : array_like, optional An array of entry times for handling left truncation (the subject is not in the risk set on or before the entry time) title : string Optional title used for plots and summary output. freq_weights : array_like Optional frequency weights exog : array_like Optional, if present used to account for violation of independent censoring. bw_factor : float Band-width multiplier for kernel-based estimation. Only used if exog is provided. Attributes ---------- surv_prob : array_like The estimated value of the survivor function at each time point in `surv_times`. surv_prob_se : array_like The standard errors for the values in `surv_prob`. Not available if exog is provided. surv_times : array_like The points where the survival function changes. n_risk : array_like The number of subjects at risk just before each time value in `surv_times`. Not available if exog is provided. n_events : array_like The number of events (e.g. deaths) that occur at each point in `surv_times`. Not available if exog is provided. Notes ----- If exog is None, the standard Kaplan-Meier estimator is used. If exog is not None, a local estimate of the marginal survival function around each point is constructed, and these are then averaged. This procedure gives an estimate of the marginal survival function that accounts for dependent censoring as long as the censoring becomes independent when conditioning on the covariates in exog. See Zeng et al. (2004) for details. References ---------- D. Zeng (2004). Estimating marginal survival function by adjusting for dependent censoring using many covariates. Annals of Statistics 32:4. https://arxiv.org/pdf/math/0409180.pdf """ def __init__(self, time, status, entry=None, title=None, freq_weights=None, exog=None, bw_factor=1.): _checkargs(time, status, entry, freq_weights, exog) time = self.time = np.asarray(time) status = self.status = np.asarray(status) if freq_weights is not None: freq_weights = self.freq_weights = np.asarray(freq_weights) if entry is not None: entry = self.entry = np.asarray(entry) if exog is not None: if entry is not None: raise ValueError("exog and entry cannot both be present") from._kernel_estimates import _kernel_survfunc exog = self.exog = np.asarray(exog) nobs = exog.shape[0] kw = nobs**(-1/3.0) * bw_factor kfunc = lambda x: np.exp(-x**2 / kw**2).sum(1) x = _kernel_survfunc(time, status, exog, kfunc, freq_weights) self.surv_prob = x[0] self.surv_times = x[1] return x = _calc_survfunc_right(time, status, weights=freq_weights, entry=entry) self.surv_prob = x[0] self.surv_prob_se = x[1] self.surv_times = x[2] self.n_risk = x[4] self.n_events = x[5] self.title = "" if not title else title def plot(self, ax=None): """ Plot the survival function. Examples -------- Change the line color: >>> import statsmodels.api as sm >>> data = sm.datasets.get_rdataset("flchain", "survival").data >>> df = data.loc[data.sex == "F", :] >>> sf = sm.SurvfuncRight(df["futime"], df["death"]) >>> fig = sf.plot() >>> ax = fig.get_axes()[0] >>> li = ax.get_lines() >>> li[0].set_color('purple') >>> li[1].set_color('purple') Don't show the censoring points: >>> fig = sf.plot() >>> ax = fig.get_axes()[0] >>> li = ax.get_lines() >>> li[1].set_visible(False) """ return plot_survfunc(self, ax) def quantile(self, p): """ Estimated quantile of a survival distribution. Parameters ---------- p : float The probability point at which the quantile is determined. Returns the estimated quantile. """ # SAS uses a strict inequality here. ii = np.flatnonzero(self.surv_prob < 1 - p) if len(ii) == 0: return np.nan return self.surv_times[ii[0]] def quantile_ci(self, p, alpha=0.05, method='cloglog'): """ Returns a confidence interval for a survival quantile. Parameters ---------- p : float The probability point for which a confidence interval is determined. alpha : float The confidence interval has nominal coverage probability 1 - `alpha`. method : string Function to use for g-transformation, must be... Returns ------- lb : float The lower confidence limit. ub : float The upper confidence limit. Notes ----- The confidence interval is obtained by inverting Z-tests. The limits of the confidence interval will always be observed event times. References ---------- The method is based on the approach used in SAS, documented here: http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_lifetest_details03.htm """ tr = norm.ppf(1 - alpha / 2) method = method.lower() if method == "cloglog": g = lambda x: np.log(-np.log(x)) gprime = lambda x: -1 / (x * np.log(x)) elif method == "linear": g = lambda x: x gprime = lambda x: 1 elif method == "log": g = lambda x: np.log(x) gprime = lambda x: 1 / x elif method == "logit": g = lambda x: np.log(x / (1 - x)) gprime = lambda x: 1 / (x * (1 - x)) elif method == "asinsqrt": g = lambda x: np.arcsin(np.sqrt(x)) gprime = lambda x: 1 / (2 * np.sqrt(x) * np.sqrt(1 - x)) else: raise ValueError("unknown method") r = g(self.surv_prob) - g(1 - p) r /= (gprime(self.surv_prob) * self.surv_prob_se) ii = np.flatnonzero(np.abs(r) <= tr) if len(ii) == 0: return np.nan, np.nan lb = self.surv_times[ii[0]] if ii[-1] == len(self.surv_times) - 1: ub = np.inf else: ub = self.surv_times[ii[-1] + 1] return lb, ub def summary(self): """ Return a summary of the estimated survival function. The summary is a datafram containing the unique event times, estimated survival function values, and related quantities. """ df = pd.DataFrame(index=self.surv_times) df.index.name = "Time" df["Surv prob"] = self.surv_prob df["Surv prob SE"] = self.surv_prob_se df["num at risk"] = self.n_risk df["num events"] = self.n_events return df def simultaneous_cb(self, alpha=0.05, method="hw", transform="log"): """ Returns a simultaneous confidence band for the survival function. Parameters ---------- alpha : float `1 - alpha` is the desired simultaneous coverage probability for the confidence region. Currently alpha must be set to 0.05, giving 95% simultaneous intervals. method : string The method used to produce the simultaneous confidence band. Only the Hall-Wellner (hw) method is currently implemented. transform : string The used to produce the interval (note that the returned interval is on the survival probability scale regardless of which transform is used). Only `log` and `arcsin` are implemented. Returns ------- lcb : array_like The lower confidence limits corresponding to the points in `surv_times`. ucb : array_like The upper confidence limits corresponding to the points in `surv_times`. """ method = method.lower() if method!= "hw": msg = "only the Hall-Wellner (hw) method is implemented" raise ValueError(msg) if alpha!= 0.05: raise ValueError("alpha must be set to 0.05") transform = transform.lower() s2 = self.surv_prob_se**2 / self.surv_prob**2 nn = self.n_risk if transform == "log": denom = np.sqrt(nn) * np.log(self.surv_prob) theta = 1.3581 * (1 + nn * s2) / denom theta = np.exp(theta) lcb = self.surv_prob**(1/theta) ucb = self.surv_prob**theta elif transform == "arcsin": k = 1.3581 k *= (1 + nn * s2) / (2 * np.sqrt(nn)) k *= np.sqrt(self.surv_prob / (1 - self.surv_prob)) f = np.arcsin(np.sqrt(self.surv_prob)) v = np.clip(f - k, 0, np.inf) lcb = np.sin(v)**2 v = np.clip(f + k, -np.inf, np.pi/2) ucb = np.sin(v)**2 else: raise ValueError("Unknown transform") return lcb, ucb def survdiff(time, status, group, weight_type=None, strata=None, entry=None, **kwargs): """ Test for the equality of two survival distributions. Parameters ---------- time : array_like The event or censoring times. status : array_like The censoring status variable, status=1 indicates that the event occured, status=0 indicates that the observation was censored. group : array_like Indicators of the two groups weight_type : string The following weight types are implemented: None (default) : logrank test fh : Fleming-Harrington, weights by S^(fh_p), requires exponent fh_p to be provided as keyword argument; the weights are derived from S defined at the previous event time, and the first weight is always 1. gb : Gehan-Breslow, weights by the number at risk tw : Tarone-Ware, weights by the square root of the number at risk strata : array_like Optional stratum indicators for a stratified test entry : array_like Entry times to handle left truncation. The subject is not in the risk set on or before the entry time. Returns ------- chisq : The chi-square (1 degree of freedom) distributed test statistic value pvalue : The p-value for the chi^2 test """ # TODO: extend to handle more than two groups time = np.asarray(time) status = np.asarray(status) group = np.asarray(group) gr = np.unique(group) if len(gr)!= 2: raise ValueError("logrank only supports two groups") if strata is None: obs, var = _survdiff(time, status, group, weight_type, gr, entry, **kwargs) else: strata = np.asarray(strata) stu = np.unique(strata) obs, var = 0., 0. for st in stu: # could be more efficient? ii = (strata == st) obs1, var1 = _survdiff(time[ii], status[ii], group[ii], weight_type, gr, entry, **kwargs) obs += obs1 var += var1 zstat = obs / np.sqrt(var) # The chi^2 test statistic and p-value. chisq = zstat**2 pvalue = 1 - chi2.cdf(chisq, 1) return chisq, pvalue def _survdiff(time, status, group, weight_type, gr, entry=None, **kwargs): # logrank test for one stratum # Get the unique times. if entry is None: utimes, rtimes = np.unique(time, return_inverse=True) else: utimes, rtimes = np.unique(np.concatenate((time, entry)), return_inverse=True) rtimes = rtimes[0:len(time)] # Split entry times by group if present (should use pandas groupby) tse = [(gr[0], None), (gr[1], None)] if entry is not None: for k in 0, 1: ii = (group == gr[k]) entry1 = entry[ii] tse[k] = (gr[k], entry1) # Event count and risk set size at each time point, per group and overall. # TODO: should use Pandas groupby nrisk, obsv = [], [] ml = len(utimes) for g, entry0 in tse: mk = (group == g) n = np.bincount(rtimes, weights=mk, minlength=ml) ob = np.bincount(rtimes, weights=status*mk, minlength=ml) obsv.append(ob) if entry is not None: n = np.cumsum(n) - n rentry = np.searchsorted(utimes, entry0, side='left') n0 = np.bincount(rentry, minlength=ml) n0 = np.cumsum(n0) - n0 nr = n0 - n else: nr = np.cumsum(n[::-1])[::-1] nrisk.append(nr) obs = sum(obsv) nrisk_tot = sum(nrisk) # The variance of event counts in the first group. r = nrisk[0] / np.clip(nrisk_tot, 1e-10, np.inf) denom = nrisk_tot - 1 denom = np.clip(denom, 1e-10, np.inf) var = obs * r * (1 - r) * (nrisk_tot - obs) / denom # The expected number of events in the first group. exp1 = obs * r weights = None if weight_type is not None: weight_type = weight_type.lower() if weight_type == "gb": weights = nrisk_tot elif weight_type == "tw": weights = np.sqrt(nrisk_tot) elif weight_type == "fh": if "fh_p" not in kwargs: msg = "weight_type type 'fh' requires specification of fh_p" raise ValueError(msg) fh_p = kwargs["fh_p"] # Calculate the survivor function directly to avoid the # overhead of creating a SurvfuncRight object sp = 1 - obs / nrisk_tot.astype(np.float64) sp = np.log(sp) sp = np.cumsum(sp) sp = np.exp(sp) weights = sp**fh_p weights = np.roll(weights, 1) weights[0] = 1 else: raise ValueError("weight_type not implemented") # The Z-scale test statistic (compare to normal reference # distribution). ix = np.flatnonzero(nrisk_tot > 1) if weights is None: obs = np.sum(obsv[0][ix] - exp1[ix]) var = np.sum(var[ix]) else: obs = np.dot(weights[ix], obsv[0][ix] - exp1[ix]) var = np.dot(weights[ix]**2, var[ix]) return obs, var def plot_survfunc(survfuncs, ax=None): """ Plot one or more survivor functions. Parameters ---------- survfuncs : object or array_like A single SurvfuncRight object, or a list or SurvfuncRight objects that are plotted together. Returns ------- A figure instance on which the plot was drawn. Examples -------- Add a legend: >>> import statsmodels.api as sm >>> from statsmodels.duration.survfunc import plot_survfunc >>> data = sm.datasets.get_rdataset("flchain", "survival").data >>> df = data.loc[data.sex == "F", :] >>> sf0 = sm.SurvfuncRight(df["futime"], df["death"]) >>> sf1 = sm.SurvfuncRight(3.0 * df["futime"], df["death"]) >>> fig = plot_survfunc([sf0, sf1]) >>> ax = fig.get_axes()[0] >>> ax.set_position([0.1, 0.1, 0.64, 0.8]) >>> ha, lb = ax.get_legend_handles_labels() >>> leg = fig.legend((ha[0], ha[1]), (lb[0], lb[1]), 'center right') Change the line colors: >>> fig = plot_survfunc([sf0, sf1]) >>> ax = fig.get_axes()[0] >>> ax.set_position([0.1, 0.1, 0.64, 0.8]) >>> ha, lb = ax.get_legend_handles_labels() >>> ha[0].set_color('purple') >>> ha[1].set_color('orange') """ fig, ax = utils.create_mpl_ax(ax) # If we have only a single survival function to plot, put it into # a list. try: assert(type(survfuncs[0]) is SurvfuncRight) except: survfuncs = [survfuncs] for gx, sf in enumerate(survfuncs): # The estimated survival function does not include a point at # time 0, include it here for plotting. surv_times = np.concatenate(([0], sf.surv_times)) surv_prob = np.concatenate(([1], sf.surv_prob)) # If the final times are censoring times they are not included # in the survival function so we add them here mxt = max(sf.time) if mxt > surv_times[-1]: surv_times = np.concatenate((surv_times, [mxt])) surv_prob = np.concatenate((surv_prob, [surv_prob[-1]])) label = getattr(sf, "title", "Group %d" % (gx + 1)) li, = ax.step(surv_times, surv_prob, '-', label=label, lw=2, where='post') # Plot the censored points. ii = np.flatnonzero(np.logical_not(sf.status)) ti = sf.time[ii] jj = np.searchsorted(surv_times, ti) - 1 sp = surv_prob[jj] ax.plot(ti, sp, '+', ms=12, color=li.get_color(), label=label + " points") ax.set_ylim(0, 1.01) return fig """ Implementation of proportional hazards regression models for duration data that may be censored ("Cox models"). References ---------- T Therneau (1996). Extending the Cox model. Technical report. http://www.mayo.edu/research/documents/biostat-58pdf/DOC-10027288 G Rodriguez (2005). Non-parametric estimation in survival models. http://data.princeton.edu/pop509/NonParametricSurvival.pdf B Gillespie (2006). Checking the assumptions in the Cox proportional hazards model. http://www.mwsug.org/proceedings/2006/stats/MWSUG-2006-SD08.pdf """ import numpy as np from statsmodels.base import model import statsmodels.base.model as base from statsmodels.tools.decorators import cache_readonly _predict_docstring = """ Returns predicted values from the proportional hazards regression model. Parameters ----------%(params_doc)s exog : array_like Data to use as `exog` in forming predictions. If not provided, the `exog` values from the model used to fit the data are used.%(cov_params_doc)s endog : array_like Duration (time) values at which the predictions are made. Only used if pred_type is either 'cumhaz' or'surv'. If using model `exog`, defaults to model `endog` (time), but may be provided explicitly to make predictions at alternative times. strata : array_like A vector of stratum values used to form the predictions. Not used (may be 'None') if pred_type is 'lhr' or 'hr'. If `exog` is None, the model stratum values are used. If `exog` is not None and pred_type is'surv' or 'cumhaz', stratum values must be provided (unless there is only one stratum). offset : array_like Offset values used to create the predicted values. pred_type : string If 'lhr', returns log hazard ratios, if 'hr' returns hazard ratios, if'surv' returns the survival function, if 'cumhaz' returns the cumulative hazard function. Returns ------- A bunch containing two fields: `predicted_values` and `standard_errors`. Notes ----- Standard errors are only returned when predicting the log hazard ratio (pred_type is 'lhr'). Types `surv` and `cumhaz` require estimation of the cumulative hazard function. """ _predict_params_doc = """ params : array_like The proportional hazards model parameters.""" _predict_cov_params_docstring = """ cov_params : array_like The covariance matrix of the estimated `params` vector, used to obtain prediction errors if pred_type='lhr', otherwise optional.""" class PHSurvivalTime(object): def __init__(self, time, status, exog, strata=None, entry=None, offset=None): """ Represent a collection of survival times with possible stratification and left truncation. Parameters ---------- time : array_like The times at which either the event (failure) occurs or the observation is censored. status : array_like Indicates whether the event (failure) occurs at `time` (`status` is 1), or if `time` is a censoring time (`status` is 0). exog : array_like The exogeneous (covariate) data matrix, cases are rows and variables are columns. strata : array_like Grouping variable defining the strata. If None, all observations are in a single stratum. entry : array_like Entry (left truncation) times. The observation is not part of the risk set for times before the entry time. If None, the entry time is treated as being zero, which gives no left truncation. The entry time must be less than or equal to `time`. offset : array_like An optional array of offsets """ # Default strata if strata is None: strata = np.zeros(len(time), dtype=np.int32) # Default entry times if entry is None: entry = np.zeros(len(time)) # Parameter validity checks. n1, n2, n3, n4 = len(time), len(status), len(strata),\ len(entry) nv = [n1, n2, n3, n4] if max(nv)!= min(nv): raise ValueError("endog, status, strata, and " + "entry must all have the same length") if min(time) < 0: raise ValueError("endog must be non-negative") if min(entry) < 0: raise ValueError("entry time must be non-negative") # In Stata, this is entry >= time, in R it is >. if np.any(entry > time): raise ValueError("entry times may not occur " + "after event or censoring times") # Get the row indices for the cases in each stratum stu = np.unique(strata) #sth = {x: [] for x in stu} # needs >=2.7 sth = dict([(x, []) for x in stu]) for i,k in enumerate(strata): sth[k].append(i) stratum_rows = [np.asarray(sth[k], dtype=np.int32) for k in stu] stratum_names = stu # Remove strata with no events ix = [i for i,ix in enumerate(stratum_rows) if status[ix].sum() > 0] self.nstrat_orig = len(stratum_rows) stratum_rows = [stratum_rows[i] for i in ix] stratum_names = [stratum_names[i] for i in ix] # The number of strata nstrat = len(stratum_rows) self.nstrat = nstrat # Remove subjects whose entry time occurs after the last event # in their stratum. for stx,ix in enumerate(stratum_rows): last_failure = max(time[ix][status[ix] == 1]) # Stata uses < here, R uses <= ii = [i for i,t in enumerate(entry[ix]) if t <= last_failure] stratum_rows[stx] = stratum_rows[stx][ii] # Remove subjects who are censored before the first event in # their stratum. for stx,ix in enumerate(stratum_rows): first_failure = min(time[ix][status[ix] == 1]) ii = [i for i,t in enumerate(time[ix]) if t >= first_failure] stratum_rows[stx] = stratum_rows[stx][ii] # Order by time within each stratum for stx,ix in enumerate(stratum_rows): ii = np.argsort(time[ix]) stratum_rows[stx] = stratum_rows[stx][ii] if offset is not None: self.offset_s = [] for stx in range(nstrat): self.offset_s.append(offset[stratum_rows[stx]]) else: self.offset_s = None # Number of informative subjects self.n_obs = sum([len(ix) for ix in stratum_rows]) # Split everything by stratum self.time_s = [] self.exog_s = [] self.status_s = [] self.entry_s = [] for ix in stratum_rows: self.time_s.append(time[ix]) self.exog_s.append(exog[ix,:]) self.status_s.append(status[ix]) self.entry_s.append(entry[ix]) self.stratum_rows = stratum_rows self.stratum_names = stratum_names # Precalculate some indices needed to fit Cox models. # Distinct failure times within a stratum are always taken to # be sorted in ascending order. # # ufailt_ix[stx][k] is a list of indices for subjects who fail # at the k^th sorted unique failure time in stratum stx # # risk_enter[stx][k] is a list of indices for subjects who # enter the risk set at the k^th sorted unique failure time in # stratum stx # # risk_exit[stx][k] is a list of indices for subjects who exit # the risk set at the k^th sorted unique failure time in # stratum stx self.ufailt_ix, self.risk_enter, self.risk_exit, self.ufailt =\ [], [], [], [] for stx in range(self.nstrat): # All failure times ift = np.flatnonzero(self.status_s[stx] == 1) ft = self.time_s[stx][ift] # Unique failure times uft = np.unique(ft) nuft = len(uft) # Indices of cases that fail at each unique failure time #uft_map = {x:i for i,x in enumerate(uft)} # requires >=2.7 uft_map = dict([(x, i) for i,x in enumerate(uft)]) # 2.6 uft_ix = [[] for k in range(nuft)] for ix,ti in zip(ift,ft): uft_ix[uft_map[ti]].append(ix) # Indices of cases (failed or censored) that enter the # risk set at each unique failure time. risk_enter1 = [[] for k in range(nuft)] for i,t in enumerate(self.time_s[stx]): ix = np.searchsorted(uft, t, "right") - 1 if ix >= 0: risk_enter1[ix].append(i) # Indices of cases (failed or censored) that exit the # risk set at each unique failure time. risk_exit1 = [[] for k in range(nuft)] for i,t in enumerate(self.entry_s[stx]): ix = np.searchsorted(uft, t) risk_exit1[ix].append(i) self.ufailt.append(uft) self.ufailt_ix.append([np.asarray(x, dtype=np.int32) for x in uft_ix]) self.risk_enter.append([np.asarray(x, dtype=np.int32) for x in risk_enter1]) self.risk_exit.append([np.asarray(x, dtype=np.int32) for x in risk_exit1]) class PHReg(model.LikelihoodModel): """ Fit the Cox proportional hazards regression model for right censored data. Parameters ---------- endog : array_like The observed times (event or censoring) exog : 2D array_like The covariates or exogeneous variables status : array_like The censoring status values; status=1 indicates that an event occured (e.g. failure or death), status=0 indicates that the observation was right censored. If None, defaults to status=1 for all cases. entry : array_like The entry times, if left truncation occurs strata : array_like Stratum labels. If None, all observations are taken to be in a single stratum. ties : string The method used to handle tied times, must be either 'breslow' or 'efron'. offset : array_like Array of offset values missing : string The method used to handle missing data Notes ----- Proportional hazards regression models should not include an explicit or implicit intercept. The effect of an intercept is not identified using the partial likelihood approach. `endog`, `event`, `strata`, `entry`, and the first dimension of `exog` all must have the same length """ def __init__(self, endog, exog, status=None, entry=None, strata=None, offset=None, ties='breslow', missing='drop', **kwargs): # Default is no censoring if status is None: status = np.ones(len(endog)) super(PHReg, self).__init__(endog, exog, status=status, entry=entry, strata=strata, offset=offset, missing=missing, **kwargs) # endog and exog are automatically converted, but these are # not if self.status is not None: self.status = np.asarray(self.status) if self.entry is not None: self.entry = np.asarray(self.entry) if self.strata is not None: self.strata = np.asarray(self.strata) if self.offset is not None: self.offset = np.asarray(self.offset) self.surv = PHSurvivalTime(self.endog, self.status, self.exog, self.strata, self.entry, self.offset) self.nobs = len(self.endog) self.groups = None # TODO: not used? self.missing = missing self.df_resid = (np.float(self.exog.shape[0] - np.linalg.matrix_rank(self.exog))) self.df_model = np.float(np.linalg.matrix_rank(self.exog)) ties = ties.lower() if ties not in ("efron", "breslow"): raise ValueError("`ties` must be either `efron` or " + "`breslow`") self.ties = ties @classmethod def from_formula(cls, formula, data, status=None, entry=None, strata=None, offset=None, subset=None, ties='breslow', missing='drop', *args, **kwargs): """ Create a proportional hazards regression model from a formula and dataframe. Parameters ---------- formula : str or generic Formula object The formula specifying the model data : array_like The data for the model. See Notes. status : array_like The censoring status values; status=1 indicates that an event occured (e.g. failure or death), status=0 indicates that the observation was right censored. If None, defaults to status=1 for all cases. entry : array_like The entry times, if left truncation occurs strata : array_like Stratum labels. If None, all observations are taken to be in a single stratum. offset : array_like Array of offset values subset : array_like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a `pandas.DataFrame` ties : string The method used to handle tied times, must be either 'breslow' or 'efron'. missing : string The method used to handle missing data args : extra arguments These are passed to the model kwargs : extra keyword arguments These are passed to the model with one exception. The ``eval_env`` keyword is passed to patsy. It can be either a :class:`patsy:patsy.EvalEnvironment` object or an integer indicating the depth of the namespace to use. For example, the default ``eval_env=0`` uses the calling namespace. If you wish to use a "clean" environment set ``eval_env=-1``. Returns ------- model : PHReg model instance """ # Allow array arguments to be passed by column name. if isinstance(status, str): status = data[status] if isinstance(entry, str): entry = data[entry] if isinstance(strata, str): strata = data[strata] if isinstance(offset, str): offset = data[offset] import re terms = re.split(r"[+\-~]", formula) for term in terms: term = term.strip() if term in ("0", "1"): import warnings warnings.warn("PHReg formulas should not include any '0' or '1' terms") mod = super(PHReg, cls).from_formula(formula, data, status=status, entry=entry, strata=strata, offset=offset, subset=subset, ties=ties, missing=missing, drop_cols=["Intercept"], *args, **kwargs) return mod def fit(self, groups=None, **args): """ Fit a proportional hazards regression model. Parameters ---------- groups : array_like Labels indicating groups of observations that may be dependent. If present, the standard errors account for this dependence. Does not affect fitted values. Returns a PHregResults instance. """ # TODO process for missing values if groups is not None: if len(groups)!= len(self.endog): msg = ("len(groups) = %d and len(endog) = %d differ" % (len(groups), len(self.endog))) raise ValueError(msg) self.groups = np.asarray(groups) else: self.groups = None if 'disp' not in args: args['disp'] = False fit_rslts = super(PHReg, self).fit(**args) if self.groups is None: cov_params = fit_rslts.cov_params() else: cov_params = self.robust_covariance(fit_rslts.params) results = PHRegResults(self, fit_rslts.params, cov_params) return results def fit_regularized(self, method="elastic_net", alpha=0., start_params=None, refit=False, **kwargs): r""" Return a regularized fit to a linear regression model. Parameters ---------- method : Only the `elastic_net` approach is currently implemented. alpha : scalar or array_like The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as `params`, and contains a penalty weight for each coefficient. start_params : array_like Starting values for `params`. refit : bool If True, the model is refit using only the variables that have non-zero coefficients in the regularized fit. The refitted model is not regularized. Returns ------- A results object. Notes ----- The penalty is the ``elastic net`` penalty, which is a combination of L1 and L2 penalties. The function that is minimized is: .. math:: -loglike/n + alpha*((1-L1\_wt)*|params|_2^2/2 + L1\_wt*|params|_1) where :math:`|*|_1` and :math:`|*|_2` are the L1 and L2 norms. Post-estimation results are based on the same data used to select variables, hence may be subject to overfitting biases. The elastic_net method uses the following keyword arguments: maxiter : int Maximum number of iterations L1_wt : float Must be in [0, 1]. The L1 penalty has weight L1_wt and the L2 penalty has weight 1 - L1_wt. cnvrg_tol : float Convergence threshold for line searches zero_tol : float Coefficients below this threshold are treated as zero. """ from statsmodels.base.elastic_net import fit_elasticnet if method!= "elastic_net": raise ValueError("method for fit_regularied must be elastic_net") defaults = {"maxiter" : 50, "L1_wt" : 1, "cnvrg_tol" : 1e-10, "zero_tol" : 1e-10} defaults.update(kwargs) return fit_elasticnet(self, method=method, alpha=alpha, start_params=start_params, refit=refit, **defaults) def loglike(self, params): """ Returns the log partial likelihood function evaluated at `params`. """ if self.ties == "breslow": return self.breslow_loglike(params) elif self.ties == "efron": return self.efron_loglike(params) def score(self, params): """ Returns the score function evaluated at `params`. """ if self.ties == "breslow": return self.breslow_gradient(params) elif self.ties == "efron": return self.efron_gradient(params) def hessian(self, params): """ Returns the Hessian matrix of the log partial likelihood function evaluated at `params`. """ if self.ties == "breslow": return self.breslow_hessian(params) else: return self.efron_hessian(params) def breslow_loglike(self, params): """ Returns the value of the log partial likelihood function evaluated at `params`, using the Breslow method to handle tied times. """ surv = self.surv like = 0. # Loop over strata for stx in range(surv.nstrat): uft_ix = surv.ufailt_ix[stx] exog_s = surv.exog_s[stx] nuft = len(uft_ix) linpred = np.dot(exog_s, params) if surv.offset_s is not None: linpred += surv.offset_s[stx] linpred -= linpred.max() e_linpred = np.exp(linpred) xp0 = 0. # Iterate backward through the unique failure times. for i in range(nuft)[::-1]: # Update for new cases entering the risk set. ix = surv.risk_enter[stx][i] xp0 += e_linpred[ix].sum() # Account for all cases that fail at this point. ix = uft_ix[i] like += (linpred[ix] - np.log(xp0)).sum() # Update for cases leaving the risk set. ix = surv.risk_exit[stx][i] xp0 -= e_linpred[ix].sum() return like def efron_loglike(self, params): """ Returns the value of the log partial likelihood function evaluated at `params`, using the Efron method to handle tied times. """ surv = self.surv like = 0. # Loop over strata for stx in range(surv.nstrat): # exog and linear predictor for this stratum exog_s = surv.exog_s[stx] linpred = np.dot(exog_s, params) if surv.offset_s is not None: linpred += surv.offset_s[stx] linpred -= linpred.max() e_linpred = np.exp(linpred) xp0 = 0. # Iterate backward through the unique failure times. uft_ix = surv.ufailt_ix[stx] nuft = len(uft_ix) for i in range(nuft)[::-1]: # Update for new cases entering the risk set. ix = surv.risk_enter[stx][i] xp0 += e_linpred[ix].sum() xp0f = e_linpred[uft_ix[i]].sum() # Account for all cases that fail at this point. ix = uft_ix[i] like += linpred[ix].sum() m = len(ix) J = np.arange(m, dtype=np.float64) / m like -= np.log(xp0 - J*xp0f).sum() # Update for cases leaving the risk set. ix = surv.risk_exit[stx][i] xp0 -= e_linpred[ix].sum() return like def breslow_gradient(self, params): """ Returns the gradient of the log partial likelihood, using the Breslow method to handle tied times. """ surv = self.surv grad = 0. # Loop over strata for stx in range(surv.nstrat): # Indices of subjects in the stratum strat_ix = surv.stratum_rows[stx] # Unique failure times in the stratum uft_ix = surv.ufailt_ix[stx] nuft = len(uft_ix) # exog and linear predictor for the stratum exog_s = surv.exog_s[stx] linpred = np.dot(exog_s, params) if surv.offset_s is not None: linpred += surv.offset_s[stx] linpred -= linpred.max() e_linpred = np.exp(linpred) xp0, xp1 = 0., 0. # Iterate backward through the unique failure times. for i in range(nuft)[::-1]: # Update for new cases entering the risk set. ix = surv.risk_enter[stx][i] if len(ix) > 0: v = exog_s[ix,:] xp0 += e_linpred[ix].sum() xp1 += (e_linpred[ix][:,None] * v).sum(0) # Account for all cases that fail at this point. ix = uft_ix[i] grad += (exog_s[ix,:] - xp1 / xp0).sum(0) # Update for cases leaving the risk set. ix = surv.risk_exit[stx][i] if len(ix) > 0: v = exog_s[ix,:] xp0 -= e_linpred[ix].sum() xp1 -= (e_linpred[ix][:,None] * v).sum(0) return grad def efron_gradient(self, params): """ Returns the gradient of the log partial likelihood evaluated at `params`, using the Efron method to handle tied times. """ surv = self.surv grad = 0. # Loop over strata for stx in range(surv.nstrat): # Indices of cases in the stratum strat_ix = surv.stratum_rows[stx] # exog and linear predictor of the stratum exog_s = surv.exog_s[stx] linpred = np.dot(exog_s, params) if surv.offset_s is not None: linpred += surv.offset_s[stx] linpred -= linpred.max() e_linpred = np.exp(linpred) xp0, xp1 = 0., 0. # Iterate backward through the unique failure times. uft_ix = surv.ufailt_ix[stx] nuft = len(uft_ix) for i in range(nuft)[::-1]: # Update for new cases entering the risk set. ix = surv.risk_enter[stx][i] if len(ix) > 0: v = exog_s[ix,:] xp0 += e_linpred[ix].sum() xp1 += (e_linpred[ix][:,None] * v).sum(0) ixf = uft_ix[i] if len(ixf) > 0: v = exog_s[ixf,:] xp0f = e_linpred[ixf].sum() xp1f = (e_linpred[ixf][:,None] * v).sum(0) # Consider all cases that fail at this point. grad += v.sum(0) m = len(ixf) J = np.arange(m, dtype=np.float64) / m numer = xp1 - np.outer(J, xp1f) denom = xp0 - np.outer(J, xp0f) ratio = numer / denom rsum = ratio.sum(0) grad -= rsum # Update for cases leaving the risk set. ix = surv.risk_exit[stx][i] if len(ix) > 0: v = exog_s[ix,:] xp0 -= e_linpred[ix].sum() xp1 -= (e_linpred[ix][:,None] * v).sum(0) return grad def breslow_hessian(self, params): """ Returns the Hessian of the log partial likelihood evaluated at `params`, using the Breslow method to handle tied times. """ surv = self.surv hess = 0. # Loop over strata for stx in range(surv.nstrat): uft_ix = surv.ufailt_ix[stx] nuft = len(uft_ix) exog_s = surv.exog_s[stx] linpred = np.dot(exog_s, params) if surv.offset_s is not None: linpred += surv.offset_s[stx] linpred -= linpred.max() e_linpred = np.exp(linpred) xp0, xp1, xp2 = 0., 0., 0. # Iterate backward through the unique failure times. for i in range(nuft)[::-1]: # Update for new cases entering the risk set. ix = surv.risk_enter[stx][i] if len(ix) > 0: xp0 += e_linpred[ix].sum() v = exog_s[ix,:] xp1 += (e_linpred[ix][:,None] * v).sum(0) elx = e_linpred[ix] xp2 += np.einsum("ij,ik,i->jk", v, v, elx) # Account for all cases that fail at this point. m = len(uft_ix[i]) hess += m*(xp2 / xp0 - np.outer(xp1, xp1) / xp0**2) # Update for new cases entering the risk set. ix = surv.risk_exit[stx][i] if len(ix) > 0: xp0 -= e_linpred[ix].sum() v = exog_s[ix,:] xp1 -= (e_linpred[ix][:,None] * v).sum(0) elx = e_linpred[ix] xp2 -= np.einsum("ij,ik,i->jk", v, v, elx) return -hess def efron_hessian(self, params): """ Returns the Hessian matrix of the partial log-likelihood evaluated at `params`, using the Efron method to handle tied times. """ surv = self.surv hess = 0. # Loop over strata for stx in range(surv.nstrat): exog_s = surv.exog_s[stx] linpred = np.dot(exog_s, params) if surv.offset_s is not None: linpred += surv.offset_s[stx] linpred -= linpred.max() e_linpred = np.exp(linpred) xp0, xp1, xp2 = 0., 0., 0. # Iterate backward through the unique failure times. uft_ix = surv.ufailt_ix[stx] nuft = len(uft_ix) for i in range(nuft)[::-1]: # Update for new cases entering the risk set. ix = surv.risk_enter[stx][i] if len(ix) > 0: xp0 += e_linpred[ix].sum() v = exog_s[ix,:] xp1 += (e_linpred[ix][:,None] * v).sum(0) elx = e_linpred[ix] xp2 += np.einsum("ij,ik,i->jk", v, v, elx) ixf = uft_ix[i] if len(ixf) > 0: v = exog_s[ixf,:] xp0f = e_linpred[ixf].sum() xp1f = (e_linpred[ixf][:,None] * v).sum(0) elx = e_linpred[ixf] xp2f = np.einsum("ij,ik,i->jk", v, v, elx) # Account for all cases that fail at this point. m = len(uft_ix[i]) J = np.arange(m, dtype=np.float64) / m c0 = xp0 - J*xp0f hess += xp2 * np.sum(1 / c0) hess -= xp2f * np.sum(J / c0) mat = (xp1[None, :] - np.outer(J, xp1f)) / c0[:, None] hess -= np.einsum("ij,ik->jk", mat, mat) # Update for new cases entering the risk set. ix = surv.risk_exit[stx][i] if len(ix) > 0: xp0 -= e_linpred[ix].sum() v = exog_s[ix,:] xp1 -= (e_linpred[ix][:,None] * v).sum(0) elx = e_linpred[ix] xp2 -= np.einsum("ij,ik,i->jk", v, v, elx) return -hess def robust_covariance(self, params): """ Returns a covariance matrix for the proportional hazards model regresion coefficient estimates that is robust to certain forms of model misspecification. Parameters ---------- params : ndarray The parameter vector at which the covariance matrix is calculated. Returns ------- The robust covariance matrix as a square ndarray. Notes ----- This function uses the `groups` argument to determine groups within which observations may be dependent. The covariance matrix is calculated using the Huber-White "sandwich" approach. """ if self.groups is None: raise ValueError("`groups` must be specified to calculate the robust covariance matrix") hess = self.hessian(params) score_obs = self.score_residuals(params) # Collapse grads = {} for i,g in enumerate(self.groups): if g not in grads: grads[g] = 0. grads[g] += score_obs[i, :] grads = np.asarray(list(grads.values())) mat = grads[None, :, :] mat = mat.T * mat mat = mat.sum(1) hess_inv = np.linalg.inv(hess) cmat = np.dot(hess_inv, np.dot(mat, hess_inv)) return cmat def score_residuals(self, params): """ Returns the score residuals calculated at a given vector of parameters. Parameters ---------- params : ndarray The parameter vector at which the score residuals are calculated. Returns ------- The score residuals, returned as a ndarray having the same shape as `exog`. Notes ----- Observations in a stratum with no observed events have undefined score residuals, and contain NaN in the returned matrix. """ surv = self.surv score_resid = np.zeros(self.exog.shape, dtype=np.float64) # Use to set undefined values to NaN. mask = np.zeros(self.exog.shape[0], dtype=np.int32) w_avg = self.weighted_covariate_averages(params) # Loop over strata for stx in range(surv.nstrat): uft_ix = surv.ufailt_ix[stx] exog_s = surv.exog_s[stx] nuft = len(uft_ix) strat_ix = surv.stratum_rows[stx] xp0 = 0. linpred = np.dot(exog_s, params) if surv.offset_s is not None: linpred += surv.offset_s[stx] linpred -= linpred.max() e_linpred = np.exp(linpred) at_risk_ix = set([]) # Iterate backward through the unique failure times. for i in range(nuft)[::-1]: # Update for new cases entering the risk set. ix = surv.risk_enter[stx][i] at_risk_ix |= set(ix) xp0 += e_linpred[ix].sum() atr_ix = list(at_risk_ix) leverage = exog_s[atr_ix, :] - w_avg[stx][i, :] # Event indicators d = np.zeros(exog_s.shape[0]) d[uft_ix[i]] = 1 # The increment in the cumulative hazard dchaz = len(uft_ix[i]) / xp0 # Piece of the martingale residual mrp = d[atr_ix] - e_linpred[atr_ix] * dchaz # Update the score residuals ii = strat_ix[atr_ix] score_resid[ii,:] += leverage * mrp[:, None] mask[ii] = 1 # Update for cases leaving the risk set. ix = surv.risk_exit[stx][i] at_risk_ix -= set(ix) xp0 -= e_linpred[ix].sum() jj = np.flatnonzero(mask == 0) if len(jj) > 0: score_resid[jj, :] = np.nan return score_resid def weighted_covariate_averages(self, params): """ Returns the hazard-weighted average of covariate values for subjects who are at-risk at a particular time. Parameters ---------- params : ndarray Parameter vector Returns ------- averages : list of ndarrays averages[stx][i,:] is a row vector containing the weighted average values (for all the covariates) of at-risk subjects a the i^th largest observed failure time in stratum `stx`, using the hazard multipliers as weights. Notes ----- Used to calculate leverages and score residuals. """ surv = self.surv averages = [] xp0, xp1 = 0., 0. # Loop over strata for stx in range(surv.nstrat): uft_ix = surv.ufailt_ix[stx] exog_s = surv.exog_s[stx] nuft = len(uft_ix) average_s = np.zeros((len(uft_ix), exog_s.shape[1]), dtype=np.float64) linpred = np.dot(exog_s, params) if surv.offset_s is not None: linpred += surv.offset_s[stx] linpred -= linpred.max() e_linpred = np.exp(linpred) # Iterate backward through the unique failure times. for i in range(nuft)[::-1]: # Update for new cases entering the risk set. ix = surv.risk_enter[stx][i] xp0 += e_linpred[ix].sum() xp1 += np.dot(e_linpred[ix], exog_s[ix, :]) average_s[i, :] = xp1 / xp0 # Update for cases leaving the risk set. ix = surv.risk_exit[stx][i] xp0 -= e_linpred[ix].sum() xp1 -= np.dot(e_linpred[ix], exog_s[ix, :]) averages.append(average_s) return averages def baseline_cumulative_hazard(self, params): """ Estimate the baseline cumulative hazard and survival functions. Parameters ---------- params : ndarray The model parameters. Returns ------- A list of triples (time, hazard, survival) containing the time values and corresponding cumulative hazard and survival function values for each stratum. Notes ----- Uses the Nelson-Aalen estimator. """ # TODO: some disagreements with R, not the same algorithm but # hard to deduce what R is doing. Our results are reasonable. surv = self.surv rslt = [] # Loop over strata for stx in range(surv.nstrat): uft = surv.ufailt[stx] uft_ix = surv.ufailt_ix[stx] exog_s = surv.exog_s[stx] nuft = len(uft_ix) linpred = np.dot(exog_s, params) if surv.offset_s is not None: linpred += surv.offset_s[stx] e_linpred = np.exp(linpred) xp0 = 0. h0 = np.zeros(nuft, dtype=np.float64) # Iterate backward through the unique failure times. for i in range(nuft)[::-1]: # Update for new cases entering the risk set. ix = surv.risk_enter[stx][i] xp0 += e_linpred[ix].sum() # Account for all cases that fail at this point. ix = uft_ix[i] h0[i] = len(ix) / xp0 # Update for cases leaving the risk set. ix = surv.risk_exit[stx][i] xp0 -= e_linpred[ix].sum() cumhaz = np.cumsum(h0) - h0 current_strata_surv = np.exp(-cumhaz) rslt.append([uft, cumhaz, current_strata_surv]) return rslt def baseline_cumulative_hazard_function(self, params): """ Returns a function that calculates the baseline cumulative hazard function for each stratum. Parameters ---------- params : ndarray The model parameters. Returns ------- A dict mapping stratum names to the estimated baseline cumulative hazard function. """ from scipy.interpolate import interp1d surv = self.surv base = self.baseline_cumulative_hazard(params) cumhaz_f = {} for stx in range(surv.nstrat): time_h = base[stx][0] cumhaz = base[stx][1] time_h = np.r_[-np.inf, time_h, np.inf] cumhaz = np.r_[cumhaz[0], cumhaz, cumhaz[-1]] func = interp1d(time_h, cumhaz, kind='zero') cumhaz_f[self.surv.stratum_names[stx]] = func return cumhaz_f def predict(self, params, exog=None, cov_params=None, endog=None, strata=None, offset=None, pred_type="lhr"): # docstring attached below pred_type = pred_type.lower() if pred_type not in ["lhr", "hr", "surv", "cumhaz"]: msg = "Type %s not allowed for prediction" % pred_type raise ValueError(msg) class bunch: predicted_values = None standard_errors = None ret_val = bunch() # Don't do anything with offset here because we want to allow # different offsets to be specified even if exog is the model # exog. exog_provided = True if exog is None: exog = self.exog exog_provided = False lhr = np.dot(exog, params) if offset is not None: lhr += offset # Never use self.offset unless we are also using self.exog elif self.offset is not None and not exog_provided: lhr += self.offset # Handle lhr and hr prediction first, since they don't make # use of the hazard function. if pred_type == "lhr": ret_val.predicted_values = lhr if cov_params is not None: mat = np.dot(exog, cov_params) va = (mat * exog).sum(1) ret_val.standard_errors = np.sqrt(va) return ret_val hr = np.exp(lhr) if pred_type == "hr": ret_val.predicted_values = hr return ret_val # Makes sure endog is defined if endog is None and exog_provided: msg = "If `exog` is provided `endog` must be provided." raise ValueError(msg) # Use model endog if using model exog elif endog is None and not exog_provided: endog = self.endog # Make sure strata is defined if strata is None: if exog_provided and self.surv.nstrat > 1: raise ValueError("`strata` must be provided") if self.strata is None: strata = [self.surv.stratum_names[0],] * len(endog) else: strata = self.strata cumhaz = np.nan * np.ones(len(endog), dtype=np.float64) stv = np.unique(strata) bhaz = self.baseline_cumulative_hazard_function(params) for stx in stv: ix = np.flatnonzero(strata == stx) func = bhaz[stx] cumhaz[ix] = func(endog[ix]) * hr[ix] if pred_type == "cumhaz": ret_val.predicted_values = cumhaz elif pred_type == "surv": ret_val.predicted_values = np.exp(-cumhaz) return ret_val predict.__doc__ = _predict_docstring % {'params_doc': _predict_params_doc, 'cov_params_doc': _predict_cov_params_docstring} def get_distribution(self, params): """ Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case. Parameters ---------- params : array_like The proportional hazards model parameters. Returns ------- A list of objects of type scipy.stats.distributions.rv_discrete Notes ----- The distributions are obtained from a simple discrete estimate of the survivor function that puts all mass on the observed failure times within a stratum. """ # TODO: this returns a Python list of rv_discrete objects, so # nothing can be vectorized. It appears that rv_discrete does # not allow vectorization. surv = self.surv bhaz = self.baseline_cumulative_hazard(params) # The arguments to rv_discrete_float, first obtained by # stratum pk, xk = [], [] for stx in range(self.surv.nstrat): exog_s = surv.exog_s[stx] linpred = np.dot(exog_s, params) if surv.offset_s is not None: linpred += surv.offset_s[stx] e_linpred = np.exp(linpred) # The unique failure times for this stratum (the support # of the distribution). pts = bhaz[stx][0] # The individual cumulative hazards for everyone in this # stratum. ichaz = np.outer(e_linpred, bhaz[stx][1]) # The individual survival functions. usurv = np.exp(-ichaz) z = np.zeros((usurv.shape[0], 1)) usurv = np.concatenate((usurv, z), axis=1) # The individual survival probability masses. probs = -np.diff(usurv, 1) pk.append(probs) xk.append(np.outer(np.ones(probs.shape[0]), pts)) # Pad to make all strata have the same shape mxc = max([x.shape[1] for x in xk]) for k in range(self.surv.nstrat): if xk[k].shape[1] < mxc: xk1 = np.zeros((xk[k].shape[0], mxc)) pk1 = np.zeros((pk[k].shape[0], mxc)) xk1[:, 0:xk[k].shape[1]] = xk[k] pk1[:, 0:pk[k].shape[1]] = pk[k] xk[k], pk[k] = xk1, pk1 # Put the support points and probabilities into single matrices xka = np.nan * np.ones((len(self.endog), mxc)) pka = np.ones((len(self.endog), mxc), dtype=np.float64) / mxc for stx in range(self.surv.nstrat): ix = self.surv.stratum_rows[stx] xka[ix, :] = xk[stx] pka[ix, :] = pk[stx] dist = rv_discrete_float(xka, pka) return dist class PHRegResults(base.LikelihoodModelResults): ''' Class to contain results of fitting a Cox proportional hazards survival model. PHregResults inherits from statsmodels.LikelihoodModelResults Parameters ---------- See statsmodels.LikelihoodModelResults Attributes ---------- model : class instance PHreg model instance that called fit. normalized_cov_params : array The sampling covariance matrix of the estimates params : array The coefficients of the fitted model. Each coefficient is the log hazard ratio corresponding to a 1 unit difference in a single covariate while holding the other covariates fixed. bse : array The standard errors of the fitted parameters. See Also -------- statsmodels.LikelihoodModelResults ''' def __init__(self, model, params, cov_params, scale=1., covariance_type="naive"): # There is no scale parameter, but we need it for # meta-procedures that work with results. self.covariance_type = covariance_type self.df_resid = model.df_resid self.df_model = model.df_model super(PHRegResults, self).__init__(model, params, scale=1., normalized_cov_params=cov_params) @cache_readonly def standard_errors(self): """ Returns the standard errors of the parameter estimates. """ return np.sqrt(np.diag(self.cov_params())) @cache_readonly def bse(self): """ Returns the standard errors of the parameter estimates. """ return self.standard_errors def get_distribution(self): """ Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case. Returns ------- A list of objects of type scipy.stats.distributions.rv_discrete Notes ----- The distributions are obtained from a simple discrete estimate of the survivor function that puts all mass on the observed failure times wihtin a stratum. """ return self.model.get_distribution(self.params) def predict(self, endog=None, exog=None, strata=None, offset=None, transform=True, pred_type="lhr"): # docstring attached below return super(PHRegResults, self).predict(exog=exog, transform=transform, cov_params=self.cov_params(), endog=endog, strata=strata, offset=offset, pred_type=pred_type) predict.__doc__ = _predict_docstring % {'params_doc': '', 'cov_params_doc': ''} def _group_stats(self, groups): """ Descriptive statistics of the groups. """ gsizes = np.unique(groups, return_counts=True) gsizes = gsizes[1] return gsizes.min(), gsizes.max(), gsizes.mean(), len(gsizes) @cache_readonly def weighted_covariate_averages(self): """ The average covariate values within the at-risk set at each event time point, weighted by hazard. """ return self.model.weighted_covariate_averages(self.params) @cache_readonly def score_residuals(self): """ A matrix containing the score residuals. """ return self.model.score_residuals(self.params) @cache_readonly def baseline_cumulative_hazard(self): """ A list (corresponding to the strata) containing the baseline cumulative hazard function evaluated at the event points. """ return self.model.baseline_cumulative_hazard(self.params) @cache_readonly def baseline_cumulative_hazard_function(self): """ A list (corresponding to the strata) containing function objects that calculate the cumulative hazard function. """ return self.model.baseline_cumulative_hazard_function(self.params) @cache_readonly def schoenfeld_residuals(self): """ A matrix containing the Schoenfeld residuals. Notes ----- Schoenfeld residuals for censored observations are set to zero. """ surv = self.model.surv w_avg = self.weighted_covariate_averages # Initialize at NaN since rows that belong to strata with no # events have undefined residuals. sch_resid = np.nan*np.ones(self.model.exog.shape, dtype=np.float64) # Loop over strata for stx in range(surv.nstrat): uft = surv.ufailt[stx] exog_s = surv.exog_s[stx] time_s = surv.time_s[stx] strat_ix = surv.stratum_rows[stx] ii = np.searchsorted(uft, time_s) # These subjects are censored after the last event in # their stratum, so have empty risk sets and undefined # residuals. jj = np.flatnonzero(ii < len(uft)) sch_resid[strat_ix[jj], :] = exog_s[jj, :] - w_avg[stx][ii[jj], :] jj = np.flatnonzero(self.model.status == 0) sch_resid[jj, :] = np.nan return sch_resid @cache_readonly def martingale_residuals(self): """ The martingale residuals. """ surv = self.model.surv # Initialize at NaN since rows that belong to strata with no # events have undefined residuals. mart_resid = np.nan*np.ones(len(self.model.endog), dtype=np.float64) cumhaz_f_list = self.baseline_cumulative_hazard_function # Loop over strata for stx in range(surv.nstrat): cumhaz_f = cumhaz_f_list[stx] exog_s = surv.exog_s[stx] time_s = surv.time_s[stx] linpred = np.dot(exog_s, self.params) if surv.offset_s is not None: linpred += surv.offset_s[stx] e_linpred = np.exp(linpred) ii = surv.stratum_rows[stx] chaz = cumhaz_f(time_s) mart_resid[ii] = self.model.status[ii] - e_linpred * chaz return mart_resid def summary(self, yname=None, xname=None, title=None, alpha=.05): """ Summarize the proportional hazards regression results. Parameters ---------- yname : str, optional Default is `y` xname : list[str], optional Names for the exogenous variables, default is `x#` for ## in p the number of regressors. Must match the number of parameters in the model title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary2.Summary : class to hold summary results """ from statsmodels.iolib import summary2 from collections import OrderedDict smry = summary2.Summary() float_format = "%8.3f" info = OrderedDict() info["Model:"] = "PH Reg" if yname is None: yname = self.model.endog_names info["Dependent variable:"] = yname info["Ties:"] = self.model.ties.capitalize() info["Sample size:"] = str(self.model.surv.n_obs) info["Num. events:"] = str(int(sum(self.model.status))) if self.model.groups is not None: mn, mx, avg, num = self._group_stats(self.model.groups) info["Num groups:"] = "%.0f" % num info["Min group size:"] = "%.0f" % mn info["Max group size:"] = "%.0f" % mx info["Avg group size:"] = "%.1f" % avg if self.model.strata is not None: mn, mx, avg, num = self._group_stats(self.model.strata) info["Num strata:"] = "%.0f" % num info["Min stratum size:"] = "%.0f" % mn info["Max stratum size:"] = "%.0f" % mx info["Avg stratum size:"] = "%.1f" % avg smry.add_dict(info, align='l', float_format=float_format) param = summary2.summary_params(self, alpha=alpha) param = param.rename(columns={"Coef.": "log HR", "Std.Err.": "log HR SE"}) param.insert(2, "HR", np.exp(param["log HR"])) a = "[%.3f" % (alpha / 2) param.loc[:, a] = np.exp(param.loc[:, a]) a = "%.3f]" % (1 - alpha / 2) param.loc[:, a] = np.exp(param.loc[:, a]) if xname is not None: param.index = xname smry.add_df(param, float_format=float_format) smry.add_title(title=title, results=self) smry.add_text("Confidence intervals are for the hazard ratios") dstrat = self.model.surv.nstrat_orig - self.model.surv.nstrat if dstrat > 0: if dstrat == 1: smry.add_text("1 stratum dropped for having no events") else: smry.add_text("%d strata dropped for having no events" % dstrat) if self.model.entry is not None: n_entry = sum(self.model.entry!= 0) if n_entry == 1: smry.add_text("1 observation has a positive entry time") else: smry.add_text("%d observations have positive entry times" % n_entry) if self.model.groups is not None: smry.add_text("Standard errors account for dependence within groups") if hasattr(self, "regularized"): smry.add_text("Standard errors do not account for the regularization") return smry class rv_discrete_float(object): """ A class representing a collection of discrete distributions. Parameters ---------- xk : 2d array_like The support points, should be non-decreasing within each row. pk : 2d array_like The probabilities, should sum to one within each row. Notes ----- Each row of `xk`, and the corresponding row of `pk` describe a discrete distribution. `xk` and `pk` should both be two-dimensional ndarrays. Each row of `pk` should sum to 1. This class is used as a substitute for scipy.distributions. rv_discrete, since that class does not allow non-integer support points, or vectorized operations. Only a limited number of methods are implemented here compared to the other scipy distribution classes. """ def __init__(self, xk, pk): self.xk = xk self.pk = pk self.cpk = np.cumsum(self.pk, axis=1) def rvs(self): """ Returns a random sample from the discrete distribution. A vector is returned containing a single draw from each row of `xk`, using the probabilities of the corresponding row of `pk` """ n = self.xk.shape[0] u = np.random.uniform(size=n) ix = (self.cpk < u[:, None]).sum(1) ii = np.arange(n, dtype=np.int32) return self.xk[(ii,ix)] def mean(self): """ Returns a vector containing the mean values of the discrete distributions. A vector is returned containing the mean value of each row of `xk`, using the probabilities in the corresponding row of `pk`. """ return (self.xk * self.pk).sum(1) def var(self): """ Returns a vector containing the variances of the discrete distributions. A vector is returned containing the variance for each row of `xk`, using the probabilities in the corresponding row of `pk`. """ mn = self.mean() xkc = self.xk - mn[:, None] return (self.pk * (self.xk - xkc)**2).sum(1) def std(self): """ Returns a vector containing the standard deviations of the discrete distributions. A vector is returned containing the standard deviation for each row of `xk`, using the probabilities in the corresponding row of `pk`. """ return np.sqrt(self.var())

statsmodels__statsmodels

gam.rst

Example / Description

Generate example for this code

BSD 3-Clause New or Revised License

statsmodels__statsmodels/docs/source/gam.rst

Generalized Additive Models (GAM) Generalized Additive Models allow for penalized estimation of smooth terms in generalized linear models. See Module Reference for commands and arguments. Examples The following illustrates a Gaussian and a Poisson regression where categorical variables are treated as linear terms and the effect of two explanatory variables is captured by penalized B-splines. The data is from the automobile dataset https://archive.ics.uci.edu/ml/datasets/automobile We can load a dataframe with selected columns from the unit test module. import statsmodels.api as sm from statsmodels.gam.api import GLMGam, BSplines # import data from statsmodels.gam.tests.test_penalized import df_autos # create spline basis for weight and hp x_spline = df_autos[['weight', 'hp']] bs = BSplines(x_spline, df=[12, 10], degree=[3, 3]) # penalization weight alpha = np.array([21833888.8, 6460.38479]) gam_bs = GLMGam.from_formula('city_mpg ~ fuel + drive', data=df_autos, smoother=bs, alpha=alpha) res_bs = gam_bs.fit() print(res_bs.summary()) # plot smooth components res_bs.plot_partial(0, cpr=True) res_bs.plot_partial(1, cpr=True) alpha = np.array([8283989284.5829611, 14628207.58927821]) gam_bs = GLMGam.from_formula('city_mpg ~ fuel + drive', data=df_autos, smoother=bs, alpha=alpha, family=sm.families.Poisson()) res_bs = gam_bs.fit() print(res_bs.summary()) # Optimal penalization weights alpha can be obtaine through generalized # cross-validation or k-fold cross-validation. # The alpha above are from the unit tests against the R mgcv package. gam_bs.select_penweight()[0] gam_bs.select_penweight_kfold()[0]

from.generalized_additive_model import GLMGam # noqa:F401 from.gam_cross_validation.gam_cross_validation import MultivariateGAMCVPath # noqa:F401,E501 from.smooth_basis import BSplines, CyclicCubicSplines # noqa:F401 # -*- coding: utf-8 -*- """ Spline and other smoother classes for Generalized Additive Models Author: Luca Puggini Author: Josef Perktold Created on Fri Jun 5 16:32:00 2015 """ # import useful only for development from abc import ABCMeta, abstractmethod from statsmodels.compat.python import with_metaclass import numpy as np import pandas as pd from patsy import dmatrix from patsy.mgcv_cubic_splines import _get_all_sorted_knots from statsmodels.tools.linalg import transf_constraints # Obtain b splines from patsy def _equally_spaced_knots(x, df): n_knots = df - 2 x_min = x.min() x_max = x.max() knots = np.linspace(x_min, x_max, n_knots) return knots def _R_compat_quantile(x, probs): # return np.percentile(x, 100 * np.asarray(probs)) probs = np.asarray(probs) quantiles = np.asarray([np.percentile(x, 100 * prob) for prob in probs.ravel(order="C")]) return quantiles.reshape(probs.shape, order="C") # FIXME: is this copy/pasted? If so, why do we need it? If not, get # rid of the try/except for scipy import # from patsy splines.py def _eval_bspline_basis(x, knots, degree, deriv='all', include_intercept=True): try: from scipy.interpolate import splev except ImportError: raise ImportError("spline functionality requires scipy") # 'knots' are assumed to be already pre-processed. E.g. usually you # want to include duplicate copies of boundary knots; you should do # that *before* calling this constructor. knots = np.atleast_1d(np.asarray(knots, dtype=float)) assert knots.ndim == 1 knots.sort() degree = int(degree) x = np.atleast_1d(x) if x.ndim == 2 and x.shape[1] == 1: x = x[:, 0] assert x.ndim == 1 # XX FIXME: when points fall outside of the boundaries, splev and R seem # to handle them differently. I don't know why yet. So until we understand # this and decide what to do with it, I'm going to play it safe and # disallow such points. if np.min(x) < np.min(knots) or np.max(x) > np.max(knots): raise NotImplementedError("some data points fall outside the " "outermost knots, and I'm not sure how " "to handle them. (Patches accepted!)") # Thanks to Charles Harris for explaining splev. It's not well # documented, but basically it computes an arbitrary b-spline basis # given knots and degree on some specificed points (or derivatives # thereof, but we don't use that functionality), and then returns some # linear combination of these basis functions. To get out the basis # functions themselves, we use linear combinations like [1, 0, 0], [0, # 1, 0], [0, 0, 1]. # NB: This probably makes it rather inefficient (though I haven't checked # to be sure -- maybe the fortran code actually skips computing the basis # function for coefficients that are zero). # Note: the order of a spline is the same as its degree + 1. # Note: there are (len(knots) - order) basis functions. k_const = 1 - int(include_intercept) n_bases = len(knots) - (degree + 1) - k_const if deriv in ['all', 0]: basis = np.empty((x.shape[0], n_bases), dtype=float) ret = basis if deriv in ['all', 1]: der1_basis = np.empty((x.shape[0], n_bases), dtype=float) ret = der1_basis if deriv in ['all', 2]: der2_basis = np.empty((x.shape[0], n_bases), dtype=float) ret = der2_basis for i in range(n_bases): coefs = np.zeros((n_bases + k_const,)) # we are skipping the first column of the basis to drop constant coefs[i + k_const] = 1 ii = i if deriv in ['all', 0]: basis[:, ii] = splev(x, (knots, coefs, degree)) if deriv in ['all', 1]: der1_basis[:, ii] = splev(x, (knots, coefs, degree), der=1) if deriv in ['all', 2]: der2_basis[:, ii] = splev(x, (knots, coefs, degree), der=2) if deriv == 'all': return basis, der1_basis, der2_basis else: return ret def compute_all_knots(x, df, degree): order = degree + 1 n_inner_knots = df - order lower_bound = np.min(x) upper_bound = np.max(x) knot_quantiles = np.linspace(0, 1, n_inner_knots + 2)[1:-1] inner_knots = _R_compat_quantile(x, knot_quantiles) all_knots = np.concatenate(([lower_bound, upper_bound] * order, inner_knots)) return all_knots, lower_bound, upper_bound, inner_knots def make_bsplines_basis(x, df, degree): ''' make a spline basis for x ''' all_knots, _, _, _ = compute_all_knots(x, df, degree) basis, der_basis, der2_basis = _eval_bspline_basis(x, all_knots, degree) return basis, der_basis, der2_basis def get_knots_bsplines(x=None, df=None, knots=None, degree=3, spacing='quantile', lower_bound=None, upper_bound=None, all_knots=None): """knots for use in B-splines There are two main options for the knot placement - quantile spacing with multiplicity of boundary knots - equal spacing extended to boundary or exterior knots The first corresponds to splines as used by patsy. the second is the knot spacing for P-Splines. """ # based on patsy memorize_finish if all_knots is not None: return all_knots x_min = x.min() x_max = x.max() if degree < 0: raise ValueError("degree must be greater than 0 (not %r)" % (degree,)) if int(degree)!= degree: raise ValueError("degree must be an integer (not %r)" % (degree,)) # These are guaranteed to all be 1d vectors by the code above # x = np.concatenate(tmp["xs"]) if df is None and knots is None: raise ValueError("must specify either df or knots") order = degree + 1 if df is not None: n_inner_knots = df - order if n_inner_knots < 0: raise ValueError("df=%r is too small for degree=%r; must be >= %s" % (df, degree, # We know that n_inner_knots is negative; # if df were that much larger, it would # have been zero, and things would work. df - n_inner_knots)) if knots is not None: if len(knots)!= n_inner_knots: raise ValueError("df=%s with degree=%r implies %s knots, " "but %s knots were provided" % (df, degree, n_inner_knots, len(knots))) elif spacing == 'quantile': # Need to compute inner knots knot_quantiles = np.linspace(0, 1, n_inner_knots + 2)[1:-1] inner_knots = _R_compat_quantile(x, knot_quantiles) elif spacing == 'equal': # Need to compute inner knots grid = np.linspace(0, 1, n_inner_knots + 2)[1:-1] inner_knots = x_min + grid * (x_max - x_min) diff_knots = inner_knots[1] - inner_knots[0] else: raise ValueError("incorrect option for spacing") if knots is not None: inner_knots = knots if lower_bound is None: lower_bound = np.min(x) if upper_bound is None: upper_bound = np.max(x) if lower_bound > upper_bound: raise ValueError("lower_bound > upper_bound (%r > %r)" % (lower_bound, upper_bound)) inner_knots = np.asarray(inner_knots) if inner_knots.ndim > 1: raise ValueError("knots must be 1 dimensional") if np.any(inner_knots < lower_bound): raise ValueError("some knot values (%s) fall below lower bound " "(%r)" % (inner_knots[inner_knots < lower_bound], lower_bound)) if np.any(inner_knots > upper_bound): raise ValueError("some knot values (%s) fall above upper bound " "(%r)" % (inner_knots[inner_knots > upper_bound], upper_bound)) if spacing == "equal": diffs = np.arange(1, order + 1) * diff_knots lower_knots = inner_knots[0] - diffs[::-1] upper_knots = inner_knots[-1] + diffs all_knots = np.concatenate((lower_knots, inner_knots, upper_knots)) else: all_knots = np.concatenate(([lower_bound, upper_bound] * order, inner_knots)) all_knots.sort() return all_knots def _get_integration_points(knots, k_points=3): """add points to each subinterval defined by knots inserts k_points between each two consecutive knots """ k_points = k_points + 1 knots = np.unique(knots) dxi = np.arange(k_points) / k_points dxk = np.diff(knots) dx = dxk[:, None] * dxi x = np.concatenate(((knots[:-1, None] + dx).ravel(), [knots[-1]])) return x def get_covder2(smoother, k_points=4, integration_points=None, skip_ctransf=False, deriv=2): """ Approximate integral of cross product of second derivative of smoother This uses scipy.integrate simps to compute an approximation to the integral of the smoother derivative cross-product at knots plus k_points in between knots. """ from scipy.integrate import simps knots = smoother.knots x = _get_integration_points(knots, k_points=3) if integration_points is None: d2 = smoother.transform(x, deriv=deriv, skip_ctransf=skip_ctransf) else: x = integration_points covd2 = simps(d2[:, :, None] * d2[:, None, :], x, axis=0) return covd2 # TODO: this function should be deleted def make_poly_basis(x, degree, intercept=True): ''' given a vector x returns poly=(1, x, x^2,..., x^degree) and its first and second derivative ''' if intercept: start = 0 else: start = 1 nobs = len(x) basis = np.zeros(shape=(nobs, degree + 1 - start)) der_basis = np.zeros(shape=(nobs, degree + 1 - start)) der2_basis = np.zeros(shape=(nobs, degree + 1 - start)) for i in range(start, degree + 1): basis[:, i - start] = x ** i der_basis[:, i - start] = i * x ** (i - 1) der2_basis[:, i - start] = i * (i - 1) * x ** (i - 2) return basis, der_basis, der2_basis # TODO: try to include other kinds of splines from patsy # x = np.linspace(0, 1, 30) # df = 10 # degree = 3 # from patsy.mgcv_cubic_splines import cc, cr, te # all_knots, lower, upper, inner = compute_all_knots(x, df, degree) # result = cc(x, df=df, knots=all_knots, lower_bound=lower, upper_bound=upper, # constraints=None) # # import matplotlib.pyplot as plt # # result = np.array(result) # print(result.shape) # plt.plot(result.T) # plt.show() class UnivariateGamSmoother(with_metaclass(ABCMeta)): """Base Class for single smooth component """ def __init__(self, x, constraints=None, variable_name='x'): self.x = x self.constraints = constraints self.variable_name = variable_name self.nobs, self.k_variables = len(x), 1 base4 = self._smooth_basis_for_single_variable() if constraints == 'center': constraints = base4[0].mean(0)[None, :] if constraints is not None and not isinstance(constraints, str): ctransf = transf_constraints(constraints) self.ctransf = ctransf else: # subclasses might set ctransf directly # only used if constraints is None if not hasattr(self, 'ctransf'): self.ctransf = None self.basis, self.der_basis, self.der2_basis, self.cov_der2 = base4 if self.ctransf is not None: ctransf = self.ctransf # transform attributes that are not None if base4[0] is not None: self.basis = base4[0].dot(ctransf) if base4[1] is not None: self.der_basis = base4[1].dot(ctransf) if base4[2] is not None: self.der2_basis = base4[2].dot(ctransf) if base4[3] is not None: self.cov_der2 = ctransf.T.dot(base4[3]).dot(ctransf) self.dim_basis = self.basis.shape[1] self.col_names = [self.variable_name + "_s" + str(i) for i in range(self.dim_basis)] @abstractmethod def _smooth_basis_for_single_variable(self): return class UnivariateGenericSmoother(UnivariateGamSmoother): """Generic single smooth component """ def __init__(self, x, basis, der_basis, der2_basis, cov_der2, variable_name='x'): self.basis = basis self.der_basis = der_basis self.der2_basis = der2_basis self.cov_der2 = cov_der2 super(UnivariateGenericSmoother, self).__init__( x, variable_name=variable_name) def _smooth_basis_for_single_variable(self): return self.basis, self.der_basis, self.der2_basis, self.cov_der2 class UnivariatePolynomialSmoother(UnivariateGamSmoother): """polynomial single smooth component """ def __init__(self, x, degree, variable_name='x'): self.degree = degree super(UnivariatePolynomialSmoother, self).__init__( x, variable_name=variable_name) def _smooth_basis_for_single_variable(self): # TODO: unclear description """ given a vector x returns poly=(1, x, x^2,..., x^degree) and its first and second derivative """ basis = np.zeros(shape=(self.nobs, self.degree)) der_basis = np.zeros(shape=(self.nobs, self.degree)) der2_basis = np.zeros(shape=(self.nobs, self.degree)) for i in range(self.degree): dg = i + 1 basis[:, i] = self.x ** dg der_basis[:, i] = dg * self.x ** (dg - 1) if dg > 1: der2_basis[:, i] = dg * (dg - 1) * self.x ** (dg - 2) else: der2_basis[:, i] = 0 cov_der2 = np.dot(der2_basis.T, der2_basis) return basis, der_basis, der2_basis, cov_der2 class UnivariateBSplines(UnivariateGamSmoother): """B-Spline single smooth component This creates and holds the B-Spline basis function for one component. Parameters ---------- x : array, 1-D underlying explanatory variable for smooth terms. df : int numer of basis functions or degrees of freedom degree : int degree of the spline include_intercept : bool If False, then the basis functions are transformed so that they do not include a constant. This avoids perfect collinearity if a constant or several components are included in the model. constraints : None, string or array Constraints are used to transform the basis functions to satisfy those constraints. `constraints = 'center'` applies a linear transform to remove the constant and center the basis functions. variable_name : None or str The name for the underlying explanatory variable, x, used in for creating the column and parameter names for the basis functions. covder2_kwds : None or dict options for computing the penalty matrix from the second derivative of the spline. knot_kwds : None or list of dict option for the knot selection. By default knots are selected in the same way as in patsy, however the number of knots is independent of keeping or removing the constant. Interior knot selection is based on quantiles of the data and is the same in patsy and mgcv. Boundary points are at the limits of the data range. The available options use with `get_knots_bsplines` are - knots : None or array interior knots - spacing : 'quantile' or 'equal' - lower_bound : None or float location of lower boundary knots, all boundary knots are at the same point - upper_bound : None or float location of upper boundary knots, all boundary knots are at the same point - all_knots : None or array If all knots are provided, then those will be taken as given and all other options will be ignored. """ def __init__(self, x, df, degree=3, include_intercept=False, constraints=None, variable_name='x', covder2_kwds=None, **knot_kwds): self.degree = degree self.df = df self.include_intercept = include_intercept self.knots = get_knots_bsplines(x, degree=degree, df=df, **knot_kwds) self.covder2_kwds = (covder2_kwds if covder2_kwds is not None else {}) super(UnivariateBSplines, self).__init__( x, constraints=constraints, variable_name=variable_name) def _smooth_basis_for_single_variable(self): basis, der_basis, der2_basis = _eval_bspline_basis( self.x, self.knots, self.degree, include_intercept=self.include_intercept) # cov_der2 = np.dot(der2_basis.T, der2_basis) cov_der2 = get_covder2(self, skip_ctransf=True, **self.covder2_kwds) return basis, der_basis, der2_basis, cov_der2 def transform(self, x_new, deriv=0, skip_ctransf=False): """create the spline basis for new observations The main use of this stateful transformation is for prediction using the same specification of the spline basis. Parameters ---------- x_new : array observations of the underlying explanatory variable deriv : int which derivative of the spline basis to compute This is an options for internal computation. skip_ctransf : bool whether to skip the constraint transform This is an options for internal computation. Returns ------- basis : ndarray design matrix for the spline basis for given ``x_new`` """ if x_new is None: x_new = self.x exog = _eval_bspline_basis(x_new, self.knots, self.degree, deriv=deriv, include_intercept=self.include_intercept) # ctransf does not exist yet when cov_der2 is computed ctransf = getattr(self, 'ctransf', None) if ctransf is not None and not skip_ctransf: exog = exog.dot(self.ctransf) return exog class UnivariateCubicSplines(UnivariateGamSmoother): """Cubic Spline single smooth component Cubic splines as described in the wood's book in chapter 3 """ def __init__(self, x, df, constraints=None, transform='domain', variable_name='x'): self.degree = 3 self.df = df self.transform_data_method = transform self.x = x = self.transform_data(x, initialize=True) self.knots = _equally_spaced_knots(x, df) super(UnivariateCubicSplines, self).__init__( x, constraints=constraints, variable_name=variable_name) def transform_data(self, x, initialize=False): tm = self.transform_data_method if tm is None: return x if initialize is True: if tm == 'domain': self.domain_low = x.min(0) self.domain_upp = x.max(0) elif isinstance(tm, tuple): self.domain_low = tm[0] self.domain_upp = tm[1] self.transform_data_method = 'domain' else: raise ValueError("transform should be None, 'domain' " "or a tuple") self.domain_diff = self.domain_upp - self.domain_low if self.transform_data_method == 'domain': x = (x - self.domain_low) / self.domain_diff return x else: raise ValueError("incorrect transform_data_method") def _smooth_basis_for_single_variable(self): basis = self._splines_x()[:, :-1] # demean except for constant, does not affect derivatives if not self.constraints == 'none': self.transf_mean = basis[:, 1:].mean(0) basis[:, 1:] -= self.transf_mean else: self.transf_mean = np.zeros(basis.shape[1]) s = self._splines_s()[:-1, :-1] if not self.constraints == 'none': ctransf = np.diag(1/np.max(np.abs(basis), axis=0)) else: ctransf = np.eye(basis.shape[1]) # use np.eye to avoid rescaling # ctransf = np.eye(basis.shape[1]) if self.constraints == 'no-const': ctransf = ctransf[1:] self.ctransf = ctransf return basis, None, None, s def _rk(self, x, z): p1 = ((z - 1 / 2) ** 2 - 1 / 12) * ((x - 1 / 2) ** 2 - 1 / 12) / 4 p2 = ((np.abs(z - x) - 1 / 2) ** 4 - 1 / 2 * (np.abs(z - x) - 1 / 2) ** 2 + 7 / 240) / 24. return p1 - p2 def _splines_x(self, x=None): if x is None: x = self.x n_columns = len(self.knots) + 2 nobs = x.shape[0] basis = np.ones(shape=(nobs, n_columns)) basis[:, 1] = x # for loop equivalent to outer(x, xk, fun=rk) for i, xi in enumerate(x): for j, xkj in enumerate(self.knots): s_ij = self._rk(xi, xkj) basis[i, j + 2] = s_ij return basis def _splines_s(self): q = len(self.knots) + 2 s = np.zeros(shape=(q, q)) for i, x1 in enumerate(self.knots): for j, x2 in enumerate(self.knots): s[i + 2, j + 2] = self._rk(x1, x2) return s def transform(self, x_new): x_new = self.transform_data(x_new, initialize=False) exog = self._splines_x(x_new) exog[:, 1:] -= self.transf_mean if self.ctransf is not None: exog = exog.dot(self.ctransf) return exog class UnivariateCubicCyclicSplines(UnivariateGamSmoother): """cyclic cubic regression spline single smooth component This creates and holds the Cyclic CubicSpline basis function for one component. Parameters ---------- x : array, 1-D underlying explanatory variable for smooth terms. df : int numer of basis functions or degrees of freedom degree : int degree of the spline include_intercept : bool If False, then the basis functions are transformed so that they do not include a constant. This avoids perfect collinearity if a constant or several components are included in the model. constraints : None, string or array Constraints are used to transform the basis functions to satisfy those constraints. `constraints = 'center'` applies a linear transform to remove the constant and center the basis functions. variable_name : None or str The name for the underlying explanatory variable, x, used in for creating the column and parameter names for the basis functions. """ def __init__(self, x, df, constraints=None, variable_name='x'): self.degree = 3 self.df = df self.x = x self.knots = _equally_spaced_knots(x, df) super(UnivariateCubicCyclicSplines, self).__init__( x, constraints=constraints, variable_name=variable_name) def _smooth_basis_for_single_variable(self): basis = dmatrix("cc(x, df=" + str(self.df) + ") - 1", {"x": self.x}) self.design_info = basis.design_info n_inner_knots = self.df - 2 + 1 # +n_constraints # TODO: from CubicRegressionSplines class all_knots = _get_all_sorted_knots(self.x, n_inner_knots=n_inner_knots, inner_knots=None, lower_bound=None, upper_bound=None) b, d = self._get_b_and_d(all_knots) s = self._get_s(b, d) return basis, None, None, s def _get_b_and_d(self, knots): """Returns mapping of cyclic cubic spline values to 2nd derivatives. .. note:: See 'Generalized Additive Models', Simon N. Wood, 2006, pp 146-147 Parameters ---------- knots : ndarray The 1-d array knots used for cubic spline parametrization, must be sorted in ascending order. Returns ------- b, d: ndarrays arrays for mapping cyclic cubic spline values at knots to second derivatives. penalty matrix is equal to ``s = d.T.dot(b^-1).dot(d)`` """ h = knots[1:] - knots[:-1] n = knots.size - 1 # b and d are defined such that the penalty matrix is equivalent to: # s = d.T.dot(b^-1).dot(d) # reference in particular to pag 146 of Wood's book b = np.zeros((n, n)) # the b matrix on page 146 of Wood's book d = np.zeros((n, n)) # the d matrix on page 146 of Wood's book b[0, 0] = (h[n - 1] + h[0]) / 3. b[0, n - 1] = h[n - 1] / 6. b[n - 1, 0] = h[n - 1] / 6. d[0, 0] = -1. / h[0] - 1. / h[n - 1] d[0, n - 1] = 1. / h[n - 1] d[n - 1, 0] = 1. / h[n - 1] for i in range(1, n): b[i, i] = (h[i - 1] + h[i]) / 3. b[i, i - 1] = h[i - 1] / 6. b[i - 1, i] = h[i - 1] / 6. d[i, i] = -1. / h[i - 1] - 1. / h[i] d[i, i - 1] = 1. / h[i - 1] d[i - 1, i] = 1. / h[i - 1] return b, d def _get_s(self, b, d): return d.T.dot(np.linalg.inv(b)).dot(d) def transform(self, x_new): exog = dmatrix(self.design_info, {"x": x_new}) if self.ctransf is not None: exog = exog.dot(self.ctransf) return exog class AdditiveGamSmoother(with_metaclass(ABCMeta)): """Base class for additive smooth components """ def __init__(self, x, variable_names=None, include_intercept=False, **kwargs): # get pandas names before using asarray if isinstance(x, pd.DataFrame): data_names = x.columns.tolist() elif isinstance(x, pd.Series): data_names = [x.name] else: data_names = None x = np.asarray(x) if x.ndim == 1: self.x = x.copy() self.x.shape = (len(x), 1) else: self.x = x self.nobs, self.k_variables = self.x.shape if isinstance(include_intercept, bool): self.include_intercept = [include_intercept] * self.k_variables else: self.include_intercept = include_intercept if variable_names is None: if data_names is not None: self.variable_names = data_names else: self.variable_names = ['x' + str(i) for i in range(self.k_variables)] else: self.variable_names = variable_names self.smoothers = self._make_smoothers_list() self.basis = np.hstack(list(smoother.basis for smoother in self.smoothers)) self.dim_basis = self.basis.shape[1] self.penalty_matrices = [smoother.cov_der2 for smoother in self.smoothers] self.col_names = [] for smoother in self.smoothers: self.col_names.extend(smoother.col_names) self.mask = [] last_column = 0 for smoother in self.smoothers: mask = np.array([False] * self.dim_basis) mask[last_column:smoother.dim_basis + last_column] = True last_column = last_column + smoother.dim_basis self.mask.append(mask) @abstractmethod def _make_smoothers_list(self): pass def transform(self, x_new): """create the spline basis for new observations The main use of this stateful transformation is for prediction using the same specification of the spline basis. Parameters ---------- x_new: array observations of the underlying explanatory variable Returns ------- basis : ndarray design matrix for the spline basis for given ``x_new``. """ exog = np.hstack(list(self.smoothers[i].transform(x_new[:, i]) for i in range(self.k_variables))) return exog class GenericSmoothers(AdditiveGamSmoother): """generic class for additive smooth components for GAM """ def __init__(self, x, smoothers): self.smoothers = smoothers super(GenericSmoothers, self).__init__(x, variable_names=None) def _make_smoothers_list(self): return self.smoothers class PolynomialSmoother(AdditiveGamSmoother): """additive polynomial components for GAM """ def __init__(self, x, degrees, variable_names=None): self.degrees = degrees super(PolynomialSmoother, self).__init__(x, variable_names=variable_names) def _make_smoothers_list(self): smoothers = [] for v in range(self.k_variables): uv_smoother = UnivariatePolynomialSmoother( self.x[:, v], degree=self.degrees[v], variable_name=self.variable_names[v]) smoothers.append(uv_smoother) return smoothers class BSplines(AdditiveGamSmoother): """additive smooth components using B-Splines This creates and holds the B-Spline basis function for several components. Parameters ---------- x : array_like, 1-D or 2-D underlying explanatory variable for smooth terms. If 2-dimensional, then observations should be in rows and explanatory variables in columns. df : int numer of basis functions or degrees of freedom degree : int degree of the spline include_intercept : bool If False, then the basis functions are transformed so that they do not include a constant. This avoids perfect collinearity if a constant or several components are included in the model. constraints : None, string or array Constraints are used to transform the basis functions to satisfy those constraints. `constraints = 'center'` applies a linear transform to remove the constant and center the basis functions. variable_names : None or list of strings The names for the underlying explanatory variables, x used in for creating the column and parameter names for the basis functions. If ``x`` is a pandas object, then the names will be taken from it. knot_kwds : None or list of dict option for the knot selection. By default knots are selected in the same way as in patsy, however the number of knots is independent of keeping or removing the constant. Interior knot selection is based on quantiles of the data and is the same in patsy and mgcv. Boundary points are at the limits of the data range. The available options use with `get_knots_bsplines` are - knots : None or array interior knots - spacing : 'quantile' or 'equal' - lower_bound : None or float location of lower boundary knots, all boundary knots are at the same point - upper_bound : None or float location of upper boundary knots, all boundary knots are at the same point - all_knots : None or array If all knots are provided, then those will be taken as given and all other options will be ignored. Attributes ---------- smoothers : list of univariate smooth component instances basis : design matrix, array of spline bases columns for all components penalty_matrices : list of penalty matrices, one for each smooth term dim_basis : number of columns in the basis k_variables : number of smooth components col_names : created names for the basis columns There are additional attributes about the specification of the splines and some attributes mainly for internal use. Notes ----- A constant in the spline basis function can be removed in two different ways. The first is by dropping one basis column and normalizing the remaining columns. This is obtained by the default ``include_intercept=False, constraints=None`` The second option is by using the centering transform which is a linear transformation of all basis functions. As a consequence of the transformation, the B-spline basis functions do not have locally bounded support anymore. This is obtained ``constraints='center'``. In this case ``include_intercept`` will be automatically set to True to avoid dropping an additional column. """ def __init__(self, x, df, degree, include_intercept=False, constraints=None, variable_names=None, knot_kwds=None): self.degrees = degree self.dfs = df self.knot_kwds = knot_kwds # TODO: move attaching constraints to super call self.constraints = constraints if constraints == 'center': include_intercept = True super(BSplines, self).__init__(x, include_intercept=include_intercept, variable_names=variable_names) def _make_smoothers_list(self): smoothers = [] for v in range(self.k_variables): kwds = self.knot_kwds[v] if self.knot_kwds else {} uv_smoother = UnivariateBSplines( self.x[:, v], df=self.dfs[v], degree=self.degrees[v], include_intercept=self.include_intercept[v], constraints=self.constraints, variable_name=self.variable_names[v], **kwds) smoothers.append(uv_smoother) return smoothers class CubicSplines(AdditiveGamSmoother): """additive smooth components using cubic splines as in Wood 2006. Note, these splines do NOT use the same spline basis as ``Cubic Regression Splines``. """ def __init__(self, x, df, constraints='center', transform='domain', variable_names=None): self.dfs = df self.constraints = constraints self.transform = transform super(CubicSplines, self).__init__(x, constraints=constraints, variable_names=variable_names) def _make_smoothers_list(self): smoothers = [] for v in range(self.k_variables): uv_smoother = UnivariateCubicSplines( self.x[:, v], df=self.dfs[v], constraints=self.constraints, transform=self.transform, variable_name=self.variable_names[v]) smoothers.append(uv_smoother) return smoothers class CyclicCubicSplines(AdditiveGamSmoother): """additive smooth components using cyclic cubic regression splines This spline basis is the same as in patsy. Parameters ---------- x : array_like, 1-D or 2-D underlying explanatory variable for smooth terms. If 2-dimensional, then observations should be in rows and explanatory variables in columns. df : int numer of basis functions or degrees of freedom constraints : None, string or array Constraints are used to transform the basis functions to satisfy those constraints. variable_names : None or list of strings The names for the underlying explanatory variables, x used in for creating the column and parameter names for the basis functions. If ``x`` is a pandas object, then the names will be taken from it. """ def __init__(self, x, df, constraints=None, variable_names=None): self.dfs = df # TODO: move attaching constraints to super call self.constraints = constraints super(CyclicCubicSplines, self).__init__(x, variable_names=variable_names) def _make_smoothers_list(self): smoothers = [] for v in range(self.k_variables): uv_smoother = UnivariateCubicCyclicSplines( self.x[:, v], df=self.dfs[v], constraints=self.constraints, variable_name=self.variable_names[v]) smoothers.append(uv_smoother) return smoothers # class CubicRegressionSplines(BaseCubicSplines): # # TODO: this class is still not tested # # def __init__(self, x, df=10): # import warnings # warnings.warn("This class is still not tested and it is probably" # " not working properly. " # "I suggest to use another smoother", Warning) # # super(CubicRegressionSplines, self).__init__(x, df) # # self.basis = dmatrix("cc(x, df=" + str(df) + ") - 1", {"x": x}) # n_inner_knots = df - 2 + 1 # +n_constraints # # TODO: ACcording to CubicRegressionSplines class this should be # # n_inner_knots = df - 2 # all_knots = _get_all_sorted_knots(x, n_inner_knots=n_inner_knots, # inner_knots=None, # lower_bound=None, upper_bound=None) # # b, d = self._get_b_and_d(all_knots) # self.s = self._get_s(b, d) # # self.dim_basis = self.basis.shape[1] # # def _get_b_and_d(self, knots): # # h = knots[1:] - knots[:-1] # n = knots.size - 1 # # # b and d are defined such that the penalty matrix is equivalent to: # # s = d.T.dot(b^-1).dot(d) # # reference in particular to pag 146 of Wood's book # b = np.zeros((n, n)) # the b matrix on page 146 of Wood's book # d = np.zeros((n, n)) # the d matrix on page 146 of Wood's book # # for i in range(n-2): # d[i, i] = 1/h[i] # d[i, i+1] = -1/h[i] - 1/h[i+1] # d[i, i+2] = 1/h[i+1] # # b[i, i] = (h[i] + h[i+1])/3 # # for i in range(n-3): # b[i, i+1] = h[i+1]/6 # b[i+1, i] = h[i+1]/6 # # return b, d # # def _get_s(self, b, d): # # return d.T.dot(np.linalg.pinv(b)).dot(d)

statsmodels__statsmodels

gee.rst

Example / Description

Generate example for this module

BSD 3-Clause New or Revised License

statsmodels__statsmodels/docs/source/gee.rst

Generalized Estimating Equations Generalized Estimating Equations estimate generalized linear models for panel, cluster or repeated measures data when the observations are possibly correlated withing a cluster but uncorrelated across clusters. It supports estimation of the same one-parameter exponential families as Generalized Linear models (GLM). See Module Reference for commands and arguments. Examples The following illustrates a Poisson regression with exchangeable correlation within clusters using data on epilepsy seizures. import statsmodels.api as sm import statsmodels.formula.api as smf data = sm.datasets.get_rdataset('epil', package='MASS').data fam = sm.families.Poisson() ind = sm.cov_struct.Exchangeable() mod = smf.gee("y ~ age + trt + base", "subject", data, cov_struct=ind, family=fam) res = mod.fit() print(res.summary()) Several notebook examples of the use of GEE can be found on the Wiki: Wiki notebooks for GEE Link Functions The link functions are the same as for GLM, currently implemented are the following. Not all link functions are available for each distribution family. The list of available link functions can be obtained by >>> sm.families.family.<familyname>.links

''' The one parameter exponential family distributions used by GLM. ''' # TODO: quasi, quasibinomial, quasipoisson # see # http://www.biostat.jhsph.edu/~qli/biostatistics_r_doc/library/stats/html/family.html # for comparison to R, and McCullagh and Nelder import warnings import inspect import numpy as np from scipy import special from. import links as L from. import varfuncs as V FLOAT_EPS = np.finfo(float).eps class Family(object): """ The parent class for one-parameter exponential families. Parameters ---------- link : a link function instance Link is the linear transformation function. See the individual families for available links. variance : a variance function Measures the variance as a function of the mean probabilities. See the individual families for the default variance function. See Also -------- :ref:`links` """ # TODO: change these class attributes, use valid somewhere... valid = [-np.inf, np.inf] links = [] def _setlink(self, link): """ Helper method to set the link for a family. Raises a ``ValueError`` exception if the link is not available. Note that the error message might not be that informative because it tells you that the link should be in the base class for the link function. See statsmodels.genmod.generalized_linear_model.GLM for a list of appropriate links for each family but note that not all of these are currently available. """ # TODO: change the links class attribute in the families to hold # meaningful information instead of a list of links instances such as # [<statsmodels.family.links.Log object at 0x9a4240c>, # <statsmodels.family.links.Power object at 0x9a423ec>, # <statsmodels.family.links.Power object at 0x9a4236c>] # for Poisson... self._link = link if not isinstance(link, L.Link): raise TypeError("The input should be a valid Link object.") if hasattr(self, "links"): validlink = max([isinstance(link, _) for _ in self.links]) if not validlink: errmsg = "Invalid link for family, should be in %s. (got %s)" raise ValueError(errmsg % (repr(self.links), link)) def _getlink(self): """ Helper method to get the link for a family. """ return self._link # link property for each family is a pointer to link instance link = property(_getlink, _setlink, doc="Link function for family") def __init__(self, link, variance): if inspect.isclass(link): warnmssg = "Calling Family(..) with a link class as argument " warnmssg += "is deprecated.\n" warnmssg += "Use an instance of a link class instead." lvl = 2 if type(self) is Family else 3 warnings.warn(warnmssg, category=DeprecationWarning, stacklevel=lvl) self.link = link() else: self.link = link self.variance = variance def starting_mu(self, y): r""" Starting value for mu in the IRLS algorithm. Parameters ---------- y : array The untransformed response variable. Returns ------- mu_0 : array The first guess on the transformed response variable. Notes ----- .. math:: \mu_0 = (Y + \overline{Y})/2 Only the Binomial family takes a different initial value. """ return (y + y.mean())/2. def weights(self, mu): r""" Weights for IRLS steps Parameters ---------- mu : array_like The transformed mean response variable in the exponential family Returns ------- w : array The weights for the IRLS steps Notes ----- .. math:: w = 1 / (g'(\mu)^2 * Var(\mu)) """ return 1. / (self.link.deriv(mu)**2 * self.variance(mu)) def deviance(self, endog, mu, var_weights=1., freq_weights=1., scale=1.): r""" The deviance function evaluated at (endog, mu, var_weights, freq_weights, scale) for the distribution. Deviance is usually defined as twice the loglikelihood ratio. Parameters ---------- endog : array_like The endogenous response variable mu : array_like The inverse of the link function at the linear predicted values. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. freq_weights : array_like 1d array of frequency weights. The default is 1. scale : float, optional An optional scale argument. The default is 1. Returns ------- Deviance : array The value of deviance function defined below. Notes ----- Deviance is defined .. math:: D = 2\sum_i (freq\_weights_i * var\_weights * (llf(endog_i, endog_i) - llf(endog_i, \mu_i))) where y is the endogenous variable. The deviance functions are analytically defined for each family. Internally, we calculate deviance as: .. math:: D = \sum_i freq\_weights_i * var\_weights * resid\_dev_i / scale """ resid_dev = self._resid_dev(endog, mu) return np.sum(resid_dev * freq_weights * var_weights / scale) def resid_dev(self, endog, mu, var_weights=1., scale=1.): r""" The deviance residuals Parameters ---------- endog : array_like The endogenous response variable mu : array_like The inverse of the link function at the linear predicted values. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float, optional An optional scale argument. The default is 1. Returns ------- resid_dev : float Deviance residuals as defined below. Notes ----- The deviance residuals are defined by the contribution D_i of observation i to the deviance as .. math:: resid\_dev_i = sign(y_i-\mu_i) \sqrt{D_i} D_i is calculated from the _resid_dev method in each family. Distribution-specific documentation of the calculation is available there. """ resid_dev = self._resid_dev(endog, mu) resid_dev *= var_weights / scale return np.sign(endog - mu) * np.sqrt(np.clip(resid_dev, 0., np.inf)) def fitted(self, lin_pred): r""" Fitted values based on linear predictors lin_pred. Parameters ---------- lin_pred : array Values of the linear predictor of the model. :math:`X \cdot \beta` in a classical linear model. Returns ------- mu : array The mean response variables given by the inverse of the link function. """ fits = self.link.inverse(lin_pred) return fits def predict(self, mu): """ Linear predictors based on given mu values. Parameters ---------- mu : array The mean response variables Returns ------- lin_pred : array Linear predictors based on the mean response variables. The value of the link function at the given mu. """ return self.link(mu) def loglike_obs(self, endog, mu, var_weights=1., scale=1.): r""" The log-likelihood function for each observation in terms of the fitted mean response for the distribution. Parameters ---------- endog : array Usually the endogenous response variable. mu : array Usually but not always the fitted mean response variable. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float The scale parameter. The default is 1. Returns ------- ll_i : float The value of the loglikelihood evaluated at (endog, mu, var_weights, scale) as defined below. Notes ----- This is defined for each family. endog and mu are not restricted to ``endog`` and ``mu`` respectively. For instance, you could call both ``loglike(endog, endog)`` and ``loglike(endog, mu)`` to get the log-likelihood ratio. """ raise NotImplementedError def loglike(self, endog, mu, var_weights=1., freq_weights=1., scale=1.): r""" The log-likelihood function in terms of the fitted mean response. Parameters ---------- endog : array Usually the endogenous response variable. mu : array Usually but not always the fitted mean response variable. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. freq_weights : array_like 1d array of frequency weights. The default is 1. scale : float The scale parameter. The default is 1. Returns ------- ll : float The value of the loglikelihood evaluated at (endog, mu, var_weights, freq_weights, scale) as defined below. Notes ----- Where :math:`ll_i` is the by-observation log-likelihood: .. math:: ll = \sum(ll_i * freq\_weights_i) ``ll_i`` is defined for each family. endog and mu are not restricted to ``endog`` and ``mu`` respectively. For instance, you could call both ``loglike(endog, endog)`` and ``loglike(endog, mu)`` to get the log-likelihood ratio. """ ll_obs = self.loglike_obs(endog, mu, var_weights, scale) return np.sum(ll_obs * freq_weights) def resid_anscombe(self, endog, mu, var_weights=1., scale=1.): r""" The Anscombe residuals Parameters ---------- endog : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float, optional An optional argument to divide the residuals by sqrt(scale). The default is 1. See Also -------- statsmodels.genmod.families.family.Family : `resid_anscombe` for the individual families for more information Notes ----- Anscombe residuals are defined by .. math:: resid\_anscombe_i = \frac{A(y)-A(\mu)}{A'(\mu)\sqrt{Var[\mu]}} * \sqrt(var\_weights) where :math:`A'(y)=v(y)^{-\frac{1}{3}}` and :math:`v(\mu)` is the variance function :math:`Var[y]=\frac{\phi}{w}v(mu)`. The transformation :math:`A(y)` makes the residuals more normal distributed. """ raise NotImplementedError def _clean(self, x): """ Helper function to trim the data so that it is in (0,inf) Notes ----- The need for this function was discovered through usage and its possible that other families might need a check for validity of the domain. """ return np.clip(x, FLOAT_EPS, np.inf) class Poisson(Family): """ Poisson exponential family. Parameters ---------- link : a link instance, optional The default link for the Poisson family is the log link. Available links are log, identity, and sqrt. See statsmodels.families.links for more information. Attributes ---------- Poisson.link : a link instance The link function of the Poisson instance. Poisson.variance : varfuncs instance ``variance`` is an instance of statsmodels.genmod.families.varfuncs.mu See Also -------- statsmodels.genmod.families.family.Family :ref:`links` """ links = [L.log, L.identity, L.sqrt] variance = V.mu valid = [0, np.inf] safe_links = [L.Log, ] def __init__(self, link=None): if link is None: link = L.log() super(Poisson, self).__init__(link=link, variance=Poisson.variance) def _resid_dev(self, endog, mu): r""" Poisson deviance residuals Parameters ---------- endog : array The endogenous response variable. mu : array The inverse of the link function at the linear predicted values. Returns ------- resid_dev : float Deviance residuals as defined below. Notes ----- .. math:: resid\_dev_i = 2 * (endog_i * \ln(endog_i / \mu_i) - (endog_i - \mu_i)) """ endog_mu = self._clean(endog / mu) resid_dev = endog * np.log(endog_mu) - (endog - mu) return 2 * resid_dev def loglike_obs(self, endog, mu, var_weights=1., scale=1.): r""" The log-likelihood function for each observation in terms of the fitted mean response for the Poisson distribution. Parameters ---------- endog : array Usually the endogenous response variable. mu : array Usually but not always the fitted mean response variable. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float The scale parameter. The default is 1. Returns ------- ll_i : float The value of the loglikelihood evaluated at (endog, mu, var_weights, scale) as defined below. Notes ----- .. math:: ll_i = var\_weights_i / scale * (endog_i * \ln(\mu_i) - \mu_i - \ln \Gamma(endog_i + 1)) """ return var_weights / scale * (endog * np.log(mu) - mu - special.gammaln(endog + 1)) def resid_anscombe(self, endog, mu, var_weights=1., scale=1.): r""" The Anscombe residuals Parameters ---------- endog : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float, optional An optional argument to divide the residuals by sqrt(scale). The default is 1. Returns ------- resid_anscombe : array The Anscome residuals for the Poisson family defined below Notes ----- .. math:: resid\_anscombe_i = (3/2) * (endog_i^{2/3} - \mu_i^{2/3}) / \mu_i^{1/6} * \sqrt(var\_weights) """ resid = ((3 / 2.) * (endog**(2 / 3.) - mu**(2 / 3.)) / (mu ** (1 / 6.) * scale ** 0.5)) resid *= np.sqrt(var_weights) return resid class Gaussian(Family): """ Gaussian exponential family distribution. Parameters ---------- link : a link instance, optional The default link for the Gaussian family is the identity link. Available links are log, identity, and inverse. See statsmodels.genmod.families.links for more information. Attributes ---------- Gaussian.link : a link instance The link function of the Gaussian instance Gaussian.variance : varfunc instance ``variance`` is an instance of statsmodels.genmod.families.varfuncs.constant See Also -------- statsmodels.genmod.families.family.Family :ref:`links` """ links = [L.log, L.identity, L.inverse_power] variance = V.constant safe_links = links def __init__(self, link=None): if link is None: link = L.identity() super(Gaussian, self).__init__(link=link, variance=Gaussian.variance) def _resid_dev(self, endog, mu): r""" Gaussian deviance residuals Parameters ---------- endog : array The endogenous response variable. mu : array The inverse of the link function at the linear predicted values. Returns ------- resid_dev : float Deviance residuals as defined below. Notes -------- .. math:: resid\_dev_i = (endog_i - \mu_i) ** 2 """ return (endog - mu) ** 2 def loglike_obs(self, endog, mu, var_weights=1., scale=1.): r""" The log-likelihood function for each observation in terms of the fitted mean response for the Gaussian distribution. Parameters ---------- endog : array Usually the endogenous response variable. mu : array Usually but not always the fitted mean response variable. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float The scale parameter. The default is 1. Returns ------- ll_i : float The value of the loglikelihood evaluated at (endog, mu, var_weights, scale) as defined below. Notes ----- If the link is the identity link function then the loglikelihood function is the same as the classical OLS model. .. math:: llf = -nobs / 2 * (\log(SSR) + (1 + \log(2 \pi / nobs))) where .. math:: SSR = \sum_i (Y_i - g^{-1}(\mu_i))^2 If the links is not the identity link then the loglikelihood function is defined as .. math:: ll_i = -1 / 2 \sum_i * var\_weights * ((Y_i - mu_i)^2 / scale + \log(2 * \pi * scale)) """ ll_obs = -var_weights * (endog - mu) ** 2 / scale ll_obs += -np.log(scale / var_weights) - np.log(2 * np.pi) ll_obs /= 2 return ll_obs def resid_anscombe(self, endog, mu, var_weights=1., scale=1.): r""" The Anscombe residuals Parameters ---------- endog : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float, optional An optional argument to divide the residuals by sqrt(scale). The default is 1. Returns ------- resid_anscombe : array The Anscombe residuals for the Gaussian family defined below Notes ----- For the Gaussian distribution, Anscombe residuals are the same as deviance residuals. .. math:: resid\_anscombe_i = (Y_i - \mu_i) / \sqrt{scale} * \sqrt(var\_weights) """ resid = (endog - mu) / scale ** 0.5 resid *= np.sqrt(var_weights) return resid class Gamma(Family): """ Gamma exponential family distribution. Parameters ---------- link : a link instance, optional The default link for the Gamma family is the inverse link. Available links are log, identity, and inverse. See statsmodels.genmod.families.links for more information. Attributes ---------- Gamma.link : a link instance The link function of the Gamma instance Gamma.variance : varfunc instance ``variance`` is an instance of statsmodels.genmod.family.varfuncs.mu_squared See Also -------- statsmodels.genmod.families.family.Family :ref:`links` """ links = [L.log, L.identity, L.inverse_power] variance = V.mu_squared safe_links = [L.Log, ] def __init__(self, link=None): if link is None: link = L.inverse_power() super(Gamma, self).__init__(link=link, variance=Gamma.variance) def _resid_dev(self, endog, mu): r""" Gamma deviance residuals Parameters ---------- endog : array The endogenous response variable. mu : array The inverse of the link function at the linear predicted values. Returns ------- resid_dev : float Deviance residuals as defined below. Notes ----- .. math:: resid\_dev_i = 2 * ((endog_i - \mu_i) / \mu_i - \log(endog_i / \mu_i)) """ endog_mu = self._clean(endog / mu) resid_dev = -np.log(endog_mu) + (endog - mu) / mu return 2 * resid_dev def loglike_obs(self, endog, mu, var_weights=1., scale=1.): r""" The log-likelihood function for each observation in terms of the fitted mean response for the Gamma distribution. Parameters ---------- endog : array Usually the endogenous response variable. mu : array Usually but not always the fitted mean response variable. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float The scale parameter. The default is 1. Returns ------- ll_i : float The value of the loglikelihood evaluated at (endog, mu, var_weights, scale) as defined below. Notes ----- .. math:: ll_i = var\_weights_i / scale * (\ln(var\_weights_i * endog_i / (scale * \mu_i)) - (var\_weights_i * endog_i) / (scale * \mu_i)) - \ln \Gamma(var\_weights_i / scale) - \ln(\mu_i) """ endog_mu = self._clean(endog / mu) weight_scale = var_weights / scale ll_obs = weight_scale * np.log(weight_scale * endog_mu) ll_obs -= weight_scale * endog_mu ll_obs -= special.gammaln(weight_scale) + np.log(endog) return ll_obs # in Stata scale is set to equal 1 for reporting llf # in R it's the dispersion, though there is a loss of precision vs. # our results due to an assumed difference in implementation def resid_anscombe(self, endog, mu, var_weights=1., scale=1.): r""" The Anscombe residuals Parameters ---------- endog : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float, optional An optional argument to divide the residuals by sqrt(scale). The default is 1. Returns ------- resid_anscombe : array The Anscombe residuals for the Gamma family defined below Notes ----- .. math:: resid\_anscombe_i = 3 * (endog_i^{1/3} - \mu_i^{1/3}) / \mu_i^{1/3} / \sqrt{scale} * \sqrt(var\_weights) """ resid = 3 * (endog**(1/3.) - mu**(1/3.)) / mu**(1/3.) / scale ** 0.5 resid *= np.sqrt(var_weights) return resid class Binomial(Family): """ Binomial exponential family distribution. Parameters ---------- link : a link instance, optional The default link for the Binomial family is the logit link. Available links are logit, probit, cauchy, log, and cloglog. See statsmodels.genmod.families.links for more information. Attributes ---------- Binomial.link : a link instance The link function of the Binomial instance Binomial.variance : varfunc instance ``variance`` is an instance of statsmodels.genmod.families.varfuncs.binary See Also -------- statsmodels.genmod.families.family.Family :ref:`links` Notes ----- endog for Binomial can be specified in one of three ways: A 1d array of 0 or 1 values, indicating failure or success respectively. A 2d array, with two columns. The first column represents the success count and the second column represents the failure count. A 1d array of proportions, indicating the proportion of successes, with parameter `var_weights` containing the number of trials for each row. """ links = [L.logit, L.probit, L.cauchy, L.log, L.cloglog, L.identity] variance = V.binary # this is not used below in an effort to include n # Other safe links, e.g. cloglog and probit are subclasses safe_links = [L.Logit, L.CDFLink] def __init__(self, link=None): #, n=1.): if link is None: link = L.logit() # TODO: it *should* work for a constant n>1 actually, if freq_weights # is equal to n self.n = 1 # overwritten by initialize if needed but always used to initialize # variance since endog is assumed/forced to be (0,1) super(Binomial, self).__init__(link=link, variance=V.Binomial(n=self.n)) def starting_mu(self, y): r""" The starting values for the IRLS algorithm for the Binomial family. A good choice for the binomial family is :math:`\mu_0 = (Y_i + 0.5)/2` """ return (y +.5)/2 def initialize(self, endog, freq_weights): ''' Initialize the response variable. Parameters ---------- endog : array Endogenous response variable freq_weights : array 1d array of frequency weights Returns ------- If `endog` is binary, returns `endog` If `endog` is a 2d array, then the input is assumed to be in the format (successes, failures) and successes/(success + failures) is returned. And n is set to successes + failures. ''' # if not np.all(np.asarray(freq_weights) == 1): # self.variance = V.Binomial(n=freq_weights) if (endog.ndim > 1 and endog.shape[1] > 1): y = endog[:, 0] # overwrite self.freq_weights for deviance below self.n = endog.sum(1) return y*1./self.n, self.n else: return endog, np.ones(endog.shape[0]) def _resid_dev(self, endog, mu): r""" Binomial deviance residuals Parameters ---------- endog : array The endogenous response variable. mu : array The inverse of the link function at the linear predicted values. Returns ------- resid_dev : float Deviance residuals as defined below. Notes ----- .. math:: resid\_dev_i = 2 * n * (endog_i * \ln(endog_i /\mu_i) + (1 - endog_i) * \ln((1 - endog_i) / (1 - \mu_i))) """ endog_mu = self._clean(endog / mu) n_endog_mu = self._clean((1. - endog) / (1. - mu)) resid_dev = endog * np.log(endog_mu) + (1 - endog) * np.log(n_endog_mu) return 2 * self.n * resid_dev def loglike_obs(self, endog, mu, var_weights=1., scale=1.): r""" The log-likelihood function for each observation in terms of the fitted mean response for the Binomial distribution. Parameters ---------- endog : array Usually the endogenous response variable. mu : array Usually but not always the fitted mean response variable. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float The scale parameter. The default is 1. Returns ------- ll_i : float The value of the loglikelihood evaluated at (endog, mu, var_weights, scale) as defined below. Notes ----- If the endogenous variable is binary: .. math:: ll_i = \sum_i (y_i * \log(\mu_i/(1-\mu_i)) + \log(1-\mu_i)) * var\_weights_i If the endogenous variable is binomial: .. math:: ll_i = \sum_i var\_weights_i * (\ln \Gamma(n+1) - \ln \Gamma(y_i + 1) - \ln \Gamma(n_i - y_i +1) + y_i * \log(\mu_i / (n_i - \mu_i)) + n * \log(1 - \mu_i/n_i)) where :math:`y_i = Y_i * n_i` with :math:`Y_i` and :math:`n_i` as defined in Binomial initialize. This simply makes :math:`y_i` the original number of successes. """ n = self.n # Number of trials y = endog * n # Number of successes # note that mu is still in (0,1), i.e. not converted back return (special.gammaln(n + 1) - special.gammaln(y + 1) - special.gammaln(n - y + 1) + y * np.log(mu / (1 - mu)) + n * np.log(1 - mu)) * var_weights def resid_anscombe(self, endog, mu, var_weights=1., scale=1.): r''' The Anscombe residuals Parameters ---------- endog : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float, optional An optional argument to divide the residuals by sqrt(scale). The default is 1. Returns ------- resid_anscombe : array The Anscombe residuals as defined below. Notes ----- .. math:: n^{2/3}*(cox\_snell(endog)-cox\_snell(mu)) / (mu*(1-mu/n)*scale^3)^{1/6} * \sqrt(var\_weights) where cox_snell is defined as cox_snell(x) = betainc(2/3., 2/3., x)*betainc(2/3.,2/3.) where betainc is the incomplete beta function as defined in scipy, which uses a regularized version (with the unregularized version, one would just have :math:`cox_snell(x) = Betainc(2/3., 2/3., x)`). The name 'cox_snell' is idiosyncratic and is simply used for convenience following the approach suggested in Cox and Snell (1968). Further note that :math:`cox\_snell(x) = \frac{3}{2}*x^{2/3} * hyp2f1(2/3.,1/3.,5/3.,x)` where hyp2f1 is the hypergeometric 2f1 function. The Anscombe residuals are sometimes defined in the literature using the hyp2f1 formulation. Both betainc and hyp2f1 can be found in scipy. References ---------- Anscombe, FJ. (1953) "Contribution to the discussion of H. Hotelling's paper." Journal of the Royal Statistical Society B. 15, 229-30. Cox, DR and Snell, EJ. (1968) "A General Definition of Residuals." Journal of the Royal Statistical Society B. 30, 248-75. ''' endog = endog * self.n # convert back to successes mu = mu * self.n # convert back to successes def cox_snell(x): return special.betainc(2/3., 2/3., x) * special.beta(2/3., 2/3.) resid = (self.n ** (2/3.) * (cox_snell(endog * 1. / self.n) - cox_snell(mu * 1. / self.n)) / (mu * (1 - mu * 1. / self.n) * scale ** 3) ** (1 / 6.)) resid *= np.sqrt(var_weights) return resid class InverseGaussian(Family): """ InverseGaussian exponential family. Parameters ---------- link : a link instance, optional The default link for the inverse Gaussian family is the inverse squared link. Available links are inverse_squared, inverse, log, and identity. See statsmodels.genmod.families.links for more information. Attributes ---------- InverseGaussian.link : a link instance The link function of the inverse Gaussian instance InverseGaussian.variance : varfunc instance ``variance`` is an instance of statsmodels.genmod.families.varfuncs.mu_cubed See Also -------- statsmodels.genmod.families.family.Family :ref:`links` Notes ----- The inverse Guassian distribution is sometimes referred to in the literature as the Wald distribution. """ links = [L.inverse_squared, L.inverse_power, L.identity, L.log] variance = V.mu_cubed safe_links = [L.inverse_squared, L.Log, ] def __init__(self, link=None): if link is None: link = L.inverse_squared() super(InverseGaussian, self).__init__( link=link, variance=InverseGaussian.variance) def _resid_dev(self, endog, mu): r""" Inverse Gaussian deviance residuals Parameters ---------- endog : array The endogenous response variable. mu : array The inverse of the link function at the linear predicted values. Returns ------- resid_dev : float Deviance residuals as defined below. Notes ----- .. math:: resid\_dev_i = 1 / (endog_i * \mu_i^2) * (endog_i - \mu_i)^2 """ return 1. / (endog * mu ** 2) * (endog - mu) ** 2 def loglike_obs(self, endog, mu, var_weights=1., scale=1.): r""" The log-likelihood function for each observation in terms of the fitted mean response for the Inverse Gaussian distribution. Parameters ---------- endog : array Usually the endogenous response variable. mu : array Usually but not always the fitted mean response variable. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float The scale parameter. The default is 1. Returns ------- ll_i : float The value of the loglikelihood evaluated at (endog, mu, var_weights, scale) as defined below. Notes ----- .. math:: ll_i = -1/2 * (var\_weights_i * (endog_i - \mu_i)^2 / (scale * endog_i * \mu_i^2) + \ln(scale * \endog_i^3 / var\_weights_i) - \ln(2 * \pi)) """ ll_obs = -var_weights * (endog - mu) ** 2 / (scale * endog * mu ** 2) ll_obs += -np.log(scale * endog ** 3 / var_weights) - np.log(2 * np.pi) ll_obs /= 2 return ll_obs def resid_anscombe(self, endog, mu, var_weights=1., scale=1.): r""" The Anscombe residuals Parameters ---------- endog : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float, optional An optional argument to divide the residuals by sqrt(scale). The default is 1. Returns ------- resid_anscombe : array The Anscombe residuals for the inverse Gaussian distribution as defined below Notes ----- .. math:: resid\_anscombe_i = \log(Y_i / \mu_i) / \sqrt{\mu_i * scale} * \sqrt(var\_weights) """ resid = np.log(endog / mu) / np.sqrt(mu * scale) resid *= np.sqrt(var_weights) return resid class NegativeBinomial(Family): r""" Negative Binomial exponential family. Parameters ---------- link : a link instance, optional The default link for the negative binomial family is the log link. Available links are log, cloglog, identity, nbinom and power. See statsmodels.genmod.families.links for more information. alpha : float, optional The ancillary parameter for the negative binomial distribution. For now ``alpha`` is assumed to be nonstochastic. The default value is 1. Permissible values are usually assumed to be between.01 and 2. Attributes ---------- NegativeBinomial.link : a link instance The link function of the negative binomial instance NegativeBinomial.variance : varfunc instance ``variance`` is an instance of statsmodels.genmod.families.varfuncs.nbinom See Also -------- statsmodels.genmod.families.family.Family :ref:`links` Notes ----- Power link functions are not yet supported. Parameterization for :math:`y=0, 1, 2, \ldots` is .. math:: f(y) = \frac{\Gamma(y+\frac{1}{\alpha})}{y!\Gamma(\frac{1}{\alpha})} \left(\frac{1}{1+\alpha\mu}\right)^{\frac{1}{\alpha}} \left(\frac{\alpha\mu}{1+\alpha\mu}\right)^y with :math:`E[Y]=\mu\,` and :math:`Var[Y]=\mu+\alpha\mu^2`. """ links = [L.log, L.cloglog, L.identity, L.nbinom, L.Power] # TODO: add the ability to use the power links with an if test # similar to below variance = V.nbinom safe_links = [L.Log, ] def __init__(self, link=None, alpha=1.): self.alpha = 1. * alpha # make it at least float if link is None: link = L.log() super(NegativeBinomial, self).__init__( link=link, variance=V.NegativeBinomial(alpha=self.alpha)) def _resid_dev(self, endog, mu): r""" Negative Binomial deviance residuals Parameters ---------- endog : array The endogenous response variable. mu : array The inverse of the link function at the linear predicted values. Returns ------- resid_dev : float Deviance residuals as defined below. Notes ----- .. math:: resid_dev_i = 2 * (endog_i * \ln(endog_i / \mu_i) - (endog_i + 1 / \alpha) * \ln((endog_i + 1 / \alpha) / (\mu_i + 1 / \alpha))) """ endog_mu = self._clean(endog / mu) endog_alpha = endog + 1 / self.alpha mu_alpha = mu + 1 / self.alpha resid_dev = endog * np.log(endog_mu) resid_dev -= endog_alpha * np.log(endog_alpha / mu_alpha) return 2 * resid_dev def loglike_obs(self, endog, mu, var_weights=1., scale=1.): r""" The log-likelihood function for each observation in terms of the fitted mean response for the Negative Binomial distribution. Parameters ---------- endog : array Usually the endogenous response variable. mu : array Usually but not always the fitted mean response variable. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float The scale parameter. The default is 1. Returns ------- ll_i : float The value of the loglikelihood evaluated at (endog, mu, var_weights, scale) as defined below. Notes ----- Defined as: .. math:: llf = \sum_i var\_weights_i / scale * (Y_i * \log{(\alpha * \mu_i / (1 + \alpha * \mu_i))} - \log{(1 + \alpha * \mu_i)}/ \alpha + Constant) where :math:`Constant` is defined as: .. math:: Constant = \ln \Gamma{(Y_i + 1/ \alpha )} - \ln \Gamma(Y_i + 1) - \ln \Gamma{(1/ \alpha )} constant = (special.gammaln(endog + 1 / self.alpha) - special.gammaln(endog+1)-special.gammaln(1/self.alpha)) return (endog * np.log(self.alpha * mu / (1 + self.alpha * mu)) - np.log(1 + self.alpha * mu) / self.alpha + constant) * var_weights / scale """ ll_obs = endog * np.log(self.alpha * mu) ll_obs -= (endog + 1 / self.alpha) * np.log(1 + self.alpha * mu) ll_obs += special.gammaln(endog + 1 / self.alpha) ll_obs -= special.gammaln(1 / self.alpha) ll_obs -= special.gammaln(endog + 1) return var_weights / scale * ll_obs def resid_anscombe(self, endog, mu, var_weights=1., scale=1.): r""" The Anscombe residuals Parameters ---------- endog : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float, optional An optional argument to divide the residuals by sqrt(scale). The default is 1. Returns ------- resid_anscombe : array The Anscombe residuals as defined below. Notes ----- Anscombe residuals for Negative Binomial are the same as for Binomial upon setting :math:`n=-\frac{1}{\alpha}`. Due to the negative value of :math:`-\alpha*Y` the representation with the hypergeometric function :math:`H2F1(x) = hyp2f1(2/3.,1/3.,5/3.,x)` is advantageous .. math:: resid\_anscombe_i = \frac{3}{2} * (Y_i^(2/3)*H2F1(-\alpha*Y_i) - \mu_i^(2/3)*H2F1(-\alpha*\mu_i)) / (\mu_i * (1+\alpha*\mu_i) * scale^3)^(1/6) * \sqrt(var\_weights) Note that for the (unregularized) Beta function, one has :math:`Beta(z,a,b) = z^a/a * H2F1(a,1-b,a+1,z)` """ def hyp2f1(x): return special.hyp2f1(2 / 3., 1 / 3., 5 / 3., x) resid = (3 / 2. * (endog ** (2 / 3.) * hyp2f1(-self.alpha * endog) - mu ** (2 / 3.) * hyp2f1(-self.alpha * mu)) / (mu * (1 + self.alpha * mu) * scale ** 3) ** (1 / 6.)) resid *= np.sqrt(var_weights) return resid class Tweedie(Family): """ Tweedie family. Parameters ---------- link : a link instance, optional The default link for the Tweedie family is the log link. Available links are log and Power. See statsmodels.genmod.families.links for more information. var_power : float, optional The variance power. The default is 1. eql : bool If True, the Extended Quasi-Likelihood is used, else the likelihood is used (however the latter is not implemented). If eql is True, var_power must be between 1 and 2. Attributes ---------- Tweedie.link : a link instance The link function of the Tweedie instance Tweedie.variance : varfunc instance ``variance`` is an instance of statsmodels.genmod.families.varfuncs.Power Tweedie.var_power : float The power of the variance function. See Also -------- statsmodels.genmod.families.family.Family :ref:`links` Notes ----- Logliklihood function not implemented because of the complexity of calculating an infinite series of summations. The variance power can be estimated using the ``estimate_tweedie_power`` function that is part of the statsmodels.genmod.generalized_linear_model.GLM class. """ links = [L.log, L.Power] variance = V.Power(power=1.5) safe_links = [L.log, L.Power] def __init__(self, link=None, var_power=1., eql=False): self.var_power = var_power self.eql = eql if eql and (var_power < 1 or var_power > 2): raise ValueError("Tweedie: if EQL=True then var_power must fall " "between 1 and 2") if link is None: link = L.log() super(Tweedie, self).__init__( link=link, variance=V.Power(power=var_power * 1.)) def _resid_dev(self, endog, mu): r""" Tweedie deviance residuals Parameters ---------- endog : array The endogenous response variable. mu : array The inverse of the link function at the linear predicted values. Returns ------- resid_dev : float Deviance residuals as defined below. Notes ----- When :math:`p = 1`, .. math:: dev_i = \mu_i when :math:`endog_i = 0` and .. math:: dev_i = endog_i * \log(endog_i / \mu_i) + (\mu_i - endog_i) otherwise. When :math:`p = 2`, .. math:: dev_i = (endog_i - \mu_i) / \mu_i - \log(endog_i / \mu_i) For all other p, .. math:: dev_i = endog_i^{2 - p} / ((1 - p) * (2 - p)) - endog_i * \mu_i^{1 - p} / (1 - p) + \mu_i^{2 - p} / (2 - p) The deviance residual is then .. math:: resid\_dev_i = 2 * dev_i """ p = self.var_power if p == 1: dev = np.where(endog == 0, mu, endog * np.log(endog / mu) + (mu - endog)) elif p == 2: endog1 = self._clean(endog) dev = ((endog - mu) / mu) - np.log(endog1 / mu) else: dev = (endog ** (2 - p) / ((1 - p) * (2 - p)) - endog * mu ** (1-p) / (1 - p) + mu ** (2 - p) / (2 - p)) return 2 * dev def loglike_obs(self, endog, mu, var_weights=1., scale=1.): r""" The log-likelihood function for each observation in terms of the fitted mean response for the Tweedie distribution. Parameters ---------- endog : array Usually the endogenous response variable. mu : array Usually but not always the fitted mean response variable. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float The scale parameter. The default is 1. Returns ------- ll_i : float The value of the loglikelihood evaluated at (endog, mu, var_weights, scale) as defined below. Notes ----- If eql is True, the Extended Quasi-Likelihood is used. At present, this method returns NaN if eql is False. When the actual likelihood is implemented, it will be accessible by setting eql to False. References ---------- JA Nelder, D Pregibon (1987). An extended quasi-likelihood function. Biometrika 74:2, pp 221-232. https://www.jstor.org/stable/2336136 """ if not self.eql: # We have not yet implemented the actual likelihood return np.nan # Equations 9-10 or Nelder and Pregibon p = self.var_power llf = np.log(2 * np.pi * scale) + p * np.log(mu) - np.log(var_weights) llf /= -2 if p == 1: u = endog * np.log(endog / mu) - (endog - mu) u *= var_weights / scale elif p == 2: yr = endog / mu u = yr - np.log(yr) - 1 u *= var_weights / scale else: u = (endog ** (2 - p) - (2 - p) * endog * mu ** (1 - p) + (1 - p) * mu ** (2 - p)) u *= var_weights / (scale * (1 - p) * (2 - p)) llf -= u return llf def resid_anscombe(self, endog, mu, var_weights=1., scale=1.): r""" The Anscombe residuals Parameters ---------- endog : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. var_weights : array_like 1d array of variance (analytic) weights. The default is 1. scale : float, optional An optional argument to divide the residuals by sqrt(scale). The default is 1. Returns ------- resid_anscombe : array The Anscombe residuals as defined below. Notes ----- When :math:`p = 3`, then .. math:: resid\_anscombe_i = \log(endog_i / \mu_i) / \sqrt{\mu_i * scale} * \sqrt(var\_weights) Otherwise, .. math:: c = (3 - p) / 3 .. math:: resid\_anscombe_i = (1 / c) * (endog_i^c - \mu_i^c) / \mu_i^{p / 6} / \sqrt{scale} * \sqrt(var\_weights) """ if self.var_power == 3: resid = np.log(endog / mu) / np.sqrt(mu * scale) else: c = (3. - self.var_power) / 3. resid = ((1. / c) * (endog ** c - mu ** c) / mu ** (self.var_power / 6.)) / scale ** 0.5 resid *= np.sqrt(var_weights) return resid import numpy as np from collections import defaultdict import statsmodels.base.model as base from statsmodels.genmod import families from statsmodels.genmod.families import links from statsmodels.genmod.families import varfuncs import statsmodels.regression.linear_model as lm import statsmodels.base.wrapper as wrap from statsmodels.tools.decorators import cache_readonly class QIFCovariance(object): """ A covariance model for quadratic inference function regression. The mat method returns a basis matrix B such that the inverse of the working covariance lies in the linear span of the basis matrices. Subclasses should set the number of basis matrices `num_terms`, so that `mat(d, j)` for j=0,..., num_terms-1 gives the basis of dimension d.` """ def mat(self, dim, term): """ Returns the term'th basis matrix, which is a dim x dim matrix. """ raise NotImplementedError class QIFIndependence(QIFCovariance): """ Independent working covariance for QIF regression. This covariance model gives identical results to GEE with the independence working covariance. When using QIFIndependence as the working covariance, the QIF value will be zero, and cannot be used for chi^2 testing, or for model selection using AIC, BIC, etc. """ def __init__(self): self.num_terms = 1 def mat(self, dim, term): if term == 0: return np.eye(dim) else: return None class QIFExchangeable(QIFCovariance): """ Exchangeable working covariance for QIF regression. """ def __init__(self): self.num_terms = 2 def mat(self, dim, term): if term == 0: return np.eye(dim) elif term == 1: return np.ones((dim, dim)) else: return None class QIFAutoregressive(QIFCovariance): """ Autoregressive working covariance for QIF regression. """ def __init__(self): self.num_terms = 3 def mat(self, dim, term): if dim < 3: msg = ("Groups must have size at least 3 for " + "autoregressive covariance.") raise ValueError(msg) if term == 0: return np.eye(dim) elif term == 1: mat = np.zeros((dim, dim)) mat.flat[1::(dim+1)] = 1 mat += mat.T return mat elif term == 2: mat = np.zeros((dim, dim)) mat[0, 0] = 1 mat[dim-1, dim-1] = 1 return mat else: return None class QIF(base.Model): """ Fit a regression model using quadratic inference functions (QIF). QIF is an alternative to GEE that can be more efficient, and that offers different approaches for model selection and inference. Parameters ---------- endog : array_like The dependent variables of the regression. exog : array_like The independent variables of the regression. groups : array_like Labels indicating which group each observation belongs to. Observations in different groups should be independent. family : genmod family An instance of a GLM family. cov_struct : QIFCovariance instance An instance of a QIFCovariance. References ---------- A. Qu, B. Lindsay, B. Li (2000). Improving Generalized Estimating Equations using Quadratic Inference Functions, Biometrika 87:4. www.jstor.org/stable/2673612 """ def __init__(self, endog, exog, groups, family=None, cov_struct=None, missing='none', **kwargs): # Handle the family argument if family is None: family = families.Gaussian() else: if not issubclass(family.__class__, families.Family): raise ValueError("QIF: `family` must be a genmod " "family instance") self.family = family self._fit_history = defaultdict(list) # Handle the cov_struct argument if cov_struct is None: cov_struct = QIFIndependence() else: if not isinstance(cov_struct, QIFCovariance): raise ValueError( "QIF: `cov_struct` must be a QIFCovariance instance") self.cov_struct = cov_struct groups = np.asarray(groups) super(QIF, self).__init__(endog, exog, groups=groups, missing=missing, **kwargs) self.group_names = list(set(groups)) self.nobs = len(self.endog) groups_ix = defaultdict(list) for i, g in enumerate(groups): groups_ix[g].append(i) self.groups_ix = [groups_ix[na] for na in self.group_names] self._check_args(groups) def _check_args(self, groups): if len(groups)!= len(self.endog): msg = "QIF: groups and endog should have the same length" raise ValueError(msg) if len(self.endog)!= self.exog.shape[0]: msg = ("QIF: the length of endog should be equal to the " "number of rows of exog.") raise ValueError(msg) def objective(self, params): """ Calculate the gradient of the QIF objective function. Parameters ---------- params : array_like The model parameters at which the gradient is evaluated. Returns ------- grad : array_like The gradient vector of the QIF objective function. gn_deriv : array_like The gradients of each estimating equation with respect to the parameter. """ endog = self.endog exog = self.exog lpr = np.dot(exog, params) mean = self.family.link.inverse(lpr) va = self.family.variance(mean) # Mean derivative idl = self.family.link.inverse_deriv(lpr) idl2 = self.family.link.inverse_deriv2(lpr) vd = self.family.variance.deriv(mean) m = self.cov_struct.num_terms p = exog.shape[1] d = p * m gn = np.zeros(d) gi = np.zeros(d) gi_deriv = np.zeros((d, p)) gn_deriv = np.zeros((d, p)) cn_deriv = [0] * p cmat = np.zeros((d, d)) fastvar = self.family.variance is varfuncs.constant fastlink = isinstance(self.family.link, links.identity) for ix in self.groups_ix: sd = np.sqrt(va[ix]) resid = endog[ix] - mean[ix] sresid = resid / sd deriv = exog[ix, :] * idl[ix, None] jj = 0 for j in range(m): # The derivative of each term in (5) of Qu et al. # There are four terms involving beta in a product. # Iterated application of the product rule gives # the gradient as a sum of four terms. c = self.cov_struct.mat(len(ix), j) crs1 = np.dot(c, sresid) / sd gi[jj:jj+p] = np.dot(deriv.T, crs1) crs2 = np.dot(c, -deriv / sd[:, None]) / sd[:, None] gi_deriv[jj:jj+p, :] = np.dot(deriv.T, crs2) if not (fastlink and fastvar): for k in range(p): m1 = np.dot(exog[ix, :].T, idl2[ix] * exog[ix, k] * crs1) if not fastvar: vx = -0.5 * vd[ix] * deriv[:, k] / va[ix]**1.5 m2 = np.dot(deriv.T, vx * np.dot(c, sresid)) m3 = np.dot(deriv.T, np.dot(c, vx * resid) / sd) else: m2, m3 = 0, 0 gi_deriv[jj:jj+p, k] += m1 + m2 + m3 jj += p for j in range(p): u = np.outer(gi, gi_deriv[:, j]) cn_deriv[j] += u + u.T gn += gi gn_deriv += gi_deriv cmat += np.outer(gi, gi) ngrp = len(self.groups_ix) gn /= ngrp gn_deriv /= ngrp cmat /= ngrp**2 qif = np.dot(gn, np.linalg.solve(cmat, gn)) gcg = np.zeros(p) for j in range(p): cn_deriv[j] /= len(self.groups_ix)**2 u = np.linalg.solve(cmat, cn_deriv[j]).T u = np.linalg.solve(cmat, u) gcg[j] = np.dot(gn, np.dot(u, gn)) grad = 2 * np.dot(gn_deriv.T, np.linalg.solve(cmat, gn)) - gcg return qif, grad, cmat, gn, gn_deriv def estimate_scale(self, params): """ Estimate the dispersion/scale. The scale parameter for binomial and Poisson families is fixed at 1, otherwise it is estimated from the data. """ if isinstance(self.family, (families.Binomial, families.Poisson)): return 1. if hasattr(self, "ddof_scale"): ddof_scale = self.ddof_scale else: ddof_scale = self.exog[1] lpr = np.dot(self.exog, params) mean = self.family.link.inverse(lpr) resid = self.endog - mean scale = np.sum(resid**2) / (self.nobs - ddof_scale) return scale @classmethod def from_formula(cls, formula, groups, data, subset=None, *args, **kwargs): """ Create a QIF model instance from a formula and dataframe. Parameters ---------- formula : str or generic Formula object The formula specifying the model groups : array_like or string Array of grouping labels. If a string, this is the name of a variable in `data` that contains the grouping labels. data : array_like The data for the model. subset : array_like An array_like object of booleans, integers, or index values that indicate the subset of the data to used when fitting the model. Returns ------- model : QIF model instance """ if isinstance(groups, str): groups = data[groups] model = super(QIF, cls).from_formula( formula, data=data, subset=subset, groups=groups, *args, **kwargs) return model def fit(self, maxiter=100, start_params=None, tol=1e-6, gtol=1e-4, ddof_scale=None): """ Fit a GLM to correlated data using QIF. Parameters ---------- maxiter : integer Maximum number of iterations. start_params : array_like, optional Starting values tol : float Convergence threshold for difference of successive estimates. gtol : float Convergence threshold for gradient. ddof_scale : int, optional Degrees of freedom for the scale parameter Returns ------- QIFResults object """ if ddof_scale is None: self.ddof_scale = self.exog.shape[1] else: self.ddof_scale = ddof_scale if start_params is None: params = np.zeros(self.exog.shape[1]) else: params = start_params for _ in range(maxiter): qif, grad, cmat, _, gn_deriv = self.objective(params) gnorm = np.sqrt(np.sum(grad * grad)) self._fit_history["qif"].append(qif) self._fit_history["gradnorm"].append(gnorm) if gnorm < gtol: break cjac = 2 * np.dot(gn_deriv.T, np.linalg.solve(cmat, gn_deriv)) step = np.linalg.solve(cjac, grad) snorm = np.sqrt(np.sum(step * step)) self._fit_history["stepnorm"].append(snorm) if snorm < tol: break params -= step vcov = np.dot(gn_deriv.T, np.linalg.solve(cmat, gn_deriv)) vcov = np.linalg.inv(vcov) scale = self.estimate_scale(params) rslt = QIFResults(self, params, vcov / scale, scale) rslt.fit_history = self._fit_history self._fit_history = defaultdict(list) return QIFResultsWrapper(rslt) class QIFResults(base.LikelihoodModelResults): """Results class for QIF Regression""" def __init__(self, model, params, cov_params, scale, use_t=False, **kwds): super(QIFResults, self).__init__( model, params, normalized_cov_params=cov_params, scale=scale) self.qif, _, _, _, _ = self.model.objective(params) @cache_readonly def aic(self): """ An AIC-like statistic for models fit using QIF. """ if isinstance(self.model.cov_struct, QIFIndependence): msg = "AIC not available with QIFIndependence covariance" raise ValueError(msg) df = self.model.exog.shape[1] return self.qif + 2*df @cache_readonly def bic(self): """ A BIC-like statistic for models fit using QIF. """ if isinstance(self.model.cov_struct, QIFIndependence): msg = "BIC not available with QIFIndependence covariance" raise ValueError(msg) df = self.model.exog.shape[1] return self.qif + np.log(self.model.nobs)*df @cache_readonly def fittedvalues(self): """ Returns the fitted values from the model. """ return self.model.family.link.inverse( np.dot(self.model.exog, self.params)) def summary(self, yname=None, xname=None, title=None, alpha=.05): """ Summarize the QIF regression results Parameters ---------- yname : str, optional Default is `y` xname : list[str], optional Names for the exogenous variables, default is `var_#` for ## in the number of regressors. Must match the number of parameters in the model title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ top_left = [('Dep. Variable:', None), ('Method:', ['QIF']), ('Family:', [self.model.family.__class__.__name__]), ('Covariance structure:', [self.model.cov_struct.__class__.__name__]), ('Date:', None), ('Time:', None), ] NY = [len(y) for y in self.model.groups_ix] top_right = [('No. Observations:', [sum(NY)]), ('No. clusters:', [len(NY)]), ('Min. cluster size:', [min(NY)]), ('Max. cluster size:', [max(NY)]), ('Mean cluster size:', ["%.1f" % np.mean(NY)]), ('Scale:', ["%.3f" % self.scale]), ] if title is None: title = self.model.__class__.__name__ +'' +\ "Regression Results" # Override the exog variable names if xname is provided as an # argument. if xname is None: xname = self.model.exog_names if yname is None: yname = self.model.endog_names # Create summary table instance from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=False) return smry class QIFResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(QIFResultsWrapper, QIFResults) ''' Defines the link functions to be used with GLM and GEE families. ''' import numpy as np import scipy.stats FLOAT_EPS = np.finfo(float).eps class Link(object): """ A generic link function for one-parameter exponential family. `Link` does nothing, but lays out the methods expected of any subclass. """ def __call__(self, p): """ Return the value of the link function. This is just a placeholder. Parameters ---------- p : array_like Probabilities Returns ------- g(p) : array_like The value of the link function g(p) = z """ return NotImplementedError def inverse(self, z): """ Inverse of the link function. Just a placeholder. Parameters ---------- z : array_like `z` is usually the linear predictor of the transformed variable in the IRLS algorithm for GLM. Returns ------- g^(-1)(z) : array The value of the inverse of the link function g^(-1)(z) = p """ return NotImplementedError def deriv(self, p): """ Derivative of the link function g'(p). Just a placeholder. Parameters ---------- p : array_like Returns ------- g'(p) : array The value of the derivative of the link function g'(p) """ return NotImplementedError def deriv2(self, p): """Second derivative of the link function g''(p) implemented through numerical differentiation """ from statsmodels.tools.numdiff import approx_fprime_cs # TODO: workaround proplem with numdiff for 1d return np.diag(approx_fprime_cs(p, self.deriv)) def inverse_deriv(self, z): """ Derivative of the inverse link function g^(-1)(z). Parameters ---------- z : array_like `z` is usually the linear predictor for a GLM or GEE model. Returns ------- g'^(-1)(z) : array The value of the derivative of the inverse of the link function Notes ----- This reference implementation gives the correct result but is inefficient, so it can be overriden in subclasses. """ return 1 / self.deriv(self.inverse(z)) def inverse_deriv2(self, z): """ Second derivative of the inverse link function g^(-1)(z). Parameters ---------- z : array_like `z` is usually the linear predictor for a GLM or GEE model. Returns ------- g'^(-1)(z) : array The value of the second derivative of the inverse of the link function Notes ----- This reference implementation gives the correct result but is inefficient, so it can be overriden in subclasses. """ iz = self.inverse(z) return -self.deriv2(iz) / self.deriv(iz)**3 class Logit(Link): """ The logit transform Notes ----- call and derivative use a private method _clean to make trim p by machine epsilon so that p is in (0,1) Alias of Logit: logit = Logit() """ def _clean(self, p): """ Clip logistic values to range (eps, 1-eps) Parameters ---------- p : array_like Probabilities Returns ------- pclip : array Clipped probabilities """ return np.clip(p, FLOAT_EPS, 1. - FLOAT_EPS) def __call__(self, p): """ The logit transform Parameters ---------- p : array_like Probabilities Returns ------- z : array Logit transform of `p` Notes ----- g(p) = log(p / (1 - p)) """ p = self._clean(p) return np.log(p / (1. - p)) def inverse(self, z): """ Inverse of the logit transform Parameters ---------- z : array_like The value of the logit transform at `p` Returns ------- p : array Probabilities Notes ----- g^(-1)(z) = exp(z)/(1+exp(z)) """ z = np.asarray(z) t = np.exp(-z) return 1. / (1. + t) def deriv(self, p): """ Derivative of the logit transform Parameters ---------- p: array_like Probabilities Returns ------- g'(p) : array Value of the derivative of logit transform at `p` Notes ----- g'(p) = 1 / (p * (1 - p)) Alias for `Logit`: logit = Logit() """ p = self._clean(p) return 1. / (p * (1 - p)) def inverse_deriv(self, z): """ Derivative of the inverse of the logit transform Parameters ---------- z : array_like `z` is usually the linear predictor for a GLM or GEE model. Returns ------- g'^(-1)(z) : array The value of the derivative of the inverse of the logit function """ t = np.exp(z) return t/(1 + t)**2 def deriv2(self, p): """ Second derivative of the logit function. Parameters ---------- p : array_like probabilities Returns ------- g''(z) : array The value of the second derivative of the logit function """ v = p * (1 - p) return (2*p - 1) / v**2 class logit(Logit): pass class Power(Link): """ The power transform Parameters ---------- power : float The exponent of the power transform Notes ----- Aliases of Power: inverse = Power(power=-1) sqrt = Power(power=.5) inverse_squared = Power(power=-2.) identity = Power(power=1.) """ def __init__(self, power=1.): self.power = power def __call__(self, p): """ Power transform link function Parameters ---------- p : array_like Mean parameters Returns ------- z : array_like Power transform of x Notes ----- g(p) = x**self.power """ if self.power == 1: return p else: return np.power(p, self.power) def inverse(self, z): """ Inverse of the power transform link function Parameters ---------- `z` : array_like Value of the transformed mean parameters at `p` Returns ------- `p` : array Mean parameters Notes ----- g^(-1)(z`) = `z`**(1/`power`) """ if self.power == 1: return z else: return np.power(z, 1. / self.power) def deriv(self, p): """ Derivative of the power transform Parameters ---------- p : array_like Mean parameters Returns ------- g'(p) : array Derivative of power transform of `p` Notes ----- g'(`p`) = `power` * `p`**(`power` - 1) """ if self.power == 1: return np.ones_like(p) else: return self.power * np.power(p, self.power - 1) def deriv2(self, p): """ Second derivative of the power transform Parameters ---------- p : array_like Mean parameters Returns ------- g''(p) : array Second derivative of the power transform of `p` Notes ----- g''(`p`) = `power` * (`power` - 1) * `p`**(`power` - 2) """ if self.power == 1: return np.zeros_like(p) else: return self.power * (self.power - 1) * np.power(p, self.power - 2) def inverse_deriv(self, z): """ Derivative of the inverse of the power transform Parameters ---------- z : array_like `z` is usually the linear predictor for a GLM or GEE model. Returns ------- g^(-1)'(z) : array The value of the derivative of the inverse of the power transform function """ if self.power == 1: return np.ones_like(z) else: return np.power(z, (1 - self.power)/self.power) / self.power def inverse_deriv2(self, z): """ Second derivative of the inverse of the power transform Parameters ---------- z : array_like `z` is usually the linear predictor for a GLM or GEE model. Returns ------- g^(-1)'(z) : array The value of the derivative of the inverse of the power transform function """ if self.power == 1: return np.zeros_like(z) else: return ((1 - self.power) * np.power(z, (1 - 2*self.power)/self.power) / self.power**2) class inverse_power(Power): """ The inverse transform Notes ----- g(p) = 1/p Alias of statsmodels.family.links.Power(power=-1.) """ def __init__(self): super(inverse_power, self).__init__(power=-1.) class sqrt(Power): """ The square-root transform Notes ----- g(`p`) = sqrt(`p`) Alias of statsmodels.family.links.Power(power=.5) """ def __init__(self): super(sqrt, self).__init__(power=.5) class inverse_squared(Power): r""" The inverse squared transform Notes ----- g(`p`) = 1/(`p`\*\*2) Alias of statsmodels.family.links.Power(power=2.) """ def __init__(self): super(inverse_squared, self).__init__(power=-2.) class identity(Power): """ The identity transform Notes ----- g(`p`) = `p` Alias of statsmodels.family.links.Power(power=1.) """ def __init__(self): super(identity, self).__init__(power=1.) class Log(Link): """ The log transform Notes ----- call and derivative call a private method _clean to trim the data by machine epsilon so that p is in (0,1). log is an alias of Log. """ def _clean(self, x): return np.clip(x, FLOAT_EPS, np.inf) def __call__(self, p, **extra): """ Log transform link function Parameters ---------- x : array_like Mean parameters Returns ------- z : array log(x) Notes ----- g(p) = log(p) """ x = self._clean(p) return np.log(x) def inverse(self, z): """ Inverse of log transform link function Parameters ---------- z : array The inverse of the link function at `p` Returns ------- p : array The mean probabilities given the value of the inverse `z` Notes ----- g^{-1}(z) = exp(z) """ return np.exp(z) def deriv(self, p): """ Derivative of log transform link function Parameters ---------- p : array_like Mean parameters Returns ------- g'(p) : array derivative of log transform of x Notes ----- g'(x) = 1/x """ p = self._clean(p) return 1. / p def deriv2(self, p): """ Second derivative of the log transform link function Parameters ---------- p : array_like Mean parameters Returns ------- g''(p) : array Second derivative of log transform of x Notes ----- g''(x) = -1/x^2 """ p = self._clean(p) return -1. / p**2 def inverse_deriv(self, z): """ Derivative of the inverse of the log transform link function Parameters ---------- z : array The inverse of the link function at `p` Returns ------- g^(-1)'(z) : array The value of the derivative of the inverse of the log function, the exponential function """ return np.exp(z) class log(Log): """ The log transform Notes ----- log is a an alias of Log. """ pass # TODO: the CDFLink is untested class CDFLink(Logit): """ The use the CDF of a scipy.stats distribution CDFLink is a subclass of logit in order to use its _clean method for the link and its derivative. Parameters ---------- dbn : scipy.stats distribution Default is dbn=scipy.stats.norm Notes ----- The CDF link is untested. """ def __init__(self, dbn=scipy.stats.norm): self.dbn = dbn def __call__(self, p): """ CDF link function Parameters ---------- p : array_like Mean parameters Returns ------- z : array (ppf) inverse of CDF transform of p Notes ----- g(`p`) = `dbn`.ppf(`p`) """ p = self._clean(p) return self.dbn.ppf(p) def inverse(self, z): """ The inverse of the CDF link Parameters ---------- z : array_like The value of the inverse of the link function at `p` Returns ------- p : array Mean probabilities. The value of the inverse of CDF link of `z` Notes ----- g^(-1)(`z`) = `dbn`.cdf(`z`) """ return self.dbn.cdf(z) def deriv(self, p): """ Derivative of CDF link Parameters ---------- p : array_like mean parameters Returns ------- g'(p) : array The derivative of CDF transform at `p` Notes ----- g'(`p`) = 1./ `dbn`.pdf(`dbn`.ppf(`p`)) """ p = self._clean(p) return 1. / self.dbn.pdf(self.dbn.ppf(p)) def deriv2(self, p): """ Second derivative of the link function g''(p) implemented through numerical differentiation """ from statsmodels.tools.numdiff import approx_fprime p = np.atleast_1d(p) # Note: special function for norm.ppf does not support complex return np.diag(approx_fprime(p, self.deriv, centered=True)) def inverse_deriv(self, z): """ Derivative of the inverse of the CDF transformation link function Parameters ---------- z : array The inverse of the link function at `p` Returns ------- g^(-1)'(z) : array The value of the derivative of the inverse of the logit function """ return 1/self.deriv(self.inverse(z)) class probit(CDFLink): """ The probit (standard normal CDF) transform Notes ----- g(p) = scipy.stats.norm.ppf(p) probit is an alias of CDFLink. """ pass class cauchy(CDFLink): """ The Cauchy (standard Cauchy CDF) transform Notes ----- g(p) = scipy.stats.cauchy.ppf(p) cauchy is an alias of CDFLink with dbn=scipy.stats.cauchy """ def __init__(self): super(cauchy, self).__init__(dbn=scipy.stats.cauchy) def deriv2(self, p): """ Second derivative of the Cauchy link function. Parameters ---------- p: array_like Probabilities Returns ------- g''(p) : array Value of the second derivative of Cauchy link function at `p` """ a = np.pi * (p - 0.5) d2 = 2 * np.pi**2 * np.sin(a) / np.cos(a)**3 return d2 class CLogLog(Logit): """ The complementary log-log transform CLogLog inherits from Logit in order to have access to its _clean method for the link and its derivative. Notes ----- CLogLog is untested. """ def __call__(self, p): """ C-Log-Log transform link function Parameters ---------- p : array Mean parameters Returns ------- z : array The CLogLog transform of `p` Notes ----- g(p) = log(-log(1-p)) """ p = self._clean(p) return np.log(-np.log(1 - p)) def inverse(self, z): """ Inverse of C-Log-Log transform link function Parameters ---------- z : array_like The value of the inverse of the CLogLog link function at `p` Returns ------- p : array Mean parameters Notes ----- g^(-1)(`z`) = 1-exp(-exp(`z`)) """ return 1 - np.exp(-np.exp(z)) def deriv(self, p): """ Derivative of C-Log-Log transform link function Parameters ---------- p : array_like Mean parameters Returns ------- g'(p) : array The derivative of the CLogLog transform link function Notes ----- g'(p) = - 1 / ((p-1)*log(1-p)) """ p = self._clean(p) return 1. / ((p - 1) * (np.log(1 - p))) def deriv2(self, p): """ Second derivative of the C-Log-Log ink function Parameters ---------- p : array_like Mean parameters Returns ------- g''(p) : array The second derivative of the CLogLog link function """ p = self._clean(p) fl = np.log(1 - p) d2 = -1 / ((1 - p)**2 * fl) d2 *= 1 + 1 / fl return d2 def inverse_deriv(self, z): """ Derivative of the inverse of the C-Log-Log transform link function Parameters ---------- z : array_like The value of the inverse of the CLogLog link function at `p` Returns ------- g^(-1)'(z) : array The derivative of the inverse of the CLogLog link function """ return np.exp(z - np.exp(z)) class cloglog(CLogLog): """ The CLogLog transform link function. Notes ----- g(`p`) = log(-log(1-`p`)) cloglog is an alias for CLogLog cloglog = CLogLog() """ pass class NegativeBinomial(Link): ''' The negative binomial link function Parameters ---------- alpha : float, optional Alpha is the ancillary parameter of the Negative Binomial link function. It is assumed to be nonstochastic. The default value is 1. Permissible values are usually assumed to be in (.01, 2). ''' def __init__(self, alpha=1.): self.alpha = alpha def _clean(self, x): return np.clip(x, FLOAT_EPS, np.inf) def __call__(self, p): ''' Negative Binomial transform link function Parameters ---------- p : array_like Mean parameters Returns ------- z : array The negative binomial transform of `p` Notes ----- g(p) = log(p/(p + 1/alpha)) ''' p = self._clean(p) return np.log(p/(p + 1/self.alpha)) def inverse(self, z): ''' Inverse of the negative binomial transform Parameters ---------- z : array_like The value of the inverse of the negative binomial link at `p`. Returns ------- p : array Mean parameters Notes ----- g^(-1)(z) = exp(z)/(alpha*(1-exp(z))) ''' return -1/(self.alpha * (1 - np.exp(-z))) def deriv(self, p): ''' Derivative of the negative binomial transform Parameters ---------- p : array_like Mean parameters Returns ------- g'(p) : array The derivative of the negative binomial transform link function Notes ----- g'(x) = 1/(x+alpha*x^2) ''' return 1/(p + self.alpha * p**2) def deriv2(self, p): ''' Second derivative of the negative binomial link function. Parameters ---------- p : array_like Mean parameters Returns ------- g''(p) : array The second derivative of the negative binomial transform link function Notes ----- g''(x) = -(1+2*alpha*x)/(x+alpha*x^2)^2 ''' numer = -(1 + 2 * self.alpha * p) denom = (p + self.alpha * p**2)**2 return numer / denom def inverse_deriv(self, z): ''' Derivative of the inverse of the negative binomial transform Parameters ---------- z : array_like Usually the linear predictor for a GLM or GEE model Returns ------- g^(-1)'(z) : array The value of the derivative of the inverse of the negative binomial link ''' t = np.exp(z) return t / (self.alpha * (1-t)**2) class nbinom(NegativeBinomial): """ The negative binomial link function. Notes ----- g(p) = log(p/(p + 1/alpha)) nbinom is an alias of NegativeBinomial. nbinom = NegativeBinomial(alpha=1.) """ pass """ Covariance models and estimators for GEE. Some details for the covariance calculations can be found in the Stata docs: http://www.stata.com/manuals13/xtxtgee.pdf """ from statsmodels.compat.python import iterkeys, itervalues, zip, range from statsmodels.stats.correlation_tools import cov_nearest import numpy as np import pandas as pd from scipy import linalg as spl from collections import defaultdict from statsmodels.tools.sm_exceptions import (ConvergenceWarning, OutputWarning, NotImplementedWarning) import warnings class CovStruct(object): """ Base class for correlation and covariance structures. An implementation of this class takes the residuals from a regression model that has been fit to grouped data, and uses them to estimate the within-group dependence structure of the random errors in the model. The current state of the covariance structure is represented through the value of the `dep_params` attribute. The default state of a newly-created instance should always be the identity correlation matrix. """ def __init__(self, cov_nearest_method="clipped"): # Parameters describing the dependency structure self.dep_params = None # Keep track of the number of times that the covariance was # adjusted. self.cov_adjust = [] # Method for projecting the covariance matrix if it is not # PSD. self.cov_nearest_method = cov_nearest_method def initialize(self, model): """ Called by GEE, used by implementations that need additional setup prior to running `fit`. Parameters ---------- model : GEE class A reference to the parent GEE class instance. """ self.model = model def update(self, params): """ Update the association parameter values based on the current regression coefficients. Parameters ---------- params : array_like Working values for the regression parameters. """ raise NotImplementedError def covariance_matrix(self, endog_expval, index): """ Returns the working covariance or correlation matrix for a given cluster of data. Parameters ---------- endog_expval: array_like The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer The index of the cluster for which the covariane or correlation matrix will be returned Returns ------- M: matrix The covariance or correlation matrix of endog is_cor: bool True if M is a correlation matrix, False if M is a covariance matrix """ raise NotImplementedError def covariance_matrix_solve(self, expval, index, stdev, rhs): """ Solves matrix equations of the form `covmat * soln = rhs` and returns the values of `soln`, where `covmat` is the covariance matrix represented by this class. Parameters ---------- expval: array_like The expected value of endog for each observed value in the group. index: integer The group index. stdev : array_like The standard deviation of endog for each observation in the group. rhs : list/tuple of array_like A set of right-hand sides; each defines a matrix equation to be solved. Returns ------- soln : list/tuple of array_like The solutions to the matrix equations. Notes ----- Returns None if the solver fails. Some dependence structures do not use `expval` and/or `index` to determine the correlation matrix. Some families (e.g. binomial) do not use the `stdev` parameter when forming the covariance matrix. If the covariance matrix is singular or not SPD, it is projected to the nearest such matrix. These projection events are recorded in the fit_history attribute of the GEE model. Systems of linear equations with the covariance matrix as the left hand side (LHS) are solved for different right hand sides (RHS); the LHS is only factorized once to save time. This is a default implementation, it can be reimplemented in subclasses to optimize the linear algebra according to the struture of the covariance matrix. """ vmat, is_cor = self.covariance_matrix(expval, index) if is_cor: vmat *= np.outer(stdev, stdev) # Factor the covariance matrix. If the factorization fails, # attempt to condition it into a factorizable matrix. threshold = 1e-2 success = False cov_adjust = 0 for itr in range(20): try: vco = spl.cho_factor(vmat) success = True break except np.linalg.LinAlgError: vmat = cov_nearest(vmat, method=self.cov_nearest_method, threshold=threshold) threshold *= 2 cov_adjust += 1 msg = "At least one covariance matrix was not PSD " msg += "and required projection." warnings.warn(msg) self.cov_adjust.append(cov_adjust) # Last resort if we still can't factor the covariance matrix. if not success: warnings.warn( "Unable to condition covariance matrix to an SPD " "matrix using cov_nearest", ConvergenceWarning) vmat = np.diag(np.diag(vmat)) vco = spl.cho_factor(vmat) soln = [spl.cho_solve(vco, x) for x in rhs] return soln def summary(self): """ Returns a text summary of the current estimate of the dependence structure. """ raise NotImplementedError class Independence(CovStruct): """ An independence working dependence structure. """ # Nothing to update def update(self, params): return def covariance_matrix(self, expval, index): dim = len(expval) return np.eye(dim, dtype=np.float64), True def covariance_matrix_solve(self, expval, index, stdev, rhs): v = stdev ** 2 rslt = [] for x in rhs: if x.ndim == 1: rslt.append(x / v) else: rslt.append(x / v[:, None]) return rslt update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__ def summary(self): return ("Observations within a cluster are modeled " "as being independent.") class Exchangeable(CovStruct): """ An exchangeable working dependence structure. """ def __init__(self): super(Exchangeable, self).__init__() # The correlation between any two values in the same cluster self.dep_params = 0. def update(self, params): endog = self.model.endog_li nobs = self.model.nobs varfunc = self.model.family.variance cached_means = self.model.cached_means has_weights = self.model.weights is not None weights_li = self.model.weights residsq_sum, scale = 0, 0 fsum1, fsum2, n_pairs = 0., 0., 0. for i in range(self.model.num_group): expval, _ = cached_means[i] stdev = np.sqrt(varfunc(expval)) resid = (endog[i] - expval) / stdev f = weights_li[i] if has_weights else 1. ssr = np.sum(resid * resid) scale += f * ssr fsum1 += f * len(endog[i]) residsq_sum += f * (resid.sum() ** 2 - ssr) / 2 ngrp = len(resid) npr = 0.5 * ngrp * (ngrp - 1) fsum2 += f * npr n_pairs += npr ddof = self.model.ddof_scale scale /= (fsum1 * (nobs - ddof) / float(nobs)) residsq_sum /= scale self.dep_params = residsq_sum / \ (fsum2 * (n_pairs - ddof) / float(n_pairs)) def covariance_matrix(self, expval, index): dim = len(expval) dp = self.dep_params * np.ones((dim, dim), dtype=np.float64) np.fill_diagonal(dp, 1) return dp, True def covariance_matrix_solve(self, expval, index, stdev, rhs): k = len(expval) c = self.dep_params / (1. - self.dep_params) c /= 1. + self.dep_params * (k - 1) rslt = [] for x in rhs: if x.ndim == 1: x1 = x / stdev y = x1 / (1. - self.dep_params) y -= c * sum(x1) y /= stdev else: x1 = x / stdev[:, None] y = x1 / (1. - self.dep_params) y -= c * x1.sum(0) y /= stdev[:, None] rslt.append(y) return rslt update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__ def summary(self): return ("The correlation between two observations in the " + "same cluster is %.3f" % self.dep_params) class Nested(CovStruct): """ A nested working dependence structure. A working dependence structure that captures a nested hierarchy of groups. Each level of grouping contributes to the random error structure of the model. When using this working covariance structure, `dep_data` of the GEE instance should contain a n_obs x k matrix of 0/1 indicators, corresponding to the k subgroups nested under the top-level `groups` of the GEE instance. These subgroups should be nested from left to right, so that two observations with the same value for column j of `dep_data` should also have the same value for all columns j' < j (this only applies to observations in the same top-level cluster given by the `groups` argument to GEE). Examples -------- Suppose our data are student test scores, and the students are in classrooms, nested in schools, nested in school districts. The school district is the highest level of grouping, so the school district id would be provided to GEE as `groups`, and the school and classroom id's would be provided to the Nested class as the `dep_data` argument, e.g. 0 0 # School 0, classroom 0, student 0 0 0 # School 0, classroom 0, student 1 0 1 # School 0, classroom 1, student 0 0 1 # School 0, classroom 1, student 1 1 0 # School 1, classroom 0, student 0 1 0 # School 1, classroom 0, student 1 1 1 # School 1, classroom 1, student 0 1 1 # School 1, classroom 1, student 1 Labels lower in the hierarchy are recycled, so that student 0 in classroom 0 is different fro student 0 in classroom 1, etc. Notes ----- The calculations for this dependence structure involve all pairs of observations within a group (that is, within the top level `group` structure passed to GEE). Large group sizes will result in slow iterations. The variance components are estimated using least squares regression of the products r*r', for standardized residuals r and r' in the same group, on a matrix of indicators defining which variance components are shared by r and r'. """ def initialize(self, model): """ Called on the first call to update `ilabels` is a list of n_i x n_i matrices containing integer labels that correspond to specific correlation parameters. Two elements of ilabels[i] with the same label share identical variance components. `designx` is a matrix, with each row containing dummy variables indicating which variance components are associated with the corresponding element of QY. """ super(Nested, self).initialize(model) if self.model.weights is not None: warnings.warn("weights not implemented for nested cov_struct, " "using unweighted covariance estimate", NotImplementedWarning) # A bit of processing of the nest data id_matrix = np.asarray(self.model.dep_data) if id_matrix.ndim == 1: id_matrix = id_matrix[:, None] self.id_matrix = id_matrix endog = self.model.endog_li designx, ilabels = [], [] # The number of layers of nesting n_nest = self.id_matrix.shape[1] for i in range(self.model.num_group): ngrp = len(endog[i]) glab = self.model.group_labels[i] rix = self.model.group_indices[glab] # Determine the number of common variance components # shared by each pair of observations. ix1, ix2 = np.tril_indices(ngrp, -1) ncm = (self.id_matrix[rix[ix1], :] == self.id_matrix[rix[ix2], :]).sum(1) # This is used to construct the working correlation # matrix. ilabel = np.zeros((ngrp, ngrp), dtype=np.int32) ilabel[(ix1, ix2)] = ncm + 1 ilabel[(ix2, ix1)] = ncm + 1 ilabels.append(ilabel) # This is used to estimate the variance components. dsx = np.zeros((len(ix1), n_nest + 1), dtype=np.float64) dsx[:, 0] = 1 for k in np.unique(ncm): ii = np.flatnonzero(ncm == k) dsx[ii, 1:k + 1] = 1 designx.append(dsx) self.designx = np.concatenate(designx, axis=0) self.ilabels = ilabels svd = np.linalg.svd(self.designx, 0) self.designx_u = svd[0] self.designx_s = svd[1] self.designx_v = svd[2].T def update(self, params): endog = self.model.endog_li nobs = self.model.nobs dim = len(params) if self.designx is None: self._compute_design(self.model) cached_means = self.model.cached_means varfunc = self.model.family.variance dvmat = [] scale = 0. for i in range(self.model.num_group): expval, _ = cached_means[i] stdev = np.sqrt(varfunc(expval)) resid = (endog[i] - expval) / stdev ix1, ix2 = np.tril_indices(len(resid), -1) dvmat.append(resid[ix1] * resid[ix2]) scale += np.sum(resid ** 2) dvmat = np.concatenate(dvmat) scale /= (nobs - dim) # Use least squares regression to estimate the variance # components vcomp_coeff = np.dot(self.designx_v, np.dot(self.designx_u.T, dvmat) / self.designx_s) self.vcomp_coeff = np.clip(vcomp_coeff, 0, np.inf) self.scale = scale self.dep_params = self.vcomp_coeff.copy() def covariance_matrix(self, expval, index): dim = len(expval) # First iteration if self.dep_params is None: return np.eye(dim, dtype=np.float64), True ilabel = self.ilabels[index] c = np.r_[self.scale, np.cumsum(self.vcomp_coeff)] vmat = c[ilabel] vmat /= self.scale return vmat, True update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ def summary(self): """ Returns a summary string describing the state of the dependence structure. """ dep_names = ["Groups"] if hasattr(self.model, "_dep_data_names"): dep_names.extend(self.model._dep_data_names) else: dep_names.extend(["Component %d:" % (k + 1) for k in range(len(self.vcomp_coeff) - 1)]) if hasattr(self.model, "_groups_name"): dep_names[0] = self.model._groups_name dep_names.append("Residual") vc = self.vcomp_coeff.tolist() vc.append(self.scale - np.sum(vc)) smry = pd.DataFrame({"Variance": vc}, index=dep_names) return smry class Stationary(CovStruct): """ A stationary covariance structure. The correlation between two observations is an arbitrary function of the distance between them. Distances up to a given maximum value are included in the covariance model. Parameters ---------- max_lag : float The largest distance that is included in the covariance model. grid : bool If True, the index positions in the data (after dropping missing values) are used to define distances, and the `time` variable is ignored. """ def __init__(self, max_lag=1, grid=False): super(Stationary, self).__init__() self.max_lag = max_lag self.grid = grid self.dep_params = np.zeros(max_lag + 1) def initialize(self, model): super(Stationary, self).initialize(model) # Time used as an index needs to be integer type. if not self.grid: time = self.model.time[:, 0].astype(np.int32) self.time = self.model.cluster_list(time) def update(self, params): if self.grid: self.update_grid(params) else: self.update_nogrid(params) def update_grid(self, params): endog = self.model.endog_li cached_means = self.model.cached_means varfunc = self.model.family.variance dep_params = np.zeros(self.max_lag + 1) for i in range(self.model.num_group): expval, _ = cached_means[i] stdev = np.sqrt(varfunc(expval)) resid = (endog[i] - expval) / stdev dep_params[0] += np.sum(resid * resid) / len(resid) for j in range(1, self.max_lag + 1): v = resid[j:] dep_params[j] += np.sum(resid[0:-j] * v) / len(v) dep_params /= dep_params[0] self.dep_params = dep_params def update_nogrid(self, params): endog = self.model.endog_li cached_means = self.model.cached_means varfunc = self.model.family.variance dep_params = np.zeros(self.max_lag + 1) dn = np.zeros(self.max_lag + 1) resid_ssq = 0 resid_ssq_n = 0 for i in range(self.model.num_group): expval, _ = cached_means[i] stdev = np.sqrt(varfunc(expval)) resid = (endog[i] - expval) / stdev j1, j2 = np.tril_indices(len(expval), -1) dx = np.abs(self.time[i][j1] - self.time[i][j2]) ii = np.flatnonzero(dx <= self.max_lag) j1 = j1[ii] j2 = j2[ii] dx = dx[ii] vs = np.bincount(dx, weights=resid[j1] * resid[j2], minlength=self.max_lag + 1) vd = np.bincount(dx, minlength=self.max_lag + 1) resid_ssq += np.sum(resid**2) resid_ssq_n += len(resid) ii = np.flatnonzero(vd > 0) if len(ii) > 0: dn[ii] += 1 dep_params[ii] += vs[ii] / vd[ii] i0 = np.flatnonzero(dn > 0) dep_params[i0] /= dn[i0] resid_msq = resid_ssq / resid_ssq_n dep_params /= resid_msq self.dep_params = dep_params def covariance_matrix(self, endog_expval, index): if self.grid: return self.covariance_matrix_grid(endog_expval, index) j1, j2 = np.tril_indices(len(endog_expval), -1) dx = np.abs(self.time[index][j1] - self.time[index][j2]) ii = np.flatnonzero(dx <= self.max_lag) j1 = j1[ii] j2 = j2[ii] dx = dx[ii] cmat = np.eye(len(endog_expval)) cmat[j1, j2] = self.dep_params[dx] cmat[j2, j1] = self.dep_params[dx] return cmat, True def covariance_matrix_grid(self, endog_expval, index): from scipy.linalg import toeplitz r = np.zeros(len(endog_expval)) r[0] = 1 r[1:self.max_lag + 1] = self.dep_params[1:] return toeplitz(r), True def covariance_matrix_solve(self, expval, index, stdev, rhs): if not self.grid: return super(Stationary, self).covariance_matrix_solve( expval, index, stdev, rhs) from statsmodels.tools.linalg import stationary_solve r = np.zeros(len(expval)) r[0:self.max_lag] = self.dep_params[1:] return [stationary_solve(r, x) for x in rhs] update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__ def summary(self): lag = np.arange(self.max_lag + 1) return pd.DataFrame({"Lag": lag, "Cov": self.dep_params}) class Autoregressive(CovStruct): """ A first-order autoregressive working dependence structure. The dependence is defined in terms of the `time` component of the parent GEE class, which defaults to the index position of each value within its cluster, based on the order of values in the input data set. Time represents a potentially multidimensional index from which distances between pairs of observations can be determined. The correlation between two observations in the same cluster is dep_params^distance, where `dep_params` contains the (scalar) autocorrelation parameter to be estimated, and `distance` is the distance between the two observations, calculated from their corresponding time values. `time` is stored as an n_obs x k matrix, where `k` represents the number of dimensions in the time index. The autocorrelation parameter is estimated using weighted nonlinear least squares, regressing each value within a cluster on each preceeding value in the same cluster. Parameters ---------- dist_func: function from R^k x R^k to R^+, optional A function that computes the distance between the two observations based on their `time` values. References ---------- B Rosner, A Munoz. Autoregressive modeling for the analysis of longitudinal data with unequally spaced examinations. Statistics in medicine. Vol 7, 59-71, 1988. """ def __init__(self, dist_func=None): super(Autoregressive, self).__init__() # The function for determining distances based on time if dist_func is None: self.dist_func = lambda x, y: np.abs(x - y).sum() else: self.dist_func = dist_func self.designx = None # The autocorrelation parameter self.dep_params = 0. def update(self, params): if self.model.weights is not None: warnings.warn("weights not implemented for autoregressive " "cov_struct, using unweighted covariance estimate", NotImplementedWarning) endog = self.model.endog_li time = self.model.time_li # Only need to compute this once if self.designx is not None: designx = self.designx else: designx = [] for i in range(self.model.num_group): ngrp = len(endog[i]) if ngrp == 0: continue # Loop over pairs of observations within a cluster for j1 in range(ngrp): for j2 in range(j1): designx.append(self.dist_func(time[i][j1, :], time[i][j2, :])) designx = np.array(designx) self.designx = designx scale = self.model.estimate_scale() varfunc = self.model.family.variance cached_means = self.model.cached_means # Weights var = 1. - self.dep_params ** (2 * designx) var /= 1. - self.dep_params ** 2 wts = 1. / var wts /= wts.sum() residmat = [] for i in range(self.model.num_group): expval, _ = cached_means[i] stdev = np.sqrt(scale * varfunc(expval)) resid = (endog[i] - expval) / stdev ngrp = len(resid) for j1 in range(ngrp): for j2 in range(j1): residmat.append([resid[j1], resid[j2]]) residmat = np.array(residmat) # Need to minimize this def fitfunc(a): dif = residmat[:, 0] - (a ** designx) * residmat[:, 1] return np.dot(dif ** 2, wts) # Left bracket point b_lft, f_lft = 0., fitfunc(0.) # Center bracket point b_ctr, f_ctr = 0.5, fitfunc(0.5) while f_ctr > f_lft: b_ctr /= 2 f_ctr = fitfunc(b_ctr) if b_ctr < 1e-8: self.dep_params = 0 return # Right bracket point b_rgt, f_rgt = 0.75, fitfunc(0.75) while f_rgt < f_ctr: b_rgt = b_rgt + (1. - b_rgt) / 2 f_rgt = fitfunc(b_rgt) if b_rgt > 1. - 1e-6: raise ValueError( "Autoregressive: unable to find right bracket") from scipy.optimize import brent self.dep_params = brent(fitfunc, brack=[b_lft, b_ctr, b_rgt]) def covariance_matrix(self, endog_expval, index): ngrp = len(endog_expval) if self.dep_params == 0: return np.eye(ngrp, dtype=np.float64), True idx = np.arange(ngrp) cmat = self.dep_params ** np.abs(idx[:, None] - idx[None, :]) return cmat, True def covariance_matrix_solve(self, expval, index, stdev, rhs): # The inverse of an AR(1) covariance matrix is tri-diagonal. k = len(expval) soln = [] # LHS has 1 column if k == 1: return [x / stdev ** 2 for x in rhs] # LHS has 2 columns if k == 2: mat = np.array([[1, -self.dep_params], [-self.dep_params, 1]]) mat /= (1. - self.dep_params ** 2) for x in rhs: if x.ndim == 1: x1 = x / stdev else: x1 = x / stdev[:, None] x1 = np.dot(mat, x1) if x.ndim == 1: x1 /= stdev else: x1 /= stdev[:, None] soln.append(x1) return soln # LHS has >= 3 columns: values c0, c1, c2 defined below give # the inverse. c0 is on the diagonal, except for the first # and last position. c1 is on the first and last position of # the diagonal. c2 is on the sub/super diagonal. c0 = (1. + self.dep_params ** 2) / (1. - self.dep_params ** 2) c1 = 1. / (1. - self.dep_params ** 2) c2 = -self.dep_params / (1. - self.dep_params ** 2) soln = [] for x in rhs: flatten = False if x.ndim == 1: x = x[:, None] flatten = True x1 = x / stdev[:, None] z0 = np.zeros((1, x.shape[1])) rhs1 = np.concatenate((x[1:, :], z0), axis=0) rhs2 = np.concatenate((z0, x[0:-1, :]), axis=0) y = c0 * x + c2 * rhs1 + c2 * rhs2 y[0, :] = c1 * x[0, :] + c2 * x[1, :] y[-1, :] = c1 * x[-1, :] + c2 * x[-2, :] y /= stdev[:, None] if flatten: y = np.squeeze(y) soln.append(y) return soln update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__ def summary(self): return ("Autoregressive(1) dependence parameter: %.3f\n" % self.dep_params) class CategoricalCovStruct(CovStruct): """ Parent class for covariance structure for categorical data models. Attributes ---------- nlevel : int The number of distinct levels for the outcome variable. ibd : list A list whose i^th element ibd[i] is an array whose rows contain integer pairs (a,b), where endog_li[i][a:b] is the subvector of binary indicators derived from the same ordinal value. """ def initialize(self, model): super(CategoricalCovStruct, self).initialize(model) self.nlevel = len(model.endog_values) self._ncut = self.nlevel - 1 from numpy.lib.stride_tricks import as_strided b = np.dtype(np.int64).itemsize ibd = [] for v in model.endog_li: jj = np.arange(0, len(v) + 1, self._ncut, dtype=np.int64) jj = as_strided(jj, shape=(len(jj) - 1, 2), strides=(b, b)) ibd.append(jj) self.ibd = ibd class GlobalOddsRatio(CategoricalCovStruct): """ Estimate the global odds ratio for a GEE with ordinal or nominal data. References ---------- PJ Heagerty and S Zeger. "Marginal Regression Models for Clustered Ordinal Measurements". Journal of the American Statistical Association Vol. 91, Issue 435 (1996). Thomas Lumley. Generalized Estimating Equations for Ordinal Data: A Note on Working Correlation Structures. Biometrics Vol. 52, No. 1 (Mar., 1996), pp. 354-361 http://www.jstor.org/stable/2533173 Notes ----- The following data structures are calculated in the class: 'ibd' is a list whose i^th element ibd[i] is a sequence of integer pairs (a,b), where endog_li[i][a:b] is the subvector of binary indicators derived from the same ordinal value. `cpp` is a dictionary where cpp[group] is a map from cut-point pairs (c,c') to the indices of all between-subject pairs derived from the given cut points. """ def __init__(self, endog_type): super(GlobalOddsRatio, self).__init__() self.endog_type = endog_type self.dep_params = 0. def initialize(self, model): super(GlobalOddsRatio, self).initialize(model) if self.model.weights is not None: warnings.warn("weights not implemented for GlobalOddsRatio " "cov_struct, using unweighted covariance estimate", NotImplementedWarning) # Need to restrict to between-subject pairs cpp = [] for v in model.endog_li: # Number of subjects in this group m = int(len(v) / self._ncut) i1, i2 = np.tril_indices(m, -1) cpp1 = {} for k1 in range(self._ncut): for k2 in range(k1 + 1): jj = np.zeros((len(i1), 2), dtype=np.int64) jj[:, 0] = i1 * self._ncut + k1 jj[:, 1] = i2 * self._ncut + k2 cpp1[(k2, k1)] = jj cpp.append(cpp1) self.cpp = cpp # Initialize the dependence parameters self.crude_or = self.observed_crude_oddsratio() if self.model.update_dep: self.dep_params = self.crude_or def pooled_odds_ratio(self, tables): """ Returns the pooled odds ratio for a list of 2x2 tables. The pooled odds ratio is the inverse variance weighted average of the sample odds ratios of the tables. """ if len(tables) == 0: return 1. # Get the sampled odds ratios and variances log_oddsratio, var = [], [] for table in tables: lor = np.log(table[1, 1]) + np.log(table[0, 0]) -\ np.log(table[0, 1]) - np.log(table[1, 0]) log_oddsratio.append(lor) var.append((1 / table.astype(np.float64)).sum()) # Calculate the inverse variance weighted average wts = [1 / v for v in var] wtsum = sum(wts) wts = [w / wtsum for w in wts] log_pooled_or = sum([w * e for w, e in zip(wts, log_oddsratio)]) return np.exp(log_pooled_or) def covariance_matrix(self, expected_value, index): vmat = self.get_eyy(expected_value, index) vmat -= np.outer(expected_value, expected_value) return vmat, False def observed_crude_oddsratio(self): """ To obtain the crude (global) odds ratio, first pool all binary indicators corresponding to a given pair of cut points (c,c'), then calculate the odds ratio for this 2x2 table. The crude odds ratio is the inverse variance weighted average of these odds ratios. Since the covariate effects are ignored, this OR will generally be greater than the stratified OR. """ cpp = self.cpp endog = self.model.endog_li # Storage for the contingency tables for each (c,c') tables = {} for ii in iterkeys(cpp[0]): tables[ii] = np.zeros((2, 2), dtype=np.float64) # Get the observed crude OR for i in range(len(endog)): # The observed joint values for the current cluster yvec = endog[i] endog_11 = np.outer(yvec, yvec) endog_10 = np.outer(yvec, 1. - yvec) endog_01 = np.outer(1. - yvec, yvec) endog_00 = np.outer(1. - yvec, 1. - yvec) cpp1 = cpp[i] for ky in iterkeys(cpp1): ix = cpp1[ky] tables[ky][1, 1] += endog_11[ix[:, 0], ix[:, 1]].sum() tables[ky][1, 0] += endog_10[ix[:, 0], ix[:, 1]].sum() tables[ky][0, 1] += endog_01[ix[:, 0], ix[:, 1]].sum() tables[ky][0, 0] += endog_00[ix[:, 0], ix[:, 1]].sum() return self.pooled_odds_ratio(list(itervalues(tables))) def get_eyy(self, endog_expval, index): """ Returns a matrix V such that V[i,j] is the joint probability that endog[i] = 1 and endog[j] = 1, based on the marginal probabilities of endog and the global odds ratio `current_or`. """ current_or = self.dep_params ibd = self.ibd[index] # The between-observation joint probabilities if current_or == 1.0: vmat = np.outer(endog_expval, endog_expval) else: psum = endog_expval[:, None] + endog_expval[None, :] pprod = endog_expval[:, None] * endog_expval[None, :] pfac = np.sqrt((1. + psum * (current_or - 1.)) ** 2 + 4 * current_or * (1. - current_or) * pprod) vmat = 1. + psum * (current_or - 1.) - pfac vmat /= 2. * (current_or - 1) # Fix E[YY'] for elements that belong to same observation for bdl in ibd: evy = endog_expval[bdl[0]:bdl[1]] if self.endog_type == "ordinal": vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] =\ np.minimum.outer(evy, evy) else: vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] = np.diag(evy) return vmat def update(self, params): """ Update the global odds ratio based on the current value of params. """ cpp = self.cpp cached_means = self.model.cached_means # This will happen if all the clusters have only # one observation if len(cpp[0]) == 0: return tables = {} for ii in cpp[0]: tables[ii] = np.zeros((2, 2), dtype=np.float64) for i in range(self.model.num_group): endog_expval, _ = cached_means[i] emat_11 = self.get_eyy(endog_expval, i) emat_10 = endog_expval[:, None] - emat_11 emat_01 = -emat_11 + endog_expval emat_00 = 1. - (emat_11 + emat_10 + emat_01) cpp1 = cpp[i] for ky in iterkeys(cpp1): ix = cpp1[ky] tables[ky][1, 1] += emat_11[ix[:, 0], ix[:, 1]].sum() tables[ky][1, 0] += emat_10[ix[:, 0], ix[:, 1]].sum() tables[ky][0, 1] += emat_01[ix[:, 0], ix[:, 1]].sum() tables[ky][0, 0] += emat_00[ix[:, 0], ix[:, 1]].sum() cor_expval = self.pooled_odds_ratio(list(itervalues(tables))) self.dep_params *= self.crude_or / cor_expval if not np.isfinite(self.dep_params): self.dep_params = 1. warnings.warn("dep_params became inf, resetting to 1", ConvergenceWarning) update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ def summary(self): return "Global odds ratio: %.3f\n" % self.dep_params class OrdinalIndependence(CategoricalCovStruct): """ An independence covariance structure for ordinal models. The working covariance between indicators derived from different observations is zero. The working covariance between indicators derived form a common observation is determined from their current mean values. There are no parameters to estimate in this covariance structure. """ def covariance_matrix(self, expected_value, index): ibd = self.ibd[index] n = len(expected_value) vmat = np.zeros((n, n)) for bdl in ibd: ev = expected_value[bdl[0]:bdl[1]] vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] =\ np.minimum.outer(ev, ev) - np.outer(ev, ev) return vmat, False # Nothing to update def update(self, params): pass class NominalIndependence(CategoricalCovStruct): """ An independence covariance structure for nominal models. The working covariance between indicators derived from different observations is zero. The working covariance between indicators derived form a common observation is determined from their current mean values. There are no parameters to estimate in this covariance structure. """ def covariance_matrix(self, expected_value, index): ibd = self.ibd[index] n = len(expected_value) vmat = np.zeros((n, n)) for bdl in ibd: ev = expected_value[bdl[0]:bdl[1]] vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] =\ np.diag(ev) - np.outer(ev, ev) return vmat, False # Nothing to update def update(self, params): pass class Equivalence(CovStruct): """ A covariance structure defined in terms of equivalence classes. An 'equivalence class' is a set of pairs of observations such that the covariance of every pair within the equivalence class has a common value. Parameters ---------- pairs : dict-like A dictionary of dictionaries, where `pairs[group][label]` provides the indices of all pairs of observations in the group that have the same covariance value. Specifically, `pairs[group][label]` is a tuple `(j1, j2)`, where `j1` and `j2` are integer arrays of the same length. `j1[i], j2[i]` is one index pair that belongs to the `label` equivalence class. Only one triangle of each covariance matrix should be included. Positions where j1 and j2 have the same value are variance parameters. labels : array_like An array of labels such that every distinct pair of labels defines an equivalence class. Either `labels` or `pairs` must be provided. When the two labels in a pair are equal two equivalence classes are defined: one for the diagonal elements (corresponding to variances) and one for the off-diagonal elements (corresponding to covariances). return_cov : boolean If True, `covariance_matrix` returns an estimate of the covariance matrix, otherwise returns an estimate of the correlation matrix. Notes ----- Using `labels` to define the class is much easier than using `pairs`, but is less general. Any pair of values not contained in `pairs` will be assigned zero covariance. The index values in `pairs` are row indices into the `exog` matrix. They are not updated if missing data are present. When using this covariance structure, missing data should be removed before constructing the model. If using `labels`, after a model is defined using the covariance structure it is possible to remove a label pair from the second level of the `pairs` dictionary to force the corresponding covariance to be zero. Examples -------- The following sets up the `pairs` dictionary for a model with two groups, equal variance for all observations, and constant covariance for all pairs of observations within each group. >> pairs = {0: {}, 1: {}} >> pairs[0][0] = (np.r_[0, 1, 2], np.r_[0, 1, 2]) >> pairs[0][1] = np.tril_indices(3, -1) >> pairs[1][0] = (np.r_[3, 4, 5], np.r_[3, 4, 5]) >> pairs[1][2] = 3 + np.tril_indices(3, -1) """ def __init__(self, pairs=None, labels=None, return_cov=False): super(Equivalence, self).__init__() if (pairs is None) and (labels is None): raise ValueError( "Equivalence cov_struct requires either `pairs` or `labels`") if (pairs is not None) and (labels is not None): raise ValueError( "Equivalence cov_struct accepts only one of `pairs` " "and `labels`") if pairs is not None: import copy self.pairs = copy.deepcopy(pairs) if labels is not None: self.labels = np.asarray(labels) self.return_cov = return_cov def _make_pairs(self, i, j): """ Create arrays containing all unique ordered pairs of i, j. The arrays i and j must be one-dimensional containing non-negative integers. """ mat = np.zeros((len(i) * len(j), 2), dtype=np.int32) # Create the pairs and order them f = np.ones(len(j)) mat[:, 0] = np.kron(f, i).astype(np.int32) f = np.ones(len(i)) mat[:, 1] = np.kron(j, f).astype(np.int32) mat.sort(1) # Remove repeated rows try: dtype = np.dtype((np.void, mat.dtype.itemsize * mat.shape[1])) bmat = np.ascontiguousarray(mat).view(dtype) _, idx = np.unique(bmat, return_index=True) except TypeError: # workaround for old numpy that can't call unique with complex # dtypes rs = np.random.RandomState(4234) bmat = np.dot(mat, rs.uniform(size=mat.shape[1])) _, idx = np.unique(bmat, return_index=True) mat = mat[idx, :] return mat[:, 0], mat[:, 1] def _pairs_from_labels(self): from collections import defaultdict pairs = defaultdict(lambda: defaultdict(lambda: None)) model = self.model df = pd.DataFrame({"labels": self.labels, "groups": model.groups}) gb = df.groupby(["groups", "labels"]) ulabels = np.unique(self.labels) for g_ix, g_lb in enumerate(model.group_labels): # Loop over label pairs for lx1 in range(len(ulabels)): for lx2 in range(lx1 + 1): lb1 = ulabels[lx1] lb2 = ulabels[lx2] try: i1 = gb.groups[(g_lb, lb1)] i2 = gb.groups[(g_lb, lb2)] except KeyError: continue i1, i2 = self._make_pairs(i1, i2) clabel = str(lb1) + "/" + str(lb2) # Variance parameters belong in their own equiv class. jj = np.flatnonzero(i1 == i2) if len(jj) > 0: clabelv = clabel + "/v" pairs[g_lb][clabelv] = (i1[jj], i2[jj]) # Covariance parameters jj = np.flatnonzero(i1!= i2) if len(jj) > 0: i1 = i1[jj] i2 = i2[jj] pairs[g_lb][clabel] = (i1, i2) self.pairs = pairs def initialize(self, model): super(Equivalence, self).initialize(model) if self.model.weights is not None: warnings.warn("weights not implemented for equalence cov_struct, " "using unweighted covariance estimate", NotImplementedWarning) if not hasattr(self, 'pairs'): self._pairs_from_labels() # Initialize so that any equivalence class containing a # variance parameter has value 1. self.dep_params = defaultdict(lambda: 0.) self._var_classes = set([]) for gp in self.model.group_labels: for lb in self.pairs[gp]: j1, j2 = self.pairs[gp][lb] if np.any(j1 == j2): if not np.all(j1 == j2): warnings.warn( "equivalence class contains both variance " "and covariance parameters", OutputWarning) self._var_classes.add(lb) self.dep_params[lb] = 1 # Need to start indexing at 0 within each group. # rx maps olds indices to new indices rx = -1 * np.ones(len(self.model.endog), dtype=np.int32) for g_ix, g_lb in enumerate(self.model.group_labels): ii = self.model.group_indices[g_lb] rx[ii] = np.arange(len(ii), dtype=np.int32) # Reindex for gp in self.model.group_labels: for lb in self.pairs[gp].keys(): a, b = self.pairs[gp][lb] self.pairs[gp][lb] = (rx[a], rx[b]) def update(self, params): endog = self.model.endog_li varfunc = self.model.family.variance cached_means = self.model.cached_means dep_params = defaultdict(lambda: [0., 0., 0.]) n_pairs = defaultdict(lambda: 0) dim = len(params) for k, gp in enumerate(self.model.group_labels): expval, _ = cached_means[k] stdev = np.sqrt(varfunc(expval)) resid = (endog[k] - expval) / stdev for lb in self.pairs[gp].keys(): if (not self.return_cov) and lb in self._var_classes: continue jj = self.pairs[gp][lb] dep_params[lb][0] += np.sum(resid[jj[0]] * resid[jj[1]]) if not self.return_cov: dep_params[lb][1] += np.sum(resid[jj[0]] ** 2) dep_params[lb][2] += np.sum(resid[jj[1]] ** 2) n_pairs[lb] += len(jj[0]) if self.return_cov: for lb in dep_params.keys(): dep_params[lb] = dep_params[lb][0] / (n_pairs[lb] - dim) else: for lb in dep_params.keys(): den = np.sqrt(dep_params[lb][1] * dep_params[lb][2]) dep_params[lb] = dep_params[lb][0] / den for lb in self._var_classes: dep_params[lb] = 1. self.dep_params = dep_params self.n_pairs = n_pairs def covariance_matrix(self, expval, index): dim = len(expval) cmat = np.zeros((dim, dim)) g_lb = self.model.group_labels[index] for lb in self.pairs[g_lb].keys(): j1, j2 = self.pairs[g_lb][lb] cmat[j1, j2] = self.dep_params[lb] cmat = cmat + cmat.T np.fill_diagonal(cmat, cmat.diagonal() / 2) return cmat, not self.return_cov update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ """ Procedures for fitting marginal regression models to dependent data using Generalized Estimating Equations. References ---------- KY Liang and S Zeger. "Longitudinal data analysis using generalized linear models". Biometrika (1986) 73 (1): 13-22. S Zeger and KY Liang. "Longitudinal Data Analysis for Discrete and Continuous Outcomes". Biometrics Vol. 42, No. 1 (Mar., 1986), pp. 121-130 A Rotnitzky and NP Jewell (1990). "Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data", Biometrika, 77, 485-497. Xu Guo and Wei Pan (2002). "Small sample performance of the score test in GEE". http://www.sph.umn.edu/faculty1/wp-content/uploads/2012/11/rr2002-013.pdf LA Mancl LA, TA DeRouen (2001). A covariance estimator for GEE with improved small-sample properties. Biometrics. 2001 Mar;57(1):126-34. """ from statsmodels.compat.python import range, lzip, zip import numpy as np from scipy import stats import pandas as pd import patsy from collections import defaultdict from statsmodels.tools.decorators import cache_readonly import statsmodels.base.model as base # used for wrapper: import statsmodels.regression.linear_model as lm import statsmodels.base.wrapper as wrap from statsmodels.genmod import families from statsmodels.genmod.generalized_linear_model import GLM from statsmodels.genmod import cov_struct as cov_structs import statsmodels.genmod.families.varfuncs as varfuncs from statsmodels.genmod.families.links import Link from statsmodels.tools.sm_exceptions import (ConvergenceWarning, DomainWarning, IterationLimitWarning, ValueWarning) import warnings from statsmodels.graphics._regressionplots_doc import ( _plot_added_variable_doc, _plot_partial_residuals_doc, _plot_ceres_residuals_doc) from statsmodels.discrete.discrete_margins import ( _get_margeff_exog, _check_margeff_args, _effects_at, margeff_cov_with_se, _check_at_is_all, _transform_names, _check_discrete_args, _get_dummy_index, _get_count_index) class ParameterConstraint(object): """ A class for managing linear equality constraints for a parameter vector. """ def __init__(self, lhs, rhs, exog): """ Parameters ---------- lhs : ndarray A q x p matrix which is the left hand side of the constraint lhs * param = rhs. The number of constraints is q >= 1 and p is the dimension of the parameter vector. rhs : ndarray A 1-dimensional vector of length q which is the right hand side of the constraint equation. exog : ndarray The n x p exognenous data for the full model. """ # In case a row or column vector is passed (patsy linear # constraints passes a column vector). rhs = np.atleast_1d(rhs.squeeze()) if rhs.ndim > 1: raise ValueError("The right hand side of the constraint " "must be a vector.") if len(rhs)!= lhs.shape[0]: raise ValueError("The number of rows of the left hand " "side constraint matrix L must equal " "the length of the right hand side " "constraint vector R.") self.lhs = lhs self.rhs = rhs # The columns of lhs0 are an orthogonal basis for the # orthogonal complement to row(lhs), the columns of lhs1 are # an orthogonal basis for row(lhs). The columns of lhsf = # [lhs0, lhs1] are mutually orthogonal. lhs_u, lhs_s, lhs_vt = np.linalg.svd(lhs.T, full_matrices=1) self.lhs0 = lhs_u[:, len(lhs_s):] self.lhs1 = lhs_u[:, 0:len(lhs_s)] self.lhsf = np.hstack((self.lhs0, self.lhs1)) # param0 is one solution to the underdetermined system # L * param = R. self.param0 = np.dot(self.lhs1, np.dot(lhs_vt, self.rhs) / lhs_s) self._offset_increment = np.dot(exog, self.param0) self.orig_exog = exog self.exog_fulltrans = np.dot(exog, self.lhsf) def offset_increment(self): """ Returns a vector that should be added to the offset vector to accommodate the constraint. Parameters ---------- exog : array_like The exogeneous data for the model. """ return self._offset_increment def reduced_exog(self): """ Returns a linearly transformed exog matrix whose columns span the constrained model space. Parameters ---------- exog : array_like The exogeneous data for the model. """ return self.exog_fulltrans[:, 0:self.lhs0.shape[1]] def restore_exog(self): """ Returns the full exog matrix before it was reduced to satisfy the constraint. """ return self.orig_exog def unpack_param(self, params): """ Converts the parameter vector `params` from reduced to full coordinates. """ return self.param0 + np.dot(self.lhs0, params) def unpack_cov(self, bcov): """ Converts the covariance matrix `bcov` from reduced to full coordinates. """ return np.dot(self.lhs0, np.dot(bcov, self.lhs0.T)) _gee_init_doc = """ Marginal regression model fit using Generalized Estimating Equations. GEE can be used to fit Generalized Linear Models (GLMs) when the data have a grouped structure, and the observations are possibly correlated within groups but not between groups. Parameters ---------- endog : array_like 1d array of endogenous values (i.e. responses, outcomes, dependent variables, or 'Y' values). exog : array_like 2d array of exogeneous values (i.e. covariates, predictors, independent variables, regressors, or 'X' values). A `nobs x k` array where `nobs` is the number of observations and `k` is the number of regressors. An intercept is not included by default and should be added by the user. See `statsmodels.tools.add_constant`. groups : array_like A 1d array of length `nobs` containing the group labels. time : array_like A 2d array of time (or other index) values, used by some dependence structures to define similarity relationships among observations within a cluster. family : family class instance %(family_doc)s cov_struct : CovStruct class instance The default is Independence. To specify an exchangeable structure use cov_struct = Exchangeable(). See statsmodels.genmod.cov_struct.CovStruct for more information. offset : array_like An offset to be included in the fit. If provided, must be an array whose length is the number of rows in exog. dep_data : array_like Additional data passed to the dependence structure. constraint : (ndarray, ndarray) If provided, the constraint is a tuple (L, R) such that the model parameters are estimated under the constraint L * param = R, where L is a q x p matrix and R is a q-dimensional vector. If constraint is provided, a score test is performed to compare the constrained model to the unconstrained model. update_dep : bool If true, the dependence parameters are optimized, otherwise they are held fixed at their starting values. weights : array_like An array of weights to use in the analysis. The weights must be constant within each group. These correspond to probability weights (pweights) in Stata. %(extra_params)s See Also -------- statsmodels.genmod.families.family :ref:`families` :ref:`links` Notes ----- Only the following combinations make sense for family and link :: + ident log logit probit cloglog pow opow nbinom loglog logc Gaussian | x x x inv Gaussian | x x x binomial | x x x x x x x x x Poission | x x x neg binomial | x x x x gamma | x x x Not all of these link functions are currently available. Endog and exog are references so that if the data they refer to are already arrays and these arrays are changed, endog and exog will change. The "robust" covariance type is the standard "sandwich estimator" (e.g. Liang and Zeger (1986)). It is the default here and in most other packages. The "naive" estimator gives smaller standard errors, but is only correct if the working correlation structure is correctly specified. The "bias reduced" estimator of Mancl and DeRouen (Biometrics, 2001) reduces the downard bias of the robust estimator. The robust covariance provided here follows Liang and Zeger (1986) and agrees with R's gee implementation. To obtain the robust standard errors reported in Stata, multiply by sqrt(N / (N - g)), where N is the total sample size, and g is the average group size. Examples -------- %(example)s """ _gee_family_doc = """\ The default is Gaussian. To specify the binomial distribution use `family=sm.families.Binomial()`. Each family can take a link instance as an argument. See statsmodels.genmod.families.family for more information.""" _gee_ordinal_family_doc = """\ The only family supported is `Binomial`. The default `Logit` link may be replaced with `probit` if desired.""" _gee_nominal_family_doc = """\ The default value `None` uses a multinomial logit family specifically designed for use with GEE. Setting this argument to a non-default value is not currently supported.""" _gee_fit_doc = """ Fits a marginal regression model using generalized estimating equations (GEE). Parameters ---------- maxiter : integer The maximum number of iterations ctol : float The convergence criterion for stopping the Gauss-Seidel iterations start_params : array_like A vector of starting values for the regression coefficients. If None, a default is chosen. params_niter : integer The number of Gauss-Seidel updates of the mean structure parameters that take place prior to each update of the dependence structure. first_dep_update : integer No dependence structure updates occur before this iteration number. cov_type : string One of "robust", "naive", or "bias_reduced". ddof_scale : scalar or None The scale parameter is estimated as the sum of squared Pearson residuals divided by `N - ddof_scale`, where N is the total sample size. If `ddof_scale` is None, the number of covariates (including an intercept if present) is used. scaling_factor : scalar The estimated covariance of the parameter estimates is scaled by this value. Default is 1, Stata uses N / (N - g), where N is the total sample size and g is the average group size. Returns ------- An instance of the GEEResults class or subclass Notes ----- If convergence difficulties occur, increase the values of `first_dep_update` and/or `params_niter`. Setting `first_dep_update` to a greater value (e.g. ~10-20) causes the algorithm to move close to the GLM solution before attempting to identify the dependence structure. For the Gaussian family, there is no benefit to setting `params_niter` to a value greater than 1, since the mean structure parameters converge in one step. """ _gee_results_doc = """ Attributes ---------- cov_params_default : ndarray default covariance of the parameter estimates. Is chosen among one of the following three based on `cov_type` cov_robust : ndarray covariance of the parameter estimates that is robust cov_naive : ndarray covariance of the parameter estimates that is not robust to correlation or variance misspecification cov_robust_bc : ndarray covariance of the parameter estimates that is robust and bias reduced converged : bool indicator for convergence of the optimization. True if the norm of the score is smaller than a threshold cov_type : string string indicating whether a "robust", "naive" or "bias_reduced" covariance is used as default fit_history : dict Contains information about the iterations. fittedvalues : array Linear predicted values for the fitted model. dot(exog, params) model : class instance Pointer to GEE model instance that called `fit`. normalized_cov_params : array See GEE docstring params : array The coefficients of the fitted model. Note that interpretation of the coefficients often depends on the distribution family and the data. scale : float The estimate of the scale / dispersion for the model fit. See GEE.fit for more information. score_norm : float norm of the score at the end of the iterative estimation. bse : array The standard errors of the fitted GEE parameters. """ _gee_example = """ Logistic regression with autoregressive working dependence: >>> import statsmodels.api as sm >>> family = sm.families.Binomial() >>> va = sm.cov_struct.Autoregressive() >>> model = sm.GEE(endog, exog, group, family=family, cov_struct=va) >>> result = model.fit() >>> print(result.summary()) Use formulas to fit a Poisson GLM with independent working dependence: >>> import statsmodels.api as sm >>> fam = sm.families.Poisson() >>> ind = sm.cov_struct.Independence() >>> model = sm.GEE.from_formula("y ~ age + trt + base", "subject", \ data, cov_struct=ind, family=fam) >>> result = model.fit() >>> print(result.summary()) Equivalent, using the formula API: >>> import statsmodels.api as sm >>> import statsmodels.formula.api as smf >>> fam = sm.families.Poisson() >>> ind = sm.cov_struct.Independence() >>> model = smf.gee("y ~ age + trt + base", "subject", \ data, cov_struct=ind, family=fam) >>> result = model.fit() >>> print(result.summary()) """ _gee_ordinal_example = """ Fit an ordinal regression model using GEE, with "global odds ratio" dependence: >>> import statsmodels.api as sm >>> gor = sm.cov_struct.GlobalOddsRatio("ordinal") >>> model = sm.OrdinalGEE(endog, exog, groups, cov_struct=gor) >>> result = model.fit() >>> print(result.summary()) Using formulas: >>> import statsmodels.formula.api as smf >>> model = smf.ordinal_gee("y ~ x1 + x2", groups, data, cov_struct=gor) >>> result = model.fit() >>> print(result.summary()) """ _gee_nominal_example = """ Fit a nominal regression model using GEE: >>> import statsmodels.api as sm >>> import statsmodels.formula.api as smf >>> gor = sm.cov_struct.GlobalOddsRatio("nominal") >>> model = sm.NominalGEE(endog, exog, groups, cov_struct=gor) >>> result = model.fit() >>> print(result.summary()) Using formulas: >>> import statsmodels.api as sm >>> model = sm.NominalGEE.from_formula("y ~ x1 + x2", groups, data, cov_struct=gor) >>> result = model.fit() >>> print(result.summary()) Using the formula API: >>> import statsmodels.formula.api as smf >>> model = smf.nominal_gee("y ~ x1 + x2", groups, data, cov_struct=gor) >>> result = model.fit() >>> print(result.summary()) """ def _check_args(endog, exog, groups, time, offset, exposure): if endog.size!= exog.shape[0]: raise ValueError("Leading dimension of 'exog' should match " "length of 'endog'") if groups.size!= endog.size: raise ValueError("'groups' and 'endog' should have the same size") if time is not None and (time.size!= endog.size): raise ValueError("'time' and 'endog' should have the same size") if offset is not None and (offset.size!= endog.size): raise ValueError("'offset and 'endog' should have the same size") if exposure is not None and (exposure.size!= endog.size): raise ValueError("'exposure' and 'endog' should have the same size") class GEE(base.Model): __doc__ = ( " Estimation of marginal regression models using Generalized\n" " Estimating Equations (GEE).\n" + _gee_init_doc % {'extra_params': base._missing_param_doc, 'family_doc': _gee_family_doc, 'example': _gee_example}) cached_means = None def __init__(self, endog, exog, groups, time=None, family=None, cov_struct=None, missing='none', offset=None, exposure=None, dep_data=None, constraint=None, update_dep=True, weights=None, **kwargs): if family is not None: if not isinstance(family.link, tuple(family.safe_links)): import warnings msg = ("The {0} link function does not respect the " "domain of the {1} family.") warnings.warn(msg.format(family.link.__class__.__name__, family.__class__.__name__), DomainWarning) groups = np.asarray(groups) # in case groups is pandas if "missing_idx" in kwargs and kwargs["missing_idx"] is not None: # If here, we are entering from super.from_formula; missing # has already been dropped from endog and exog, but not from # the other variables. ii = ~kwargs["missing_idx"] groups = groups[ii] if time is not None: time = time[ii] if offset is not None: offset = offset[ii] if exposure is not None: exposure = exposure[ii] del kwargs["missing_idx"] _check_args(endog, exog, groups, time, offset, exposure) self.missing = missing self.dep_data = dep_data self.constraint = constraint self.update_dep = update_dep self._fit_history = defaultdict(list) # Pass groups, time, offset, and dep_data so they are # processed for missing data along with endog and exog. # Calling super creates self.exog, self.endog, etc. as # ndarrays and the original exog, endog, etc. are # self.data.endog, etc. super(GEE, self).__init__(endog, exog, groups=groups, time=time, offset=offset, exposure=exposure, weights=weights, dep_data=dep_data, missing=missing, **kwargs) self._init_keys.extend(["update_dep", "constraint", "family", "cov_struct"]) # Handle the family argument if family is None: family = families.Gaussian() else: if not issubclass(family.__class__, families.Family): raise ValueError("GEE: `family` must be a genmod " "family instance") self.family = family # Handle the cov_struct argument if cov_struct is None: cov_struct = cov_structs.Independence() else: if not issubclass(cov_struct.__class__, cov_structs.CovStruct): raise ValueError("GEE: `cov_struct` must be a genmod " "cov_struct instance") self.cov_struct = cov_struct # Handle the offset and exposure self._offset_exposure = None if offset is not None: self._offset_exposure = self.offset.copy() self.offset = offset if exposure is not None: if not isinstance(self.family.link, families.links.Log): raise ValueError( "exposure can only be used with the log link function") if self._offset_exposure is not None: self._offset_exposure += np.log(exposure) else: self._offset_exposure = np.log(exposure) self.exposure = exposure # Handle the constraint self.constraint = None if constraint is not None: if len(constraint)!= 2: raise ValueError("GEE: `constraint` must be a 2-tuple.") if constraint[0].shape[1]!= self.exog.shape[1]: raise ValueError( "GEE: the left hand side of the constraint must have " "the same number of columns as the exog matrix.") self.constraint = ParameterConstraint(constraint[0], constraint[1], self.exog) if self._offset_exposure is not None: self._offset_exposure += self.constraint.offset_increment() else: self._offset_exposure = ( self.constraint.offset_increment().copy()) self.exog = self.constraint.reduced_exog() # Create list of row indices for each group group_labels, ix = np.unique(self.groups, return_inverse=True) se = pd.Series(index=np.arange(len(ix))) gb = se.groupby(ix).groups dk = [(lb, np.asarray(gb[k])) for k, lb in enumerate(group_labels)] self.group_indices = dict(dk) self.group_labels = group_labels # Convert the data to the internal representation, which is a # list of arrays, corresponding to the groups. self.endog_li = self.cluster_list(self.endog) self.exog_li = self.cluster_list(self.exog) if self.weights is not None: self.weights_li = self.cluster_list(self.weights) self.weights_li = [x[0] for x in self.weights_li] self.weights_li = np.asarray(self.weights_li) self.num_group = len(self.endog_li) # Time defaults to a 1d grid with equal spacing if self.time is not None: self.time = np.asarray(self.time, np.float64) if self.time.ndim == 1: self.time = self.time[:, None] self.time_li = self.cluster_list(self.time) else: self.time_li = \ [np.arange(len(y), dtype=np.float64)[:, None] for y in self.endog_li] self.time = np.concatenate(self.time_li) if self._offset_exposure is not None: self.offset_li = self.cluster_list(self._offset_exposure) else: self.offset_li = None if constraint is not None: self.constraint.exog_fulltrans_li = \ self.cluster_list(self.constraint.exog_fulltrans) self.family = family self.cov_struct.initialize(self) # Total sample size group_ns = [len(y) for y in self.endog_li] self.nobs = sum(group_ns) # The following are column based, not on rank see #1928 self.df_model = self.exog.shape[1] - 1 # assumes constant self.df_resid = self.nobs - self.exog.shape[1] # Skip the covariance updates if all groups have a single # observation (reduces to fitting a GLM). maxgroup = max([len(x) for x in self.endog_li]) if maxgroup == 1: self.update_dep = False # Override to allow groups and time to be passed as variable # names. @classmethod def from_formula(cls, formula, groups, data, subset=None, time=None, offset=None, exposure=None, *args, **kwargs): """ Create a GEE model instance from a formula and dataframe. Parameters ---------- formula : str or generic Formula object The formula specifying the model groups : array_like or string Array of grouping labels. If a string, this is the name of a variable in `data` that contains the grouping labels. data : array_like The data for the model. subset : array_like An array-like object of booleans, integers, or index values that indicate the subset of the data to used when fitting the model. time : array_like or string The time values, used for dependence structures involving distances between observations. If a string, this is the name of a variable in `data` that contains the time values. offset : array_like or string The offset values, added to the linear predictor. If a string, this is the name of a variable in `data` that contains the offset values. exposure : array_like or string The exposure values, only used if the link function is the logarithm function, in which case the log of `exposure` is added to the offset (if any). If a string, this is the name of a variable in `data` that contains the offset values. %(missing_param_doc)s args : extra arguments These are passed to the model kwargs : extra keyword arguments These are passed to the model with two exceptions. `dep_data` is processed as described below. The ``eval_env`` keyword is passed to patsy. It can be either a :class:`patsy:patsy.EvalEnvironment` object or an integer indicating the depth of the namespace to use. For example, the default ``eval_env=0`` uses the calling namespace. If you wish to use a "clean" environment set ``eval_env=-1``. Optional arguments ------------------ dep_data : string or array_like Data used for estimating the dependence structure. See specific dependence structure classes (e.g. Nested) for details. If `dep_data` is a string, it is interpreted as a formula that is applied to `data`. If it is an array, it must be an array of strings corresponding to column names in `data`. Otherwise it must be an array-like with the same number of rows as data. Returns ------- model : GEE model instance Notes ----- `data` must define __getitem__ with the keys in the formula terms args and kwargs are passed on to the model instantiation. E.g., a numpy structured or rec array, a dictionary, or a pandas DataFrame. """ % {'missing_param_doc': base._missing_param_doc} groups_name = "Groups" if isinstance(groups, str): groups_name = groups groups = data[groups] if isinstance(time, str): time = data[time] if isinstance(offset, str): offset = data[offset] if isinstance(exposure, str): exposure = data[exposure] dep_data = kwargs.get("dep_data") dep_data_names = None if dep_data is not None: if isinstance(dep_data, str): dep_data = patsy.dmatrix(dep_data, data, return_type='dataframe') dep_data_names = dep_data.columns.tolist() else: dep_data_names = list(dep_data) dep_data = data[dep_data] kwargs["dep_data"] = np.asarray(dep_data) model = super(GEE, cls).from_formula(formula, data=data, subset=subset, groups=groups, time=time, offset=offset, exposure=exposure, *args, **kwargs) if dep_data_names is not None: model._dep_data_names = dep_data_names model._groups_name = groups_name return model def cluster_list(self, array): """ Returns `array` split into subarrays corresponding to the cluster structure. """ if array.ndim == 1: return [np.array(array[self.group_indices[k]]) for k in self.group_labels] else: return [np.array(array[self.group_indices[k], :]) for k in self.group_labels] def compare_score_test(self, submodel): """ Perform a score test for the given submodel against this model. Parameters ---------- submodel : GEEResults instance A fitted GEE model that is a submodel of this model. Returns ------- A dictionary with keys "statistic", "p-value", and "df", containing the score test statistic, its chi^2 p-value, and the degrees of freedom used to compute the p-value. Notes ----- The score test can be performed without calling 'fit' on the larger model. The provided submodel must be obtained from a fitted GEE. This method performs the same score test as can be obtained by fitting the GEE with a linear constraint and calling `score_test` on the results. References ---------- Xu Guo and Wei Pan (2002). "Small sample performance of the score test in GEE". http://www.sph.umn.edu/faculty1/wp-content/uploads/2012/11/rr2002-013.pdf """ # Check consistency between model and submodel (not a comprehensive # check) submod = submodel.model if self.exog.shape[0]!= submod.exog.shape[0]: msg = "Model and submodel have different numbers of cases." raise ValueError(msg) if self.exog.shape[1] == submod.exog.shape[1]: msg = "Model and submodel have the same number of variables" warnings.warn(msg) if not isinstance(self.family, type(submod.family)): msg = "Model and submodel have different GLM families." warnings.warn(msg) if not isinstance(self.cov_struct, type(submod.cov_struct)): warnings.warn("Model and submodel have different GEE covariance " "structures.") if not np.equal(self.weights, submod.weights).all(): msg = "Model and submodel should have the same weights." warnings.warn(msg) # Get the positions of the submodel variables in the # parent model qm, qc = _score_test_submodel(self, submodel.model) if qm is None: msg = "The provided model is not a submodel." raise ValueError(msg) # Embed the submodel params into a params vector for the # parent model params_ex = np.dot(qm, submodel.params) # Attempt to preserve the state of the parent model cov_struct_save = self.cov_struct import copy cached_means_save = copy.deepcopy(self.cached_means) # Get the score vector of the submodel params in # the parent model self.cov_struct = submodel.cov_struct self.update_cached_means(params_ex) _, score = self._update_mean_params() if score is None: msg = "Singular matrix encountered in GEE score test" warnings.warn(msg, ConvergenceWarning) return None if not hasattr(self, "ddof_scale"): self.ddof_scale = self.exog.shape[1] if not hasattr(self, "scaling_factor"): self.scaling_factor = 1 _, ncov1, cmat = self._covmat() scale = self.estimate_scale() cmat = cmat / scale ** 2 score2 = np.dot(qc.T, score) / scale amat = np.linalg.inv(ncov1) bmat_11 = np.dot(qm.T, np.dot(cmat, qm)) bmat_22 = np.dot(qc.T, np.dot(cmat, qc)) bmat_12 = np.dot(qm.T, np.dot(cmat, qc)) amat_11 = np.dot(qm.T, np.dot(amat, qm)) amat_12 = np.dot(qm.T, np.dot(amat, qc)) score_cov = bmat_22 - np.dot(amat_12.T, np.linalg.solve(amat_11, bmat_12)) score_cov -= np.dot(bmat_12.T, np.linalg.solve(amat_11, amat_12)) score_cov += np.dot(amat_12.T, np.dot(np.linalg.solve(amat_11, bmat_11), np.linalg.solve(amat_11, amat_12))) # Attempt to restore state self.cov_struct = cov_struct_save self.cached_means = cached_means_save from scipy.stats.distributions import chi2 score_statistic = np.dot(score2, np.linalg.solve(score_cov, score2)) score_df = len(score2) score_pvalue = 1 - chi2.cdf(score_statistic, score_df) return {"statistic": score_statistic, "df": score_df, "p-value": score_pvalue} def estimate_scale(self): """ Estimate the dispersion/scale. The scale parameter for binomial, Poisson, and multinomial families is fixed at 1, otherwise it is estimated from the data. """ if isinstance(self.family, (families.Binomial, families.Poisson, _Multinomial)): return 1. endog = self.endog_li cached_means = self.cached_means nobs = self.nobs varfunc = self.family.variance scale = 0. fsum = 0. for i in range(self.num_group): if len(endog[i]) == 0: continue expval, _ = cached_means[i] f = self.weights_li[i] if self.weights is not None else 1. sdev = np.sqrt(varfunc(expval)) resid = (endog[i] - expval) / sdev scale += f * np.sum(resid ** 2) fsum += f * len(endog[i]) scale /= (fsum * (nobs - self.ddof_scale) / float(nobs)) return scale def mean_deriv(self, exog, lin_pred): """ Derivative of the expected endog with respect to the parameters. Parameters ---------- exog : array_like The exogeneous data at which the derivative is computed. lin_pred : array_like The values of the linear predictor. Returns ------- The value of the derivative of the expected endog with respect to the parameter vector. Notes ----- If there is an offset or exposure, it should be added to `lin_pred` prior to calling this function. """ idl = self.family.link.inverse_deriv(lin_pred) dmat = exog * idl[:, None] return dmat def mean_deriv_exog(self, exog, params, offset_exposure=None): """ Derivative of the expected endog with respect to exog. Parameters ---------- exog : array_like Values of the independent variables at which the derivative is calculated. params : array_like Parameter values at which the derivative is calculated. offset_exposure : array_like, optional Combined offset and exposure. Returns ------- The derivative of the expected endog with respect to exog. """ lin_pred = np.dot(exog, params) if offset_exposure is not None: lin_pred += offset_exposure idl = self.family.link.inverse_deriv(lin_pred) dmat = np.outer(idl, params) return dmat def _update_mean_params(self): """ Returns ------- update : array_like The update vector such that params + update is the next iterate when solving the score equations. score : array_like The current value of the score equations, not incorporating the scale parameter. If desired, multiply this vector by the scale parameter to incorporate the scale. """ endog = self.endog_li exog = self.exog_li cached_means = self.cached_means varfunc = self.family.variance bmat, score = 0, 0 for i in range(self.num_group): expval, lpr = cached_means[i] resid = endog[i] - expval dmat = self.mean_deriv(exog[i], lpr) sdev = np.sqrt(varfunc(expval)) rslt = self.cov_struct.covariance_matrix_solve(expval, i, sdev, (dmat, resid)) if rslt is None: return None, None vinv_d, vinv_resid = tuple(rslt) f = self.weights_li[i] if self.weights is not None else 1. bmat += f * np.dot(dmat.T, vinv_d) score += f * np.dot(dmat.T, vinv_resid) update = np.linalg.solve(bmat, score) self._fit_history["cov_adjust"].append( self.cov_struct.cov_adjust) return update, score def update_cached_means(self, mean_params): """ cached_means should always contain the most recent calculation of the group-wise mean vectors. This function should be called every time the regression parameters are changed, to keep the cached means up to date. """ endog = self.endog_li exog = self.exog_li offset = self.offset_li linkinv = self.family.link.inverse self.cached_means = [] for i in range(self.num_group): if len(endog[i]) == 0: continue lpr = np.dot(exog[i], mean_params) if offset is not None: lpr += offset[i] expval = linkinv(lpr) self.cached_means.append((expval, lpr)) def _covmat(self): """ Returns the sampling covariance matrix of the regression parameters and related quantities. Returns ------- cov_robust : array_like The robust, or sandwich estimate of the covariance, which is meaningful even if the working covariance structure is incorrectly specified. cov_naive : array_like The model-based estimate of the covariance, which is meaningful if the covariance structure is correctly specified. cmat : array_like The center matrix of the sandwich expression, used in obtaining score test results. """ endog = self.endog_li exog = self.exog_li varfunc = self.family.variance cached_means = self.cached_means # Calculate the naive (model-based) and robust (sandwich) # covariances. bmat, cmat = 0, 0 for i in range(self.num_group): expval, lpr = cached_means[i] resid = endog[i] - expval dmat = self.mean_deriv(exog[i], lpr) sdev = np.sqrt(varfunc(expval)) rslt = self.cov_struct.covariance_matrix_solve( expval, i, sdev, (dmat, resid)) if rslt is None: return None, None, None, None vinv_d, vinv_resid = tuple(rslt) f = self.weights_li[i] if self.weights is not None else 1. bmat += f * np.dot(dmat.T, vinv_d) dvinv_resid = f * np.dot(dmat.T, vinv_resid) cmat += np.outer(dvinv_resid, dvinv_resid) scale = self.estimate_scale() bmati = np.linalg.inv(bmat) cov_naive = bmati * scale cov_robust = np.dot(bmati, np.dot(cmat, bmati)) cov_naive *= self.scaling_factor cov_robust *= self.scaling_factor return cov_robust, cov_naive, cmat # Calculate the bias-corrected sandwich estimate of Mancl and # DeRouen. def _bc_covmat(self, cov_naive): cov_naive = cov_naive / self.scaling_factor endog = self.endog_li exog = self.exog_li varfunc = self.family.variance cached_means = self.cached_means scale = self.estimate_scale() bcm = 0 for i in range(self.num_group): expval, lpr = cached_means[i] resid = endog[i] - expval dmat = self.mean_deriv(exog[i], lpr) sdev = np.sqrt(varfunc(expval)) rslt = self.cov_struct.covariance_matrix_solve( expval, i, sdev, (dmat,)) if rslt is None: return None vinv_d = rslt[0] vinv_d /= scale hmat = np.dot(vinv_d, cov_naive) hmat = np.dot(hmat, dmat.T).T f = self.weights_li[i] if self.weights is not None else 1. aresid = np.linalg.solve(np.eye(len(resid)) - hmat, resid) rslt = self.cov_struct.covariance_matrix_solve( expval, i, sdev, (aresid,)) if rslt is None: return None srt = rslt[0] srt = f * np.dot(dmat.T, srt) / scale bcm += np.outer(srt, srt) cov_robust_bc = np.dot(cov_naive, np.dot(bcm, cov_naive)) cov_robust_bc *= self.scaling_factor return cov_robust_bc def predict(self, params, exog=None, offset=None, exposure=None, linear=False): """ Return predicted values for a marginal regression model fit using GEE. Parameters ---------- params : array_like Parameters / coefficients of a marginal regression model. exog : array_like, optional Design / exogenous data. If exog is None, model exog is used. offset : array_like, optional Offset for exog if provided. If offset is None, model offset is used. exposure : array_like, optional Exposure for exog, if exposure is None, model exposure is used. Only allowed if link function is the logarithm. linear : bool If True, returns the linear predicted values. If False, returns the value of the inverse of the model's link function at the linear predicted values. Returns ------- An array of fitted values Notes ----- Using log(V) as the offset is equivalent to using V as the exposure. If exposure U and offset V are both provided, then log(U) + V is added to the linear predictor. """ # TODO: many paths through this, not well covered in tests if exposure is not None: if not isinstance(self.family.link, families.links.Log): raise ValueError( "exposure can only be used with the log link function") # This is the combined offset and exposure _offset = 0. # Using model exog if exog is None: exog = self.exog if not isinstance(self.family.link, families.links.Log): # Don't need to worry about exposure if offset is None: if self._offset_exposure is not None: _offset = self._offset_exposure.copy() else: _offset = offset else: if offset is None and exposure is None: if self._offset_exposure is not None: _offset = self._offset_exposure elif offset is None and exposure is not None: _offset = np.log(exposure) if hasattr(self, "offset"): _offset = _offset + self.offset elif offset is not None and exposure is None: _offset = offset if hasattr(self, "exposure"): _offset = offset + np.log(self.exposure) else: _offset = offset + np.log(exposure) # exog is provided: this is simpler than above because we # never use model exog or exposure if exog is provided. else: if offset is not None: _offset = _offset + offset if exposure is not None: _offset += np.log(exposure) lin_pred = _offset + np.dot(exog, params) if not linear: return self.family.link.inverse(lin_pred) return lin_pred def _starting_params(self): model = GLM(self.endog, self.exog, family=self.family, offset=self._offset_exposure, freq_weights=self.weights) result = model.fit() return result.params def fit(self, maxiter=60, ctol=1e-6, start_params=None, params_niter=1, first_dep_update=0, cov_type='robust', ddof_scale=None, scaling_factor=1.): # Docstring attached below # Subtract this number from the total sample size when # normalizing the scale parameter estimate. if ddof_scale is None: self.ddof_scale = self.exog.shape[1] else: if not ddof_scale >= 0: raise ValueError( "ddof_scale must be a non-negative number or None") self.ddof_scale = ddof_scale self.scaling_factor = scaling_factor self._fit_history = defaultdict(list) if self.weights is not None and cov_type == 'naive': raise ValueError("when using weights, cov_type may not be naive") if start_params is None: mean_params = self._starting_params() else: start_params = np.asarray(start_params) mean_params = start_params.copy() self.update_cached_means(mean_params) del_params = -1. num_assoc_updates = 0 for itr in range(maxiter): update, score = self._update_mean_params() if update is None: warnings.warn("Singular matrix encountered in GEE update", ConvergenceWarning) break mean_params += update self.update_cached_means(mean_params) # L2 norm of the change in mean structure parameters at # this iteration. del_params = np.sqrt(np.sum(score ** 2)) self._fit_history['params'].append(mean_params.copy()) self._fit_history['score'].append(score) self._fit_history['dep_params'].append( self.cov_struct.dep_params) # Don't exit until the association parameters have been # updated at least once. if (del_params < ctol and (num_assoc_updates > 0 or self.update_dep is False)): break # Update the dependence structure if (self.update_dep and (itr % params_niter) == 0 and (itr >= first_dep_update)): self._update_assoc(mean_params) num_assoc_updates += 1 if del_params >= ctol: warnings.warn("Iteration limit reached prior to convergence", IterationLimitWarning) if mean_params is None: warnings.warn("Unable to estimate GEE parameters.", ConvergenceWarning) return None bcov, ncov, _ = self._covmat() if bcov is None: warnings.warn("Estimated covariance structure for GEE " "estimates is singular", ConvergenceWarning) return None bc_cov = None if cov_type == "bias_reduced": bc_cov = self._bc_covmat(ncov) if self.constraint is not None: x = mean_params.copy() mean_params, bcov = self._handle_constraint(mean_params, bcov) if mean_params is None: warnings.warn("Unable to estimate constrained GEE " "parameters.", ConvergenceWarning) return None y, ncov = self._handle_constraint(x, ncov) if y is None: warnings.warn("Unable to estimate constrained GEE " "parameters.", ConvergenceWarning) return None if bc_cov is not None: y, bc_cov = self._handle_constraint(x, bc_cov) if x is None: warnings.warn("Unable to estimate constrained GEE " "parameters.", ConvergenceWarning) return None scale = self.estimate_scale() # kwargs to add to results instance, need to be available in __init__ res_kwds = dict(cov_type=cov_type, cov_robust=bcov, cov_naive=ncov, cov_robust_bc=bc_cov) # The superclass constructor will multiply the covariance # matrix argument bcov by scale, which we don't want, so we # divide bcov by the scale parameter here results = GEEResults(self, mean_params, bcov / scale, scale, cov_type=cov_type, use_t=False, attr_kwds=res_kwds) # attributes not needed during results__init__ results.fit_history = self._fit_history self.fit_history = defaultdict(list) results.score_norm = del_params results.converged = (del_params < ctol) results.cov_struct = self.cov_struct results.params_niter = params_niter results.first_dep_update = first_dep_update results.ctol = ctol results.maxiter = maxiter # These will be copied over to subclasses when upgrading. results._props = ["cov_type", "use_t", "cov_params_default", "cov_robust", "cov_naive", "cov_robust_bc", "fit_history", "score_norm", "converged", "cov_struct", "params_niter", "first_dep_update", "ctol", "maxiter"] return GEEResultsWrapper(results) fit.__doc__ = _gee_fit_doc def _update_regularized(self, params, pen_wt, scad_param, eps): sn, hm = 0, 0 for i in range(self.num_group): expval, _ = self.cached_means[i] resid = self.endog_li[i] - expval sdev = np.sqrt(self.family.variance(expval)) ex = self.exog_li[i] * sdev[:, None]**2 rslt = self.cov_struct.covariance_matrix_solve( expval, i, sdev, (resid, ex)) sn0 = rslt[0] sn += np.dot(ex.T, sn0) hm0 = rslt[1] hm += np.dot(ex.T, hm0) # Wang et al. divide sn here by num_group, but that # seems to be incorrect ap = np.abs(params) clipped = np.clip(scad_param * pen_wt - ap, 0, np.inf) en = pen_wt * clipped * (ap > pen_wt) en /= (scad_param - 1) * pen_wt en += pen_wt * (ap <= pen_wt) en /= eps + ap hm.flat[::hm.shape[0] + 1] += self.num_group * en hm *= self.estimate_scale() sn -= self.num_group * en * params return np.linalg.solve(hm, sn), hm def _regularized_covmat(self, mean_params): self.update_cached_means(mean_params) ma = 0 for i in range(self.num_group): expval, _ = self.cached_means[i] resid = self.endog_li[i] - expval sdev = np.sqrt(self.family.variance(expval)) ex = self.exog_li[i] * sdev[:, None]**2 rslt = self.cov_struct.covariance_matrix_solve( expval, i, sdev, (resid,)) ma0 = np.dot(ex.T, rslt[0]) ma += np.outer(ma0, ma0) return ma def fit_regularized(self, pen_wt, scad_param=3.7, maxiter=100, ddof_scale=None, update_assoc=5, ctol=1e-5, ztol=1e-3, eps=1e-6): """ Regularized estimation for GEE. Parameters ---------- pen_wt : float The penalty weight (a non-negative scalar). scad_param : float Non-negative scalar determining the shape of the Scad penalty. maxiter : integer The maximum number of iterations. ddof_scale : integer Value to subtract from `nobs` when calculating the denominator degrees of freedom for t-statistics, defaults to the number of columns in `exog`. update_assoc : integer The dependence parameters are updated every `update_assoc` iterations of the mean structure parameter updates. ctol : float Convergence criterion, default is one order of magnitude smaller than proposed in section 3.1 of Wang et al. ztol : float Coefficients smaller than this value are treated as being zero, default is based on section 5 of Wang et al. eps : non-negative scalar Numerical constant, see section 3.2 of Wang et al. Returns ------- GEEResults instance. Note that not all methods of the results class make sense when the model has been fit with regularization. Notes ----- This implementation assumes that the link is canonical. References ---------- Wang L, Zhou J, Qu A. (2012). Penalized generalized estimating equations for high-dimensional longitudinal data analysis. Biometrics. 2012 Jun;68(2):353-60. doi: 10.1111/j.1541-0420.2011.01678.x. https://www.ncbi.nlm.nih.gov/pubmed/21955051 http://users.stat.umn.edu/~wangx346/research/GEE_selection.pdf """ mean_params = np.zeros(self.exog.shape[1]) self.update_cached_means(mean_params) converged = False fit_history = defaultdict(list) # Subtract this number from the total sample size when # normalizing the scale parameter estimate. if ddof_scale is None: self.ddof_scale = self.exog.shape[1] else: if not ddof_scale >= 0: raise ValueError( "ddof_scale must be a non-negative number or None") self.ddof_scale = ddof_scale for itr in range(maxiter): update, hm = self._update_regularized( mean_params, pen_wt, scad_param, eps) if update is None: msg = "Singular matrix encountered in regularized GEE update", warnings.warn(msg, ConvergenceWarning) break if np.sqrt(np.sum(update**2)) < ctol: converged = True break mean_params += update fit_history['params'].append(mean_params.copy()) self.update_cached_means(mean_params) if itr!= 0 and (itr % update_assoc == 0): self._update_assoc(mean_params) if not converged: msg = "GEE.fit_regularized did not converge" warnings.warn(msg) mean_params[np.abs(mean_params) < ztol] = 0 self._update_assoc(mean_params) ma = self._regularized_covmat(mean_params) cov = np.linalg.solve(hm, ma) cov = np.linalg.solve(hm, cov.T) # kwargs to add to results instance, need to be available in __init__ res_kwds = dict(cov_type="robust", cov_robust=cov) scale = self.estimate_scale() rslt = GEEResults(self, mean_params, cov, scale, regularized=True, attr_kwds=res_kwds) rslt.fit_history = fit_history return GEEResultsWrapper(rslt) def _handle_constraint(self, mean_params, bcov): """ Expand the parameter estimate `mean_params` and covariance matrix `bcov` to the coordinate system of the unconstrained model. Parameters ---------- mean_params : array_like A parameter vector estimate for the reduced model. bcov : array_like The covariance matrix of mean_params. Returns ------- mean_params : array_like The input parameter vector mean_params, expanded to the coordinate system of the full model bcov : array_like The input covariance matrix bcov, expanded to the coordinate system of the full model """ # The number of variables in the full model red_p = len(mean_params) full_p = self.constraint.lhs.shape[1] mean_params0 = np.r_[mean_params, np.zeros(full_p - red_p)] # Get the score vector under the full model. save_exog_li = self.exog_li self.exog_li = self.constraint.exog_fulltrans_li import copy save_cached_means = copy.deepcopy(self.cached_means) self.update_cached_means(mean_params0) _, score = self._update_mean_params() if score is None: warnings.warn("Singular matrix encountered in GEE score test", ConvergenceWarning) return None, None _, ncov1, cmat = self._covmat() scale = self.estimate_scale() cmat = cmat / scale ** 2 score2 = score[red_p:] / scale amat = np.linalg.inv(ncov1) bmat_11 = cmat[0:red_p, 0:red_p] bmat_22 = cmat[red_p:, red_p:] bmat_12 = cmat[0:red_p, red_p:] amat_11 = amat[0:red_p, 0:red_p] amat_12 = amat[0:red_p, red_p:] score_cov = bmat_22 - np.dot(amat_12.T, np.linalg.solve(amat_11, bmat_12)) score_cov -= np.dot(bmat_12.T, np.linalg.solve(amat_11, amat_12)) score_cov += np.dot(amat_12.T, np.dot(np.linalg.solve(amat_11, bmat_11), np.linalg.solve(amat_11, amat_12))) from scipy.stats.distributions import chi2 score_statistic = np.dot(score2, np.linalg.solve(score_cov, score2)) score_df = len(score2) score_pvalue = 1 - chi2.cdf(score_statistic, score_df) self.score_test_results = {"statistic": score_statistic, "df": score_df, "p-value": score_pvalue} mean_params = self.constraint.unpack_param(mean_params) bcov = self.constraint.unpack_cov(bcov) self.exog_li = save_exog_li self.cached_means = save_cached_means self.exog = self.constraint.restore_exog() return mean_params, bcov def _update_assoc(self, params): """ Update the association parameters """ self.cov_struct.update(params) def _derivative_exog(self, params, exog=None, transform='dydx', dummy_idx=None, count_idx=None): """ For computing marginal effects, returns dF(XB) / dX where F(.) is the fitted mean. transform can be 'dydx', 'dyex', 'eydx', or 'eyex'. Not all of these make sense in the presence of discrete regressors, but checks are done in the results in get_margeff. """ # This form should be appropriate for group 1 probit, logit, # logistic, cloglog, heckprob, xtprobit. offset_exposure = None if exog is None: exog = self.exog offset_exposure = self._offset_exposure margeff = self.mean_deriv_exog(exog, params, offset_exposure) if 'ex' in transform: margeff *= exog if 'ey' in transform: margeff /= self.predict(params, exog)[:, None] if count_idx is not None: from statsmodels.discrete.discrete_margins import ( _get_count_effects) margeff = _get_count_effects(margeff, exog, count_idx, transform, self, params) if dummy_idx is not None: from statsmodels.discrete.discrete_margins import ( _get_dummy_effects) margeff = _get_dummy_effects(margeff, exog, dummy_idx, transform, self, params) return margeff def qic(self, params, scale, cov_params): """ Returns quasi-information criteria and quasi-likelihood values. Parameters ---------- params : array_like The GEE estimates of the regression parameters. scale : scalar Estimated scale parameter cov_params : array_like An estimate of the covariance matrix for the model parameters. Conventionally this is the robust covariance matrix. Returns ------- ql : scalar The quasi-likelihood value qic : scalar A QIC that can be used to compare the mean and covariance structures of the model. qicu : scalar A simplified QIC that can be used to compare mean structures but not covariance structures Notes ----- The quasi-likelihood used here is obtained by numerically evaluating Wedderburn's integral representation of the quasi-likelihood function. This approach is valid for all families and links. Many other packages use analytical expressions for quasi-likelihoods that are valid in special cases where the link function is canonical. These analytical expressions may omit additive constants that only depend on the data. Therefore, the numerical values of our QL and QIC values will differ from the values reported by other packages. However only the differences between two QIC values calculated for different models using the same data are meaningful. Our QIC should produce the same QIC differences as other software. When using the QIC for models with unknown scale parameter, use a common estimate of the scale parameter for all models being compared. References ---------- .. [*] W. Pan (2001). Akaike's information criterion in generalized estimating equations. Biometrics (57) 1. """ varfunc = self.family.variance means = [] omega = 0.0 # omega^-1 is the model-based covariance assuming independence for i in range(self.num_group): expval, lpr = self.cached_means[i] means.append(expval) dmat = self.mean_deriv(self.exog_li[i], lpr) omega += np.dot(dmat.T, dmat) / scale means = np.concatenate(means) # The quasi-likelihood, use change of variables so the integration is # from -1 to 1. du = means - self.endog nstep = 10000 qv = np.empty(nstep) xv = np.linspace(-0.99999, 1, nstep) for i, g in enumerate(xv): u = self.endog + (g + 1) * du / 2.0 vu = varfunc(u) qv[i] = -np.sum(du**2 * (g + 1) / vu) qv /= (4 * scale) from scipy.integrate import trapz ql = trapz(qv, dx=xv[1] - xv[0]) qicu = -2 * ql + 2 * self.exog.shape[1] qic = -2 * ql + 2 * np.trace(np.dot(omega, cov_params)) return ql, qic, qicu class GEEResults(base.LikelihoodModelResults): __doc__ = ( "This class summarizes the fit of a marginal regression model " "using GEE.\n" + _gee_results_doc) def __init__(self, model, params, cov_params, scale, cov_type='robust', use_t=False, regularized=False, **kwds): super(GEEResults, self).__init__( model, params, normalized_cov_params=cov_params, scale=scale) # not added by super self.df_resid = model.df_resid self.df_model = model.df_model self.family = model.family attr_kwds = kwds.pop('attr_kwds', {}) self.__dict__.update(attr_kwds) # we don't do this if the cov_type has already been set # subclasses can set it through attr_kwds if not (hasattr(self, 'cov_type') and hasattr(self, 'cov_params_default')): self.cov_type = cov_type # keep alias covariance_type = self.cov_type.lower() allowed_covariances = ["robust", "naive", "bias_reduced"] if covariance_type not in allowed_covariances: msg = ("GEE: `cov_type` must be one of " + ", ".join(allowed_covariances)) raise ValueError(msg) if cov_type == "robust": cov = self.cov_robust elif cov_type == "naive": cov = self.cov_naive elif cov_type == "bias_reduced": cov = self.cov_robust_bc self.cov_params_default = cov else: if self.cov_type!= cov_type: raise ValueError('cov_type in argument is different from ' 'already attached cov_type') def standard_errors(self, cov_type="robust"): """ This is a convenience function that returns the standard errors for any covariance type. The value of `bse` is the standard errors for whichever covariance type is specified as an argument to `fit` (defaults to "robust"). Parameters ---------- cov_type : string One of "robust", "naive", or "bias_reduced". Determines the covariance used to compute standard errors. Defaults to "robust". """ # Check covariance_type covariance_type = cov_type.lower() allowed_covariances = ["robust", "naive", "bias_reduced"] if covariance_type not in allowed_covariances: msg = ("GEE: `covariance_type` must be one of " + ", ".join(allowed_covariances)) raise ValueError(msg) if covariance_type == "robust": return np.sqrt(np.diag(self.cov_robust)) elif covariance_type == "naive": return np.sqrt(np.diag(self.cov_naive)) elif covariance_type == "bias_reduced": if self.cov_robust_bc is None: raise ValueError( "GEE: `bias_reduced` covariance not available") return np.sqrt(np.diag(self.cov_robust_bc)) # Need to override to allow for different covariance types. @cache_readonly def bse(self): return self.standard_errors(self.cov_type) @cache_readonly def resid(self): """ Returns the residuals, the endogeneous data minus the fitted values from the model. """ return self.model.endog - self.fittedvalues def score_test(self): """ Return the results of a score test for a linear constraint. Returns ------- Adictionary containing the p-value, the test statistic, and the degrees of freedom for the score test. Notes ----- See also GEE.compare_score_test for an alternative way to perform a score test. GEEResults.score_test is more general, in that it supports testing arbitrary linear equality constraints. However GEE.compare_score_test might be easier to use when comparing two explicit models. References ---------- Xu Guo and Wei Pan (2002). "Small sample performance of the score test in GEE". http://www.sph.umn.edu/faculty1/wp-content/uploads/2012/11/rr2002-013.pdf """ if not hasattr(self.model, "score_test_results"): msg = "score_test on results instance only available when " msg += " model was fit with constraints" raise ValueError(msg) return self.model.score_test_results @cache_readonly def resid_split(self): """ Returns the residuals, the endogeneous data minus the fitted values from the model. The residuals are returned as a list of arrays containing the residuals for each cluster. """ sresid = [] for v in self.model.group_labels: ii = self.model.group_indices[v] sresid.append(self.resid[ii]) return sresid @cache_readonly def resid_centered(self): """ Returns the residuals centered within each group. """ cresid = self.resid.copy() for v in self.model.group_labels: ii = self.model.group_indices[v] cresid[ii] -= cresid[ii].mean() return cresid @cache_readonly def resid_centered_split(self): """ Returns the residuals centered within each group. The residuals are returned as a list of arrays containing the centered residuals for each cluster. """ sresid = [] for v in self.model.group_labels: ii = self.model.group_indices[v] sresid.append(self.centered_resid[ii]) return sresid def qic(self, scale=None): """ Returns the QIC and QICu information criteria. For families with a scale parameter (e.g. Gaussian), provide as the scale argument the estimated scale from the largest model under consideration. If the scale parameter is not provided, the estimated scale parameter is used. Doing this does not allow comparisons of QIC values between models. """ # It is easy to forget to set the scale parameter. Sometimes # this is intentional, so we warn. if scale is None: warnings.warn("QIC values obtained using scale=None are not " "appropriate for comparing models") if scale is None: scale = self.scale _, qic, qicu = self.model.qic(self.params, scale, self.cov_params()) return qic, qicu # FIXME: alias to be removed, temporary backwards compatibility split_resid = resid_split centered_resid = resid_centered split_centered_resid = resid_centered_split @cache_readonly def resid_response(self): return self.model.endog - self.fittedvalues @cache_readonly def resid_pearson(self): val = self.model.endog - self.fittedvalues val = val / np.sqrt(self.family.variance(self.fittedvalues)) return val @cache_readonly def resid_working(self): val = self.resid_response val = val * self.family.link.deriv(self.fittedvalues) return val @cache_readonly def resid_anscombe(self): return self.family.resid_anscombe(self.model.endog, self.fittedvalues) @cache_readonly def resid_deviance(self): return self.family.resid_dev(self.model.endog, self.fittedvalues) @cache_readonly def fittedvalues(self): """ Returns the fitted values from the model. """ return self.model.family.link.inverse(np.dot(self.model.exog, self.params)) def plot_added_variable(self, focus_exog, resid_type=None, use_glm_weights=True, fit_kwargs=None, ax=None): # Docstring attached below from statsmodels.graphics.regressionplots import plot_added_variable fig = plot_added_variable(self, focus_exog, resid_type=resid_type, use_glm_weights=use_glm_weights, fit_kwargs=fit_kwargs, ax=ax) return fig plot_added_variable.__doc__ = _plot_added_variable_doc % { 'extra_params_doc': ''} def plot_partial_residuals(self, focus_exog, ax=None): # Docstring attached below from statsmodels.graphics.regressionplots import plot_partial_residuals return plot_partial_residuals(self, focus_exog, ax=ax) plot_partial_residuals.__doc__ = _plot_partial_residuals_doc % { 'extra_params_doc': ''} def plot_ceres_residuals(self, focus_exog, frac=0.66, cond_means=None, ax=None): # Docstring attached below from statsmodels.graphics.regressionplots import plot_ceres_residuals return plot_ceres_residuals(self, focus_exog, frac, cond_means=cond_means, ax=ax) plot_ceres_residuals.__doc__ = _plot_ceres_residuals_doc % { 'extra_params_doc': ''} def conf_int(self, alpha=.05, cols=None, cov_type=None): """ Returns confidence intervals for the fitted parameters. Parameters ---------- alpha : float, optional The `alpha` level for the confidence interval. i.e., The default `alpha` =.05 returns a 95% confidence interval. cols : array_like, optional `cols` specifies which confidence intervals to return cov_type : string The covariance type used for computing standard errors; must be one of 'robust', 'naive', and 'bias reduced'. See `GEE` for details. Notes ----- The confidence interval is based on the Gaussian distribution. """ # super doesn't allow to specify cov_type and method is not # implemented, # FIXME: remove this method here if cov_type is None: bse = self.bse else: bse = self.standard_errors(cov_type=cov_type) params = self.params dist = stats.norm q = dist.ppf(1 - alpha / 2) if cols is None: lower = self.params - q * bse upper = self.params + q * bse else: cols = np.asarray(cols) lower = params[cols] - q * bse[cols] upper = params[cols] + q * bse[cols] return np.asarray(lzip(lower, upper)) def summary(self, yname=None, xname=None, title=None, alpha=.05): """ Summarize the GEE regression results Parameters ---------- yname : str, optional Default is `y` xname : list[str], optional Names for the exogenous variables, default is `var_#` for ## in the number of regressors. Must match the number of parameters in the model title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals cov_type : str The covariance type used to compute the standard errors; one of 'robust' (the usual robust sandwich-type covariance estimate), 'naive' (ignores dependence), and 'bias reduced' (the Mancl/DeRouen estimate). Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ top_left = [('Dep. Variable:', None), ('Model:', None), ('Method:', ['Generalized']), ('', ['Estimating Equations']), ('Family:', [self.model.family.__class__.__name__]), ('Dependence structure:', [self.model.cov_struct.__class__.__name__]), ('Date:', None), ('Covariance type: ', [self.cov_type, ]) ] NY = [len(y) for y in self.model.endog_li] top_right = [('No. Observations:', [sum(NY)]), ('No. clusters:', [len(self.model.endog_li)]), ('Min. cluster size:', [min(NY)]), ('Max. cluster size:', [max(NY)]), ('Mean cluster size:', ["%.1f" % np.mean(NY)]), ('Num. iterations:', ['%d' % len(self.fit_history['params'])]), ('Scale:', ["%.3f" % self.scale]), ('Time:', None), ] # The skew of the residuals skew1 = stats.skew(self.resid) kurt1 = stats.kurtosis(self.resid) skew2 = stats.skew(self.centered_resid) kurt2 = stats.kurtosis(self.centered_resid) diagn_left = [('Skew:', ["%12.4f" % skew1]), ('Centered skew:', ["%12.4f" % skew2])] diagn_right = [('Kurtosis:', ["%12.4f" % kurt1]), ('Centered kurtosis:', ["%12.4f" % kurt2]) ] if title is None: title = self.model.__class__.__name__ +'' +\ "Regression Results" # Override the exog variable names if xname is provided as an # argument. if xname is None: xname = self.model.exog_names if yname is None: yname = self.model.endog_names # Create summary table instance from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=False) smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right, yname=yname, xname=xname, title="") return smry def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): """Get marginal effects of the fitted model. Parameters ---------- at : str, optional Options are: - 'overall', The average of the marginal effects at each observation. -'mean', The marginal effects at the mean of each regressor. -'median', The marginal effects at the median of each regressor. - 'zero', The marginal effects at zero for each regressor. - 'all', The marginal effects at each observation. If `at` is 'all' only margeff will be available. Note that if `exog` is specified, then marginal effects for all variables not specified by `exog` are calculated using the `at` option. method : str, optional Options are: - 'dydx' - dy/dx - No transformation is made and marginal effects are returned. This is the default. - 'eyex' - estimate elasticities of variables in `exog` -- d(lny)/d(lnx) - 'dyex' - estimate semielasticity -- dy/d(lnx) - 'eydx' - estimate semeilasticity -- d(lny)/dx Note that tranformations are done after each observation is calculated. Semi-elasticities for binary variables are computed using the midpoint method. 'dyex' and 'eyex' do not make sense for discrete variables. atexog : array_like, optional Optionally, you can provide the exogenous variables over which to get the marginal effects. This should be a dictionary with the key as the zero-indexed column number and the value of the dictionary. Default is None for all independent variables less the constant. dummy : bool, optional If False, treats binary variables (if present) as continuous. This is the default. Else if True, treats binary variables as changing from 0 to 1. Note that any variable that is either 0 or 1 is treated as binary. Each binary variable is treated separately for now. count : bool, optional If False, treats count variables (if present) as continuous. This is the default. Else if True, the marginal effect is the change in probabilities when each observation is increased by one. Returns ------- effects : ndarray the marginal effect corresponding to the input options Notes ----- When using after Poisson, returns the expected number of events per period, assuming that the model is loglinear. """ if self.model.constraint is not None: warnings.warn("marginal effects ignore constraints", ValueWarning) return GEEMargins(self, (at, method, atexog, dummy, count)) def plot_isotropic_dependence(self, ax=None, xpoints=10, min_n=50): """ Create a plot of the pairwise products of within-group residuals against the corresponding time differences. This plot can be used to assess the possible form of an isotropic covariance structure. Parameters ---------- ax : Matplotlib axes instance An axes on which to draw the graph. If None, new figure and axes objects are created xpoints : scalar or array_like If scalar, the number of points equally spaced points on the time difference axis used to define bins for calculating local means. If an array, the specific points that define the bins. min_n : integer The minimum sample size in a bin for the mean residual product to be included on the plot. """ from statsmodels.graphics import utils as gutils resid = self.model.cluster_list(self.resid) time = self.model.cluster_list(self.model.time) # All within-group pairwise time distances (xdt) and the # corresponding products of scaled residuals (xre). xre, xdt = [], [] for re, ti in zip(resid, time): ix = np.tril_indices(re.shape[0], 0) re = re[ix[0]] * re[ix[1]] / self.scale ** 2 xre.append(re) dists = np.sqrt(((ti[ix[0], :] - ti[ix[1], :]) ** 2).sum(1)) xdt.append(dists) xre = np.concatenate(xre) xdt = np.concatenate(xdt) if ax is None: fig, ax = gutils.create_mpl_ax(ax) else: fig = ax.get_figure() # Convert to a correlation ii = np.flatnonzero(xdt == 0) v0 = np.mean(xre[ii]) xre /= v0 # Use the simple average to smooth, since fancier smoothers # that trim and downweight outliers give biased results (we # need the actual mean of a skewed distribution). if np.isscalar(xpoints): xpoints = np.linspace(0, max(xdt), xpoints) dg = np.digitize(xdt, xpoints) dgu = np.unique(dg) hist = np.asarray([np.sum(dg == k) for k in dgu]) ii = np.flatnonzero(hist >= min_n) dgu = dgu[ii] dgy = np.asarray([np.mean(xre[dg == k]) for k in dgu]) dgx = np.asarray([np.mean(xdt[dg == k]) for k in dgu]) ax.plot(dgx, dgy, '-', color='orange', lw=5) ax.set_xlabel("Time difference") ax.set_ylabel("Product of scaled residuals") return fig def sensitivity_params(self, dep_params_first, dep_params_last, num_steps): """ Refits the GEE model using a sequence of values for the dependence parameters. Parameters ---------- dep_params_first : array_like The first dep_params in the sequence dep_params_last : array_like The last dep_params in the sequence num_steps : int The number of dep_params in the sequence Returns ------- results : array_like The GEEResults objects resulting from the fits. """ model = self.model import copy cov_struct = copy.deepcopy(self.model.cov_struct) # We are fixing the dependence structure in each run. update_dep = model.update_dep model.update_dep = False dep_params = [] results = [] for x in np.linspace(0, 1, num_steps): dp = x * dep_params_last + (1 - x) * dep_params_first dep_params.append(dp) model.cov_struct = copy.deepcopy(cov_struct) model.cov_struct.dep_params = dp rslt = model.fit(start_params=self.params, ctol=self.ctol, params_niter=self.params_niter, first_dep_update=self.first_dep_update, cov_type=self.cov_type) results.append(rslt) model.update_dep = update_dep return results # FIXME: alias to be removed, temporary backwards compatibility params_sensitivity = sensitivity_params class GEEResultsWrapper(lm.RegressionResultsWrapper): _attrs = { 'centered_resid': 'rows', } _wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs, _attrs) wrap.populate_wrapper(GEEResultsWrapper, GEEResults) # noqa:E305 class OrdinalGEE(GEE): __doc__ = ( " Estimation of ordinal response marginal regression models\n" " using Generalized Estimating Equations (GEE).\n" + _gee_init_doc % {'extra_params': base._missing_param_doc, 'family_doc': _gee_ordinal_family_doc, 'example': _gee_ordinal_example}) def __init__(self, endog, exog, groups, time=None, family=None, cov_struct=None, missing='none', offset=None, dep_data=None, constraint=None, **kwargs): if family is None: family = families.Binomial() else: if not isinstance(family, families.Binomial): raise ValueError("ordinal GEE must use a Binomial family") if cov_struct is None: cov_struct = cov_structs.OrdinalIndependence() endog, exog, groups, time, offset = self.setup_ordinal( endog, exog, groups, time, offset) super(OrdinalGEE, self).__init__(endog, exog, groups, time, family, cov_struct, missing, offset, dep_data, constraint) def setup_ordinal(self, endog, exog, groups, time, offset): """ Restructure ordinal data as binary indicators so that they can be analysed using Generalized Estimating Equations. """ self.endog_orig = endog.copy() self.exog_orig = exog.copy() self.groups_orig = groups.copy() if offset is not None: self.offset_orig = offset.copy() else: self.offset_orig = None offset = np.zeros(len(endog)) if time is not None: self.time_orig = time.copy() else: self.time_orig = None time = np.zeros((len(endog), 1)) exog = np.asarray(exog) endog = np.asarray(endog) groups = np.asarray(groups) time = np.asarray(time) offset = np.asarray(offset) # The unique outcomes, except the greatest one. self.endog_values = np.unique(endog) endog_cuts = self.endog_values[0:-1] ncut = len(endog_cuts) nrows = ncut * len(endog) exog_out = np.zeros((nrows, exog.shape[1]), dtype=np.float64) endog_out = np.zeros(nrows, dtype=np.float64) intercepts = np.zeros((nrows, ncut), dtype=np.float64) groups_out = np.zeros(nrows, dtype=groups.dtype) time_out = np.zeros((nrows, time.shape[1]), dtype=np.float64) offset_out = np.zeros(nrows, dtype=np.float64) jrow = 0 zipper = zip(exog, endog, groups, time, offset) for (exog_row, endog_value, group_value, time_value, offset_value) in zipper: # Loop over thresholds for the indicators for thresh_ix, thresh in enumerate(endog_cuts): exog_out[jrow, :] = exog_row endog_out[jrow] = (int(endog_value > thresh)) intercepts[jrow, thresh_ix] = 1 groups_out[jrow] = group_value time_out[jrow] = time_value offset_out[jrow] = offset_value jrow += 1 exog_out = np.concatenate((intercepts, exog_out), axis=1) # exog column names, including intercepts xnames = ["I(y>%.1f)" % v for v in endog_cuts] if type(self.exog_orig) == pd.DataFrame: xnames.extend(self.exog_orig.columns) else: xnames.extend(["x%d" % k for k in range(1, exog.shape[1] + 1)]) exog_out = pd.DataFrame(exog_out, columns=xnames) # Preserve the endog name if there is one if type(self.endog_orig) == pd.Series: endog_out = pd.Series(endog_out, name=self.endog_orig.name) return endog_out, exog_out, groups_out, time_out, offset_out def _starting_params(self): model = GEE(self.endog, self.exog, self.groups, time=self.time, family=families.Binomial(), offset=self.offset, exposure=self.exposure) result = model.fit() return result.params def fit(self, maxiter=60, ctol=1e-6, start_params=None, params_niter=1, first_dep_update=0, cov_type='robust'): rslt = super(OrdinalGEE, self).fit(maxiter, ctol, start_params, params_niter, first_dep_update, cov_type=cov_type) rslt = rslt._results # use unwrapped instance res_kwds = dict(((k, getattr(rslt, k)) for k in rslt._props)) # Convert the GEEResults to an OrdinalGEEResults ord_rslt = OrdinalGEEResults(self, rslt.params, rslt.cov_params() / rslt.scale, rslt.scale, cov_type=cov_type, attr_kwds=res_kwds) # for k in rslt._props: # setattr(ord_rslt, k, getattr(rslt, k)) return OrdinalGEEResultsWrapper(ord_rslt) fit.__doc__ = _gee_fit_doc class OrdinalGEEResults(GEEResults): __doc__ = ( "This class summarizes the fit of a marginal regression model" "for an ordinal response using GEE.\n" + _gee_results_doc) def plot_distribution(self, ax=None, exog_values=None): """ Plot the fitted probabilities of endog in an ordinal model, for specifed values of the predictors. Parameters ---------- ax : Matplotlib axes instance An axes on which to draw the graph. If None, new figure and axes objects are created exog_values : array_like A list of dictionaries, with each dictionary mapping variable names to values at which the variable is held fixed. The values P(endog=y | exog) are plotted for all possible values of y, at the given exog value. Variables not included in a dictionary are held fixed at the mean value. Example: -------- We have a model with covariates 'age' and'sex', and wish to plot the probabilities P(endog=y | exog) for males (sex=0) and for females (sex=1), as separate paths on the plot. Since 'age' is not included below in the map, it is held fixed at its mean value. >>> ev = [{"sex": 1}, {"sex": 0}] >>> rslt.distribution_plot(exog_values=ev) """ from statsmodels.graphics import utils as gutils if ax is None: fig, ax = gutils.create_mpl_ax(ax) else: fig = ax.get_figure() # If no covariate patterns are specified, create one with all # variables set to their mean values. if exog_values is None: exog_values = [{}, ] exog_means = self.model.exog.mean(0) ix_icept = [i for i, x in enumerate(self.model.exog_names) if x.startswith("I(")] for ev in exog_values: for k in ev.keys(): if k not in self.model.exog_names: raise ValueError("%s is not a variable in the model" % k) # Get the fitted probability for each level, at the given # covariate values. pr = [] for j in ix_icept: xp = np.zeros_like(self.params) xp[j] = 1. for i, vn in enumerate(self.model.exog_names): if i in ix_icept: continue # User-specified value if vn in ev: xp[i] = ev[vn] # Mean value else: xp[i] = exog_means[i] p = 1 / (1 + np.exp(-np.dot(xp, self.params))) pr.append(p) pr.insert(0, 1) pr.append(0) pr = np.asarray(pr) prd = -np.diff(pr) ax.plot(self.model.endog_values, prd, 'o-') ax.set_xlabel("Response value") ax.set_ylabel("Probability") ax.set_ylim(0, 1) return fig def _score_test_submodel(par, sub): """ Return transformation matrices for design matrices. Parameters ---------- par : instance The parent model sub : instance The sub-model Returns ------- qm : array_like Matrix mapping the design matrix of the parent to the design matrix for the sub-model. qc : array_like Matrix mapping the design matrix of the parent to the orthogonal complement of the columnspace of the submodel in the columnspace of the parent. Notes ----- Returns None, None if the provided submodel is not actually a submodel. """ x1 = par.exog x2 = sub.exog u, s, vt = np.linalg.svd(x1, 0) # Get the orthogonal complement of col(x2) in col(x1). a, _, _ = np.linalg.svd(x2, 0) a = u - np.dot(a, np.dot(a.T, u)) x2c, sb, _ = np.linalg.svd(a, 0) x2c = x2c[:, sb > 1e-12] # x1 * qm = x2 qm = np.dot(vt.T, np.dot(u.T, x2) / s[:, None]) e = np.max(np.abs(x2 - np.dot(x1, qm))) if e > 1e-8: return None, None # x1 * qc = x2c qc = np.dot(vt.T, np.dot(u.T, x2c) / s[:, None]) return qm, qc class OrdinalGEEResultsWrapper(GEEResultsWrapper): pass wrap.populate_wrapper(OrdinalGEEResultsWrapper, OrdinalGEEResults) # noqa:E305 class NominalGEE(GEE): __doc__ = ( " Estimation of nominal response marginal regression models\n" " using Generalized Estimating Equations (GEE).\n" + _gee_init_doc % {'extra_params': base._missing_param_doc, 'family_doc': _gee_nominal_family_doc, 'example': _gee_nominal_example}) def __init__(self, endog, exog, groups, time=None, family=None, cov_struct=None, missing='none', offset=None, dep_data=None, constraint=None, **kwargs): endog, exog, groups, time, offset = self.setup_nominal( endog, exog, groups, time, offset) if family is None: family = _Multinomial(self.ncut + 1) if cov_struct is None: cov_struct = cov_structs.NominalIndependence() super(NominalGEE, self).__init__( endog, exog, groups, time, family, cov_struct, missing, offset, dep_data, constraint) def _starting_params(self): model = GEE(self.endog, self.exog, self.groups, time=self.time, family=families.Binomial(), offset=self.offset, exposure=self.exposure) result = model.fit() return result.params def setup_nominal(self, endog, exog, groups, time, offset): """ Restructure nominal data as binary indicators so that they can be analysed using Generalized Estimating Equations. """ self.endog_orig = endog.copy() self.exog_orig = exog.copy() self.groups_orig = groups.copy() if offset is not None: self.offset_orig = offset.copy() else: self.offset_orig = None offset = np.zeros(len(endog)) if time is not None: self.time_orig = time.copy() else: self.time_orig = None time = np.zeros((len(endog), 1)) exog = np.asarray(exog) endog = np.asarray(endog) groups = np.asarray(groups) time = np.asarray(time) offset = np.asarray(offset) # The unique outcomes, except the greatest one. self.endog_values = np.unique(endog) endog_cuts = self.endog_values[0:-1] ncut = len(endog_cuts) self.ncut = ncut nrows = len(endog_cuts) * exog.shape[0] ncols = len(endog_cuts) * exog.shape[1] exog_out = np.zeros((nrows, ncols), dtype=np.float64) endog_out = np.zeros(nrows, dtype=np.float64) groups_out = np.zeros(nrows, dtype=np.float64) time_out = np.zeros((nrows, time.shape[1]), dtype=np.float64) offset_out = np.zeros(nrows, dtype=np.float64) jrow = 0 zipper = zip(exog, endog, groups, time, offset) for (exog_row, endog_value, group_value, time_value, offset_value) in zipper: # Loop over thresholds for the indicators for thresh_ix, thresh in enumerate(endog_cuts): u = np.zeros(len(endog_cuts), dtype=np.float64) u[thresh_ix] = 1 exog_out[jrow, :] = np.kron(u, exog_row) endog_out[jrow] = (int(endog_value == thresh)) groups_out[jrow] = group_value time_out[jrow] = time_value offset_out[jrow] = offset_value jrow += 1 # exog names if isinstance(self.exog_orig, pd.DataFrame): xnames_in = self.exog_orig.columns else: xnames_in = ["x%d" % k for k in range(1, exog.shape[1] + 1)] xnames = [] for tr in endog_cuts: xnames.extend(["%s[%.1f]" % (v, tr) for v in xnames_in]) exog_out = pd.DataFrame(exog_out, columns=xnames) exog_out = pd.DataFrame(exog_out, columns=xnames) # Preserve endog name if there is one if isinstance(self.endog_orig, pd.Series): endog_out = pd.Series(endog_out, name=self.endog_orig.name) return endog_out, exog_out, groups_out, time_out, offset_out def mean_deriv(self, exog, lin_pred): """ Derivative of the expected endog with respect to the parameters. Parameters ---------- exog : array_like The exogeneous data at which the derivative is computed, number of rows must be a multiple of `ncut`. lin_pred : array_like The values of the linear predictor, length must be multiple of `ncut`. Returns ------- The derivative of the expected endog with respect to the parameters. """ expval = np.exp(lin_pred) # Reshape so that each row contains all the indicators # corresponding to one multinomial observation. expval_m = np.reshape(expval, (len(expval) // self.ncut, self.ncut)) # The normalizing constant for the multinomial probabilities. denom = 1 + expval_m.sum(1) denom = np.kron(denom, np.ones(self.ncut, dtype=np.float64)) # The multinomial probabilities mprob = expval / denom # First term of the derivative: denom * expval' / denom^2 = # expval' / denom. dmat = mprob[:, None] * exog # Second term of the derivative: -expval * denom' / denom^2 ddenom = expval[:, None] * exog dmat -= mprob[:, None] * ddenom / denom[:, None] return dmat def mean_deriv_exog(self, exog, params, offset_exposure=None): """ Derivative of the expected endog with respect to exog for the multinomial model, used in analyzing marginal effects. Parameters ---------- exog : array_like The exogeneous data at which the derivative is computed, number of rows must be a multiple of `ncut`. lpr : array_like The linear predictor values, length must be multiple of `ncut`. Returns ------- The value of the derivative of the expected endog with respect to exog. Notes ----- offset_exposure must be set at None for the multinoial family. """ if offset_exposure is not None: warnings.warn("Offset/exposure ignored for the multinomial family", ValueWarning) lpr = np.dot(exog, params) expval = np.exp(lpr) expval_m = np.reshape(expval, (len(expval) // self.ncut, self.ncut)) denom = 1 + expval_m.sum(1) denom = np.kron(denom, np.ones(self.ncut, dtype=np.float64)) bmat0 = np.outer(np.ones(exog.shape[0]), params) # Masking matrix qmat = [] for j in range(self.ncut): ee = np.zeros(self.ncut, dtype=np.float64) ee[j] = 1 qmat.append(np.kron(ee, np.ones(len(params) // self.ncut))) qmat = np.array(qmat) qmat = np.kron(np.ones((exog.shape[0] // self.ncut, 1)), qmat) bmat = bmat0 * qmat dmat = expval[:, None] * bmat / denom[:, None] expval_mb = np.kron(expval_m, np.ones((self.ncut, 1))) expval_mb = np.kron(expval_mb, np.ones((1, self.ncut))) dmat -= expval[:, None] * (bmat * expval_mb) / denom[:, None] ** 2 return dmat def fit(self, maxiter=60, ctol=1e-6, start_params=None, params_niter=1, first_dep_update=0, cov_type='robust'): rslt = super(NominalGEE, self).fit(maxiter, ctol, start_params, params_niter, first_dep_update, cov_type=cov_type) if rslt is None: warnings.warn("GEE updates did not converge", ConvergenceWarning) return None rslt = rslt._results # use unwrapped instance res_kwds = dict(((k, getattr(rslt, k)) for k in rslt._props)) # Convert the GEEResults to a NominalGEEResults nom_rslt = NominalGEEResults(self, rslt.params, rslt.cov_params() / rslt.scale, rslt.scale, cov_type=cov_type, attr_kwds=res_kwds) # for k in rslt._props: # setattr(nom_rslt, k, getattr(rslt, k)) return NominalGEEResultsWrapper(nom_rslt) fit.__doc__ = _gee_fit_doc class NominalGEEResults(GEEResults): __doc__ = ( "This class summarizes the fit of a marginal regression model" "for a nominal response using GEE.\n" + _gee_results_doc) def plot_distribution(self, ax=None, exog_values=None): """ Plot the fitted probabilities of endog in an nominal model, for specifed values of the predictors. Parameters ---------- ax : Matplotlib axes instance An axes on which to draw the graph. If None, new figure and axes objects are created exog_values : array_like A list of dictionaries, with each dictionary mapping variable names to values at which the variable is held fixed. The values P(endog=y | exog) are plotted for all possible values of y, at the given exog value. Variables not included in a dictionary are held fixed at the mean value. Example: -------- We have a model with covariates 'age' and'sex', and wish to plot the probabilities P(endog=y | exog) for males (sex=0) and for females (sex=1), as separate paths on the plot. Since 'age' is not included below in the map, it is held fixed at its mean value. >>> ex = [{"sex": 1}, {"sex": 0}] >>> rslt.distribution_plot(exog_values=ex) """ from statsmodels.graphics import utils as gutils if ax is None: fig, ax = gutils.create_mpl_ax(ax) else: fig = ax.get_figure() # If no covariate patterns are specified, create one with all # variables set to their mean values. if exog_values is None: exog_values = [{}, ] link = self.model.family.link.inverse ncut = self.model.family.ncut k = int(self.model.exog.shape[1] / ncut) exog_means = self.model.exog.mean(0)[0:k] exog_names = self.model.exog_names[0:k] exog_names = [x.split("[")[0] for x in exog_names] params = np.reshape(self.params, (ncut, len(self.params) // ncut)) for ev in exog_values: exog = exog_means.copy() for k in ev.keys(): if k not in exog_names: raise ValueError("%s is not a variable in the model" % k) ii = exog_names.index(k) exog[ii] = ev[k] lpr = np.dot(params, exog) pr = link(lpr) pr = np.r_[pr, 1 - pr.sum()] ax.plot(self.model.endog_values, pr, 'o-') ax.set_xlabel("Response value") ax.set_ylabel("Probability") ax.set_xticks(self.model.endog_values) ax.set_xticklabels(self.model.endog_values) ax.set_ylim(0, 1) return fig class NominalGEEResultsWrapper(GEEResultsWrapper): pass wrap.populate_wrapper(NominalGEEResultsWrapper, NominalGEEResults) # noqa:E305 class _MultinomialLogit(Link): """ The multinomial logit transform, only for use with GEE. Notes ----- The data are assumed coded as binary indicators, where each observed multinomial value y is coded as I(y == S[0]),..., I(y == S[-1]), where S is the set of possible response labels, excluding the largest one. Thererefore functions in this class should only be called using vector argument whose length is a multiple of |S| = ncut, which is an argument to be provided when initializing the class. call and derivative use a private method _clean to trim p by 1e-10 so that p is in (0, 1) """ def __init__(self, ncut): self.ncut = ncut def inverse(self, lpr): """ Inverse of the multinomial logit transform, which gives the expected values of the data as a function of the linear predictors. Parameters ---------- lpr : array_like (length must be divisible by `ncut`) The linear predictors Returns ------- prob : array Probabilities, or expected values """ expval = np.exp(lpr) denom = 1 + np.reshape(expval, (len(expval) // self.ncut, self.ncut)).sum(1) denom = np.kron(denom, np.ones(self.ncut, dtype=np.float64)) prob = expval / denom return prob class _Multinomial(families.Family): """ Pseudo-link function for fitting nominal multinomial models with GEE. Not for use outside the GEE class. """ links = [_MultinomialLogit, ] variance = varfuncs.binary safe_links = [_MultinomialLogit, ] def __init__(self, nlevels): """ Parameters ---------- nlevels : integer The number of distinct categories for the multinomial distribution. """ self.initialize(nlevels) def initialize(self, nlevels): self.ncut = nlevels - 1 self.link = _MultinomialLogit(self.ncut) class GEEMargins(object): """ Estimated marginal effects for a regression model fit with GEE. Parameters ---------- results : GEEResults instance The results instance of a fitted discrete choice model args : tuple Args are passed to `get_margeff`. This is the same as results.get_margeff. See there for more information. kwargs : dict Keyword args are passed to `get_margeff`. This is the same as results.get_margeff. See there for more information. """ def __init__(self, results, args, kwargs={}): self._cache = {} self.results = results self.get_margeff(*args, **kwargs) def _reset(self): self._cache = {} @cache_readonly def tvalues(self): _check_at_is_all(self.margeff_options) return self.margeff / self.margeff_se def summary_frame(self, alpha=.05): """ Returns a DataFrame summarizing the marginal effects. Parameters ---------- alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns ------- frame : DataFrames A DataFrame summarizing the marginal effects. """ _check_at_is_all(self.margeff_options) from pandas import DataFrame names = [_transform_names[self.margeff_options['method']], 'Std. Err.', 'z', 'Pr(>|z|)', 'Conf. Int. Low', 'Cont. Int. Hi.'] ind = self.results.model.exog.var(0)!= 0 # True if not a constant exog_names = self.results.model.exog_names var_names = [name for i, name in enumerate(exog_names) if ind[i]] table = np.column_stack((self.margeff, self.margeff_se, self.tvalues, self.pvalues, self.conf_int(alpha))) return DataFrame(table, columns=names, index=var_names) @cache_readonly def pvalues(self): _check_at_is_all(self.margeff_options) return stats.norm.sf(np.abs(self.tvalues)) * 2 def conf_int(self, alpha=.05): """ Returns the confidence intervals of the marginal effects Parameters ---------- alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns ------- conf_int : ndarray An array with lower, upper confidence intervals for the marginal effects. """ _check_at_is_all(self.margeff_options) me_se = self.margeff_se q = stats.norm.ppf(1 - alpha / 2) lower = self.margeff - q * me_se upper = self.margeff + q * me_se return np.asarray(lzip(lower, upper)) def summary(self, alpha=.05): """ Returns a summary table for marginal effects Parameters ---------- alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns ------- Summary : SummaryTable A SummaryTable instance """ _check_at_is_all(self.margeff_options) results = self.results model = results.model title = model.__class__.__name__ + " Marginal Effects" method = self.margeff_options['method'] top_left = [('Dep. Variable:', [model.endog_names]), ('Method:', [method]), ('At:', [self.margeff_options['at']]), ] from statsmodels.iolib.summary import (Summary, summary_params, table_extend) exog_names = model.exog_names[:] # copy smry = Summary() const_idx = model.data.const_idx if const_idx is not None: exog_names.pop(const_idx) J = int(getattr(model, "J", 1)) if J > 1: yname, yname_list = results._get_endog_name(model.endog_names, None, all=True) else: yname = model.endog_names yname_list = [yname] smry.add_table_2cols(self, gleft=top_left, gright=[], yname=yname, xname=exog_names, title=title) # NOTE: add_table_params is not general enough yet for margeff # could use a refactor with getattr instead of hard-coded params # tvalues etc. table = [] conf_int = self.conf_int(alpha) margeff = self.margeff margeff_se = self.margeff_se tvalues = self.tvalues pvalues = self.pvalues if J > 1: for eq in range(J): restup = (results, margeff[:, eq], margeff_se[:, eq], tvalues[:, eq], pvalues[:, eq], conf_int[:, :, eq]) tble = summary_params(restup, yname=yname_list[eq], xname=exog_names, alpha=alpha, use_t=False, skip_header=True) tble.title = yname_list[eq] # overwrite coef with method name header = ['', _transform_names[method],'std err', 'z', 'P>|z|', '[%3.1f%% Conf. Int.]' % (100 - alpha * 100)] tble.insert_header_row(0, header) # from IPython.core.debugger import Pdb; Pdb().set_trace() table.append(tble) table = table_extend(table, keep_headers=True) else: restup = (results, margeff, margeff_se, tvalues, pvalues, conf_int) table = summary_params(restup, yname=yname, xname=exog_names, alpha=alpha, use_t=False, skip_header=True) header = ['', _transform_names[method],'std err', 'z', 'P>|z|', '[%3.1f%% Conf. Int.]' % (100 - alpha * 100)] table.insert_header_row(0, header) smry.tables.append(table) return smry def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): self._reset() # always reset the cache when this is called # TODO: if at is not all or overall, we can also put atexog values # in summary table head method = method.lower() at = at.lower() _check_margeff_args(at, method) self.margeff_options = dict(method=method, at=at) results = self.results model = results.model params = results.params exog = model.exog.copy() # copy because values are changed effects_idx = exog.var(0)!= 0 const_idx = model.data.const_idx if dummy: _check_discrete_args(at, method) dummy_idx, dummy = _get_dummy_index(exog, const_idx) else: dummy_idx = None if count: _check_discrete_args(at, method) count_idx, count = _get_count_index(exog, const_idx) else: count_idx = None # get the exogenous variables exog = _get_margeff_exog(exog, at, atexog, effects_idx) # get base marginal effects, handled by sub-classes effects = model._derivative_exog(params, exog, method, dummy_idx, count_idx) effects = _effects_at(effects, at) if at == 'all': self.margeff = effects[:, effects_idx] else: # Set standard error of the marginal effects by Delta method. margeff_cov, margeff_se = margeff_cov_with_se( model, params, exog, results.cov_params(), at, model._derivative_exog, dummy_idx, count_idx, method, 1) # don't care about at constant self.margeff_cov = margeff_cov[effects_idx][:, effects_idx] self.margeff_se = margeff_se[effects_idx] self.margeff = effects[effects_idx]

statsmodels__statsmodels

gmm.rst

Description

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BSD 3-Clause New or Revised License

statsmodels__statsmodels/docs/source/gmm.rst

Generalized Method of Moments gmm statsmodels.gmm contains model classes and functions that are based on estimation with Generalized Method of Moments. Currently the general non-linear case is implemented. An example class for the standard linear instrumental variable model is included. This has been introduced as a test case, it works correctly but it does not take the linear structure into account. For the linear case we intend to introduce a specific implementation which will be faster and numerically more accurate. Currently, GMM takes arbitrary non-linear moment conditions and calculates the estimates either for a given weighting matrix or iteratively by alternating between estimating the optimal weighting matrix and estimating the parameters. Implementing models with different moment conditions is done by subclassing GMM. In the minimal implementation only the moment conditions, momcond have to be defined.

'''Generalized Method of Moments, GMM, and Two-Stage Least Squares for instrumental variables IV2SLS Issues ------ * number of parameters, nparams, and starting values for parameters Where to put them? start was initially taken from global scope (bug) * When optimal weighting matrix cannot be calculated numerically In DistQuantilesGMM, we only have one row of moment conditions, not a moment condition for each observation, calculation for cov of moments breaks down. iter=1 works (weights is identity matrix) -> need method to do one iteration with an identity matrix or an analytical weighting matrix given as parameter. -> add result statistics for this case, e.g. cov_params, I have it in the standalone function (and in calc_covparams which is a copy of it), but not tested yet. DONE `fitonce` in DistQuantilesGMM, params are the same as in direct call to fitgmm move it to GMM class (once it's clearer for which cases I need this.) * GMM doesn't know anything about the underlying model, e.g. y = X beta + u or panel data model. It would be good if we can reuse methods from regressions, e.g. predict, fitted values, calculating the error term, and some result statistics. What's the best way to do this, multiple inheritance, outsourcing the functions, mixins or delegation (a model creates a GMM instance just for estimation). Unclear ------- * dof in Hausman - based on rank - differs between IV2SLS method and function used with GMM or (IV2SLS) - with GMM, covariance matrix difference has negative eigenvalues in iv example,??? * jtest/jval - I'm not sure about the normalization (multiply or divide by nobs) in jtest. need a test case. Scaling of jval is irrelevant for estimation. jval in jtest looks to large in example, but I have no idea about the size * bse for fitonce look too large (no time for checking now) formula for calc_cov_params for the case without optimal weighting matrix is wrong. I don't have an estimate for omega in that case. And I'm confusing between weights and omega, which are *not* the same in this case. Author: josef-pktd License: BSD (3-clause) ''' from statsmodels.compat.python import lrange import numpy as np from scipy import optimize, stats from statsmodels.tools.numdiff import approx_fprime from statsmodels.base.model import (Model, LikelihoodModel, LikelihoodModelResults) from statsmodels.regression.linear_model import (OLS, RegressionResults, RegressionResultsWrapper) import statsmodels.stats.sandwich_covariance as smcov from statsmodels.tools.decorators import cache_readonly from statsmodels.tools.tools import _ensure_2d DEBUG = 0 def maxabs(x): '''just a shortcut to np.abs(x).max() ''' return np.abs(x).max() class IV2SLS(LikelihoodModel): """ Instrumental variables estimation using Two-Stage Least-Squares (2SLS) Parameters ---------- endog: array Endogenous variable, 1-dimensional or 2-dimensional array nobs by 1 exog : array Explanatory variables, 1-dimensional or 2-dimensional array nobs by k instrument : array Instruments for explanatory variables. Must contain both exog variables that are not being instrumented and instruments Notes ----- All variables in exog are instrumented in the calculations. If variables in exog are not supposed to be instrumented, then these variables must also to be included in the instrument array. Degrees of freedom in the calculation of the standard errors uses `df_resid = (nobs - k_vars)`. (This corresponds to the `small` option in Stata's ivreg2.) """ def __init__(self, endog, exog, instrument=None): self.instrument, self.instrument_names = _ensure_2d(instrument, True) super(IV2SLS, self).__init__(endog, exog) # where is this supposed to be handled # Note: Greene p.77/78 dof correction is not necessary (because only # asy results), but most packages do it anyway self.df_resid = self.exog.shape[0] - self.exog.shape[1] #self.df_model = float(self.rank - self.k_constant) self.df_model = float(self.exog.shape[1] - self.k_constant) def initialize(self): self.wendog = self.endog self.wexog = self.exog def whiten(self, X): """Not implemented""" pass def fit(self): '''estimate model using 2SLS IV regression Returns ------- results : instance of RegressionResults regression result Notes ----- This returns a generic RegressioResults instance as defined for the linear models. Parameter estimates and covariance are correct, but other results haven't been tested yet, to seee whether they apply without changes. ''' #Greene 5th edt., p.78 section 5.4 #move this maybe y,x,z = self.endog, self.exog, self.instrument # TODO: this uses "textbook" calculation, improve linalg ztz = np.dot(z.T, z) ztx = np.dot(z.T, x) self.xhatparams = xhatparams = np.linalg.solve(ztz, ztx) #print 'x.T.shape, xhatparams.shape', x.shape, xhatparams.shape F = xhat = np.dot(z, xhatparams) FtF = np.dot(F.T, F) self.xhatprod = FtF #store for Housman specification test Ftx = np.dot(F.T, x) Fty = np.dot(F.T, y) params = np.linalg.solve(FtF, Fty) Ftxinv = np.linalg.inv(Ftx) self.normalized_cov_params = np.dot(Ftxinv.T, np.dot(FtF, Ftxinv)) lfit = IVRegressionResults(self, params, normalized_cov_params=self.normalized_cov_params) lfit.exog_hat_params = xhatparams lfit.exog_hat = xhat # TODO: do we want to store this, might be large self._results_ols2nd = OLS(y, xhat).fit() return RegressionResultsWrapper(lfit) # copied from GLS, because I subclass currently LikelihoodModel and not GLS def predict(self, params, exog=None): """ Return linear predicted values from a design matrix. Parameters ---------- exog : array_like Design / exogenous data params : array_like, optional after fit has been called Parameters of a linear model Returns ------- An array of fitted values Notes ----- If the model as not yet been fit, params is not optional. """ if exog is None: exog = self.exog return np.dot(exog, params) class IVRegressionResults(RegressionResults): """ Results class for for an OLS model. Most of the methods and attributes are inherited from RegressionResults. The special methods that are only available for OLS are: - get_influence - outlier_test - el_test - conf_int_el See Also -------- RegressionResults """ @cache_readonly def fvalue(self): const_idx = self.model.data.const_idx # if constant is implicit or missing, return nan see #2444, #3544 if const_idx is None: return np.nan else: k_vars = len(self.params) restriction = np.eye(k_vars) idx_noconstant = lrange(k_vars) del idx_noconstant[const_idx] fval = self.f_test(restriction[idx_noconstant]).fvalue # without constant return fval def spec_hausman(self, dof=None): '''Hausman's specification test See Also -------- spec_hausman : generic function for Hausman's specification test ''' #use normalized cov_params for OLS endog, exog = self.model.endog, self.model.exog resols = OLS(endog, exog).fit() normalized_cov_params_ols = resols.model.normalized_cov_params # Stata `ivendog` doesn't use df correction for se #se2 = resols.mse_resid #* resols.df_resid * 1. / len(endog) se2 = resols.ssr / len(endog) params_diff = self.params - resols.params cov_diff = np.linalg.pinv(self.model.xhatprod) - normalized_cov_params_ols #TODO: the following is very inefficient, solves problem (svd) twice #use linalg.lstsq or svd directly #cov_diff will very often be in-definite (singular) if not dof: dof = np.linalg.matrix_rank(cov_diff) cov_diffpinv = np.linalg.pinv(cov_diff) H = np.dot(params_diff, np.dot(cov_diffpinv, params_diff))/se2 pval = stats.chi2.sf(H, dof) return H, pval, dof # copied from regression results with small changes, no llf def summary(self, yname=None, xname=None, title=None, alpha=.05): """Summarize the Regression Results Parameters ---------- yname : str, optional Default is `y` xname : list of strings, optional Default is `var_##` for ## in p the number of regressors title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ #TODO: import where we need it (for now), add as cached attributes from statsmodels.stats.stattools import (jarque_bera, omni_normtest, durbin_watson) jb, jbpv, skew, kurtosis = jarque_bera(self.wresid) omni, omnipv = omni_normtest(self.wresid) #TODO: reuse condno from somewhere else? #condno = np.linalg.cond(np.dot(self.wexog.T, self.wexog)) wexog = self.model.wexog eigvals = np.linalg.linalg.eigvalsh(np.dot(wexog.T, wexog)) eigvals = np.sort(eigvals) #in increasing order condno = np.sqrt(eigvals[-1]/eigvals[0]) # TODO: check what is valid. # box-pierce, breusch-pagan, durbin's h are not with endogenous on rhs # use Cumby Huizinga 1992 instead self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis, omni=omni, omnipv=omnipv, condno=condno, mineigval=eigvals[0]) #TODO not used yet #diagn_left_header = ['Models stats'] #diagn_right_header = ['Residual stats'] #TODO: requiring list/iterable is a bit annoying #need more control over formatting #TODO: default don't work if it's not identically spelled top_left = [('Dep. Variable:', None), ('Model:', None), ('Method:', ['Two Stage']), ('', ['Least Squares']), ('Date:', None), ('Time:', None), ('No. Observations:', None), ('Df Residuals:', None), #[self.df_resid]), #TODO: spelling ('Df Model:', None), #[self.df_model]) ] top_right = [('R-squared:', ["%#8.3f" % self.rsquared]), ('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]), ('F-statistic:', ["%#8.4g" % self.fvalue] ), ('Prob (F-statistic):', ["%#6.3g" % self.f_pvalue]), #('Log-Likelihood:', None), #["%#6.4g" % self.llf]), #('AIC:', ["%#8.4g" % self.aic]), #('BIC:', ["%#8.4g" % self.bic]) ] diagn_left = [('Omnibus:', ["%#6.3f" % omni]), ('Prob(Omnibus):', ["%#6.3f" % omnipv]), ('Skew:', ["%#6.3f" % skew]), ('Kurtosis:', ["%#6.3f" % kurtosis]) ] diagn_right = [('Durbin-Watson:', ["%#8.3f" % durbin_watson(self.wresid)]), ('Jarque-Bera (JB):', ["%#8.3f" % jb]), ('Prob(JB):', ["%#8.3g" % jbpv]), ('Cond. No.', ["%#8.3g" % condno]) ] if title is None: title = self.model.__class__.__name__ +'' + "Regression Results" #create summary table instance from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=True) smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right, yname=yname, xname=xname, title="") return smry ############# classes for Generalized Method of Moments GMM _gmm_options = '''\ Options for GMM --------------- Type of GMM ~~~~~~~~~~~ - one-step - iterated - CUE : not tested yet weight matrix ~~~~~~~~~~~~~ - `weights_method` : string, defines method for robust Options here are similar to :mod:`statsmodels.stats.robust_covariance` default is heteroscedasticity consistent, HC0 currently available methods are - `cov` : HC0, optionally with degrees of freedom correction - `hac` : - `iid` : untested, only for Z*u case, IV cases with u as error indep of Z - `ac` : not available yet - `cluster` : not connected yet - others from robust_covariance other arguments: - `wargs` : tuple or dict, required arguments for weights_method - `centered` : bool, indicates whether moments are centered for the calculation of the weights and covariance matrix, applies to all weight_methods - `ddof` : int degrees of freedom correction, applies currently only to `cov` - maxlag : int number of lags to include in HAC calculation, applies only to `hac` - others not yet, e.g. groups for cluster robust covariance matrix ~~~~~~~~~~~~~~~~~ The same options as for weight matrix also apply to the calculation of the estimate of the covariance matrix of the parameter estimates. The additional option is - `has_optimal_weights`: If true, then the calculation of the covariance matrix assumes that we have optimal GMM with :math:`W = S^{-1}`. Default is True. TODO: do we want to have a different default after `onestep`? ''' class GMM(Model): ''' Class for estimation by Generalized Method of Moments needs to be subclassed, where the subclass defined the moment conditions `momcond` Parameters ---------- endog : array endogenous variable, see notes exog : array array of exogenous variables, see notes instrument : array array of instruments, see notes nmoms : None or int number of moment conditions, if None then it is set equal to the number of columns of instruments. Mainly needed to determin the shape or size of start parameters and starting weighting matrix. kwds : anything this is mainly if additional variables need to be stored for the calculations of the moment conditions Attributes ---------- results : instance of GMMResults currently just a storage class for params and cov_params without it's own methods bse : property return bse Notes ----- The GMM class only uses the moment conditions and does not use any data directly. endog, exog, instrument and kwds in the creation of the class instance are only used to store them for access in the moment conditions. Which of this are required and how they are used depends on the moment conditions of the subclass. Warning: Options for various methods have not been fully implemented and are still missing in several methods. TODO: currently onestep (maxiter=0) still produces an updated estimate of bse and cov_params. ''' results_class = 'GMMResults' def __init__(self, endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds): ''' maybe drop and use mixin instead TODO: GMM doesn't really care about the data, just the moment conditions ''' instrument = self._check_inputs(instrument, endog) # attaches if needed super(GMM, self).__init__(endog, exog, missing=missing, instrument=instrument) # self.endog = endog # self.exog = exog # self.instrument = instrument self.nobs = endog.shape[0] if k_moms is not None: self.nmoms = k_moms elif instrument is not None: self.nmoms = instrument.shape[1] else: self.nmoms = np.nan if k_params is not None: self.k_params = k_params elif instrument is not None: self.k_params = exog.shape[1] else: self.k_params = np.nan self.__dict__.update(kwds) self.epsilon_iter = 1e-6 def _check_inputs(self, instrument, endog): if instrument is not None: offset = np.asarray(instrument) if offset.shape[0]!= endog.shape[0]: raise ValueError("instrument is not the same length as endog") return instrument def _fix_param_names(self, params, param_names=None): # TODO: this is a temporary fix, need xnames = self.data.xnames if param_names is not None: if len(params) == len(param_names): self.data.xnames = param_names else: raise ValueError('param_names has the wrong length') else: if len(params) < len(xnames): # cut in front for poisson multiplicative self.data.xnames = xnames[-len(params):] elif len(params) > len(xnames): # use generic names self.data.xnames = ['p%2d' % i for i in range(len(params))] def set_param_names(self, param_names, k_params=None): """set the parameter names in the model Parameters ---------- param_names : list of strings param_names should have the same length as the number of params k_params : None or int If k_params is None, then the k_params attribute is used, unless it is None. If k_params is not None, then it will also set the k_params attribute. """ if k_params is not None: self.k_params = k_params else: k_params = self.k_params if k_params == len(param_names): self.data.xnames = param_names else: raise ValueError('param_names has the wrong length') def fit(self, start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None): ''' Estimate parameters using GMM and return GMMResults TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results Parameters ---------- start_params : array (optional) starting value for parameters ub minimization. If None then fitstart method is called for the starting values. maxiter : int or 'cue' Number of iterations in iterated GMM. The onestep estimate can be obtained with maxiter=0 or 1. If maxiter is large, then the iteration will stop either at maxiter or on convergence of the parameters (TODO: no options for convergence criteria yet.) If `maxiter == 'cue'`, the the continuously updated GMM is calculated which updates the weight matrix during the minimization of the GMM objective function. The CUE estimation uses the onestep parameters as starting values. inv_weights : None or ndarray inverse of the starting weighting matrix. If inv_weights are not given then the method `start_weights` is used which depends on the subclass, for IV subclasses `inv_weights = z'z` where `z` are the instruments, otherwise an identity matrix is used. weights_method : string, defines method for robust Options here are similar to :mod:`statsmodels.stats.robust_covariance` default is heteroscedasticity consistent, HC0 currently available methods are - `cov` : HC0, optionally with degrees of freedom correction - `hac` : - `iid` : untested, only for Z*u case, IV cases with u as error indep of Z - `ac` : not available yet - `cluster` : not connected yet - others from robust_covariance wargs` : tuple or dict, required and optional arguments for weights_method - `centered` : bool, indicates whether moments are centered for the calculation of the weights and covariance matrix, applies to all weight_methods - `ddof` : int degrees of freedom correction, applies currently only to `cov` - `maxlag` : int number of lags to include in HAC calculation, applies only to `hac` - others not yet, e.g. groups for cluster robust has_optimal_weights: If true, then the calculation of the covariance matrix assumes that we have optimal GMM with :math:`W = S^{-1}`. Default is True. TODO: do we want to have a different default after `onestep`? optim_method : string, default is 'bfgs' numerical optimization method. Currently not all optimizers that are available in LikelihoodModels are connected. optim_args : dict keyword arguments for the numerical optimizer. Returns ------- results : instance of GMMResults this is also attached as attribute results Notes ----- Warning: One-step estimation, `maxiter` either 0 or 1, still has problems (at least compared to Stata's gmm). By default it uses a heteroscedasticity robust covariance matrix, but uses the assumption that the weight matrix is optimal. See options for cov_params in the results instance. The same options as for weight matrix also apply to the calculation of the estimate of the covariance matrix of the parameter estimates. ''' # TODO: add check for correct wargs keys # currently a misspelled key is not detected, # because I'm still adding options # TODO: check repeated calls to fit with different options # arguments are dictionaries, i.e. mutable # unit test if anything is stale or spilled over. #bug: where does start come from??? start = start_params # alias for renaming if start is None: start = self.fitstart() #TODO: temporary hack if inv_weights is None: inv_weights if optim_args is None: optim_args = {} if 'disp' not in optim_args: optim_args['disp'] = 1 if maxiter == 0 or maxiter == 'cue': if inv_weights is not None: weights = np.linalg.pinv(inv_weights) else: # let start_weights handle the inv=False for maxiter=0 weights = self.start_weights(inv=False) params = self.fitgmm(start, weights=weights, optim_method=optim_method, optim_args=optim_args) weights_ = weights # temporary alias used in jval else: params, weights = self.fititer(start, maxiter=maxiter, start_invweights=inv_weights, weights_method=weights_method, wargs=wargs, optim_method=optim_method, optim_args=optim_args) # TODO weights returned by fititer is inv_weights - not true anymore # weights_ currently not necessary and used anymore weights_ = np.linalg.pinv(weights) if maxiter == 'cue': #we have params from maxiter= 0 as starting value # TODO: need to give weights options to gmmobjective_cu params = self.fitgmm_cu(params, optim_method=optim_method, optim_args=optim_args) # weights is stored as attribute weights = self._weights_cu #TODO: use Bunch instead? options_other = {'weights_method':weights_method, 'has_optimal_weights':has_optimal_weights, 'optim_method':optim_method} # check that we have the right number of xnames self._fix_param_names(params, param_names=None) results = results_class_dict[self.results_class]( model = self, params = params, weights = weights, wargs = wargs, options_other = options_other, optim_args = optim_args) self.results = results # FIXME: remove, still keeping it temporarily return results def fitgmm(self, start, weights=None, optim_method='bfgs', optim_args=None): '''estimate parameters using GMM Parameters ---------- start : array_like starting values for minimization weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used Returns ------- paramest : array estimated parameters Notes ----- todo: add fixed parameter option, not here??? uses scipy.optimize.fmin ''' ## if not fixed is None: #fixed not defined in this version ## raise NotImplementedError # TODO: should start_weights only be in `fit` if weights is None: weights = self.start_weights(inv=False) if optim_args is None: optim_args = {} if optim_method == 'nm': optimizer = optimize.fmin elif optim_method == 'bfgs': optimizer = optimize.fmin_bfgs # TODO: add score optim_args['fprime'] = self.score #lambda params: self.score(params, weights) elif optim_method == 'ncg': optimizer = optimize.fmin_ncg optim_args['fprime'] = self.score elif optim_method == 'cg': optimizer = optimize.fmin_cg optim_args['fprime'] = self.score elif optim_method == 'fmin_l_bfgs_b': optimizer = optimize.fmin_l_bfgs_b optim_args['fprime'] = self.score elif optim_method == 'powell': optimizer = optimize.fmin_powell elif optim_method =='slsqp': optimizer = optimize.fmin_slsqp else: raise ValueError('optimizer method not available') if DEBUG: print(np.linalg.det(weights)) #TODO: add other optimization options and results return optimizer(self.gmmobjective, start, args=(weights,), **optim_args) def fitgmm_cu(self, start, optim_method='bfgs', optim_args=None): '''estimate parameters using continuously updating GMM Parameters ---------- start : array_like starting values for minimization Returns ------- paramest : array estimated parameters Notes ----- todo: add fixed parameter option, not here??? uses scipy.optimize.fmin ''' ## if not fixed is None: #fixed not defined in this version ## raise NotImplementedError if optim_args is None: optim_args = {} if optim_method == 'nm': optimizer = optimize.fmin elif optim_method == 'bfgs': optimizer = optimize.fmin_bfgs optim_args['fprime'] = self.score_cu elif optim_method == 'ncg': optimizer = optimize.fmin_ncg else: raise ValueError('optimizer method not available') #TODO: add other optimization options and results return optimizer(self.gmmobjective_cu, start, args=(), **optim_args) def start_weights(self, inv=True): """Create identity matrix for starting weights""" return np.eye(self.nmoms) def gmmobjective(self, params, weights): ''' objective function for GMM minimization Parameters ---------- params : array parameter values at which objective is evaluated weights : array weighting matrix Returns ------- jval : float value of objective function ''' moms = self.momcond_mean(params) return np.dot(np.dot(moms, weights), moms) #moms = self.momcond(params) #return np.dot(np.dot(moms.mean(0),weights), moms.mean(0)) def gmmobjective_cu(self, params, weights_method='cov', wargs=()): ''' objective function for continuously updating GMM minimization Parameters ---------- params : array parameter values at which objective is evaluated Returns ------- jval : float value of objective function ''' moms = self.momcond(params) inv_weights = self.calc_weightmatrix(moms, weights_method=weights_method, wargs=wargs) weights = np.linalg.pinv(inv_weights) self._weights_cu = weights # store if we need it later return np.dot(np.dot(moms.mean(0), weights), moms.mean(0)) def fititer(self, start, maxiter=2, start_invweights=None, weights_method='cov', wargs=(), optim_method='bfgs', optim_args=None): '''iterative estimation with updating of optimal weighting matrix stopping criteria are maxiter or change in parameter estimate less than self.epsilon_iter, with default 1e-6. Parameters ---------- start : array starting value for parameters maxiter : int maximum number of iterations start_weights : array (nmoms, nmoms) initial weighting matrix; if None, then the identity matrix is used weights_method : {'cov',...} method to use to estimate the optimal weighting matrix, see calc_weightmatrix for details Returns ------- params : array estimated parameters weights : array optimal weighting matrix calculated with final parameter estimates Notes ----- ''' self.history = [] momcond = self.momcond if start_invweights is None: w = self.start_weights(inv=True) else: w = start_invweights #call fitgmm function #args = (self.endog, self.exog, self.instrument) #args is not used in the method version winv_new = w for it in range(maxiter): winv = winv_new w = np.linalg.pinv(winv) #this is still calling function not method ## resgmm = fitgmm(momcond, (), start, weights=winv, fixed=None, ## weightsoptimal=False) resgmm = self.fitgmm(start, weights=w, optim_method=optim_method, optim_args=optim_args) moms = momcond(resgmm) # the following is S = cov_moments winv_new = self.calc_weightmatrix(moms, weights_method=weights_method, wargs=wargs, params=resgmm) if it > 2 and maxabs(resgmm - start) < self.epsilon_iter: #check rule for early stopping # TODO: set has_optimal_weights = True break start = resgmm return resgmm, w def calc_weightmatrix(self, moms, weights_method='cov', wargs=(), params=None): ''' calculate omega or the weighting matrix Parameters ---------- moms : array moment conditions (nobs x nmoms) for all observations evaluated at a parameter value weights_method : string 'cov' If method='cov' is cov then the matrix is calculated as simple covariance of the moment conditions. see fit method for available aoptions for the weight and covariance matrix wargs : tuple or dict parameters that are required by some kernel methods to estimate the long-run covariance. Not used yet. Returns ------- w : array (nmoms, nmoms) estimate for the weighting matrix or covariance of the moment condition Notes ----- currently a constant cutoff window is used TODO: implement long-run cov estimators, kernel-based Newey-West Andrews Andrews-Moy???? References ---------- Greene Hansen, Bruce ''' nobs, k_moms = moms.shape # TODO: wargs are tuple or dict? if DEBUG: print(' momcov wargs', wargs) centered = not ('centered' in wargs and not wargs['centered']) if not centered: # caller doesn't want centered moment conditions moms_ = moms else: moms_ = moms - moms.mean() # TODO: store this outside to avoid doing this inside optimization loop # TODO: subclasses need to be able to add weights_methods, and remove # IVGMM can have homoscedastic (OLS), # some options won't make sense in some cases # possible add all here and allow subclasses to define a list # TODO: should other weights_methods also have `ddof` if weights_method == 'cov': w = np.dot(moms_.T, moms_) if 'ddof' in wargs: # caller requests degrees of freedom correction if wargs['ddof'] == 'k_params': w /= (nobs - self.k_params) else: if DEBUG: print(' momcov ddof', wargs['ddof']) w /= (nobs - wargs['ddof']) else: # default: divide by nobs w /= nobs elif weights_method == 'flatkernel': #uniform cut-off window # This was a trial version, can use HAC with flatkernel if'maxlag' not in wargs: raise ValueError('flatkernel requires maxlag') maxlag = wargs['maxlag'] h = np.ones(maxlag + 1) w = np.dot(moms_.T, moms_)/nobs for i in range(1,maxlag+1): w += (h[i] * np.dot(moms_[i:].T, moms_[:-i]) / (nobs-i)) elif weights_method == 'hac': maxlag = wargs['maxlag'] if 'kernel' in wargs: weights_func = wargs['kernel'] else: weights_func = smcov.weights_bartlett wargs['kernel'] = weights_func w = smcov.S_hac_simple(moms_, nlags=maxlag, weights_func=weights_func) w /= nobs #(nobs - self.k_params) elif weights_method == 'iid': # only when we have instruments and residual mom = Z * u # TODO: problem we don't have params in argument # I cannot keep everything in here w/o params as argument u = self.get_error(params) if centered: # Note: I'm not centering instruments, # shouldn't we always center u? Ok, with centered as default u -= u.mean(0) #demean inplace, we don't need original u instrument = self.instrument w = np.dot(instrument.T, instrument).dot(np.dot(u.T, u)) / nobs if 'ddof' in wargs: # caller requests degrees of freedom correction if wargs['ddof'] == 'k_params': w /= (nobs - self.k_params) else: # assume ddof is a number if DEBUG: print(' momcov ddof', wargs['ddof']) w /= (nobs - wargs['ddof']) else: # default: divide by nobs w /= nobs else: raise ValueError('weight method not available') return w def momcond_mean(self, params): ''' mean of moment conditions, ''' momcond = self.momcond(params) self.nobs_moms, self.k_moms = momcond.shape return momcond.mean(0) def gradient_momcond(self, params, epsilon=1e-4, centered=True): '''gradient of moment conditions Parameters ---------- params : ndarray parameter at which the moment conditions are evaluated epsilon : float stepsize for finite difference calculation centered : bool This refers to the finite difference calculation. If `centered` is true, then the centered finite difference calculation is used. Otherwise the one-sided forward differences are used. TODO: looks like not used yet missing argument `weights` ''' momcond = self.momcond_mean # TODO: approx_fprime has centered keyword if centered: gradmoms = (approx_fprime(params, momcond, epsilon=epsilon) + approx_fprime(params, momcond, epsilon=-epsilon))/2 else: gradmoms = approx_fprime(params, momcond, epsilon=epsilon) return gradmoms def score(self, params, weights, epsilon=None, centered=True): """Score""" deriv = approx_fprime(params, self.gmmobjective, args=(weights,), centered=centered, epsilon=epsilon) return deriv def score_cu(self, params, epsilon=None, centered=True): """Score cu""" deriv = approx_fprime(params, self.gmmobjective_cu, args=(), centered=centered, epsilon=epsilon) return deriv # TODO: wrong superclass, I want tvalues,... right now class GMMResults(LikelihoodModelResults): '''just a storage class right now''' use_t = False def __init__(self, *args, **kwds): self.__dict__.update(kwds) self.nobs = self.model.nobs self.df_resid = np.inf self.cov_params_default = self._cov_params() @cache_readonly def q(self): """Objective function at params""" return self.model.gmmobjective(self.params, self.weights) @cache_readonly def jval(self): """nobs_moms attached by momcond_mean""" return self.q * self.model.nobs_moms def _cov_params(self, **kwds): #TODO add options???) # this should use by default whatever options have been specified in # fit # TODO: don't do this when we want to change options # if hasattr(self, '_cov_params'): # #replace with decorator later # return self._cov_params # set defaults based on fit arguments if 'wargs' not in kwds: # Note: we don't check the keys in wargs, use either all or nothing kwds['wargs'] = self.wargs if 'weights_method' not in kwds: kwds['weights_method'] = self.options_other['weights_method'] if 'has_optimal_weights' not in kwds: kwds['has_optimal_weights'] = self.options_other['has_optimal_weights'] gradmoms = self.model.gradient_momcond(self.params) moms = self.model.momcond(self.params) covparams = self.calc_cov_params(moms, gradmoms, **kwds) return covparams def calc_cov_params(self, moms, gradmoms, weights=None, use_weights=False, has_optimal_weights=True, weights_method='cov', wargs=()): '''calculate covariance of parameter estimates not all options tried out yet If weights matrix is given, then the formula use to calculate cov_params depends on whether has_optimal_weights is true. If no weights are given, then the weight matrix is calculated with the given method, and has_optimal_weights is assumed to be true. (API Note: The latter assumption could be changed if we allow for has_optimal_weights=None.) ''' nobs = moms.shape[0] if weights is None: #omegahat = self.model.calc_weightmatrix(moms, method=method, wargs=wargs) #has_optimal_weights = True #add other options, Barzen,... longrun var estimators # TODO: this might still be inv_weights after fititer weights = self.weights else: pass #omegahat = weights #2 different names used, #TODO: this is wrong, I need an estimate for omega if use_weights: omegahat = weights else: omegahat = self.model.calc_weightmatrix( moms, weights_method=weights_method, wargs=wargs, params=self.params) if has_optimal_weights: #has_optimal_weights: # TOD0 make has_optimal_weights depend on convergence or iter >2 cov = np.linalg.inv(np.dot(gradmoms.T, np.dot(np.linalg.inv(omegahat), gradmoms))) else: gw = np.dot(gradmoms.T, weights) gwginv = np.linalg.inv(np.dot(gw, gradmoms)) cov = np.dot(np.dot(gwginv, np.dot(np.dot(gw, omegahat), gw.T)), gwginv) #cov /= nobs return cov/nobs @property def bse_(self): '''standard error of the parameter estimates ''' return self.get_bse() def get_bse(self, **kwds): '''standard error of the parameter estimates with options Parameters ---------- kwds : optional keywords options for calculating cov_params Returns ------- bse : ndarray estimated standard error of parameter estimates ''' return np.sqrt(np.diag(self.cov_params(**kwds))) def jtest(self): '''overidentification test I guess this is missing a division by nobs, what's the normalization in jval? ''' jstat = self.jval nparams = self.params.size #self.nparams df = self.model.nmoms - nparams return jstat, stats.chi2.sf(jstat, df), df def compare_j(self, other): '''overidentification test for comparing two nested gmm estimates This assumes that some moment restrictions have been dropped in one of the GMM estimates relative to the other. Not tested yet We are comparing two separately estimated models, that use different weighting matrices. It is not guaranteed that the resulting difference is positive. TODO: Check in which cases Stata programs use the same weigths ''' jstat1 = self.jval k_moms1 = self.model.nmoms jstat2 = other.jval k_moms2 = other.model.nmoms jdiff = jstat1 - jstat2 df = k_moms1 - k_moms2 if df < 0: # possible nested in other way, TODO allow this or not # flip sign instead of absolute df = - df jdiff = - jdiff return jdiff, stats.chi2.sf(jdiff, df), df def summary(self, yname=None, xname=None, title=None, alpha=.05): """Summarize the Regression Results Parameters ---------- yname : str, optional Default is `y` xname : list of strings, optional Default is `var_##` for ## in p the number of regressors title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ #TODO: add a summary text for options that have been used jvalue, jpvalue, jdf = self.jtest() top_left = [('Dep. Variable:', None), ('Model:', None), ('Method:', ['GMM']), ('Date:', None), ('Time:', None), ('No. Observations:', None), #('Df Residuals:', None), #[self.df_resid]), #TODO: spelling #('Df Model:', None), #[self.df_model]) ] top_right = [#('R-squared:', ["%#8.3f" % self.rsquared]), #('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]), ('Hansen J:', ["%#8.4g" % jvalue] ), ('Prob (Hansen J):', ["%#6.3g" % jpvalue]), #('F-statistic:', ["%#8.4g" % self.fvalue] ), #('Prob (F-statistic):', ["%#6.3g" % self.f_pvalue]), #('Log-Likelihood:', None), #["%#6.4g" % self.llf]), #('AIC:', ["%#8.4g" % self.aic]), #('BIC:', ["%#8.4g" % self.bic]) ] if title is None: title = self.model.__class__.__name__ +'' + "Results" # create summary table instance from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=self.use_t) return smry class IVGMM(GMM): ''' Basic class for instrumental variables estimation using GMM A linear function for the conditional mean is defined as default but the methods should be overwritten by subclasses, currently `LinearIVGMM` and `NonlinearIVGMM` are implemented as subclasses. See Also -------- LinearIVGMM NonlinearIVGMM ''' results_class = 'IVGMMResults' def fitstart(self): """Create array of zeros""" return np.zeros(self.exog.shape[1]) def start_weights(self, inv=True): """Starting weights""" zz = np.dot(self.instrument.T, self.instrument) nobs = self.instrument.shape[0] if inv: return zz / nobs else: return np.linalg.pinv(zz / nobs) def get_error(self, params): """Get error at params""" return self.endog - self.predict(params) def predict(self, params, exog=None): """Get prediction at params""" if exog is None: exog = self.exog return np.dot(exog, params) def momcond(self, params): """Error times instrument""" instrument = self.instrument return instrument * self.get_error(params)[:, None] class LinearIVGMM(IVGMM): """class for linear instrumental variables models estimated with GMM Uses closed form expression instead of nonlinear optimizers for each step of the iterative GMM. The model is assumed to have the following moment condition E( z * (y - x beta)) = 0 Where `y` is the dependent endogenous variable, `x` are the explanatory variables and `z` are the instruments. Variables in `x` that are exogenous need also be included in `z`. Notation Warning: our name `exog` stands for the explanatory variables, and includes both exogenous and explanatory variables that are endogenous, i.e. included endogenous variables Parameters ---------- endog : array_like dependent endogenous variable exog : array_like explanatory, right hand side variables, including explanatory variables that are endogenous instrument : array_like Instrumental variables, variables that are exogenous to the error in the linear model containing both included and excluded exogenous variables """ def fitgmm(self, start, weights=None, optim_method=None, **kwds): '''estimate parameters using GMM for linear model Uses closed form expression instead of nonlinear optimizers Parameters ---------- start : not used starting values for minimization, not used, only for consistency of method signature weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used optim_method : not used, optimization method, not used, only for consistency of method signature **kwds : keyword arguments not used, will be silently ignored (for compatibility with generic) Returns ------- paramest : array estimated parameters ''' ## if not fixed is None: #fixed not defined in this version ## raise NotImplementedError # TODO: should start_weights only be in `fit` if weights is None: weights = self.start_weights(inv=False) y, x, z = self.endog, self.exog, self.instrument zTx = np.dot(z.T, x) zTy = np.dot(z.T, y) # normal equation, solved with pinv part0 = zTx.T.dot(weights) part1 = part0.dot(zTx) part2 = part0.dot(zTy) params = np.linalg.pinv(part1).dot(part2) return params def predict(self, params, exog=None): if exog is None: exog = self.exog return np.dot(exog, params) def gradient_momcond(self, params, **kwds): # **kwds for compatibility not used x, z = self.exog, self.instrument gradmoms = -np.dot(z.T, x) / self.nobs return gradmoms def score(self, params, weights, **kwds): # **kwds for compatibility, not used # Note: I coud use general formula with gradient_momcond instead x, z = self.exog, self.instrument nobs = z.shape[0] u = self.get_errors(params) score = -2 * np.dot(x.T, z).dot(weights.dot(np.dot(z.T, u))) score /= nobs * nobs return score class NonlinearIVGMM(IVGMM): """ Class for non-linear instrumental variables estimation wusing GMM The model is assumed to have the following moment condition E[ z * (y - f(X, beta)] = 0 Where `y` is the dependent endogenous variable, `x` are the explanatory variables and `z` are the instruments. Variables in `x` that are exogenous need also be included in z. `f` is a nonlinear function. Notation Warning: our name `exog` stands for the explanatory variables, and includes both exogenous and explanatory variables that are endogenous, i.e. included endogenous variables Parameters ---------- endog : array_like dependent endogenous variable exog : array_like explanatory, right hand side variables, including explanatory variables that are endogenous. instruments : array_like Instrumental variables, variables that are exogenous to the error in the linear model containing both included and excluded exogenous variables func : callable function for the mean or conditional expectation of the endogenous variable. The function will be called with parameters and the array of explanatory, right hand side variables, `func(params, exog)` Notes ----- This class uses numerical differences to obtain the derivative of the objective function. If the jacobian of the conditional mean function, `func` is available, then it can be used by subclassing this class and defining a method `jac_func`. TODO: check required signature of jac_error and jac_func """ # This should be reversed: # NonlinearIVGMM is IVGMM and need LinearIVGMM as special case (fit, predict) def fitstart(self): #might not make sense for more general functions return np.zeros(self.exog.shape[1]) def __init__(self, endog, exog, instrument, func, **kwds): self.func = func super(NonlinearIVGMM, self).__init__(endog, exog, instrument, **kwds) def predict(self, params, exog=None): if exog is None: exog = self.exog return self.func(params, exog) #---------- the following a semi-general versions, # TODO: move to higher class after testing def jac_func(self, params, weights, args=None, centered=True, epsilon=None): # TODO: Why are ther weights in the signature - copy-paste error? deriv = approx_fprime(params, self.func, args=(self.exog,), centered=centered, epsilon=epsilon) return deriv def jac_error(self, params, weights, args=None, centered=True, epsilon=None): jac_func = self.jac_func(params, weights, args=None, centered=True, epsilon=None) return -jac_func def score(self, params, weights, **kwds): # **kwds for compatibility not used # Note: I coud use general formula with gradient_momcond instead z = self.instrument nobs = z.shape[0] jac_u = self.jac_error(params, weights, args=None, epsilon=None, centered=True) x = -jac_u # alias, plays the same role as X in linear model u = self.get_error(params) score = -2 * np.dot(np.dot(x.T, z), weights).dot(np.dot(z.T, u)) score /= nobs * nobs return score class IVGMMResults(GMMResults): """Results class of IVGMM""" # this assumes that we have an additive error model `(y - f(x, params))` @cache_readonly def fittedvalues(self): """Fitted values""" return self.model.predict(self.params) @cache_readonly def resid(self): """Residuals""" return self.model.endog - self.fittedvalues @cache_readonly def ssr(self): """Sum of square errors""" return (self.resid * self.resid).sum(0) def spec_hausman(params_e, params_i, cov_params_e, cov_params_i, dof=None): '''Hausmans specification test Parameters ---------- params_e : array efficient and consistent under Null hypothesis, inconsistent under alternative hypothesis params_i: array consistent under Null hypothesis, consistent under alternative hypothesis cov_params_e : array, 2d covariance matrix of parameter estimates for params_e cov_params_i : array, 2d covariance matrix of parameter estimates for params_i example instrumental variables OLS estimator is `e`, IV estimator is `i` Notes ----- Todos,Issues - check dof calculations and verify for linear case - check one-sided hypothesis References ---------- Greene section 5.5 p.82/83 ''' params_diff = (params_i - params_e) cov_diff = cov_params_i - cov_params_e #TODO: the following is very inefficient, solves problem (svd) twice #use linalg.lstsq or svd directly #cov_diff will very often be in-definite (singular) if not dof: dof = np.linalg.matrix_rank(cov_diff) cov_diffpinv = np.linalg.pinv(cov_diff) H = np.dot(params_diff, np.dot(cov_diffpinv, params_diff)) pval = stats.chi2.sf(H, dof) evals = np.linalg.eigvalsh(cov_diff) return H, pval, dof, evals ########### class DistQuantilesGMM(GMM): ''' Estimate distribution parameters by GMM based on matching quantiles Currently mainly to try out different requirements for GMM when we cannot calculate the optimal weighting matrix. ''' def __init__(self, endog, exog, instrument, **kwds): #TODO: something wrong with super super(DistQuantilesGMM, self).__init__(endog, exog, instrument) #self.func = func self.epsilon_iter = 1e-5 self.distfn = kwds['distfn'] #done by super doesn't work yet #TypeError: super does not take keyword arguments self.endog = endog #make this optional for fit if 'pquant' not in kwds: self.pquant = pquant = np.array([0.01, 0.05,0.1,0.4,0.6,0.9,0.95,0.99]) else: self.pquant = pquant = kwds['pquant'] #TODO: vectorize this: use edf self.xquant = np.array([stats.scoreatpercentile(endog, p) for p in pquant*100]) self.nmoms = len(self.pquant) #TODOcopied from GMM, make super work self.endog = endog self.exog = exog self.instrument = instrument self.results = GMMResults(model=self) #self.__dict__.update(kwds) self.epsilon_iter = 1e-6 def fitstart(self): #todo: replace with or add call to distfn._fitstart # added but not used during testing, avoid Travis distfn = self.distfn if hasattr(distfn, '_fitstart'): start = distfn._fitstart(self.endog) else: start = [1]*distfn.numargs + [0.,1.] return np.asarray(start) def momcond(self, params): #drop distfn as argument #, mom2, quantile=None, shape=None '''moment conditions for estimating distribution parameters by matching quantiles, defines as many moment conditions as quantiles. Returns ------- difference : array difference between theoretical and empirical quantiles Notes ----- This can be used for method of moments or for generalized method of moments. ''' #this check looks redundant/unused know if len(params) == 2: loc, scale = params elif len(params) == 3: shape, loc, scale = params else: #raise NotImplementedError pass #see whether this might work, seems to work for beta with 2 shape args #mom2diff = np.array(distfn.stats(*params)) - mom2 #if not quantile is None: pq, xq = self.pquant, self.xquant #ppfdiff = distfn.ppf(pq, alpha) cdfdiff = self.distfn.cdf(xq, *params) - pq #return np.concatenate([mom2diff, cdfdiff[:1]]) return np.atleast_2d(cdfdiff) def fitonce(self, start=None, weights=None, has_optimal_weights=False): '''fit without estimating an optimal weighting matrix and return results This is a convenience function that calls fitgmm and covparams with a given weight matrix or the identity weight matrix. This is useful if the optimal weight matrix is know (or is analytically given) or if an optimal weight matrix cannot be calculated. (Developer Notes: this function could go into GMM, but is needed in this class, at least at the moment.) Parameters ---------- Returns ------- results : GMMResult instance result instance with params and _cov_params attached See Also -------- fitgmm cov_params ''' if weights is None: weights = np.eye(self.nmoms) params = self.fitgmm(start=start) # TODO: rewrite this old hack, should use fitgmm or fit maxiter=0 self.results.params = params #required before call to self.cov_params self.results.wargs = {} #required before call to self.cov_params self.results.options_other = {'weights_method':'cov'} # TODO: which weights_method? There shouldn't be any needed? _cov_params = self.results.cov_params(weights=weights, has_optimal_weights=has_optimal_weights) self.results.weights = weights self.results.jval = self.gmmobjective(params, weights) self.results.options_other.update({'has_optimal_weights':has_optimal_weights}) return self.results results_class_dict = {'GMMResults': GMMResults, 'IVGMMResults': IVGMMResults, 'DistQuantilesGMM': GMMResults} #TODO: should be a default

statsmodels__statsmodels

imputation.rst

Description

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BSD 3-Clause New or Revised License

statsmodels__statsmodels/docs/source/imputation.rst

Multiple Imputation with Chained Equations The MICE module allows most Statsmodels models to be fit to a dataset with missing values on the independent and/or dependent variables, and provides rigorous standard errors for the fitted parameters. The basic idea is to treat each variable with missing values as the dependent variable in a regression, with some or all of the remaining variables as its predictors. The MICE procedure cycles through these models, fitting each in turn, then uses a procedure called "predictive mean matching" (PMM) to generate random draws from the predictive distributions determined by the fitted models. These random draws become the imputed values for one imputed data set. By default, each variable with missing variables is modeled using a linear regression with main effects for all other variables in the data set. Note that even when the imputation model is linear, the PMM procedure preserves the domain of each variable. Thus, for example, if all observed values for a given variable are positive, all imputed values for the variable will always be positive. The user also has the option to specify which model is used to produce imputed values for each variable.

""" Overview -------- This module implements the Multiple Imputation through Chained Equations (MICE) approach to handling missing data in statistical data analyses. The approach has the following steps: 0. Impute each missing value with the mean of the observed values of the same variable. 1. For each variable in the data set with missing values (termed the 'focus variable'), do the following: 1a. Fit an 'imputation model', which is a regression model for the focus variable, regressed on the observed and (current) imputed values of some or all of the other variables. 1b. Impute the missing values for the focus variable. Currently this imputation must use the 'predictive mean matching' (pmm) procedure. 2. Once all variables have been imputed, fit the 'analysis model' to the data set. 3. Repeat steps 1-2 multiple times and combine the results using a 'combining rule' to produce point estimates of all parameters in the analysis model and standard errors for them. The imputations for each variable are based on an imputation model that is specified via a model class and a formula for the regression relationship. The default model is OLS, with a formula specifying main effects for all other variables. The MICE procedure can be used in one of two ways: * If the goal is only to produce imputed data sets, the MICEData class can be used to wrap a data frame, providing facilities for doing the imputation. Summary plots are available for assessing the performance of the imputation. * If the imputed data sets are to be used to fit an additional 'analysis model', a MICE instance can be used. After specifying the MICE instance and running it, the results are combined using the `combine` method. Results and various summary plots are then available. Terminology ----------- The primary goal of the analysis is usually to fit and perform inference using an 'analysis model'. If an analysis model is not specified, then imputed datasets are produced for later use. The MICE procedure involves a family of imputation models. There is one imputation model for each variable with missing values. An imputation model may be conditioned on all or a subset of the remaining variables, using main effects, transformations, interactions, etc. as desired. A 'perturbation method' is a method for setting the parameter estimate in an imputation model. The 'gaussian' perturbation method first fits the model (usually using maximum likelihood, but it could use any statsmodels fit procedure), then sets the parameter vector equal to a draw from the Gaussian approximation to the sampling distribution for the fit. The 'bootstrap' perturbation method sets the parameter vector equal to a fitted parameter vector obtained when fitting the conditional model to a bootstrapped version of the data set. Class structure --------------- There are two main classes in the module: * 'MICEData' wraps a Pandas dataframe, incorporating information about the imputation model for each variable with missing values. It can be used to produce multiply imputed data sets that are to be further processed or distributed to other researchers. A number of plotting procedures are provided to visualize the imputation results and missing data patterns. The `history_func` hook allows any features of interest of the imputed data sets to be saved for further analysis. * 'MICE' takes both a 'MICEData' object and an analysis model specification. It runs the multiple imputation, fits the analysis models, and combines the results to produce a `MICEResults` object. The summary method of this results object can be used to see the key estimands and inferential quantities. Notes ----- By default, to conserve memory 'MICEData' saves very little information from one iteration to the next. The data set passed by the user is copied on entry, but then is over-written each time new imputations are produced. If using 'MICE', the fitted analysis models and results are saved. MICEData includes a `history_callback` hook that allows arbitrary information from the intermediate datasets to be saved for future use. References ---------- JL Schafer: 'Multiple Imputation: A Primer', Stat Methods Med Res, 1999. TE Raghunathan et al.: 'A Multivariate Technique for Multiply Imputing Missing Values Using a Sequence of Regression Models', Survey Methodology, 2001. SAS Institute: 'Predictive Mean Matching Method for Monotone Missing Data', SAS 9.2 User's Guide, 2014. A Gelman et al.: 'Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box', Journal of Statistical Software, 2009. """ import pandas as pd import numpy as np import patsy from statsmodels.base.model import LikelihoodModelResults from statsmodels.regression.linear_model import OLS from collections import defaultdict _mice_data_example_1 = """ >>> imp = mice.MICEData(data) >>> imp.set_imputer('x1', formula='x2 + np.square(x2) + x3') >>> for j in range(20): ... imp.update_all() ... imp.data.to_csv('data%02d.csv' % j)""" _mice_data_example_2 = """ >>> imp = mice.MICEData(data) >>> j = 0 >>> for data in imp: ... imp.data.to_csv('data%02d.csv' % j) ... j += 1""" class PatsyFormula(object): """ A simple wrapper for a string to be interpreted as a Patsy formula. """ def __init__(self, formula): self.formula = "0 + " + formula class MICEData(object): __doc__ = """\ Wrap a data set to allow missing data handling with MICE. Parameters ---------- data : Pandas data frame The data set, whch is copied internally. perturbation_method : string The default perturbation method k_pmm : int The number of nearest neighbors to use during predictive mean matching. Can also be specified in `fit`. history_callback : function A function that is called after each complete imputation cycle. The return value is appended to `history`. The MICEData object is passed as the sole argument to `history_callback`. Examples -------- Draw 20 imputations from a data set called `data` and save them in separate files with filename pattern `dataXX.csv`. The variables other than `x1` are imputed using linear models fit with OLS, with mean structures containing main effects of all other variables in `data`. The variable named `x1` has a condtional mean structure that includes an additional term for x2^2. %(_mice_data_example_1)s Impute using default models, using the MICEData object as an iterator. %(_mice_data_example_2)s Notes ----- Allowed perturbation methods are 'gaussian' (the model parameters are set to a draw from the Gaussian approximation to the posterior distribution), and 'boot' (the model parameters are set to the estimated values obtained when fitting a bootstrapped version of the data set). `history_callback` can be implemented to have side effects such as saving the current imputed data set to disk. """ % {'_mice_data_example_1': _mice_data_example_1, '_mice_data_example_2': _mice_data_example_2} def __init__(self, data, perturbation_method='gaussian', k_pmm=20, history_callback=None): if data.columns.dtype!= np.dtype('O'): msg = "MICEData data column names should be string type" raise ValueError(msg) self.regularized = dict() # Drop observations where all variables are missing. This # also has the effect of copying the data frame. self.data = data.dropna(how='all').reset_index(drop=True) self.history_callback = history_callback self.history = [] self.predict_kwds = {} # Assign the same perturbation method for all variables. # Can be overriden when calling'set_imputer'. self.perturbation_method = defaultdict(lambda: perturbation_method) # Map from variable name to indices of observed/missing # values. self.ix_obs = {} self.ix_miss = {} for col in self.data.columns: ix_obs, ix_miss = self._split_indices(self.data[col]) self.ix_obs[col] = ix_obs self.ix_miss[col] = ix_miss # Most recent model instance and results instance for each variable. self.models = {} self.results = {} # Map from variable names to the conditional formula. self.conditional_formula = {} # Map from variable names to init/fit args of the conditional # models. self.init_kwds = defaultdict(lambda: dict()) self.fit_kwds = defaultdict(lambda: dict()) # Map from variable names to the model class. self.model_class = {} # Map from variable names to most recent params update. self.params = {} # Set default imputers. for vname in data.columns: self.set_imputer(vname) # The order in which variables are imputed in each cycle. # Impute variables with the fewest missing values first. vnames = list(data.columns) nmiss = [len(self.ix_miss[v]) for v in vnames] nmiss = np.asarray(nmiss) ii = np.argsort(nmiss) ii = ii[sum(nmiss == 0):] self._cycle_order = [vnames[i] for i in ii] self._initial_imputation() self.k_pmm = k_pmm def next_sample(self): """ Returns the next imputed dataset in the imputation process. Returns ------- data : array_like An imputed dataset from the MICE chain. Notes ----- `MICEData` does not have a `skip` parameter. Consecutive values returned by `next_sample` are immediately consecutive in the imputation chain. The returned value is a reference to the data attribute of the class and should be copied before making any changes. """ self.update_all(1) return self.data def _initial_imputation(self): """ Use a PMM-like procedure for initial imputed values. For each variable, missing values are imputed as the observed value that is closest to the mean over all observed values. """ for col in self.data.columns: di = self.data[col] - self.data[col].mean() di = np.abs(di) ix = di.idxmin() imp = self.data[col].loc[ix] self.data[col].fillna(imp, inplace=True) def _split_indices(self, vec): null = pd.isnull(vec) ix_obs = np.flatnonzero(~null) ix_miss = np.flatnonzero(null) if len(ix_obs) == 0: raise ValueError("variable to be imputed has no observed values") return ix_obs, ix_miss def set_imputer(self, endog_name, formula=None, model_class=None, init_kwds=None, fit_kwds=None, predict_kwds=None, k_pmm=20, perturbation_method=None, regularized=False): """ Specify the imputation process for a single variable. Parameters ---------- endog_name : string Name of the variable to be imputed. formula : string Conditional formula for imputation. Defaults to a formula with main effects for all other variables in dataset. The formula should only include an expression for the mean structure, e.g. use 'x1 + x2' not 'x4 ~ x1 + x2'. model_class : statsmodels model Conditional model for imputation. Defaults to OLS. See below for more information. init_kwds : dit-like Keyword arguments passed to the model init method. fit_kwds : dict-like Keyword arguments passed to the model fit method. predict_kwds : dict-like Keyword arguments passed to the model predict method. k_pmm : int Determines number of neighboring observations from which to randomly sample when using predictive mean matching. perturbation_method : string Either 'gaussian' or 'bootstrap'. Determines the method for perturbing parameters in the imputation model. If None, uses the default specified at class initialization. regularized : dict If regularized[name]=True, `fit_regularized` rather than `fit` is called when fitting imputation models for this variable. When regularized[name]=True for any variable, pertrurbation_method must be set to boot. Notes ----- The model class must meet the following conditions: * A model must have a 'fit' method that returns an object. * The object returned from `fit` must have a `params` attribute that is an array-like object. * The object returned from `fit` must have a cov_params method that returns a square array-like object. * The model must have a `predict` method. """ if formula is None: main_effects = [x for x in self.data.columns if x!= endog_name] fml = endog_name + " ~ " + " + ".join(main_effects) self.conditional_formula[endog_name] = fml else: fml = endog_name + " ~ " + formula self.conditional_formula[endog_name] = fml if model_class is None: self.model_class[endog_name] = OLS else: self.model_class[endog_name] = model_class if init_kwds is not None: self.init_kwds[endog_name] = init_kwds if fit_kwds is not None: self.fit_kwds[endog_name] = fit_kwds if predict_kwds is not None: self.predict_kwds[endog_name] = predict_kwds if perturbation_method is not None: self.perturbation_method[endog_name] = perturbation_method self.k_pmm = k_pmm self.regularized[endog_name] = regularized def _store_changes(self, col, vals): """ Fill in dataset with imputed values. Parameters ---------- col : string Name of variable to be filled in. vals : array Array of imputed values to use for filling-in missing values. """ ix = self.ix_miss[col] if len(ix) > 0: self.data.iloc[ix, self.data.columns.get_loc(col)] = np.atleast_1d(vals) def update_all(self, n_iter=1): """ Perform a specified number of MICE iterations. Parameters ---------- n_iter : int The number of updates to perform. Only the result of the final update will be available. Notes ----- The imputed values are stored in the class attribute `self.data`. """ for k in range(n_iter): for vname in self._cycle_order: self.update(vname) if self.history_callback is not None: hv = self.history_callback(self) self.history.append(hv) def get_split_data(self, vname): """ Return endog and exog for imputation of a given variable. Parameters ---------- vname : string The variable for which the split data is returned. Returns ------- endog_obs : DataFrame Observed values of the variable to be imputed. exog_obs : DataFrame Current values of the predictors where the variable to be imputed is observed. exog_miss : DataFrame Current values of the predictors where the variable to be Imputed is missing. init_kwds : dict-like The init keyword arguments for `vname`, processed through Patsy as required. fit_kwds : dict-like The fit keyword arguments for `vname`, processed through Patsy as required. """ formula = self.conditional_formula[vname] endog, exog = patsy.dmatrices(formula, self.data, return_type="dataframe") # Rows with observed endog ixo = self.ix_obs[vname] endog_obs = np.asarray(endog.iloc[ixo]) exog_obs = np.asarray(exog.iloc[ixo, :]) # Rows with missing endog ixm = self.ix_miss[vname] exog_miss = np.asarray(exog.iloc[ixm, :]) predict_obs_kwds = {} if vname in self.predict_kwds: kwds = self.predict_kwds[vname] predict_obs_kwds = self._process_kwds(kwds, ixo) predict_miss_kwds = {} if vname in self.predict_kwds: kwds = self.predict_kwds[vname] predict_miss_kwds = self._process_kwds(kwds, ixo) return (endog_obs, exog_obs, exog_miss, predict_obs_kwds, predict_miss_kwds) def _process_kwds(self, kwds, ix): kwds = kwds.copy() for k in kwds: v = kwds[k] if isinstance(v, PatsyFormula): mat = patsy.dmatrix(v.formula, self.data, return_type="dataframe") mat = np.asarray(mat)[ix, :] if mat.shape[1] == 1: mat = mat[:, 0] kwds[k] = mat return kwds def get_fitting_data(self, vname): """ Return the data needed to fit a model for imputation. The data is used to impute variable `vname`, and therefore only includes cases for which `vname` is observed. Values of type `PatsyFormula` in `init_kwds` or `fit_kwds` are processed through Patsy and subset to align with the model's endog and exog. Parameters ---------- vname : string The variable for which the fitting data is returned. Returns ------- endog : DataFrame Observed values of `vname`. exog : DataFrame Regression design matrix for imputing `vname`. init_kwds : dict-like The init keyword arguments for `vname`, processed through Patsy as required. fit_kwds : dict-like The fit keyword arguments for `vname`, processed through Patsy as required. """ # Rows with observed endog ix = self.ix_obs[vname] formula = self.conditional_formula[vname] endog, exog = patsy.dmatrices(formula, self.data, return_type="dataframe") endog = np.asarray(endog.iloc[ix, 0]) exog = np.asarray(exog.iloc[ix, :]) init_kwds = self._process_kwds(self.init_kwds[vname], ix) fit_kwds = self._process_kwds(self.fit_kwds[vname], ix) return endog, exog, init_kwds, fit_kwds def plot_missing_pattern(self, ax=None, row_order="pattern", column_order="pattern", hide_complete_rows=False, hide_complete_columns=False, color_row_patterns=True): """ Generate an image showing the missing data pattern. Parameters ---------- ax : matplotlib axes Axes on which to draw the plot. row_order : string The method for ordering the rows. Must be one of 'pattern', 'proportion', or 'raw'. column_order : string The method for ordering the columns. Must be one of 'pattern', 'proportion', or 'raw'. hide_complete_rows : boolean If True, rows with no missing values are not drawn. hide_complete_columns : boolean If True, columns with no missing values are not drawn. color_row_patterns : boolean If True, color the unique row patterns, otherwise use grey and white as colors. Returns ------- A figure containing a plot of the missing data pattern. """ # Create an indicator matrix for missing values. miss = np.zeros(self.data.shape) cols = self.data.columns for j, col in enumerate(cols): ix = self.ix_miss[col] miss[ix, j] = 1 # Order the columns as requested if column_order == "proportion": ix = np.argsort(miss.mean(0)) elif column_order == "pattern": cv = np.cov(miss.T) u, s, vt = np.linalg.svd(cv, 0) ix = np.argsort(cv[:, 0]) elif column_order == "raw": ix = np.arange(len(cols)) else: raise ValueError( column_order + " is not an allowed value for `column_order`.") miss = miss[:, ix] cols = [cols[i] for i in ix] # Order the rows as requested if row_order == "proportion": ix = np.argsort(miss.mean(1)) elif row_order == "pattern": x = 2**np.arange(miss.shape[1]) rky = np.dot(miss, x) ix = np.argsort(rky) elif row_order == "raw": ix = np.arange(miss.shape[0]) else: raise ValueError( row_order + " is not an allowed value for `row_order`.") miss = miss[ix, :] if hide_complete_rows: ix = np.flatnonzero((miss == 1).any(1)) miss = miss[ix, :] if hide_complete_columns: ix = np.flatnonzero((miss == 1).any(0)) miss = miss[:, ix] cols = [cols[i] for i in ix] from statsmodels.graphics import utils as gutils from matplotlib.colors import LinearSegmentedColormap if ax is None: fig, ax = gutils.create_mpl_ax(ax) else: fig = ax.get_figure() if color_row_patterns: x = 2**np.arange(miss.shape[1]) rky = np.dot(miss, x) _, rcol = np.unique(rky, return_inverse=True) miss *= 1 + rcol[:, None] ax.imshow(miss, aspect="auto", interpolation="nearest", cmap='gist_ncar_r') else: cmap = LinearSegmentedColormap.from_list("_", ["white", "darkgrey"]) ax.imshow(miss, aspect="auto", interpolation="nearest", cmap=cmap) ax.set_ylabel("Cases") ax.set_xticks(range(len(cols))) ax.set_xticklabels(cols, rotation=90) return fig def plot_bivariate(self, col1_name, col2_name, lowess_args=None, lowess_min_n=40, jitter=None, plot_points=True, ax=None): """ Plot observed and imputed values for two variables. Displays a scatterplot of one variable against another. The points are colored according to whether the values are observed or imputed. Parameters ---------- col1_name : string The variable to be plotted on the horizontal axis. col2_name : string The variable to be plotted on the vertical axis. lowess_args : dictionary A dictionary of dictionaries, keys are 'ii', 'io', 'oi' and 'oo', where 'o' denotes 'observed' and 'i' denotes imputed. See Notes for details. lowess_min_n : integer Minimum sample size to plot a lowess fit jitter : float or tuple Standard deviation for jittering points in the plot. Either a single scalar applied to both axes, or a tuple containing x-axis jitter and y-axis jitter, respectively. plot_points : bool If True, the data points are plotted. ax : matplotlib axes object Axes on which to plot, created if not provided. Returns ------- The matplotlib figure on which the plot id drawn. """ from statsmodels.graphics import utils as gutils from statsmodels.nonparametric.smoothers_lowess import lowess if lowess_args is None: lowess_args = {} if ax is None: fig, ax = gutils.create_mpl_ax(ax) else: fig = ax.get_figure() ax.set_position([0.1, 0.1, 0.7, 0.8]) ix1i = self.ix_miss[col1_name] ix1o = self.ix_obs[col1_name] ix2i = self.ix_miss[col2_name] ix2o = self.ix_obs[col2_name] ix_ii = np.intersect1d(ix1i, ix2i) ix_io = np.intersect1d(ix1i, ix2o) ix_oi = np.intersect1d(ix1o, ix2i) ix_oo = np.intersect1d(ix1o, ix2o) vec1 = np.asarray(self.data[col1_name]) vec2 = np.asarray(self.data[col2_name]) if jitter is not None: if np.isscalar(jitter): jitter = (jitter, jitter) vec1 += jitter[0] * np.random.normal(size=len(vec1)) vec2 += jitter[1] * np.random.normal(size=len(vec2)) # Plot the points keys = ['oo', 'io', 'oi', 'ii'] lak = {'i': 'imp', 'o': 'obs'} ixs = {'ii': ix_ii, 'io': ix_io, 'oi': ix_oi, 'oo': ix_oo} color = {'oo': 'grey', 'ii':'red', 'io': 'orange', 'oi': 'lime'} if plot_points: for ky in keys: ix = ixs[ky] lab = lak[ky[0]] + "/" + lak[ky[1]] ax.plot(vec1[ix], vec2[ix], 'o', color=color[ky], label=lab, alpha=0.6) # Plot the lowess fits for ky in keys: ix = ixs[ky] if len(ix) < lowess_min_n: continue if ky in lowess_args: la = lowess_args[ky] else: la = {} ix = ixs[ky] lfit = lowess(vec2[ix], vec1[ix], **la) if plot_points: ax.plot(lfit[:, 0], lfit[:, 1], '-', color=color[ky], alpha=0.6, lw=4) else: lab = lak[ky[0]] + "/" + lak[ky[1]] ax.plot(lfit[:, 0], lfit[:, 1], '-', color=color[ky], alpha=0.6, lw=4, label=lab) ha, la = ax.get_legend_handles_labels() pad = 0.0001 if plot_points else 0.5 leg = fig.legend(ha, la, 'center right', numpoints=1, handletextpad=pad) leg.draw_frame(False) ax.set_xlabel(col1_name) ax.set_ylabel(col2_name) return fig def plot_fit_obs(self, col_name, lowess_args=None, lowess_min_n=40, jitter=None, plot_points=True, ax=None): """ Plot fitted versus imputed or observed values as a scatterplot. Parameters ---------- col_name : string The variable to be plotted on the horizontal axis. lowess_args : dict-like Keyword arguments passed to lowess fit. A dictionary of dictionaries, keys are 'o' and 'i' denoting 'observed' and 'imputed', respectively. lowess_min_n : integer Minimum sample size to plot a lowess fit jitter : float or tuple Standard deviation for jittering points in the plot. Either a single scalar applied to both axes, or a tuple containing x-axis jitter and y-axis jitter, respectively. plot_points : bool If True, the data points are plotted. ax : matplotlib axes object Axes on which to plot, created if not provided. Returns ------- The matplotlib figure on which the plot is drawn. """ from statsmodels.graphics import utils as gutils from statsmodels.nonparametric.smoothers_lowess import lowess if lowess_args is None: lowess_args = {} if ax is None: fig, ax = gutils.create_mpl_ax(ax) else: fig = ax.get_figure() ax.set_position([0.1, 0.1, 0.7, 0.8]) ixi = self.ix_miss[col_name] ixo = self.ix_obs[col_name] vec1 = np.asarray(self.data[col_name]) # Fitted values formula = self.conditional_formula[col_name] endog, exog = patsy.dmatrices(formula, self.data, return_type="dataframe") results = self.results[col_name] vec2 = results.predict(exog=exog) vec2 = self._get_predicted(vec2) if jitter is not None: if np.isscalar(jitter): jitter = (jitter, jitter) vec1 += jitter[0] * np.random.normal(size=len(vec1)) vec2 += jitter[1] * np.random.normal(size=len(vec2)) # Plot the points keys = ['o', 'i'] ixs = {'o': ixo, 'i': ixi} lak = {'o': 'obs', 'i': 'imp'} color = {'o': 'orange', 'i': 'lime'} if plot_points: for ky in keys: ix = ixs[ky] ax.plot(vec1[ix], vec2[ix], 'o', color=color[ky], label=lak[ky], alpha=0.6) # Plot the lowess fits for ky in keys: ix = ixs[ky] if len(ix) < lowess_min_n: continue if ky in lowess_args: la = lowess_args[ky] else: la = {} ix = ixs[ky] lfit = lowess(vec2[ix], vec1[ix], **la) ax.plot(lfit[:, 0], lfit[:, 1], '-', color=color[ky], alpha=0.6, lw=4, label=lak[ky]) ha, la = ax.get_legend_handles_labels() leg = fig.legend(ha, la, 'center right', numpoints=1) leg.draw_frame(False) ax.set_xlabel(col_name + " observed or imputed") ax.set_ylabel(col_name + " fitted") return fig def plot_imputed_hist(self, col_name, ax=None, imp_hist_args=None, obs_hist_args=None, all_hist_args=None): """ Display imputed values for one variable as a histogram. Parameters ---------- col_name : string The name of the variable to be plotted. ax : matplotlib axes An axes on which to draw the histograms. If not provided, one is created. imp_hist_args : dict Keyword arguments to be passed to pyplot.hist when creating the histogram for imputed values. obs_hist_args : dict Keyword arguments to be passed to pyplot.hist when creating the histogram for observed values. all_hist_args : dict Keyword arguments to be passed to pyplot.hist when creating the histogram for all values. Returns ------- The matplotlib figure on which the histograms were drawn """ from statsmodels.graphics import utils as gutils if imp_hist_args is None: imp_hist_args = {} if obs_hist_args is None: obs_hist_args = {} if all_hist_args is None: all_hist_args = {} if ax is None: fig, ax = gutils.create_mpl_ax(ax) else: fig = ax.get_figure() ax.set_position([0.1, 0.1, 0.7, 0.8]) ixm = self.ix_miss[col_name] ixo = self.ix_obs[col_name] imp = self.data[col_name].iloc[ixm] obs = self.data[col_name].iloc[ixo] for di in imp_hist_args, obs_hist_args, all_hist_args: if 'histtype' not in di: di['histtype'] ='step' ha, la = [], [] if len(imp) > 0: h = ax.hist(np.asarray(imp), **imp_hist_args) ha.append(h[-1][0]) la.append("Imp") h1 = ax.hist(np.asarray(obs), **obs_hist_args) h2 = ax.hist(np.asarray(self.data[col_name]), **all_hist_args) ha.extend([h1[-1][0], h2[-1][0]]) la.extend(["Obs", "All"]) leg = fig.legend(ha, la, 'center right', numpoints=1) leg.draw_frame(False) ax.set_xlabel(col_name) ax.set_ylabel("Frequency") return fig # Try to identify any auxiliary arrays (e.g. status vector in # PHReg) that need to be bootstrapped along with exog and endog. def _boot_kwds(self, kwds, rix): for k in kwds: v = kwds[k] # This is only relevant for ndarrays if not isinstance(v, np.ndarray): continue # Handle 1d vectors if (v.ndim == 1) and (v.shape[0] == len(rix)): kwds[k] = v[rix] # Handle 2d arrays if (v.ndim == 2) and (v.shape[0] == len(rix)): kwds[k] = v[rix, :] return kwds def _perturb_bootstrap(self, vname): """ Perturbs the model's parameters using a bootstrap. """ endog, exog, init_kwds, fit_kwds = self.get_fitting_data(vname) m = len(endog) rix = np.random.randint(0, m, m) endog = endog[rix] exog = exog[rix, :] init_kwds = self._boot_kwds(init_kwds, rix) fit_kwds = self._boot_kwds(fit_kwds, rix) klass = self.model_class[vname] self.models[vname] = klass(endog, exog, **init_kwds) if vname in self.regularized and self.regularized[vname]: self.results[vname] = ( self.models[vname].fit_regularized(**fit_kwds)) else: self.results[vname] = self.models[vname].fit(**fit_kwds) self.params[vname] = self.results[vname].params def _perturb_gaussian(self, vname): """ Gaussian perturbation of model parameters. The normal approximation to the sampling distribution of the parameter estimates is used to define the mean and covariance structure of the perturbation distribution. """ endog, exog, init_kwds, fit_kwds = self.get_fitting_data(vname) klass = self.model_class[vname] self.models[vname] = klass(endog, exog, **init_kwds) self.results[vname] = self.models[vname].fit(**fit_kwds) cov = self.results[vname].cov_params() mu = self.results[vname].params self.params[vname] = np.random.multivariate_normal(mean=mu, cov=cov) def perturb_params(self, vname): if self.perturbation_method[vname] == "gaussian": self._perturb_gaussian(vname) elif self.perturbation_method[vname] == "boot": self._perturb_bootstrap(vname) else: raise ValueError("unknown perturbation method") def impute(self, vname): # Wrap this in case we later add additional imputation # methods. self.impute_pmm(vname) def update(self, vname): """ Impute missing values for a single variable. This is a two-step process in which first the parameters are perturbed, then the missing values are re-imputed. Parameters ---------- vname : string The name of the variable to be updated. """ self.perturb_params(vname) self.impute(vname) # work-around for inconsistent predict return values def _get_predicted(self, obj): if isinstance(obj, np.ndarray): return obj elif isinstance(obj, pd.Series): return obj.values elif hasattr(obj, 'predicted_values'): return obj.predicted_values else: raise ValueError( "cannot obtain predicted values from %s" % obj.__class__) def impute_pmm(self, vname): """ Use predictive mean matching to impute missing values. Notes ----- The `perturb_params` method must be called first to define the model. """ k_pmm = self.k_pmm endog_obs, exog_obs, exog_miss, predict_obs_kwds, predict_miss_kwds = ( self.get_split_data(vname)) # Predict imputed variable for both missing and non-missing # observations model = self.models[vname] pendog_obs = model.predict(self.params[vname], exog_obs, **predict_obs_kwds) pendog_miss = model.predict(self.params[vname], exog_miss, **predict_miss_kwds) pendog_obs = self._get_predicted(pendog_obs) pendog_miss = self._get_predicted(pendog_miss) # Jointly sort the observed and predicted endog values for the # cases with observed values. ii = np.argsort(pendog_obs) endog_obs = endog_obs[ii] pendog_obs = pendog_obs[ii] # Find the closest match to the predicted endog values for # cases with missing endog values. ix = np.searchsorted(pendog_obs, pendog_miss) # Get the indices for the closest k_pmm values on # either side of the closest index. ixm = ix[:, None] + np.arange(-k_pmm, k_pmm)[None, :] # Account for boundary effects msk = np.nonzero((ixm < 0) | (ixm > len(endog_obs) - 1)) ixm = np.clip(ixm, 0, len(endog_obs) - 1) # Get the distances dx = pendog_miss[:, None] - pendog_obs[ixm] dx = np.abs(dx) dx[msk] = np.inf # Closest positions in ix, row-wise. dxi = np.argsort(dx, 1)[:, 0:k_pmm] # Choose a column for each row. ir = np.random.randint(0, k_pmm, len(pendog_miss)) # Unwind the indices jj = np.arange(dxi.shape[0]) ix = dxi[(jj, ir)] iz = ixm[(jj, ix)] imputed_miss = np.array(endog_obs[iz]).squeeze() self._store_changes(vname, imputed_miss) _mice_example_1 = """ >>> imp = mice.MICEData(data) >>> fml = 'y ~ x1 + x2 + x3 + x4' >>> mice = mice.MICE(fml, sm.OLS, imp) >>> results = mice.fit(10, 10) >>> print(results.summary()) .. literalinclude::../plots/mice_example_1.txt """ _mice_example_2 = """ >>> imp = mice.MICEData(data) >>> fml = 'y ~ x1 + x2 + x3 + x4' >>> mice = mice.MICE(fml, sm.OLS, imp) >>> results = [] >>> for k in range(10): >>> x = mice.next_sample() >>> results.append(x) """ class MICE(object): __doc__ = """\ Multiple Imputation with Chained Equations. This class can be used to fit most Statsmodels models to data sets with missing values using the'multiple imputation with chained equations' (MICE) approach.. Parameters ---------- model_formula : string The model formula to be fit to the imputed data sets. This formula is for the 'analysis model'. model_class : statsmodels model The model to be fit to the imputed data sets. This model class if for the 'analysis model'. data : MICEData instance MICEData object containing the data set for which missing values will be imputed n_skip : int The number of imputed datasets to skip between consecutive imputed datasets that are used for analysis. init_kwds : dict-like Dictionary of keyword arguments passed to the init method of the analysis model. fit_kwds : dict-like Dictionary of keyword arguments passed to the fit method of the analysis model. Examples -------- Run all MICE steps and obtain results: %(mice_example_1)s Obtain a sequence of fitted analysis models without combining to obtain summary:: %(mice_example_2)s """ % {'mice_example_1': _mice_example_1, 'mice_example_2': _mice_example_2} def __init__(self, model_formula, model_class, data, n_skip=3, init_kwds=None, fit_kwds=None): self.model_formula = model_formula self.model_class = model_class self.n_skip = n_skip self.data = data self.results_list = [] self.init_kwds = init_kwds if init_kwds is not None else {} self.fit_kwds = fit_kwds if fit_kwds is not None else {} def next_sample(self): """ Perform one complete MICE iteration. A single MICE iteration updates all missing values using their respective imputation models, then fits the analysis model to the imputed data. Returns ------- params : array_like The model parameters for the analysis model. Notes ----- This function fits the analysis model and returns its parameter estimate. The parameter vector is not stored by the class and is not used in any subsequent calls to `combine`. Use `fit` to run all MICE steps together and obtain summary results. The complete cycle of missing value imputation followed by fitting the analysis model is repeated `n_skip + 1` times and the analysis model parameters from the final fit are returned. """ # Impute missing values self.data.update_all(self.n_skip + 1) start_params = None if len(self.results_list) > 0: start_params = self.results_list[-1].params # Fit the analysis model. model = self.model_class.from_formula(self.model_formula, self.data.data, **self.init_kwds) self.fit_kwds.update({"start_params": start_params}) result = model.fit(**self.fit_kwds) return result def fit(self, n_burnin=10, n_imputations=10): """ Fit a model using MICE. Parameters ---------- n_burnin : int The number of burn-in cycles to skip. n_imputations : int The number of data sets to impute """ # Run without fitting the analysis model self.data.update_all(n_burnin) for j in range(n_imputations): result = self.next_sample() self.results_list.append(result) self.endog_names = result.model.endog_names self.exog_names = result.model.exog_names return self.combine() def combine(self): """ Pools MICE imputation results. This method can only be used after the `run` method has been called. Returns estimates and standard errors of the analysis model parameters. Returns a MICEResults instance. """ # Extract a few things from the models that were fit to # imputed data sets. params_list = [] cov_within = 0. scale_list = [] for results in self.results_list: results_uw = results._results params_list.append(results_uw.params) cov_within += results_uw.cov_params() scale_list.append(results.scale) params_list = np.asarray(params_list) scale_list = np.asarray(scale_list) # The estimated parameters for the MICE analysis params = params_list.mean(0) # The average of the within-imputation covariances cov_within /= len(self.results_list) # The between-imputation covariance cov_between = np.cov(params_list.T) # The estimated covariance matrix for the MICE analysis f = 1 + 1 / float(len(self.results_list)) cov_params = cov_within + f * cov_between # Fraction of missing information fmi = f * np.diag(cov_between) / np.diag(cov_params) # Set up a results instance scale = np.mean(scale_list) results = MICEResults(self, params, cov_params / scale) results.scale = scale results.frac_miss_info = fmi results.exog_names = self.exog_names results.endog_names = self.endog_names results.model_class = self.model_class return results class MICEResults(LikelihoodModelResults): def __init__(self, model, params, normalized_cov_params): super(MICEResults, self).__init__(model, params, normalized_cov_params) def summary(self, title=None, alpha=.05): """ Summarize the results of running MICE. Parameters ---------- title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float Significance level for the confidence intervals Returns ------- smry : Summary instance This holds the summary tables and text, which can be printed or converted to various output formats. """ from statsmodels.iolib import summary2 from collections import OrderedDict smry = summary2.Summary() float_format = "%8.3f" info = OrderedDict() info["Method:"] = "MICE" info["Model:"] = self.model_class.__name__ info["Dependent variable:"] = self.endog_names info["Sample size:"] = "%d" % self.model.data.data.shape[0] info["Scale"] = "%.2f" % self.scale info["Num. imputations"] = "%d" % len(self.model.results_list) smry.add_dict(info, align='l', float_format=float_format) param = summary2.summary_params(self, alpha=alpha) param["FMI"] = self.frac_miss_info smry.add_df(param, float_format=float_format) smry.add_title(title=title, results=self) return smry

statsmodels__statsmodels

large_data.rst

Tutorial

Working with Large Data Sets

BSD 3-Clause New or Revised License

statsmodels__statsmodels/docs/source/large_data.rst

Working with Large Data Sets Big data is something of a buzzword in the modern world. While statsmodels works well with small and moderately-sized data sets that can be loaded in memory--perhaps tens of thousands of observations--use cases exist with millions of observations or more. Depending your use case, statsmodels may or may not be a sufficient tool. statsmodels and most of the software stack it is written on operates in memory. Resultantly, building models on larger data sets can be challenging or even impractical. With that said, there are 2 general strategies for building models on larger data sets with statsmodels. Divide and Conquer - Distributing Jobs If your system is capable of loading all the data, but the analysis you are attempting to perform is slow, you might be able to build models on horizontal slices of the data and then aggregate the individual models once fit. A current limitation of this approach is that it generally does not support patsy so constructing your design matrix (known as exog) in statsmodels, is a little challenging. Subsetting your data If your entire data set is too large to store in memory, you might try storing it in a columnar container like Apache Parquet or bcolz. Using the patsy formula interface, statsmodels will use the __getitem__ function (i.e. data['Item']) to pull only the specified columns. import pyarrow as pa import pyarrow.parquet as pq import statsmodels.formula.api as smf class DataSet(dict): def __init__(self, path): self.parquet = pq.ParquetFile(path) def __getitem__(self, key): try: return self.parquet.read([key]).to_pandas()[key] except: raise KeyError LargeData = DataSet('LargeData.parquet') res = smf.ols('Profit ~ Sugar + Power + Women', data=LargeData).fit() Additionally, you can add code to this example DataSet object to return only a subset of the rows until you have built a good model. Then, you can refit your final model on more data.

from statsmodels.base.elastic_net import RegularizedResults from statsmodels.stats.regularized_covariance import _calc_nodewise_row, \ _calc_nodewise_weight, _calc_approx_inv_cov from statsmodels.base.model import LikelihoodModelResults from statsmodels.regression.linear_model import OLS import numpy as np """ Distributed estimation routines. Currently, we support several methods of distribution - sequential, has no extra dependencies - parallel - with joblib A variety of backends are supported through joblib This allows for different types of clusters besides standard local clusters. Some examples of backends supported by joblib are - dask.distributed - yarn - ipyparallel The framework is very general and allows for a variety of estimation methods. Currently, these include - debiased regularized estimation - simple coefficient averaging (naive) - regularized - unregularized Currently, the default is regularized estimation with debiasing which follows the methods outlined in Jason D. Lee, Qiang Liu, Yuekai Sun and Jonathan E. Taylor. "Communication-Efficient Sparse Regression: A One-Shot Approach." arXiv:1503.04337. 2015. https://arxiv.org/abs/1503.04337. There are several variables that are taken from the source paper for which the interpretation may not be directly clear from the code, these are mostly used to help form the estimate of the approximate inverse covariance matrix as part of the debiasing procedure. wexog A weighted design matrix used to perform the node-wise regression procedure. nodewise_row nodewise_row is produced as part of the node-wise regression procedure used to produce the approximate inverse covariance matrix. One is produced for each variable using the LASSO. nodewise_weight nodewise_weight is produced using the gamma_hat values for each p to produce weights to reweight the gamma_hat values which are ultimately used to form approx_inv_cov. approx_inv_cov This is the estimate of the approximate inverse covariance matrix. This is used to debiase the coefficient average along with the average gradient. For the OLS case, approx_inv_cov is an approximation for n * (X^T X)^{-1} formed by node-wise regression. """ def _est_regularized_naive(mod, pnum, partitions, fit_kwds=None): """estimates the regularized fitted parameters. Parameters ---------- mod : statsmodels model class instance The model for the current partition. pnum : scalar Index of current partition partitions : scalar Total number of partitions fit_kwds : dict-like or None Keyword arguments to be given to fit_regularized Returns ------- An array of the paramters for the regularized fit """ if fit_kwds is None: raise ValueError("_est_regularized_naive currently " + "requires that fit_kwds not be None.") return mod.fit_regularized(**fit_kwds).params def _est_unregularized_naive(mod, pnum, partitions, fit_kwds=None): """estimates the unregularized fitted parameters. Parameters ---------- mod : statsmodels model class instance The model for the current partition. pnum : scalar Index of current partition partitions : scalar Total number of partitions fit_kwds : dict-like or None Keyword arguments to be given to fit Returns ------- An array of the parameters for the fit """ if fit_kwds is None: raise ValueError("_est_unregularized_naive currently " + "requires that fit_kwds not be None.") return mod.fit(**fit_kwds).params def _join_naive(params_l, threshold=0): """joins the results from each run of _est_<type>_naive and returns the mean estimate of the coefficients Parameters ---------- params_l : list A list of arrays of coefficients. threshold : scalar The threshold at which the coefficients will be cut. """ p = len(params_l[0]) partitions = len(params_l) params_mn = np.zeros(p) for params in params_l: params_mn += params params_mn /= partitions params_mn[np.abs(params_mn) < threshold] = 0 return params_mn def _calc_grad(mod, params, alpha, L1_wt, score_kwds): """calculates the log-likelihood gradient for the debiasing Parameters ---------- mod : statsmodels model class instance The model for the current partition. params : array_like The estimated coefficients for the current partition. alpha : scalar or array_like The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as `params`, and contains a penalty weight for each coefficient. L1_wt : scalar The fraction of the penalty given to the L1 penalty term. Must be between 0 and 1 (inclusive). If 0, the fit is a ridge fit, if 1 it is a lasso fit. score_kwds : dict-like or None Keyword arguments for the score function. Returns ------- An array-like object of the same dimension as params Notes ----- In general: gradient l_k(params) where k corresponds to the index of the partition For OLS: X^T(y - X^T params) """ grad = -mod.score(np.asarray(params), **score_kwds) grad += alpha * (1 - L1_wt) return grad def _calc_wdesign_mat(mod, params, hess_kwds): """calculates the weighted design matrix necessary to generate the approximate inverse covariance matrix Parameters ---------- mod : statsmodels model class instance The model for the current partition. params : array_like The estimated coefficients for the current partition. hess_kwds : dict-like or None Keyword arguments for the hessian function. Returns ------- An array-like object, updated design matrix, same dimension as mod.exog """ rhess = np.sqrt(mod.hessian_factor(np.asarray(params), **hess_kwds)) return rhess[:, None] * mod.exog def _est_regularized_debiased(mod, mnum, partitions, fit_kwds=None, score_kwds=None, hess_kwds=None): """estimates the regularized fitted parameters, is the default estimation_method for class DistributedModel. Parameters ---------- mod : statsmodels model class instance The model for the current partition. mnum : scalar Index of current partition. partitions : scalar Total number of partitions. fit_kwds : dict-like or None Keyword arguments to be given to fit_regularized score_kwds : dict-like or None Keyword arguments for the score function. hess_kwds : dict-like or None Keyword arguments for the Hessian function. Returns ------- A tuple of parameters for regularized fit An array-like object of the fitted parameters, params An array-like object for the gradient A list of array like objects for nodewise_row A list of array like objects for nodewise_weight """ score_kwds = {} if score_kwds is None else score_kwds hess_kwds = {} if hess_kwds is None else hess_kwds if fit_kwds is None: raise ValueError("_est_regularized_debiased currently " + "requires that fit_kwds not be None.") else: alpha = fit_kwds["alpha"] if "L1_wt" in fit_kwds: L1_wt = fit_kwds["L1_wt"] else: L1_wt = 1 nobs, p = mod.exog.shape p_part = int(np.ceil((1. * p) / partitions)) params = mod.fit_regularized(**fit_kwds).params grad = _calc_grad(mod, params, alpha, L1_wt, score_kwds) / nobs wexog = _calc_wdesign_mat(mod, params, hess_kwds) nodewise_row_l = [] nodewise_weight_l = [] for idx in range(mnum * p_part, min((mnum + 1) * p_part, p)): nodewise_row = _calc_nodewise_row(wexog, idx, alpha) nodewise_row_l.append(nodewise_row) nodewise_weight = _calc_nodewise_weight(wexog, nodewise_row, idx, alpha) nodewise_weight_l.append(nodewise_weight) return params, grad, nodewise_row_l, nodewise_weight_l def _join_debiased(results_l, threshold=0): """joins the results from each run of _est_regularized_debiased and returns the debiased estimate of the coefficients Parameters ---------- results_l : list A list of tuples each one containing the params, grad, nodewise_row and nodewise_weight values for each partition. threshold : scalar The threshold at which the coefficients will be cut. """ p = len(results_l[0][0]) partitions = len(results_l) params_mn = np.zeros(p) grad_mn = np.zeros(p) nodewise_row_l = [] nodewise_weight_l = [] for r in results_l: params_mn += r[0] grad_mn += r[1] nodewise_row_l.extend(r[2]) nodewise_weight_l.extend(r[3]) nodewise_row_l = np.array(nodewise_row_l) nodewise_weight_l = np.array(nodewise_weight_l) params_mn /= partitions grad_mn *= -1. / partitions approx_inv_cov = _calc_approx_inv_cov(nodewise_row_l, nodewise_weight_l) debiased_params = params_mn + approx_inv_cov.dot(grad_mn) debiased_params[np.abs(debiased_params) < threshold] = 0 return debiased_params def _helper_fit_partition(self, pnum, endog, exog, fit_kwds, init_kwds_e={}): """handles the model fitting for each machine. NOTE: this is primarily handled outside of DistributedModel because joblib can't handle class methods. Parameters ---------- self : DistributedModel class instance An instance of DistributedModel. pnum : scalar index of current partition. endog : array_like endogenous data for current partition. exog : array_like exogenous data for current partition. fit_kwds : dict-like Keywords needed for the model fitting. init_kwds_e : dict-like Additional init_kwds to add for each partition. Returns ------- estimation_method result. For the default, _est_regularized_debiased, a tuple. """ temp_init_kwds = self.init_kwds.copy() temp_init_kwds.update(init_kwds_e) model = self.model_class(endog, exog, **temp_init_kwds) results = self.estimation_method(model, pnum, self.partitions, fit_kwds=fit_kwds, **self.estimation_kwds) return results class DistributedModel(object): __doc__ = """ Distributed model class Parameters ---------- partitions : scalar The number of partitions that the data will be split into. model_class : statsmodels model class The model class which will be used for estimation. If None this defaults to OLS. init_kwds : dict-like or None Keywords needed for initializing the model, in addition to endog and exog. init_kwds_generator : generator or None Additional keyword generator that produces model init_kwds that may vary based on data partition. The current usecase is for WLS and GLS estimation_method : function or None The method that performs the estimation for each partition. If None this defaults to _est_regularized_debiased. estimation_kwds : dict-like or None Keywords to be passed to estimation_method. join_method : function or None The method used to recombine the results from each partition. If None this defaults to _join_debiased. join_kwds : dict-like or None Keywords to be passed to join_method. results_class : results class or None The class of results that should be returned. If None this defaults to RegularizedResults. results_kwds : dict-like or None Keywords to be passed to results class. Attributes ---------- partitions : scalar See Parameters. model_class : statsmodels model class See Parameters. init_kwds : dict-like See Parameters. init_kwds_generator : generator or None See Parameters. estimation_method : function See Parameters. estimation_kwds : dict-like See Parameters. join_method : function See Parameters. join_kwds : dict-like See Parameters. results_class : results class See Parameters. results_kwds : dict-like See Parameters. Examples -------- Notes ----- """ def __init__(self, partitions, model_class=None, init_kwds=None, estimation_method=None, estimation_kwds=None, join_method=None, join_kwds=None, results_class=None, results_kwds=None): self.partitions = partitions if model_class is None: self.model_class = OLS else: self.model_class = model_class if init_kwds is None: self.init_kwds = {} else: self.init_kwds = init_kwds if estimation_method is None: self.estimation_method = _est_regularized_debiased else: self.estimation_method = estimation_method if estimation_kwds is None: self.estimation_kwds = {} else: self.estimation_kwds = estimation_kwds if join_method is None: self.join_method = _join_debiased else: self.join_method = join_method if join_kwds is None: self.join_kwds = {} else: self.join_kwds = join_kwds if results_class is None: self.results_class = RegularizedResults else: self.results_class = results_class if results_kwds is None: self.results_kwds = {} else: self.results_kwds = results_kwds def fit(self, data_generator, fit_kwds=None, parallel_method="sequential", parallel_backend=None, init_kwds_generator=None): """Performs the distributed estimation using the corresponding DistributedModel Parameters ---------- data_generator : generator A generator that produces a sequence of tuples where the first element in the tuple corresponds to an endog array and the element corresponds to an exog array. fit_kwds : dict-like or None Keywords needed for the model fitting. parallel_method : str type of distributed estimation to be used, currently "sequential", "joblib" and "dask" are supported. parallel_backend : None or joblib parallel_backend object used to allow support for more complicated backends, ex: dask.distributed init_kwds_generator : generator or None Additional keyword generator that produces model init_kwds that may vary based on data partition. The current usecase is for WLS and GLS Returns ------- join_method result. For the default, _join_debiased, it returns a p length array. """ if fit_kwds is None: fit_kwds = {} if parallel_method == "sequential": results_l = self.fit_sequential(data_generator, fit_kwds, init_kwds_generator) elif parallel_method == "joblib": results_l = self.fit_joblib(data_generator, fit_kwds, parallel_backend, init_kwds_generator) else: raise ValueError("parallel_method: %s is currently not supported" % parallel_method) params = self.join_method(results_l, **self.join_kwds) # NOTE that currently, the dummy result model that is initialized # here does not use any init_kwds from the init_kwds_generator event # if it is provided. It is possible to imagine an edge case where # this might be a problem but given that the results model instance # does not correspond to any data partition this seems reasonable. res_mod = self.model_class([0], [0], **self.init_kwds) return self.results_class(res_mod, params, **self.results_kwds) def fit_sequential(self, data_generator, fit_kwds, init_kwds_generator=None): """Sequentially performs the distributed estimation using the corresponding DistributedModel Parameters ---------- data_generator : generator A generator that produces a sequence of tuples where the first element in the tuple corresponds to an endog array and the element corresponds to an exog array. fit_kwds : dict-like Keywords needed for the model fitting. init_kwds_generator : generator or None Additional keyword generator that produces model init_kwds that may vary based on data partition. The current usecase is for WLS and GLS Returns ------- join_method result. For the default, _join_debiased, it returns a p length array. """ results_l = [] if init_kwds_generator is None: for pnum, (endog, exog) in enumerate(data_generator): results = _helper_fit_partition(self, pnum, endog, exog, fit_kwds) results_l.append(results) else: tup_gen = enumerate(zip(data_generator, init_kwds_generator)) for pnum, ((endog, exog), init_kwds_e) in tup_gen: results = _helper_fit_partition(self, pnum, endog, exog, fit_kwds, init_kwds_e) results_l.append(results) return results_l def fit_joblib(self, data_generator, fit_kwds, parallel_backend, init_kwds_generator=None): """Performs the distributed estimation in parallel using joblib Parameters ---------- data_generator : generator A generator that produces a sequence of tuples where the first element in the tuple corresponds to an endog array and the element corresponds to an exog array. fit_kwds : dict-like Keywords needed for the model fitting. parallel_backend : None or joblib parallel_backend object used to allow support for more complicated backends, ex: dask.distributed init_kwds_generator : generator or None Additional keyword generator that produces model init_kwds that may vary based on data partition. The current usecase is for WLS and GLS Returns ------- join_method result. For the default, _join_debiased, it returns a p length array. """ from statsmodels.tools.parallel import parallel_func par, f, n_jobs = parallel_func(_helper_fit_partition, self.partitions) if parallel_backend is None and init_kwds_generator is None: results_l = par(f(self, pnum, endog, exog, fit_kwds) for pnum, (endog, exog) in enumerate(data_generator)) elif parallel_backend is not None and init_kwds_generator is None: with parallel_backend: results_l = par(f(self, pnum, endog, exog, fit_kwds) for pnum, (endog, exog) in enumerate(data_generator)) elif parallel_backend is None and init_kwds_generator is not None: tup_gen = enumerate(zip(data_generator, init_kwds_generator)) results_l = par(f(self, pnum, endog, exog, fit_kwds, init_kwds) for pnum, ((endog, exog), init_kwds) in tup_gen) elif parallel_backend is not None and init_kwds_generator is not None: tup_gen = enumerate(zip(data_generator, init_kwds_generator)) with parallel_backend: results_l = par(f(self, pnum, endog, exog, fit_kwds, init_kwds) for pnum, ((endog, exog), init_kwds) in tup_gen) return results_l class DistributedResults(LikelihoodModelResults): """ Class to contain model results Parameters ---------- model : class instance class instance for model used for distributed data, this particular instance uses fake data and is really only to allow use of methods like predict. params : array parameter estimates from the fit model. """ def __init__(self, model, params): super(DistributedResults, self).__init__(model, params) def predict(self, exog, *args, **kwargs): """Calls self.model.predict for the provided exog. See Results.predict. Parameters ---------- exog : array_like NOT optional The values for which we want to predict, unlike standard predict this is NOT optional since the data in self.model is fake. args, kwargs : Some models can take additional arguments or keywords, see the predict method of the model for the details. Returns ------- prediction : ndarray, pandas.Series or pandas.DataFrame See self.model.predict """ return self.model.predict(self.params, exog, *args, **kwargs)

statsmodels__statsmodels

miscmodels.rst

Description

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BSD 3-Clause New or Revised License

statsmodels__statsmodels/docs/source/miscmodels.rst

statsmodels__statsmodels/statsmodels/miscmodels

Other Models miscmodels statsmodels.miscmodels contains model classes and that do not yet fit into any other category, or are basic implementations that are not yet polished and will most likely still change. Some of these models were written as examples for the generic maximum likelihood framework, and there will be others that might be based on general method of moments. The models in this category have been checked for basic cases, but might be more exposed to numerical problems than the complete implementation. For example, count.Poisson has been added using only the generic maximum likelihood framework, the standard errors are based on the numerical evaluation of the Hessian, while discretemod.Poisson uses analytical Gradients and Hessian and will be more precise, especially in cases when there is strong multicollinearity. On the other hand, by subclassing GenericLikelihoodModel, it is easy to add new models, another example can be seen in the zero inflated Poisson model, miscmodels.count.

"""Linear Model with Student-t distributed errors Because the t distribution has fatter tails than the normal distribution, it can be used to model observations with heavier tails and observations that have some outliers. For the latter case, the t-distribution provides more robust estimators for mean or mean parameters (what about var?). References ---------- Kenneth L. Lange, Roderick J. A. Little, Jeremy M. G. Taylor (1989) Robust Statistical Modeling Using the t Distribution Journal of the American Statistical Association Vol. 84, No. 408 (Dec., 1989), pp. 881-896 Published by: American Statistical Association Stable URL: http://www.jstor.org/stable/2290063 not read yet Created on 2010-09-24 Author: josef-pktd License: BSD TODO ---- * add starting values based on OLS * bugs: store_params doesn't seem to be defined, I think this was a module global for debugging - commented out * parameter restriction: check whether version with some fixed parameters works """ #mostly copied from the examples directory written for trying out generic mle. import numpy as np from scipy import special, stats from statsmodels.base.model import GenericLikelihoodModel from statsmodels.tsa.arma_mle import Arma #redefine some shortcuts np_log = np.log np_pi = np.pi sps_gamln = special.gammaln class TLinearModel(GenericLikelihoodModel): '''Maximum Likelihood Estimation of Linear Model with t-distributed errors This is an example for generic MLE. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation. ''' def initialize(self): # TODO: here or in __init__ self.k_vars = self.exog.shape[1] if not hasattr(self, 'fix_df'): self.fix_df = False if self.fix_df is False: # df will be estimated, no parameter restrictions self.fixed_params = None self.fixed_paramsmask = None self.k_params = self.exog.shape[1] + 2 extra_params_names = ['df','scale'] else: # df fixed self.k_params = self.exog.shape[1] + 1 fixdf = np.nan * np.zeros(self.exog.shape[1] + 2) fixdf[-2] = self.fix_df self.fixed_params = fixdf self.fixed_paramsmask = np.isnan(fixdf) extra_params_names = ['scale'] self._set_extra_params_names(extra_params_names) self._set_start_params() super(TLinearModel, self).initialize() def _set_start_params(self, start_params=None, use_kurtosis=False): if start_params is not None: self.start_params = start_params else: from statsmodels.regression.linear_model import OLS res_ols = OLS(self.endog, self.exog).fit() start_params = 0.1*np.ones(self.k_params) start_params[:self.k_vars] = res_ols.params if self.fix_df is False: if use_kurtosis: kurt = stats.kurtosis(res_ols.resid) df = 6./kurt + 4 else: df = 5 start_params[-2] = df #TODO adjust scale for df start_params[-1] = np.sqrt(res_ols.scale) self.start_params = start_params def loglike(self, params): return -self.nloglikeobs(params).sum(0) def nloglikeobs(self, params): """ Loglikelihood of linear model with t distributed errors. Parameters ---------- params : array The parameters of the model. The last 2 parameters are degrees of freedom and scale. Returns ------- loglike : array The log likelihood of the model evaluated at `params` for each observation defined by self.endog and self.exog. Notes ----- .. math:: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] The t distribution is the standard t distribution and not a standardized t distribution, which means that the scale parameter is not equal to the standard deviation. self.fixed_params and self.expandparams can be used to fix some parameters. (I doubt this has been tested in this model.) """ #print len(params), #store_params.append(params) if self.fixed_params is not None: #print 'using fixed' params = self.expandparams(params) beta = params[:-2] df = params[-2] scale = np.abs(params[-1]) #TODO check behavior around zero loc = np.dot(self.exog, beta) endog = self.endog x = (endog - loc)/scale #next part is stats.t._logpdf lPx = sps_gamln((df+1)/2) - sps_gamln(df/2.) lPx -= 0.5*np_log(df*np_pi) + (df+1)/2.*np_log(1+(x**2)/df) lPx -= np_log(scale) # correction for scale return -lPx def predict(self, params, exog=None): if exog is None: exog = self.exog return np.dot(exog, params[:self.exog.shape[1]]) class TArma(Arma): '''Univariate Arma Model with t-distributed errors This inherit all methods except loglike from tsa.arma_mle.Arma This uses the standard t-distribution, the implied variance of the error is not equal to scale, but :: error_variance = df/(df-2)*scale**2 Notes ----- This might be replaced by a standardized t-distribution with scale**2 equal to variance ''' def loglike(self, params): return -self.nloglikeobs(params).sum(0) #add for Jacobian calculation bsejac in GenericMLE, copied from loglike def nloglikeobs(self, params): """ Loglikelihood for arma model for each observation, t-distribute Notes ----- The ancillary parameter is assumed to be the last element of the params vector """ errorsest = self.geterrors(params[:-2]) #sigma2 = np.maximum(params[-1]**2, 1e-6) #do I need this #axis = 0 #nobs = len(errorsest) df = params[-2] scale = np.abs(params[-1]) llike = - stats.t._logpdf(errorsest/scale, df) + np_log(scale) return llike #TODO rename fit_mle -> fit, fit -> fit_ls def fit_mle(self, order, start_params=None, method='nm', maxiter=5000, tol=1e-08, **kwds): nar, nma = order if start_params is not None: if len(start_params)!= nar + nma + 2: raise ValueError('start_param need sum(order) + 2 elements') else: start_params = np.concatenate((0.05*np.ones(nar + nma), [5, 1])) res = super(TArma, self).fit_mle(order=order, start_params=start_params, method=method, maxiter=maxiter, tol=tol, **kwds) return res # -*- coding: utf-8 -*- """ Created on Mon Jul 26 08:34:59 2010 Author: josef-pktd changes: added offset and zero-inflated version of Poisson - kind of ok, need better test cases, - a nan in ZIP bse, need to check hessian calculations - found error in ZIP loglike - all tests pass with Issues ------ * If true model is not zero-inflated then numerical Hessian for ZIP has zeros for the inflation probability and is not invertible. -> hessian inverts and bse look ok if row and column are dropped, pinv also works * GenericMLE: still get somewhere (where?) "CacheWriteWarning: The attribute 'bse' cannot be overwritten" * bfgs is too fragile, doesn't come back * `nm` is slow but seems to work * need good start_params and their use in genericmle needs to be checked for consistency, set as attribute or method (called as attribute) * numerical hessian needs better scaling * check taking parts out of the loop, e.g. factorial(endog) could be precalculated """ import numpy as np from scipy import stats from scipy.special import factorial from statsmodels.base.model import GenericLikelihoodModel def maxabs(arr1, arr2): return np.max(np.abs(arr1 - arr2)) def maxabsrel(arr1, arr2): return np.max(np.abs(arr2 / arr1 - 1)) class NonlinearDeltaCov(object): '''Asymptotic covariance by Deltamethod the function is designed for 2d array, with rows equal to the number of equations and columns equal to the number of parameters. 1d params work by chance? fun: R^{m*k) -> R^{m} where m is number of equations and k is the number of parameters. equations follow Greene ''' def __init__(self, fun, params, cov_params): self.fun = fun self.params = params self.cov_params = cov_params def grad(self, params=None, **kwds): if params is None: params = self.params kwds.setdefault('epsilon', 1e-4) from statsmodels.tools.numdiff import approx_fprime return approx_fprime(params, self.fun, **kwds) def cov(self): g = self.grad() covar = np.dot(np.dot(g, self.cov_params), g.T) return covar def expected(self): # rename: misnomer, this is the MLE of the fun return self.fun(self.params) def wald(self, value): m = self.expected() v = self.cov() df = np.size(m) diff = m - value lmstat = np.dot(np.dot(diff.T, np.linalg.inv(v)), diff) return lmstat, stats.chi2.sf(lmstat, df) class PoissonGMLE(GenericLikelihoodModel): '''Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation. ''' # copied from discretemod.Poisson def nloglikeobs(self, params): """ Loglikelihood of Poisson model Parameters ---------- params : array_like The parameters of the model. Returns ------- The log likelihood of the model evaluated at `params` Notes -------- .. math:: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] """ XB = np.dot(self.exog, params) endog = self.endog return np.exp(XB) - endog*XB + np.log(factorial(endog)) def predict_distribution(self, exog): '''return frozen scipy.stats distribution with mu at estimated prediction ''' if not hasattr(self, "result"): # TODO: why would this be ValueError instead of AttributeError? # TODO: Why even make this a Model attribute in the first place? # It belongs on the Results class raise ValueError else: result = self.result params = result.params mu = np.exp(np.dot(exog, params)) return stats.poisson(mu, loc=0) class PoissonOffsetGMLE(GenericLikelihoodModel): '''Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation. ''' def __init__(self, endog, exog=None, offset=None, missing='none', **kwds): # let them be none in case user wants to use inheritance if offset is not None: if offset.ndim == 1: offset = offset[:,None] #need column self.offset = offset.ravel() else: self.offset = 0. super(PoissonOffsetGMLE, self).__init__(endog, exog, missing=missing, **kwds) #this was added temporarily for bug-hunting, but shouldn't be needed # def loglike(self, params): # return -self.nloglikeobs(params).sum(0) # original copied from discretemod.Poisson def nloglikeobs(self, params): """ Loglikelihood of Poisson model Parameters ---------- params : array_like The parameters of the model. Returns ------- The log likelihood of the model evaluated at `params` Notes -------- .. math:: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] """ XB = self.offset + np.dot(self.exog, params) endog = self.endog nloglik = np.exp(XB) - endog*XB + np.log(factorial(endog)) return nloglik class PoissonZiGMLE(GenericLikelihoodModel): '''Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset and zero-inflation. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation. There are numerical problems if there is no zero-inflation. ''' def __init__(self, endog, exog=None, offset=None, missing='none', **kwds): # let them be none in case user wants to use inheritance super(PoissonZiGMLE, self).__init__(endog, exog, missing=missing, **kwds) if offset is not None: if offset.ndim == 1: offset = offset[:,None] #need column self.offset = offset.ravel() #which way? else: self.offset = 0. #TODO: it's not standard pattern to use default exog if exog is None: self.exog = np.ones((self.nobs,1)) self.nparams = self.exog.shape[1] #what's the shape in regression for exog if only constant self.start_params = np.hstack((np.ones(self.nparams), 0)) self.cloneattr = ['start_params'] #needed for t_test and summary self.exog_names.append('zi') # original copied from discretemod.Poisson def nloglikeobs(self, params): """ Loglikelihood of Poisson model Parameters ---------- params : array_like The parameters of the model. Returns ------- The log likelihood of the model evaluated at `params` Notes -------- .. math:: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] """ beta = params[:-1] gamm = 1 / (1 + np.exp(params[-1])) #check this # replace with np.dot(self.exogZ, gamma) #print(np.shape(self.offset), self.exog.shape, beta.shape XB = self.offset + np.dot(self.exog, beta) endog = self.endog nloglik = -np.log(1-gamm) + np.exp(XB) - endog*XB + np.log(factorial(endog)) nloglik[endog==0] = - np.log(gamm + np.exp(-nloglik[endog==0])) return nloglik

statsmodels__statsmodels

mixed_glm.rst

Description

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BSD 3-Clause New or Revised License

statsmodels__statsmodels/docs/source/mixed_glm.rst

Generalized Linear Mixed Effects Models Generalized Linear Mixed Effects (GLIMMIX) models are generalized linear models with random effects in the linear predictors. Statsmodels currently supports estimation of binomial and Poisson GLIMMIX models using two Bayesian methods: the Laplace approximation to the posterior, and a variational Bayes approximation to the posterior. Both methods provide point estimates (posterior means) and assessments of uncertainty (posterior standard deviation). The current implementation only supports independent random effects. Technical Documentation Unlike Statsmodels mixed linear models, the GLIMMIX implementation is not group-based. Groups are created by interacting all random effects with a categorical variable. Note that this creates large, sparse random effects design matrices exog_vc. Internally, exog_vc is converted to a scipy sparse matrix. When passing the arguments directly to the class initializer, a sparse matrix may be passed. When using formulas, a dense matrix is created then converted to sparse. For very large problems, it may not be feasible to use formulas due to the size of this dense intermediate matrix.

r""" Bayesian inference for generalized linear mixed models. Currently only families without additional scale or shape parameters are supported (binomial and Poisson). Two estimation approaches are supported: Laplace approximation ('maximum a posteriori'), and variational Bayes (mean field approximation to the posterior distribution). All realizations of random effects are modeled to be mutually independent in this implementation. The `exog_vc` matrix is the design matrix for the random effects. Every column of `exog_vc` corresponds to an independent realization of a random effect. These random effects have mean zero and an unknown standard deviation. The standard deviation parameters are constrained to be equal within subsets of the columns. When not using formulas, these subsets are specified through the parameter `ident`. `ident` must have the same length as the number of columns of `exog_vc`, and two columns whose `ident` values are equal have the same standard deviation. When formulas are used, the columns of `exog_vc` derived from a common formula are constrained to have the same standard deviation. In many applications, `exog_vc` will be sparse. A sparse matrix may be passed when constructing a model class. If a dense matrix is passed, it will be converted internally to a sparse matrix. There currently is no way to avoid creating a temporary dense version of `exog_vc` when using formulas. Model and parameterization -------------------------- The joint density of data and parameters factors as: .. math:: p(y | vc, fep) p(vc | vcp) p(vcp) p(fe) The terms :math:`p(vcp)` and :math:`p(fe)` are prior distributions that are taken to be Gaussian (the :math:`vcp` parameters are log standard deviations so the standard deviations have log-normal distributions). The random effects distribution :math:`p(vc | vcp)` is independent Gaussian (random effect realizations are independent within and between values of the `ident` array). The model :math:`p(y | vc, fep)` depends on the specific GLM being fit. """ import numpy as np from scipy.optimize import minimize from scipy import sparse import statsmodels.base.model as base from statsmodels.iolib import summary2 from statsmodels.genmod import families import pandas as pd import warnings import patsy # Gauss-Legendre weights glw = [ [0.2955242247147529, -0.1488743389816312], [0.2955242247147529, 0.1488743389816312], [0.2692667193099963, -0.4333953941292472], [0.2692667193099963, 0.4333953941292472], [0.2190863625159820, -0.6794095682990244], [0.2190863625159820, 0.6794095682990244], [0.1494513491505806, -0.8650633666889845], [0.1494513491505806, 0.8650633666889845], [0.0666713443086881, -0.9739065285171717], [0.0666713443086881, 0.9739065285171717], ] _init_doc = r""" Fit a generalized linear mixed model using Bayesian methods. The class implements the Laplace approximation to the posterior distribution (`fit_map`) and a variational Bayes approximation to the posterior (`fit_vb`). See the two fit method docstrings for more information about the fitting approaches. Parameters ---------- endog : array_like Vector of response values. exog : array_like Array of covariates for the fixed effects part of the mean structure. exog_vc : array_like Array of covariates for the random part of the model. A scipy.sparse array may be provided, or else the passed array will be converted to sparse internally. ident : array_like Array of integer labels showing which random terms (columns of `exog_vc`) have a common variance. vcp_p : float Prior standard deviation for variance component parameters (the prior standard deviation of log(s) is vcp_p, where s is the standard deviation of a random effect). fe_p : float Prior standard deviation for fixed effects parameters. family : statsmodels.genmod.families instance The GLM family. fep_names : list of strings The names of the fixed effects parameters (corresponding to columns of exog). If None, default names are constructed. vcp_names : list of strings The names of the variance component parameters (corresponding to distinct labels in ident). If None, default names are constructed. vc_names : list of strings The names of the random effect realizations. Returns ------- MixedGLMResults object Notes ----- There are three types of values in the posterior distribution: fixed effects parameters (fep), corresponding to the columns of `exog`, random effects realizations (vc), corresponding to the columns of `exog_vc`, and the standard deviations of the random effects realizations (vcp), corresponding to the unique integer labels in `ident`. All random effects are modeled as being independent Gaussian values (given the variance structure parameters). Every column of `exog_vc` has a distinct realized random effect that is used to form the linear predictors. The elements of `ident` determine the distinct variance structure parameters. Two random effect realizations that have the same value in `ident` have the same variance. When fitting with a formula, `ident` is constructed internally (each element of `vc_formulas` yields a distinct label in `ident`). The random effect standard deviation parameters (`vcp`) have log-normal prior distributions with mean 0 and standard deviation `vcp_p`. Note that for some families, e.g. Binomial, the posterior mode may be difficult to find numerically if `vcp_p` is set to too large of a value. Setting `vcp_p` to 0.5 seems to work well. The prior for the fixed effects parameters is Gaussian with mean 0 and standard deviation `fe_p`. Examples --------{example} References ---------- Introduction to generalized linear mixed models: https://stats.idre.ucla.edu/other/mult-pkg/introduction-to-generalized-linear-mixed-models SAS documentation: https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_intromix_a0000000215.htm An assessment of estimation methods for generalized linear mixed models with binary outcomes https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866838/ """ # The code in the example should be identical to what appears in # the test_doc_examples unit test _logit_example = """ A binomial (logistic) random effects model with random intercepts for villages and random slopes for each year within each village: >>> random = {"a": '0 + C(Village)', "b": '0 + C(Village)*year_cen'} >>> model = BinomialBayesMixedGLM.from_formula( 'y ~ year_cen', random, data) >>> result = model.fit_vb() """ # The code in the example should be identical to what appears in # the test_doc_examples unit test _poisson_example = """ A Poisson random effects model with random intercepts for villages and random slopes for each year within each village: >>> random = {"a": '0 + C(Village)', "b": '0 + C(Village)*year_cen'} >>> model = PoissonBayesMixedGLM.from_formula( 'y ~ year_cen', random, data) >>> result = model.fit_vb() """ class _BayesMixedGLM(base.Model): def __init__(self, endog, exog, exog_vc=None, ident=None, family=None, vcp_p=1, fe_p=2, fep_names=None, vcp_names=None, vc_names=None, **kwargs): if exog.ndim == 1: if isinstance(exog, np.ndarray): exog = exog[:, None] else: exog = pd.DataFrame(exog) if exog.ndim!= 2: msg = "'exog' must have one or two columns" raise ValueError(msg) if exog_vc.ndim == 1: if isinstance(exog_vc, np.ndarray): exog_vc = exog_vc[:, None] else: exog_vc = pd.DataFrame(exog_vc) if exog_vc.ndim!= 2: msg = "'exog_vc' must have one or two columns" raise ValueError(msg) ident = np.asarray(ident) if ident.ndim!= 1: msg = "ident must be a one-dimensional array" raise ValueError(msg) if len(ident)!= exog_vc.shape[1]: msg = "len(ident) should match the number of columns of exog_vc" raise ValueError(msg) if not np.issubdtype(ident.dtype, np.integer): msg = "ident must have an integer dtype" raise ValueError(msg) # Get the fixed effects parameter names if fep_names is None: if hasattr(exog, "columns"): fep_names = exog.columns.tolist() else: fep_names = ["FE_%d" % (k + 1) for k in range(exog.shape[1])] # Get the variance parameter names if vcp_names is None: vcp_names = ["VC_%d" % (k + 1) for k in range(int(max(ident)) + 1)] else: if len(vcp_names)!= len(set(ident)): msg = "The lengths of vcp_names and ident should be the same" raise ValueError(msg) if not sparse.issparse(exog_vc): exog_vc = sparse.csr_matrix(exog_vc) ident = ident.astype(np.int) vcp_p = float(vcp_p) fe_p = float(fe_p) # Number of fixed effects parameters if exog is None: k_fep = 0 else: k_fep = exog.shape[1] # Number of variance component structure parameters and # variance component realizations. if exog_vc is None: k_vc = 0 k_vcp = 0 else: k_vc = exog_vc.shape[1] k_vcp = max(ident) + 1 # power might be better but not available in older scipy exog_vc2 = exog_vc.multiply(exog_vc) super(_BayesMixedGLM, self).__init__(endog, exog, **kwargs) self.exog_vc = exog_vc self.exog_vc2 = exog_vc2 self.ident = ident self.family = family self.k_fep = k_fep self.k_vc = k_vc self.k_vcp = k_vcp self.fep_names = fep_names self.vcp_names = vcp_names self.vc_names = vc_names self.fe_p = fe_p self.vcp_p = vcp_p self.names = fep_names + vcp_names if vc_names is not None: self.names += vc_names def _unpack(self, vec): ii = 0 # Fixed effects parameters fep = vec[:ii + self.k_fep] ii += self.k_fep # Variance component structure parameters (standard # deviations). These are on the log scale. The standard # deviation for random effect j is exp(vcp[ident[j]]). vcp = vec[ii:ii + self.k_vcp] ii += self.k_vcp # Random effect realizations vc = vec[ii:] return fep, vcp, vc def logposterior(self, params): """ The overall log-density: log p(y, fe, vc, vcp). This differs by an additive constant from the log posterior log p(fe, vc, vcp | y). """ fep, vcp, vc = self._unpack(params) # Contributions from p(y | x, vc) lp = 0 if self.k_fep > 0: lp += np.dot(self.exog, fep) if self.k_vc > 0: lp += self.exog_vc.dot(vc) mu = self.family.link.inverse(lp) ll = self.family.loglike(self.endog, mu) if self.k_vc > 0: # Contributions from p(vc | vcp) vcp0 = vcp[self.ident] s = np.exp(vcp0) ll -= 0.5 * np.sum(vc**2 / s**2) + np.sum(vcp0) # Contributions from p(vc) ll -= 0.5 * np.sum(vcp**2 / self.vcp_p**2) # Contributions from p(fep) if self.k_fep > 0: ll -= 0.5 * np.sum(fep**2 / self.fe_p**2) return ll def logposterior_grad(self, params): """ The gradient of the log posterior. """ fep, vcp, vc = self._unpack(params) lp = 0 if self.k_fep > 0: lp += np.dot(self.exog, fep) if self.k_vc > 0: lp += self.exog_vc.dot(vc) mu = self.family.link.inverse(lp) score_factor = (self.endog - mu) / self.family.link.deriv(mu) score_factor /= self.family.variance(mu) te = [None, None, None] # Contributions from p(y | x, z, vc) if self.k_fep > 0: te[0] = np.dot(score_factor, self.exog) if self.k_vc > 0: te[2] = self.exog_vc.transpose().dot(score_factor) if self.k_vc > 0: # Contributions from p(vc | vcp) # vcp0 = vcp[self.ident] # s = np.exp(vcp0) # ll -= 0.5 * np.sum(vc**2 / s**2) + np.sum(vcp0) vcp0 = vcp[self.ident] s = np.exp(vcp0) u = vc**2 / s**2 - 1 te[1] = np.bincount(self.ident, weights=u) te[2] -= vc / s**2 # Contributions from p(vcp) # ll -= 0.5 * np.sum(vcp**2 / self.vcp_p**2) te[1] -= vcp / self.vcp_p**2 # Contributions from p(fep) if self.k_fep > 0: te[0] -= fep / self.fe_p**2 te = [x for x in te if x is not None] return np.concatenate(te) def _get_start(self): start_fep = np.zeros(self.k_fep) start_vcp = np.ones(self.k_vcp) start_vc = np.random.normal(size=self.k_vc) start = np.concatenate((start_fep, start_vcp, start_vc)) return start @classmethod def from_formula(cls, formula, vc_formulas, data, family=None, vcp_p=1, fe_p=2): """ Fit a BayesMixedGLM using a formula. Parameters ---------- formula : string Formula for the endog and fixed effects terms (use ~ to separate dependent and independent expressions). vc_formulas : dictionary vc_formulas[name] is a one-sided formula that creates one collection of random effects with a common variance prameter. If using categorical (factor) variables to produce variance components, note that generally `0 +...` should be used so that an intercept is not included. data : data frame The data to which the formulas are applied. family : genmod.families instance A GLM family. vcp_p : float The prior standard deviation for the logarithms of the standard deviations of the random effects. fe_p : float The prior standard deviation for the fixed effects parameters. """ ident = [] exog_vc = [] vcp_names = [] j = 0 for na, fml in vc_formulas.items(): mat = patsy.dmatrix(fml, data, return_type='dataframe') exog_vc.append(mat) vcp_names.append(na) ident.append(j * np.ones(mat.shape[1], dtype=np.integer)) j += 1 exog_vc = pd.concat(exog_vc, axis=1) vc_names = exog_vc.columns.tolist() ident = np.concatenate(ident) model = super(_BayesMixedGLM, cls).from_formula( formula, data=data, family=family, subset=None, exog_vc=exog_vc, ident=ident, vc_names=vc_names, vcp_names=vcp_names, fe_p=fe_p, vcp_p=vcp_p) return model def fit(self, method="BFGS", minim_opts=None): """ fit is equivalent to fit_map. See fit_map for parameter information. Use `fit_vb` to fit the model using variational Bayes. """ self.fit_map(method, minim_opts) def fit_map(self, method="BFGS", minim_opts=None, scale_fe=False): """ Construct the Laplace approximation to the posterior distribution. Parameters ---------- method : string Optimization method for finding the posterior mode. minim_opts : dict-like Options passed to scipy.minimize. scale_fe : bool If True, the columns of the fixed effects design matrix are centered and scaled to unit variance before fitting the model. The results are back-transformed so that the results are presented on the original scale. Returns ------- BayesMixedGLMResults instance. """ if scale_fe: mn = self.exog.mean(0) sc = self.exog.std(0) self._exog_save = self.exog self.exog = self.exog.copy() ixs = np.flatnonzero(sc > 1e-8) self.exog[:, ixs] -= mn[ixs] self.exog[:, ixs] /= sc[ixs] def fun(params): return -self.logposterior(params) def grad(params): return -self.logposterior_grad(params) start = self._get_start() r = minimize(fun, start, method=method, jac=grad, options=minim_opts) if not r.success: msg = ("Laplace fitting did not converge, |gradient|=%.6f" % np.sqrt(np.sum(r.jac**2))) warnings.warn(msg) from statsmodels.tools.numdiff import approx_fprime hess = approx_fprime(r.x, grad) cov = np.linalg.inv(hess) params = r.x if scale_fe: self.exog = self._exog_save del self._exog_save params[ixs] /= sc[ixs] cov[ixs, :][:, ixs] /= np.outer(sc[ixs], sc[ixs]) return BayesMixedGLMResults(self, params, cov, optim_retvals=r) def predict(self, params, exog=None, linear=False): """ Return the fitted mean structure. Parameters ---------- params : array_like The parameter vector, may be the full parameter vector, or may be truncated to include only the mean parameters. exog : array_like The design matrix for the mean structure. If omitted, use the model's design matrix. linear : bool If True, return the linear predictor without passing through the link function. Returns ------- A 1-dimensional array of predicted values """ if exog is None: exog = self.exog q = exog.shape[1] pr = np.dot(exog, params[0:q]) if not linear: pr = self.family.link.inverse(pr) return pr class _VariationalBayesMixedGLM(object): """ A mixin providing generic (not family-specific) methods for variational Bayes mean field fitting. """ # Integration range (from -rng to +rng). The integrals are with # respect to a standard Gaussian distribution so (-5, 5) will be # sufficient in many cases. rng = 5 verbose = False # Returns the mean and variance of the linear predictor under the # given distribution parameters. def _lp_stats(self, fep_mean, fep_sd, vc_mean, vc_sd): tm = np.dot(self.exog, fep_mean) tv = np.dot(self.exog**2, fep_sd**2) tm += self.exog_vc.dot(vc_mean) tv += self.exog_vc2.dot(vc_sd**2) return tm, tv def vb_elbo_base(self, h, tm, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd): """ Returns the evidence lower bound (ELBO) for the model. This function calculates the family-specific ELBO function based on information provided from a subclass. Parameters ---------- h : function mapping 1d vector to 1d vector The contribution of the model to the ELBO function can be expressed as y_i*lp_i + Eh_i(z), where y_i and lp_i are the response and linear predictor for observation i, and z is a standard normal rangom variable. This formulation can be achieved for any GLM with a canonical link function. """ # p(y | vc) contributions iv = 0 for w in glw: z = self.rng * w[1] iv += w[0] * h(z) * np.exp(-z**2 / 2) iv /= np.sqrt(2 * np.pi) iv *= self.rng iv += self.endog * tm iv = iv.sum() # p(vc | vcp) * p(vcp) * p(fep) contributions iv += self._elbo_common(fep_mean, fep_sd, vcp_mean, vcp_sd, vc_mean, vc_sd) r = (iv + np.sum(np.log(fep_sd)) + np.sum(np.log(vcp_sd)) + np.sum( np.log(vc_sd))) return r def vb_elbo_grad_base(self, h, tm, tv, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd): """ Return the gradient of the ELBO function. See vb_elbo_base for parameters. """ fep_mean_grad = 0. fep_sd_grad = 0. vcp_mean_grad = 0. vcp_sd_grad = 0. vc_mean_grad = 0. vc_sd_grad = 0. # p(y | vc) contributions for w in glw: z = self.rng * w[1] u = h(z) * np.exp(-z**2 / 2) / np.sqrt(2 * np.pi) r = u / np.sqrt(tv) fep_mean_grad += w[0] * np.dot(u, self.exog) vc_mean_grad += w[0] * self.exog_vc.transpose().dot(u) fep_sd_grad += w[0] * z * np.dot(r, self.exog**2 * fep_sd) v = self.exog_vc2.multiply(vc_sd).transpose().dot(r) v = np.squeeze(np.asarray(v)) vc_sd_grad += w[0] * z * v fep_mean_grad *= self.rng vc_mean_grad *= self.rng fep_sd_grad *= self.rng vc_sd_grad *= self.rng fep_mean_grad += np.dot(self.endog, self.exog) vc_mean_grad += self.exog_vc.transpose().dot(self.endog) (fep_mean_grad_i, fep_sd_grad_i, vcp_mean_grad_i, vcp_sd_grad_i, vc_mean_grad_i, vc_sd_grad_i) = self._elbo_grad_common( fep_mean, fep_sd, vcp_mean, vcp_sd, vc_mean, vc_sd) fep_mean_grad += fep_mean_grad_i fep_sd_grad += fep_sd_grad_i vcp_mean_grad += vcp_mean_grad_i vcp_sd_grad += vcp_sd_grad_i vc_mean_grad += vc_mean_grad_i vc_sd_grad += vc_sd_grad_i fep_sd_grad += 1 / fep_sd vcp_sd_grad += 1 / vcp_sd vc_sd_grad += 1 / vc_sd mean_grad = np.concatenate((fep_mean_grad, vcp_mean_grad, vc_mean_grad)) sd_grad = np.concatenate((fep_sd_grad, vcp_sd_grad, vc_sd_grad)) if self.verbose: print( "|G|=%f" % np.sqrt(np.sum(mean_grad**2) + np.sum(sd_grad**2))) return mean_grad, sd_grad def fit_vb(self, mean=None, sd=None, fit_method="BFGS", minim_opts=None, scale_fe=False, verbose=False): """ Fit a model using the variational Bayes mean field approximation. Parameters ---------- mean : array_like Starting value for VB mean vector sd : array_like Starting value for VB standard deviation vector fit_method : string Algorithm for scipy.minimize minim_opts : dict-like Options passed to scipy.minimize scale_fe : bool If true, the columns of the fixed effects design matrix are centered and scaled to unit variance before fitting the model. The results are back-transformed so that the results are presented on the original scale. verbose : bool If True, print the gradient norm to the screen each time it is calculated. Notes ----- The goal is to find a factored Gaussian approximation q1*q2*... to the posterior distribution, approximately minimizing the KL divergence from the factored approximation to the actual posterior. The KL divergence, or ELBO function has the form E* log p(y, fe, vcp, vc) - E* log q where E* is expectation with respect to the product of qj. References ---------- Blei, Kucukelbir, McAuliffe (2017). Variational Inference: A review for Statisticians https://arxiv.org/pdf/1601.00670.pdf """ self.verbose = verbose if scale_fe: mn = self.exog.mean(0) sc = self.exog.std(0) self._exog_save = self.exog self.exog = self.exog.copy() ixs = np.flatnonzero(sc > 1e-8) self.exog[:, ixs] -= mn[ixs] self.exog[:, ixs] /= sc[ixs] n = self.k_fep + self.k_vcp + self.k_vc ml = self.k_fep + self.k_vcp + self.k_vc if mean is None: m = np.zeros(n) else: if len(mean)!= ml: raise ValueError( "mean has incorrect length, %d!= %d" % (len(mean), ml)) m = mean.copy() if sd is None: s = -0.5 + 0.1 * np.random.normal(size=n) else: if len(sd)!= ml: raise ValueError( "sd has incorrect length, %d!= %d" % (len(sd), ml)) # s is parameterized on the log-scale internally when # optimizing the ELBO function (this is transparent to the # caller) s = np.log(sd) # Don't allow the variance parameter starting mean values to # be too small. i1, i2 = self.k_fep, self.k_fep + self.k_vcp m[i1:i2] = np.where(m[i1:i2] < -1, -1, m[i1:i2]) # Don't allow the posterior standard deviation starting values # to be too small. s = np.where(s < -1, -1, s) def elbo(x): n = len(x) // 2 return -self.vb_elbo(x[:n], np.exp(x[n:])) def elbo_grad(x): n = len(x) // 2 gm, gs = self.vb_elbo_grad(x[:n], np.exp(x[n:])) gs *= np.exp(x[n:]) return -np.concatenate((gm, gs)) start = np.concatenate((m, s)) mm = minimize( elbo, start, jac=elbo_grad, method=fit_method, options=minim_opts) if not mm.success: warnings.warn("VB fitting did not converge") n = len(mm.x) // 2 params = mm.x[0:n] va = np.exp(2 * mm.x[n:]) if scale_fe: self.exog = self._exog_save del self._exog_save params[ixs] /= sc[ixs] va[ixs] /= sc[ixs]**2 return BayesMixedGLMResults(self, params, va, mm) # Handle terms in the ELBO that are common to all models. def _elbo_common(self, fep_mean, fep_sd, vcp_mean, vcp_sd, vc_mean, vc_sd): iv = 0 # p(vc | vcp) contributions m = vcp_mean[self.ident] s = vcp_sd[self.ident] iv -= np.sum((vc_mean**2 + vc_sd**2) * np.exp(2 * (s**2 - m))) / 2 iv -= np.sum(m) # p(vcp) contributions iv -= 0.5 * (vcp_mean**2 + vcp_sd**2).sum() / self.vcp_p**2 # p(b) contributions iv -= 0.5 * (fep_mean**2 + fep_sd**2).sum() / self.fe_p**2 return iv def _elbo_grad_common(self, fep_mean, fep_sd, vcp_mean, vcp_sd, vc_mean, vc_sd): # p(vc | vcp) contributions m = vcp_mean[self.ident] s = vcp_sd[self.ident] u = vc_mean**2 + vc_sd**2 ve = np.exp(2 * (s**2 - m)) dm = u * ve - 1 ds = -2 * u * ve * s vcp_mean_grad = np.bincount(self.ident, weights=dm) vcp_sd_grad = np.bincount(self.ident, weights=ds) vc_mean_grad = -vc_mean.copy() * ve vc_sd_grad = -vc_sd.copy() * ve # p(vcp) contributions vcp_mean_grad -= vcp_mean / self.vcp_p**2 vcp_sd_grad -= vcp_sd / self.vcp_p**2 # p(b) contributions fep_mean_grad = -fep_mean.copy() / self.fe_p**2 fep_sd_grad = -fep_sd.copy() / self.fe_p**2 return (fep_mean_grad, fep_sd_grad, vcp_mean_grad, vcp_sd_grad, vc_mean_grad, vc_sd_grad) class BayesMixedGLMResults(object): """ Class to hold results from a Bayesian estimation of a Mixed GLM model. Attributes ---------- fe_mean : array_like Posterior mean of the fixed effects coefficients. fe_sd : array_like Posterior standard deviation of the fixed effects coefficients vcp_mean : array_like Posterior mean of the logged variance component standard deviations. vcp_sd : array_like Posterior standard deviation of the logged variance component standard deviations. vc_mean : array_like Posterior mean of the random coefficients vc_sd : array_like Posterior standard deviation of the random coefficients """ def __init__(self, model, params, cov_params, optim_retvals=None): self.model = model self.params = params self._cov_params = cov_params self.optim_retvals = optim_retvals self.fe_mean, self.vcp_mean, self.vc_mean = (model._unpack(params)) if cov_params.ndim == 2: cp = np.diag(cov_params) else: cp = cov_params self.fe_sd, self.vcp_sd, self.vc_sd = model._unpack(cp) self.fe_sd = np.sqrt(self.fe_sd) self.vcp_sd = np.sqrt(self.vcp_sd) self.vc_sd = np.sqrt(self.vc_sd) def cov_params(self): if hasattr(self.model.data, "frame"): # Return the covariance matrix as a dataframe or series na = (self.model.fep_names + self.model.vcp_names + self.model.vc_names) if self._cov_params.ndim == 2: return pd.DataFrame(self._cov_params, index=na, columns=na) else: return pd.Series(self._cov_params, index=na) # Return the covariance matrix as a ndarray return self._cov_params def summary(self): df = pd.DataFrame() m = self.model.k_fep + self.model.k_vcp df["Type"] = (["M" for k in range(self.model.k_fep)] + ["V" for k in range(self.model.k_vcp)]) df["Post. Mean"] = self.params[0:m] if self._cov_params.ndim == 2: v = np.diag(self._cov_params)[0:m] df["Post. SD"] = np.sqrt(v) else: df["Post. SD"] = np.sqrt(self._cov_params[0:m]) # Convert variance parameters to natural scale df["SD"] = np.exp(df["Post. Mean"]) df["SD (LB)"] = np.exp(df["Post. Mean"] - 2 * df["Post. SD"]) df["SD (UB)"] = np.exp(df["Post. Mean"] + 2 * df["Post. SD"]) df["SD"] = ["%.3f" % x for x in df.SD] df["SD (LB)"] = ["%.3f" % x for x in df["SD (LB)"]] df["SD (UB)"] = ["%.3f" % x for x in df["SD (UB)"]] df.loc[df.index < self.model.k_fep, "SD"] = "" df.loc[df.index < self.model.k_fep, "SD (LB)"] = "" df.loc[df.index < self.model.k_fep, "SD (UB)"] = "" df.index = self.model.fep_names + self.model.vcp_names summ = summary2.Summary() summ.add_title(self.model.family.__class__.__name__ + " Mixed GLM Results") summ.add_df(df) summ.add_text("Parameter types are mean structure (M) and " "variance structure (V)") summ.add_text("Variance parameters are modeled as log " "standard deviations") return summ def random_effects(self, term=None): """ Posterior mean and standard deviation of random effects. Parameters ---------- term : int or None If None, results for all random effects are returned. If an integer, returns results for a given set of random effects. The value of `term` refers to an element of the `ident` vector, or to a position in the `vc_formulas` list. Returns ------- Data frame of posterior means and posterior standard deviations of random effects. """ z = self.vc_mean s = self.vc_sd na = self.model.vc_names if term is not None: termix = self.model.vcp_names.index(term) ii = np.flatnonzero(self.model.ident == termix) z = z[ii] s = s[ii] na = [na[i] for i in ii] x = pd.DataFrame({"Mean": z, "SD": s}) if na is not None: x.index = na return x def predict(self, exog=None, linear=False): """ Return predicted values for the mean structure. Parameters ---------- exog : array_like The design matrix for the mean structure. If None, use the model's design matrix. linear : bool If True, returns the linear predictor, otherwise transform the linear predictor using the link function. Returns ------- A one-dimensional array of fitted values. """ return self.model.predict(self.params, exog, linear) class BinomialBayesMixedGLM(_VariationalBayesMixedGLM, _BayesMixedGLM): __doc__ = _init_doc.format(example=_logit_example) def __init__(self, endog, exog, exog_vc, ident, vcp_p=1, fe_p=2, fep_names=None, vcp_names=None, vc_names=None): super(BinomialBayesMixedGLM, self).__init__( endog, exog, exog_vc=exog_vc, ident=ident, vcp_p=vcp_p, fe_p=fe_p, family=families.Binomial(), fep_names=fep_names, vcp_names=vcp_names, vc_names=vc_names) if not np.all(np.unique(endog) == np.r_[0, 1]): msg = "endog values must be 0 and 1, and not all identical" raise ValueError(msg) @classmethod def from_formula(cls, formula, vc_formulas, data, vcp_p=1, fe_p=2): fam = families.Binomial() x = _BayesMixedGLM.from_formula( formula, vc_formulas, data, family=fam, vcp_p=vcp_p, fe_p=fe_p) # Copy over to the intended class structure mod = BinomialBayesMixedGLM( x.endog, x.exog, exog_vc=x.exog_vc, ident=x.ident, vcp_p=x.vcp_p, fe_p=x.fe_p, fep_names=x.fep_names, vcp_names=x.vcp_names, vc_names=x.vc_names) mod.data = x.data return mod def vb_elbo(self, vb_mean, vb_sd): """ Returns the evidence lower bound (ELBO) for the model. """ fep_mean, vcp_mean, vc_mean = self._unpack(vb_mean) fep_sd, vcp_sd, vc_sd = self._unpack(vb_sd) tm, tv = self._lp_stats(fep_mean, fep_sd, vc_mean, vc_sd) def h(z): return -np.log(1 + np.exp(tm + np.sqrt(tv) * z)) return self.vb_elbo_base(h, tm, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd) def vb_elbo_grad(self, vb_mean, vb_sd): """ Returns the gradient of the model's evidence lower bound (ELBO). """ fep_mean, vcp_mean, vc_mean = self._unpack(vb_mean) fep_sd, vcp_sd, vc_sd = self._unpack(vb_sd) tm, tv = self._lp_stats(fep_mean, fep_sd, vc_mean, vc_sd) def h(z): u = tm + np.sqrt(tv) * z x = np.zeros_like(u) ii = np.flatnonzero(u > 0) uu = u[ii] x[ii] = 1 / (1 + np.exp(-uu)) ii = np.flatnonzero(u <= 0) uu = u[ii] x[ii] = np.exp(uu) / (1 + np.exp(uu)) return -x return self.vb_elbo_grad_base(h, tm, tv, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd) class PoissonBayesMixedGLM(_VariationalBayesMixedGLM, _BayesMixedGLM): __doc__ = _init_doc.format(example=_poisson_example) def __init__(self, endog, exog, exog_vc, ident, vcp_p=1, fe_p=2, fep_names=None, vcp_names=None, vc_names=None): super(PoissonBayesMixedGLM, self).__init__( endog=endog, exog=exog, exog_vc=exog_vc, ident=ident, vcp_p=vcp_p, fe_p=fe_p, family=families.Poisson(), fep_names=fep_names, vcp_names=vcp_names, vc_names=vc_names) @classmethod def from_formula(cls, formula, vc_formulas, data, vcp_p=1, fe_p=2, vcp_names=None, vc_names=None): fam = families.Poisson() x = _BayesMixedGLM.from_formula( formula, vc_formulas, data, family=fam, vcp_p=vcp_p, fe_p=fe_p) # Copy over to the intended class structure mod = PoissonBayesMixedGLM( endog=x.endog, exog=x.exog, exog_vc=x.exog_vc, ident=x.ident, vcp_p=x.vcp_p, fe_p=x.fe_p, fep_names=x.fep_names, vcp_names=x.vcp_names, vc_names=x.vc_names) mod.data = x.data return mod def vb_elbo(self, vb_mean, vb_sd): """ Returns the evidence lower bound (ELBO) for the model. """ fep_mean, vcp_mean, vc_mean = self._unpack(vb_mean) fep_sd, vcp_sd, vc_sd = self._unpack(vb_sd) tm, tv = self._lp_stats(fep_mean, fep_sd, vc_mean, vc_sd) def h(z): return -np.exp(tm + np.sqrt(tv) * z) return self.vb_elbo_base(h, tm, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd) def vb_elbo_grad(self, vb_mean, vb_sd): """ Returns the gradient of the model's evidence lower bound (ELBO). """ fep_mean, vcp_mean, vc_mean = self._unpack(vb_mean) fep_sd, vcp_sd, vc_sd = self._unpack(vb_sd) tm, tv = self._lp_stats(fep_mean, fep_sd, vc_mean, vc_sd) def h(z): y = -np.exp(tm + np.sqrt(tv) * z) return y return self.vb_elbo_grad_base(h, tm, tv, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd)

statsmodels__statsmodels

mixed_linear.rst

Description

Generate description to this module

BSD 3-Clause New or Revised License

statsmodels__statsmodels/docs/source/mixed_linear.rst

Linear Mixed Effects Models Linear Mixed Effects models are used for regression analyses involving dependent data. Such data arise when working with longitudinal and other study designs in which multiple observations are made on each subject. Some specific linear mixed effects models are - Random intercepts models, where all responses in a group are additively shifted by a value that is specific to the group. - Random slopes models, where the responses in a group follow a (conditional) mean trajectory that is linear in the observed covariates, with the slopes (and possibly intercepts) varying by group. - Variance components models, where the levels of one or more categorical covariates are associated with draws from distributions. These random terms additively determine the conditional mean of each observation based on its covariate values. The Statsmodels implementation of LME is primarily group-based, meaning that random effects must be independently-realized for responses in different groups. There are two types of random effects in our implementation of mixed models: (i) random coefficients (possibly vectors) that have an unknown covariance matrix, and (ii) random coefficients that are independent draws from a common univariate distribution. For both (i) and (ii), the random effects influence the conditional mean of a group through their matrix/vector product with a group-specific design matrix. A simple example of random coefficients, as in (i) above, is: Y_(ij) = β₀ + β₁X_(ij) + γ_(0i) + γ_(1i)X_(ij) + ϵ_(ij) Here, Y_(ij) is the $j^\rm{th}$ measured response for subject i, and X_(ij) is a covariate for this response. The "fixed effects parameters" β₀ and β₁ are shared by all subjects, and the errors ϵ_(ij) are independent of everything else, and identically distributed (with mean zero). The "random effects parameters" γ_(0i) and γ_(1i) follow a bivariate distribution with mean zero, described by three parameters: ${\rm var}(\gamma_{0i})$, ${\rm var}(\gamma_{1i})$, and ${\rm cov}(\gamma_{0i}, \gamma_{1i})$. There is also a parameter for ${\rm var}(\epsilon_{ij})$. A simple example of variance components, as in (ii) above, is: Y_(ijk) = β₀ + η_(1i) + η_(2j) + ϵ_(ijk) Here, Y_(ijk) is the $k^\rm{th}$ measured response under conditions i, j. The only "mean structure parameter" is β₀. The η_(1i) are independent and identically distributed with zero mean, and variance τ₁², and the η_(2j) are independent and identically distributed with zero mean, and variance τ₂². Statsmodels MixedLM handles most non-crossed random effects models, and some crossed models. To include crossed random effects in a model, it is necessary to treat the entire dataset as a single group. The variance components arguments to the model can then be used to define models with various combinations of crossed and non-crossed random effects. The Statsmodels LME framework currently supports post-estimation inference via Wald tests and confidence intervals on the coefficients, profile likelihood analysis, likelihood ratio testing, and AIC. Examples import statsmodels.api as sm import statsmodels.formula.api as smf data = sm.datasets.get_rdataset("dietox", "geepack").data md = smf.mixedlm("Weight ~ Time", data, groups=data["Pig"]) mdf = md.fit() print(mdf.summary()) Detailed examples can be found here - Mixed LM There are some notebook examples on the Wiki: Wiki notebooks for MixedLM Technical Documentation The data are partitioned into disjoint groups. The probability model for group i is: Y = Xβ + Zγ + Q₁η₁ + ⋯ + Q_(k)η_(k) + ϵ where - n_(i) is the number of observations in group i - Y is a n_(i) dimensional response vector - X is a n_(i) * k_(fe) dimensional matrix of fixed effects coefficients - β is a k_(fe)-dimensional vector of fixed effects slopes - Z is a n_(i) * k_(re) dimensional matrix of random effects coefficients - γ is a k_(re)-dimensional random vector with mean 0 and covariance matrix Ψ; note that each group gets its own independent realization of gamma. - Q_(j) is a n_(i) × q_(j) dimensional design matrix for the $j^\rm{th}$ variance component. - η_(j) is a q_(j)-dimensional random vector containing independent and identically distributed values with variance τ_(j)². - ϵ is a n_(i) dimensional vector of i.i.d normal errors with mean 0 and variance σ²; the ϵ values are independent both within and between groups Y, X, {Q_(j)} and Z must be entirely observed. β, Ψ, and σ² are estimated using ML or REML estimation, and γ, {η_(j)} and ϵ are random so define the probability model. The marginal mean structure is E[Y|X,Z] = X * β. If only the marginal mean structure is of interest, GEE is a good alternative to mixed models. Notation: - cov_(re) is the random effects covariance matrix (referred to above as Ψ) and scale is the (scalar) error variance. There is also a single estimated variance parameter τ_(j)² for each variance component. For a single group, the marginal covariance matrix of endog given exog is scale * I + Z * cov_(re) * Z, where Z is the design matrix for the random effects in one group.

""" Linear mixed effects models are regression models for dependent data. They can be used to estimate regression relationships involving both means and variances. These models are also known as multilevel linear models, and hierarchical linear models. The MixedLM class fits linear mixed effects models to data, and provides support for some common post-estimation tasks. This is a group-based implementation that is most efficient for models in which the data can be partitioned into independent groups. Some models with crossed effects can be handled by specifying a model with a single group. The data are partitioned into disjoint groups. The probability model for group i is: Y = X*beta + Z*gamma + epsilon where * n_i is the number of observations in group i * Y is a n_i dimensional response vector (called endog in MixedLM) * X is a n_i x k_fe dimensional design matrix for the fixed effects (called exog in MixedLM) * beta is a k_fe-dimensional vector of fixed effects parameters (called fe_params in MixedLM) * Z is a design matrix for the random effects with n_i rows (called exog_re in MixedLM). The number of columns in Z can vary by group as discussed below. * gamma is a random vector with mean 0. The covariance matrix for the first `k_re` elements of `gamma` (called cov_re in MixedLM) is common to all groups. The remaining elements of `gamma` are variance components as discussed in more detail below. Each group receives its own independent realization of gamma. * epsilon is a n_i dimensional vector of iid normal errors with mean 0 and variance sigma^2; the epsilon values are independent both within and between groups Y, X and Z must be entirely observed. beta, Psi, and sigma^2 are estimated using ML or REML estimation, and gamma and epsilon are random so define the probability model. The marginal mean structure is E[Y | X, Z] = X*beta. If only the mean structure is of interest, GEE is an alternative to using linear mixed models. Two types of random effects are supported. Standard random effects are correlated with each other in arbitrary ways. Every group has the same number (`k_re`) of standard random effects, with the same joint distribution (but with independent realizations across the groups). Variance components are uncorrelated with each other, and with the standard random effects. Each variance component has mean zero, and all realizations of a given variance component have the same variance parameter. The number of realized variance components per variance parameter can differ across the groups. The primary reference for the implementation details is: MJ Lindstrom, DM Bates (1988). "Newton Raphson and EM algorithms for linear mixed effects models for repeated measures data". Journal of the American Statistical Association. Volume 83, Issue 404, pages 1014-1022. See also this more recent document: http://econ.ucsb.edu/~doug/245a/Papers/Mixed%20Effects%20Implement.pdf All the likelihood, gradient, and Hessian calculations closely follow Lindstrom and Bates 1988, adapted to support variance components. The following two documents are written more from the perspective of users: http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf http://lme4.r-forge.r-project.org/slides/2009-07-07-Rennes/3Longitudinal-4.pdf Notation: * `cov_re` is the random effects covariance matrix (referred to above as Psi) and `scale` is the (scalar) error variance. For a single group, the marginal covariance matrix of endog given exog is scale*I + Z * cov_re * Z', where Z is the design matrix for the random effects in one group. * `vcomp` is a vector of variance parameters. The length of `vcomp` is determined by the number of keys in either the `exog_vc` argument to ``MixedLM``, or the `vc_formula` argument when using formulas to fit a model. Notes: 1. Three different parameterizations are used in different places. The regression slopes (usually called `fe_params`) are identical in all three parameterizations, but the variance parameters differ. The parameterizations are: * The "user parameterization" in which cov(endog) = scale*I + Z * cov_re * Z', as described above. This is the main parameterization visible to the user. * The "profile parameterization" in which cov(endog) = I + Z * cov_re1 * Z'. This is the parameterization of the profile likelihood that is maximized to produce parameter estimates. (see Lindstrom and Bates for details). The "user" cov_re is equal to the "profile" cov_re1 times the scale. * The "square root parameterization" in which we work with the Cholesky factor of cov_re1 instead of cov_re directly. This is hidden from the user. All three parameterizations can be packed into a vector by (optionally) concatenating `fe_params` together with the lower triangle or Cholesky square root of the dependence structure, followed by the variance parameters for the variance components. The are stored as square roots if (and only if) the random effects covariance matrix is stored as its Choleky factor. Note that when unpacking, it is important to either square or reflect the dependence structure depending on which parameterization is being used. Two score methods are implemented. One takes the score with respect to the elements of the random effects covariance matrix (used for inference once the MLE is reached), and the other takes the score with respect to the parameters of the Choleky square root of the random effects covariance matrix (used for optimization). The numerical optimization uses GLS to avoid explicitly optimizing over the fixed effects parameters. The likelihood that is optimized is profiled over both the scale parameter (a scalar) and the fixed effects parameters (if any). As a result of this profiling, it is difficult and unnecessary to calculate the Hessian of the profiled log likelihood function, so that calculation is not implemented here. Therefore, optimization methods requiring the Hessian matrix such as the Newton-Raphson algorithm cannot be used for model fitting. """ import numpy as np import statsmodels.base.model as base from statsmodels.tools.decorators import cache_readonly from statsmodels.tools import data as data_tools from scipy.stats.distributions import norm from scipy import sparse import pandas as pd import patsy from collections import OrderedDict from statsmodels.compat.python import string_types from statsmodels.compat import range import warnings from statsmodels.tools.sm_exceptions import ConvergenceWarning from statsmodels.base._penalties import Penalty def _dot(x, y): """ Returns the dot product of the arrays, works for sparse and dense. """ if isinstance(x, np.ndarray) and isinstance(y, np.ndarray): return np.dot(x, y) elif sparse.issparse(x): return x.dot(y) elif sparse.issparse(y): return y.T.dot(x.T).T # From numpy, adapted to work with sparse and dense arrays. def _multi_dot_three(A, B, C): """ Find best ordering for three arrays and do the multiplication. Doing in manually instead of using dynamic programing is approximately 15 times faster. """ # cost1 = cost((AB)C) cost1 = (A.shape[0] * A.shape[1] * B.shape[1] + # (AB) A.shape[0] * B.shape[1] * C.shape[1]) # (--)C # cost2 = cost((AB)C) cost2 = (B.shape[0] * B.shape[1] * C.shape[1] + # (BC) A.shape[0] * A.shape[1] * C.shape[1]) # A(--) if cost1 < cost2: return _dot(_dot(A, B), C) else: return _dot(A, _dot(B, C)) def _dotsum(x, y): """ Returns sum(x * y), where '*' is the pointwise product, computed efficiently for dense and sparse matrices. """ if sparse.issparse(x): return x.multiply(y).sum() else: # This way usually avoids allocating a temporary. return np.dot(x.ravel(), y.ravel()) class VCSpec(object): """ Define the variance component structure of a multilevel model. An instance of the class contains three attributes: - names : names[k] is the name of variance component k. - mats : mats[k][i] is the design matrix for group index i in variance component k. - colnames : colnames[k][i] is the list of column names for mats[k][i]. The groups in colnames and mats must be in sorted order. """ def __init__(self, names, colnames, mats): self.names = names self.colnames = colnames self.mats = mats def _get_exog_re_names(self, exog_re): """ Passes through if given a list of names. Otherwise, gets pandas names or creates some generic variable names as needed. """ if self.k_re == 0: return [] if isinstance(exog_re, pd.DataFrame): return exog_re.columns.tolist() elif isinstance(exog_re, pd.Series) and exog_re.name is not None: return [exog_re.name] elif isinstance(exog_re, list): return exog_re # Default names defnames = ["x_re{0:1d}".format(k + 1) for k in range(exog_re.shape[1])] return defnames class MixedLMParams(object): """ This class represents a parameter state for a mixed linear model. Parameters ---------- k_fe : integer The number of covariates with fixed effects. k_re : integer The number of covariates with random coefficients (excluding variance components). k_vc : integer The number of variance components parameters. Notes ----- This object represents the parameter state for the model in which the scale parameter has been profiled out. """ def __init__(self, k_fe, k_re, k_vc): self.k_fe = k_fe self.k_re = k_re self.k_re2 = k_re * (k_re + 1) // 2 self.k_vc = k_vc self.k_tot = self.k_fe + self.k_re2 + self.k_vc self._ix = np.tril_indices(self.k_re) def from_packed(params, k_fe, k_re, use_sqrt, has_fe): """ Create a MixedLMParams object from packed parameter vector. Parameters ---------- params : array_like The mode parameters packed into a single vector. k_fe : integer The number of covariates with fixed effects k_re : integer The number of covariates with random effects (excluding variance components). use_sqrt : boolean If True, the random effects covariance matrix is provided as its Cholesky factor, otherwise the lower triangle of the covariance matrix is stored. has_fe : boolean If True, `params` contains fixed effects parameters. Otherwise, the fixed effects parameters are set to zero. Returns ------- A MixedLMParams object. """ k_re2 = int(k_re * (k_re + 1) / 2) # The number of covariance parameters. if has_fe: k_vc = len(params) - k_fe - k_re2 else: k_vc = len(params) - k_re2 pa = MixedLMParams(k_fe, k_re, k_vc) cov_re = np.zeros((k_re, k_re)) ix = pa._ix if has_fe: pa.fe_params = params[0:k_fe] cov_re[ix] = params[k_fe:k_fe+k_re2] else: pa.fe_params = np.zeros(k_fe) cov_re[ix] = params[0:k_re2] if use_sqrt: cov_re = np.dot(cov_re, cov_re.T) else: cov_re = (cov_re + cov_re.T) - np.diag(np.diag(cov_re)) pa.cov_re = cov_re if k_vc > 0: if use_sqrt: pa.vcomp = params[-k_vc:]**2 else: pa.vcomp = params[-k_vc:] else: pa.vcomp = np.array([]) return pa from_packed = staticmethod(from_packed) def from_components(fe_params=None, cov_re=None, cov_re_sqrt=None, vcomp=None): """ Create a MixedLMParams object from each parameter component. Parameters ---------- fe_params : array_like The fixed effects parameter (a 1-dimensional array). If None, there are no fixed effects. cov_re : array_like The random effects covariance matrix (a square, symmetric 2-dimensional array). cov_re_sqrt : array_like The Cholesky (lower triangular) square root of the random effects covariance matrix. vcomp : array_like The variance component parameters. If None, there are no variance components. Returns ------- A MixedLMParams object. """ if vcomp is None: vcomp = np.empty(0) if fe_params is None: fe_params = np.empty(0) if cov_re is None and cov_re_sqrt is None: cov_re = np.empty((0, 0)) k_fe = len(fe_params) k_vc = len(vcomp) k_re = cov_re.shape[0] if cov_re is not None else cov_re_sqrt.shape[0] pa = MixedLMParams(k_fe, k_re, k_vc) pa.fe_params = fe_params if cov_re_sqrt is not None: pa.cov_re = np.dot(cov_re_sqrt, cov_re_sqrt.T) elif cov_re is not None: pa.cov_re = cov_re pa.vcomp = vcomp return pa from_components = staticmethod(from_components) def copy(self): """ Returns a copy of the object. """ obj = MixedLMParams(self.k_fe, self.k_re, self.k_vc) obj.fe_params = self.fe_params.copy() obj.cov_re = self.cov_re.copy() obj.vcomp = self.vcomp.copy() return obj def get_packed(self, use_sqrt, has_fe=False): """ Return the model parameters packed into a single vector. Parameters ---------- use_sqrt : bool If True, the Cholesky square root of `cov_re` is included in the packed result. Otherwise the lower triangle of `cov_re` is included. has_fe : bool If True, the fixed effects parameters are included in the packed result, otherwise they are omitted. """ if self.k_re > 0: if use_sqrt: L = np.linalg.cholesky(self.cov_re) cpa = L[self._ix] else: cpa = self.cov_re[self._ix] else: cpa = np.zeros(0) if use_sqrt: vcomp = np.sqrt(self.vcomp) else: vcomp = self.vcomp if has_fe: pa = np.concatenate((self.fe_params, cpa, vcomp)) else: pa = np.concatenate((cpa, vcomp)) return pa def _smw_solver(s, A, AtA, Qi, di): r""" Returns a solver for the linear system: .. math:: (sI + ABA^\prime) y = x The returned function f satisfies f(x) = y as defined above. B and its inverse matrix are block diagonal. The upper left block of :math:`B^{-1}` is Qi and its lower right block is diag(di). Parameters ---------- s : scalar See above for usage A : ndarray p x q matrix, in general q << p, may be sparse. AtA : square ndarray :math:`A^\prime A`, a q x q matrix. Qi : square symmetric ndarray The matrix `B` is q x q, where q = r + d. `B` consists of a r x r diagonal block whose inverse is `Qi`, and a d x d diagonal block, whose inverse is diag(di). di : 1d array_like See documentation for Qi. Returns ------- A function for solving a linear system, as documented above. Notes ----- Uses Sherman-Morrison-Woodbury identity: https://en.wikipedia.org/wiki/Woodbury_matrix_identity """ # Use SMW identity qmat = AtA / s if sparse.issparse(qmat): qmat = qmat.todense() m = Qi.shape[0] qmat[0:m, 0:m] += Qi d = qmat.shape[0] qmat.flat[m*(d+1)::d+1] += di if sparse.issparse(A): qmati = sparse.linalg.spsolve(sparse.csc_matrix(qmat), A.T) else: qmati = np.linalg.solve(qmat, A.T) if sparse.issparse(A): def solver(rhs): ql = qmati.dot(rhs) ql = A.dot(ql) return rhs / s - ql / s**2 else: def solver(rhs): ql = np.dot(qmati, rhs) ql = np.dot(A, ql) return rhs / s - ql / s**2 return solver def _smw_logdet(s, A, AtA, Qi, di, B_logdet): r""" Returns the log determinant of .. math:: sI + ABA^\prime Uses the matrix determinant lemma to accelerate the calculation. B is assumed to be positive definite, and s > 0, therefore the determinant is positive. Parameters ---------- s : positive scalar See above for usage A : ndarray p x q matrix, in general q << p. AtA : square ndarray :math:`A^\prime A`, a q x q matrix. Qi : square symmetric ndarray The matrix `B` is q x q, where q = r + d. `B` consists of a r x r diagonal block whose inverse is `Qi`, and a d x d diagonal block, whose inverse is diag(di). di : 1d array_like See documentation for Qi. B_logdet : real The log determinant of B Returns ------- The log determinant of s*I + A*B*A'. Notes ----- Uses the matrix determinant lemma: https://en.wikipedia.org/wiki/Matrix_determinant_lemma """ p = A.shape[0] ld = p * np.log(s) qmat = AtA / s m = Qi.shape[0] qmat[0:m, 0:m] += Qi d = qmat.shape[0] qmat.flat[m*(d+1)::d+1] += di _, ld1 = np.linalg.slogdet(qmat) return B_logdet + ld + ld1 def _convert_vc(exog_vc): vc_names = [] vc_colnames = [] vc_mats = [] # Get the groups in sorted order groups = set([]) for k, v in exog_vc.items(): groups |= set(v.keys()) groups = list(groups) groups.sort() for k, v in exog_vc.items(): vc_names.append(k) colnames, mats = [], [] for g in groups: try: colnames.append(v[g].columns) except AttributeError: colnames.append([str(j) for j in range(v[g].shape[1])]) mats.append(v[g]) vc_colnames.append(colnames) vc_mats.append(mats) ii = np.argsort(vc_names) vc_names = [vc_names[i] for i in ii] vc_colnames = [vc_colnames[i] for i in ii] vc_mats = [vc_mats[i] for i in ii] return VCSpec(vc_names, vc_colnames, vc_mats) class MixedLM(base.LikelihoodModel): """ An object specifying a linear mixed effects model. Use the `fit` method to fit the model and obtain a results object. Parameters ---------- endog : 1d array_like The dependent variable exog : 2d array_like A matrix of covariates used to determine the mean structure (the "fixed effects" covariates). groups : 1d array_like A vector of labels determining the groups -- data from different groups are independent exog_re : 2d array_like A matrix of covariates used to determine the variance and covariance structure (the "random effects" covariates). If None, defaults to a random intercept for each group. exog_vc : VCSpec instance or dict-like (deprecated) A VCSPec instance defines the structure of the variance components in the model. Alternatively, see notes below for a dictionary-based format. The dictionary format is deprecated and may be removed at some point in the future. use_sqrt : bool If True, optimization is carried out using the lower triangle of the square root of the random effects covariance matrix, otherwise it is carried out using the lower triangle of the random effects covariance matrix. missing : string The approach to missing data handling Notes ----- If `exog_vc` is not a `VCSpec` instance, then it must be a dictionary of dictionaries. Specifically, `exog_vc[a][g]` is a matrix whose columns are linearly combined using independent random coefficients. This random term then contributes to the variance structure of the data for group `g`. The random coefficients all have mean zero, and have the same variance. The matrix must be `m x k`, where `m` is the number of observations in group `g`. The number of columns may differ among the top-level groups. The covariates in `exog`, `exog_re` and `exog_vc` may (but need not) partially or wholly overlap. `use_sqrt` should almost always be set to True. The main use case for use_sqrt=False is when complicated patterns of fixed values in the covariance structure are set (using the `free` argument to `fit`) that cannot be expressed in terms of the Cholesky factor L. Examples -------- A basic mixed model with fixed effects for the columns of ``exog`` and a random intercept for each distinct value of ``group``: >>> model = sm.MixedLM(endog, exog, groups) >>> result = model.fit() A mixed model with fixed effects for the columns of ``exog`` and correlated random coefficients for the columns of ``exog_re``: >>> model = sm.MixedLM(endog, exog, groups, exog_re=exog_re) >>> result = model.fit() A mixed model with fixed effects for the columns of ``exog`` and independent random coefficients for the columns of ``exog_re``: >>> free = MixedLMParams.from_components( fe_params=np.ones(exog.shape[1]), cov_re=np.eye(exog_re.shape[1])) >>> model = sm.MixedLM(endog, exog, groups, exog_re=exog_re) >>> result = model.fit(free=free) A different way to specify independent random coefficients for the columns of ``exog_re``. In this example ``groups`` must be a Pandas Series with compatible indexing with ``exog_re``, and ``exog_re`` has two columns. >>> g = pd.groupby(groups, by=groups).groups >>> vc = {} >>> vc['1'] = {k : exog_re.loc[g[k], 0] for k in g} >>> vc['2'] = {k : exog_re.loc[g[k], 1] for k in g} >>> model = sm.MixedLM(endog, exog, groups, vcomp=vc) >>> result = model.fit() """ def __init__(self, endog, exog, groups, exog_re=None, exog_vc=None, use_sqrt=True, missing='none', **kwargs): _allowed_kwargs = ["missing_idx", "design_info", "formula"] for x in kwargs.keys(): if x not in _allowed_kwargs: raise ValueError( "argument %s not permitted for MixedLM initialization" % x) self.use_sqrt = use_sqrt # Some defaults self.reml = True self.fe_pen = None self.re_pen = None if isinstance(exog_vc, dict): warnings.warn("Using deprecated variance components format") # Convert from old to new representation exog_vc = _convert_vc(exog_vc) if exog_vc is not None: self.k_vc = len(exog_vc.names) self.exog_vc = exog_vc else: self.k_vc = 0 self.exog_vc = VCSpec([], [], []) # If there is one covariate, it may be passed in as a column # vector, convert these to 2d arrays. # TODO: Can this be moved up in the class hierarchy? # yes, it should be done up the hierarchy if (exog is not None and data_tools._is_using_ndarray_type(exog, None) and exog.ndim == 1): exog = exog[:, None] if (exog_re is not None and data_tools._is_using_ndarray_type(exog_re, None) and exog_re.ndim == 1): exog_re = exog_re[:, None] # Calling super creates self.endog, etc. as ndarrays and the # original exog, endog, etc. are self.data.endog, etc. super(MixedLM, self).__init__(endog, exog, groups=groups, exog_re=exog_re, missing=missing, **kwargs) self._init_keys.extend(["use_sqrt", "exog_vc"]) # Number of fixed effects parameters self.k_fe = exog.shape[1] if exog_re is None and len(self.exog_vc.names) == 0: # Default random effects structure (random intercepts). self.k_re = 1 self.k_re2 = 1 self.exog_re = np.ones((len(endog), 1), dtype=np.float64) self.data.exog_re = self.exog_re names = ['Group Var'] self.data.param_names = self.exog_names + names self.data.exog_re_names = names self.data.exog_re_names_full = names elif exog_re is not None: # Process exog_re the same way that exog is handled # upstream # TODO: this is wrong and should be handled upstream wholly self.data.exog_re = exog_re self.exog_re = np.asarray(exog_re) if self.exog_re.ndim == 1: self.exog_re = self.exog_re[:, None] # Model dimensions # Number of random effect covariates self.k_re = self.exog_re.shape[1] # Number of covariance parameters self.k_re2 = self.k_re * (self.k_re + 1) // 2 else: # All random effects are variance components self.k_re = 0 self.k_re2 = 0 if not self.data._param_names: # HACK: could've been set in from_formula already # needs refactor (param_names, exog_re_names, exog_re_names_full) = self._make_param_names(exog_re) self.data.param_names = param_names self.data.exog_re_names = exog_re_names self.data.exog_re_names_full = exog_re_names_full self.k_params = self.k_fe + self.k_re2 # Convert the data to the internal representation, which is a # list of arrays, corresponding to the groups. group_labels = list(set(groups)) group_labels.sort() row_indices = dict((s, []) for s in group_labels) for i, g in enumerate(groups): row_indices[g].append(i) self.row_indices = row_indices self.group_labels = group_labels self.n_groups = len(self.group_labels) # Split the data by groups self.endog_li = self.group_list(self.endog) self.exog_li = self.group_list(self.exog) self.exog_re_li = self.group_list(self.exog_re) # Precompute this. if self.exog_re is None: self.exog_re2_li = None else: self.exog_re2_li = [np.dot(x.T, x) for x in self.exog_re_li] # The total number of observations, summed over all groups self.nobs = len(self.endog) self.n_totobs = self.nobs # Set the fixed effects parameter names if self.exog_names is None: self.exog_names = ["FE%d" % (k + 1) for k in range(self.exog.shape[1])] # Precompute this self._aex_r = [] self._aex_r2 = [] for i in range(self.n_groups): a = self._augment_exog(i) self._aex_r.append(a) # This matrix is not very sparse so convert it to dense. ma = _dot(a.T, a) if sparse.issparse(ma): ma = ma.todense() self._aex_r2.append(ma) # Precompute this self._lin, self._quad = self._reparam() def _make_param_names(self, exog_re): """ Returns the full parameter names list, just the exogenous random effects variables, and the exogenous random effects variables with the interaction terms. """ exog_names = list(self.exog_names) exog_re_names = _get_exog_re_names(self, exog_re) param_names = [] jj = self.k_fe for i in range(len(exog_re_names)): for j in range(i + 1): if i == j: param_names.append(exog_re_names[i] + " Var") else: param_names.append(exog_re_names[j] + " x " + exog_re_names[i] + " Cov") jj += 1 vc_names = [x + " Var" for x in self.exog_vc.names] return exog_names + param_names + vc_names, exog_re_names, param_names @classmethod def from_formula(cls, formula, data, re_formula=None, vc_formula=None, subset=None, use_sparse=False, missing='none', *args, **kwargs): """ Create a Model from a formula and dataframe. Parameters ---------- formula : str or generic Formula object The formula specifying the model data : array_like The data for the model. See Notes. re_formula : string A one-sided formula defining the variance structure of the model. The default gives a random intercept for each group. vc_formula : dict-like Formulas describing variance components. `vc_formula[vc]` is the formula for the component with variance parameter named `vc`. The formula is processed into a matrix, and the columns of this matrix are linearly combined with independent random coefficients having mean zero and a common variance. subset : array_like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a `pandas.DataFrame` missing : string Either 'none' or 'drop' args : extra arguments These are passed to the model kwargs : extra keyword arguments These are passed to the model with one exception. The ``eval_env`` keyword is passed to patsy. It can be either a :class:`patsy:patsy.EvalEnvironment` object or an integer indicating the depth of the namespace to use. For example, the default ``eval_env=0`` uses the calling namespace. If you wish to use a "clean" environment set ``eval_env=-1``. Returns ------- model : Model instance Notes ----- `data` must define __getitem__ with the keys in the formula terms args and kwargs are passed on to the model instantiation. E.g., a numpy structured or rec array, a dictionary, or a pandas DataFrame. If the variance component is intended to produce random intercepts for disjoint subsets of a group, specified by string labels or a categorical data value, always use '0 +' in the formula so that no overall intercept is included. If the variance components specify random slopes and you do not also want a random group-level intercept in the model, then use '0 +' in the formula to exclude the intercept. The variance components formulas are processed separately for each group. If a variable is categorical the results will not be affected by whether the group labels are distinct or re-used over the top-level groups. Examples -------- Suppose we have data from an educational study with students nested in classrooms nested in schools. The students take a test, and we want to relate the test scores to the students' ages, while accounting for the effects of classrooms and schools. The school will be the top-level group, and the classroom is a nested group that is specified as a variance component. Note that the schools may have different number of classrooms, and the classroom labels may (but need not be) different across the schools. >>> vc = {'classroom': '0 + C(classroom)'} >>> MixedLM.from_formula('test_score ~ age', vc_formula=vc, \ re_formula='1', groups='school', data=data) Now suppose we also have a previous test score called 'pretest'. If we want the relationship between pretest scores and the current test to vary by classroom, we can specify a random slope for the pretest score >>> vc = {'classroom': '0 + C(classroom)', 'pretest': '0 + pretest'} >>> MixedLM.from_formula('test_score ~ age + pretest', vc_formula=vc, \ re_formula='1', groups='school', data=data) The following model is almost equivalent to the previous one, but here the classroom random intercept and pretest slope may be correlated. >>> vc = {'classroom': '0 + C(classroom)'} >>> MixedLM.from_formula('test_score ~ age + pretest', vc_formula=vc, \ re_formula='1 + pretest', groups='school', \ data=data) """ if "groups" not in kwargs.keys(): raise AttributeError("'groups' is a required keyword argument " + "in MixedLM.from_formula") groups = kwargs["groups"] # If `groups` is a variable name, retrieve the data for the # groups variable. group_name = "Group" if isinstance(groups, string_types): group_name = groups groups = np.asarray(data[groups]) else: groups = np.asarray(groups) del kwargs["groups"] # Bypass all upstream missing data handling to properly handle # variance components if missing == 'drop': data, groups = _handle_missing(data, groups, formula, re_formula, vc_formula) missing = 'none' if re_formula is not None: if re_formula.strip() == "1": # Work around Patsy bug, fixed by 0.3. exog_re = np.ones((data.shape[0], 1)) exog_re_names = [group_name] else: eval_env = kwargs.get('eval_env', None) if eval_env is None: eval_env = 1 elif eval_env == -1: from patsy import EvalEnvironment eval_env = EvalEnvironment({}) exog_re = patsy.dmatrix(re_formula, data, eval_env=eval_env) exog_re_names = exog_re.design_info.column_names exog_re_names = [x.replace("Intercept", group_name) for x in exog_re_names] exog_re = np.asarray(exog_re) if exog_re.ndim == 1: exog_re = exog_re[:, None] else: exog_re = None if vc_formula is None: exog_re_names = [group_name] else: exog_re_names = [] if vc_formula is not None: eval_env = kwargs.get('eval_env', None) if eval_env is None: eval_env = 1 elif eval_env == -1: from patsy import EvalEnvironment eval_env = EvalEnvironment({}) vc_mats = [] vc_colnames = [] vc_names = [] gb = data.groupby(groups) kylist = sorted(gb.groups.keys()) vcf = sorted(vc_formula.keys()) for vc_name in vcf: md = patsy.ModelDesc.from_formula(vc_formula[vc_name]) vc_names.append(vc_name) evc_mats, evc_colnames = [], [] for group_ix, group in enumerate(kylist): ii = gb.groups[group] mat = patsy.dmatrix( md, data.loc[ii, :], eval_env=eval_env, return_type='dataframe') evc_colnames.append(mat.columns.tolist()) if use_sparse: evc_mats.append(sparse.csr_matrix(mat)) else: evc_mats.append(np.asarray(mat)) vc_mats.append(evc_mats) vc_colnames.append(evc_colnames) exog_vc = VCSpec(vc_names, vc_colnames, vc_mats) else: exog_vc = VCSpec([], [], []) kwargs["subset"] = None kwargs["exog_re"] = exog_re kwargs["exog_vc"] = exog_vc kwargs["groups"] = groups mod = super(MixedLM, cls).from_formula( formula, data, *args, **kwargs) # expand re names to account for pairs of RE (param_names, exog_re_names, exog_re_names_full) = mod._make_param_names(exog_re_names) mod.data.param_names = param_names mod.data.exog_re_names = exog_re_names mod.data.exog_re_names_full = exog_re_names_full if vc_formula is not None: mod.data.vcomp_names = mod.exog_vc.names return mod def predict(self, params, exog=None): """ Return predicted values from a design matrix. Parameters ---------- params : array_like Parameters of a mixed linear model. Can be either a MixedLMParams instance, or a vector containing the packed model parameters in which the fixed effects parameters are at the beginning of the vector, or a vector containing only the fixed effects parameters. exog : array_like, optional Design / exogenous data for the fixed effects. Model exog is used if None. Returns ------- An array of fitted values. Note that these predicted values only reflect the fixed effects mean structure of the model. """ if exog is None: exog = self.exog if isinstance(params, MixedLMParams): params = params.fe_params else: params = params[0:self.k_fe] return np.dot(exog, params) def group_list(self, array): """ Returns `array` split into subarrays corresponding to the grouping structure. """ if array is None: return None if array.ndim == 1: return [np.array(array[self.row_indices[k]]) for k in self.group_labels] else: return [np.array(array[self.row_indices[k], :]) for k in self.group_labels] def fit_regularized(self, start_params=None, method='l1', alpha=0, ceps=1e-4, ptol=1e-6, maxit=200, **fit_kwargs): """ Fit a model in which the fixed effects parameters are penalized. The dependence parameters are held fixed at their estimated values in the unpenalized model. Parameters ---------- method : string of Penalty object Method for regularization. If a string, must be 'l1'. alpha : array_like Scalar or vector of penalty weights. If a scalar, the same weight is applied to all coefficients; if a vector, it contains a weight for each coefficient. If method is a Penalty object, the weights are scaled by alpha. For L1 regularization, the weights are used directly. ceps : positive real scalar Fixed effects parameters smaller than this value in magnitude are treaded as being zero. ptol : positive real scalar Convergence occurs when the sup norm difference between successive values of `fe_params` is less than `ptol`. maxit : integer The maximum number of iterations. fit_kwargs : keywords Additional keyword arguments passed to fit. Returns ------- A MixedLMResults instance containing the results. Notes ----- The covariance structure is not updated as the fixed effects parameters are varied. The algorithm used here for L1 regularization is a"shooting" or cyclic coordinate descent algorithm. If method is 'l1', then `fe_pen` and `cov_pen` are used to obtain the covariance structure, but are ignored during the L1-penalized fitting. References ---------- Friedman, J. H., Hastie, T. and Tibshirani, R. Regularized Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1) (2008) http://www.jstatsoft.org/v33/i01/paper http://statweb.stanford.edu/~tibs/stat315a/Supplements/fuse.pdf """ if isinstance(method, string_types) and (method.lower()!= 'l1'): raise ValueError("Invalid regularization method") # If method is a smooth penalty just optimize directly. if isinstance(method, Penalty): # Scale the penalty weights by alpha method.alpha = alpha fit_kwargs.update({"fe_pen": method}) return self.fit(**fit_kwargs) if np.isscalar(alpha): alpha = alpha * np.ones(self.k_fe, dtype=np.float64) # Fit the unpenalized model to get the dependence structure. mdf = self.fit(**fit_kwargs) fe_params = mdf.fe_params cov_re = mdf.cov_re vcomp = mdf.vcomp scale = mdf.scale try: cov_re_inv = np.linalg.inv(cov_re) except np.linalg.LinAlgError: cov_re_inv = None for itr in range(maxit): fe_params_s = fe_params.copy() for j in range(self.k_fe): if abs(fe_params[j]) < ceps: continue # The residuals fe_params[j] = 0. expval = np.dot(self.exog, fe_params) resid_all = self.endog - expval # The loss function has the form # a*x^2 + b*x + pwt*|x| a, b = 0., 0. for group_ix, group in enumerate(self.group_labels): vc_var = self._expand_vcomp(vcomp, group_ix) exog = self.exog_li[group_ix] ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix] resid = resid_all[self.row_indices[group]] solver = _smw_solver(scale, ex_r, ex2_r, cov_re_inv, 1 / vc_var) x = exog[:, j] u = solver(x) a += np.dot(u, x) b -= 2 * np.dot(u, resid) pwt1 = alpha[j] if b > pwt1: fe_params[j] = -(b - pwt1) / (2 * a) elif b < -pwt1: fe_params[j] = -(b + pwt1) / (2 * a) if np.abs(fe_params_s - fe_params).max() < ptol: break # Replace the fixed effects estimates with their penalized # values, leave the dependence parameters in their unpenalized # state. params_prof = mdf.params.copy() params_prof[0:self.k_fe] = fe_params scale = self.get_scale(fe_params, mdf.cov_re_unscaled, mdf.vcomp) # Get the Hessian including only the nonzero fixed effects, # then blow back up to the full size after inverting. hess = self.hessian(params_prof) pcov = np.nan * np.ones_like(hess) ii = np.abs(params_prof) > ceps ii[self.k_fe:] = True ii = np.flatnonzero(ii) hess1 = hess[ii, :][:, ii] pcov[np.ix_(ii, ii)] = np.linalg.inv(-hess1) params_object = MixedLMParams.from_components(fe_params, cov_re=cov_re) results = MixedLMResults(self, params_prof, pcov / scale) results.params_object = params_object results.fe_params = fe_params results.cov_re = cov_re results.scale = scale results.cov_re_unscaled = mdf.cov_re_unscaled results.method = mdf.method results.converged = True results.cov_pen = self.cov_pen results.k_fe = self.k_fe results.k_re = self.k_re results.k_re2 = self.k_re2 results.k_vc = self.k_vc return MixedLMResultsWrapper(results) def get_fe_params(self, cov_re, vcomp): """ Use GLS to update the fixed effects parameter estimates. Parameters ---------- cov_re : array_like The covariance matrix of the random effects. Returns ------- The GLS estimates of the fixed effects parameters. """ if self.k_fe == 0: return np.array([]) if self.k_re == 0: cov_re_inv = np.empty((0, 0)) else: cov_re_inv = np.linalg.inv(cov_re) # Cache these quantities that don't change. if not hasattr(self, "_endex_li"): self._endex_li = [] for group_ix, _ in enumerate(self.group_labels): mat = np.concatenate( (self.exog_li[group_ix], self.endog_li[group_ix][:, None]), axis=1) self._endex_li.append(mat) xtxy = 0. for group_ix, group in enumerate(self.group_labels): vc_var = self._expand_vcomp(vcomp, group_ix) exog = self.exog_li[group_ix] ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix] solver = _smw_solver(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var) u = solver(self._endex_li[group_ix]) xtxy += np.dot(exog.T, u) fe_params = np.linalg.solve(xtxy[:, 0:-1], xtxy[:, -1]) return fe_params def _reparam(self): """ Returns parameters of the map converting parameters from the form used in optimization to the form returned to the user. Returns ------- lin : list-like Linear terms of the map quad : list-like Quadratic terms of the map Notes ----- If P are the standard form parameters and R are the transformed parameters (i.e. with the Cholesky square root covariance and square root transformed variance components), then P[i] = lin[i] * R + R' * quad[i] * R """ k_fe, k_re, k_re2, k_vc = self.k_fe, self.k_re, self.k_re2, self.k_vc k_tot = k_fe + k_re2 + k_vc ix = np.tril_indices(self.k_re) lin = [] for k in range(k_fe): e = np.zeros(k_tot) e[k] = 1 lin.append(e) for k in range(k_re2): lin.append(np.zeros(k_tot)) for k in range(k_vc): lin.append(np.zeros(k_tot)) quad = [] # Quadratic terms for fixed effects. for k in range(k_tot): quad.append(np.zeros((k_tot, k_tot))) # Quadratic terms for random effects covariance. ii = np.tril_indices(k_re) ix = [(a, b) for a, b in zip(ii[0], ii[1])] for i1 in range(k_re2): for i2 in range(k_re2): ix1 = ix[i1] ix2 = ix[i2] if (ix1[1] == ix2[1]) and (ix1[0] <= ix2[0]): ii = (ix2[0], ix1[0]) k = ix.index(ii) quad[k_fe+k][k_fe+i2, k_fe+i1] += 1 for k in range(k_tot): quad[k] = 0.5*(quad[k] + quad[k].T) # Quadratic terms for variance components. km = k_fe + k_re2 for k in range(km, km+k_vc): quad[k][k, k] = 1 return lin, quad def _expand_vcomp(self, vcomp, group_ix): """ Replicate variance parameters to match a group's design. Parameters ---------- vcomp : array_like The variance parameters for the variance components. group_ix : integer The group index Returns an expanded version of vcomp, in which each variance parameter is copied as many times as there are independent realizations of the variance component in the given group. """ if len(vcomp) == 0: return np.empty(0) vc_var = [] for j in range(len(self.exog_vc.names)): d = self.exog_vc.mats[j][group_ix].shape[1] vc_var.append(vcomp[j] * np.ones(d)) if len(vc_var) > 0: return np.concatenate(vc_var) else: # Cannot reach here? return np.empty(0) def _augment_exog(self, group_ix): """ Concatenate the columns for variance components to the columns for other random effects to obtain a single random effects exog matrix for a given group. """ ex_r = self.exog_re_li[group_ix] if self.k_re > 0 else None if self.k_vc == 0: return ex_r ex = [ex_r] if self.k_re > 0 else [] any_sparse = False for j, _ in enumerate(self.exog_vc.names): ex.append(self.exog_vc.mats[j][group_ix]) any_sparse |= sparse.issparse(ex[-1]) if any_sparse: for j, x in enumerate(ex): if not sparse.issparse(x): ex[j] = sparse.csr_matrix(x) ex = sparse.hstack(ex) ex = sparse.csr_matrix(ex) else: ex = np.concatenate(ex, axis=1) return ex def loglike(self, params, profile_fe=True): """ Evaluate the (profile) log-likelihood of the linear mixed effects model. Parameters ---------- params : MixedLMParams, or array_like. The parameter value. If array-like, must be a packed parameter vector containing only the covariance parameters. profile_fe : boolean If True, replace the provided value of `fe_params` with the GLS estimates. Returns ------- The log-likelihood value at `params`. Notes ----- The scale parameter `scale` is always profiled out of the log-likelihood. In addition, if `profile_fe` is true the fixed effects parameters are also profiled out. """ if type(params) is not MixedLMParams: params = MixedLMParams.from_packed(params, self.k_fe, self.k_re, self.use_sqrt, has_fe=False) cov_re = params.cov_re vcomp = params.vcomp # Move to the profile set if profile_fe: fe_params = self.get_fe_params(cov_re, vcomp) else: fe_params = params.fe_params if self.k_re > 0: try: cov_re_inv = np.linalg.inv(cov_re) except np.linalg.LinAlgError: cov_re_inv = None _, cov_re_logdet = np.linalg.slogdet(cov_re) else: cov_re_inv = np.zeros((0, 0)) cov_re_logdet = 0 # The residuals expval = np.dot(self.exog, fe_params) resid_all = self.endog - expval likeval = 0. # Handle the covariance penalty if (self.cov_pen is not None) and (self.k_re > 0): likeval -= self.cov_pen.func(cov_re, cov_re_inv) # Handle the fixed effects penalty if (self.fe_pen is not None): likeval -= self.fe_pen.func(fe_params) xvx, qf = 0., 0. for group_ix, group in enumerate(self.group_labels): vc_var = self._expand_vcomp(vcomp, group_ix) cov_aug_logdet = cov_re_logdet + np.sum(np.log(vc_var)) exog = self.exog_li[group_ix] ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix] solver = _smw_solver(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var) resid = resid_all[self.row_indices[group]] # Part 1 of the log likelihood (for both ML and REML) ld = _smw_logdet(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var, cov_aug_logdet) likeval -= ld / 2. # Part 2 of the log likelihood (for both ML and REML) u = solver(resid) qf += np.dot(resid, u) # Adjustment for REML if self.reml: mat = solver(exog) xvx += np.dot(exog.T, mat) if self.reml: likeval -= (self.n_totobs - self.k_fe) * np.log(qf) / 2. _, ld = np.linalg.slogdet(xvx) likeval -= ld / 2. likeval -= (self.n_totobs - self.k_fe) * np.log(2 * np.pi) / 2. likeval += ((self.n_totobs - self.k_fe) * np.log(self.n_totobs - self.k_fe) / 2.) likeval -= (self.n_totobs - self.k_fe) / 2. else: likeval -= self.n_totobs * np.log(qf) / 2. likeval -= self.n_totobs * np.log(2 * np.pi) / 2. likeval += self.n_totobs * np.log(self.n_totobs) / 2. likeval -= self.n_totobs / 2. return likeval def _gen_dV_dPar(self, ex_r, solver, group_ix, max_ix=None): """ A generator that yields the element-wise derivative of the marginal covariance matrix with respect to the random effects variance and covariance parameters. ex_r : array_like The random effects design matrix solver : function A function that given x returns V^{-1}x, where V is the group's marginal covariance matrix. group_ix : integer The group index max_ix : integer or None If not None, the generator ends when this index is reached. """ axr = solver(ex_r) # Regular random effects jj = 0 for j1 in range(self.k_re): for j2 in range(j1 + 1): if max_ix is not None and jj > max_ix: return # Need 2d mat_l, mat_r = ex_r[:, j1:j1+1], ex_r[:, j2:j2+1] vsl, vsr = axr[:, j1:j1+1], axr[:, j2:j2+1] yield jj, mat_l, mat_r, vsl, vsr, j1 == j2 jj += 1 # Variance components for j, _ in enumerate(self.exog_vc.names): if max_ix is not None and jj > max_ix: return mat = self.exog_vc.mats[j][group_ix] axmat = solver(mat) yield jj, mat, mat, axmat, axmat, True jj += 1 def score(self, params, profile_fe=True): """ Returns the score vector of the profile log-likelihood. Notes ----- The score vector that is returned is computed with respect to the parameterization defined by this model instance's `use_sqrt` attribute. """ if type(params) is not MixedLMParams: params = MixedLMParams.from_packed( params, self.k_fe, self.k_re, self.use_sqrt, has_fe=False) if profile_fe: params.fe_params = self.get_fe_params(params.cov_re, params.vcomp) if self.use_sqrt: score_fe, score_re, score_vc = self.score_sqrt( params, calc_fe=not profile_fe) else: score_fe, score_re, score_vc = self.score_full( params, calc_fe=not profile_fe) if self._freepat is not None: score_fe *= self._freepat.fe_params score_re *= self._freepat.cov_re[self._freepat._ix] score_vc *= self._freepat.vcomp if profile_fe: return np.concatenate((score_re, score_vc)) else: return np.concatenate((score_fe, score_re, score_vc)) def score_full(self, params, calc_fe): """ Returns the score with respect to untransformed parameters. Calculates the score vector for the profiled log-likelihood of the mixed effects model with respect to the parameterization in which the random effects covariance matrix is represented in its full form (not using the Cholesky factor). Parameters ---------- params : MixedLMParams or array_like The parameter at which the score function is evaluated. If array-like, must contain the packed random effects parameters (cov_re and vcomp) without fe_params. calc_fe : boolean If True, calculate the score vector for the fixed effects parameters. If False, this vector is not calculated, and a vector of zeros is returned in its place. Returns ------- score_fe : array_like The score vector with respect to the fixed effects parameters. score_re : array_like The score vector with respect to the random effects parameters (excluding variance components parameters). score_vc : array_like The score vector with respect to variance components parameters. Notes ----- `score_re` is taken with respect to the parameterization in which `cov_re` is represented through its lower triangle (without taking the Cholesky square root). """ fe_params = params.fe_params cov_re = params.cov_re vcomp = params.vcomp try: cov_re_inv = np.linalg.inv(cov_re) except np.linalg.LinAlgError: cov_re_inv = None score_fe = np.zeros(self.k_fe) score_re = np.zeros(self.k_re2) score_vc = np.zeros(self.k_vc) # Handle the covariance penalty. if self.cov_pen is not None: score_re -= self.cov_pen.deriv(cov_re, cov_re_inv) # Handle the fixed effects penalty. if calc_fe and (self.fe_pen is not None): score_fe -= self.fe_pen.deriv(fe_params) # resid' V^{-1} resid, summed over the groups (a scalar) rvir = 0. # exog' V^{-1} resid, summed over the groups (a k_fe # dimensional vector) xtvir = 0. # exog' V^{_1} exog, summed over the groups (a k_fe x k_fe # matrix) xtvix = 0. # V^{-1} exog' dV/dQ_jj exog V^{-1}, where Q_jj is the jj^th # covariance parameter. xtax = [0., ] * (self.k_re2 + self.k_vc) # Temporary related to the gradient of log |V| dlv = np.zeros(self.k_re2 + self.k_vc) # resid' V^{-1} dV/dQ_jj V^{-1} resid (a scalar) rvavr = np.zeros(self.k_re2 + self.k_vc) for group_ix, group in enumerate(self.group_labels): vc_var = self._expand_vcomp(vcomp, group_ix) exog = self.exog_li[group_ix] ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix] solver = _smw_solver(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var) # The residuals resid = self.endog_li[group_ix] if self.k_fe > 0: expval = np.dot(exog, fe_params) resid = resid - expval if self.reml: viexog = solver(exog) xtvix += np.dot(exog.T, viexog) # Contributions to the covariance parameter gradient vir = solver(resid) for (jj, matl, matr, vsl, vsr, sym) in\ self._gen_dV_dPar(ex_r, solver, group_ix): dlv[jj] = _dotsum(matr, vsl) if not sym: dlv[jj] += _dotsum(matl, vsr) ul = _dot(vir, matl) ur = ul.T if sym else _dot(matr.T, vir) ulr = np.dot(ul, ur) rvavr[jj] += ulr if not sym: rvavr[jj] += ulr.T if self.reml: ul = _dot(viexog.T, matl) ur = ul.T if sym else _dot(matr.T, viexog) ulr = np.dot(ul, ur) xtax[jj] += ulr if not sym: xtax[jj] += ulr.T # Contribution of log|V| to the covariance parameter # gradient. if self.k_re > 0: score_re -= 0.5 * dlv[0:self.k_re2] if self.k_vc > 0: score_vc -= 0.5 * dlv[self.k_re2:] rvir += np.dot(resid, vir) if calc_fe: xtvir += np.dot(exog.T, vir) fac = self.n_totobs if self.reml: fac -= self.k_fe if calc_fe and self.k_fe > 0: score_fe += fac * xtvir / rvir if self.k_re > 0: score_re += 0.5 * fac * rvavr[0:self.k_re2] / rvir if self.k_vc > 0: score_vc += 0.5 * fac * rvavr[self.k_re2:] / rvir if self.reml: xtvixi = np.linalg.inv(xtvix) for j in range(self.k_re2): score_re[j] += 0.5 * _dotsum(xtvixi.T, xtax[j]) for j in range(self.k_vc): score_vc[j] += 0.5 * _dotsum(xtvixi.T, xtax[self.k_re2 + j]) return score_fe, score_re, score_vc def score_sqrt(self, params, calc_fe=True): """ Returns the score with respect to transformed parameters. Calculates the score vector with respect to the parameterization in which the random effects covariance matrix is represented through its Cholesky square root. Parameters ---------- params : MixedLMParams or array_like The model parameters. If array-like must contain packed parameters that are compatible with this model instance. calc_fe : boolean If True, calculate the score vector for the fixed effects parameters. If False, this vector is not calculated, and a vector of zeros is returned in its place. Returns ------- score_fe : array_like The score vector with respect to the fixed effects parameters. score_re : array_like The score vector with respect to the random effects parameters (excluding variance components parameters). score_vc : array_like The score vector with respect to variance components parameters. """ score_fe, score_re, score_vc = self.score_full(params, calc_fe=calc_fe) params_vec = params.get_packed(use_sqrt=True, has_fe=True) score_full = np.concatenate((score_fe, score_re, score_vc)) scr = 0. for i in range(len(params_vec)): v = self._lin[i] + 2 * np.dot(self._quad[i], params_vec) scr += score_full[i] * v score_fe = scr[0:self.k_fe] score_re = scr[self.k_fe:self.k_fe + self.k_re2] score_vc = scr[self.k_fe + self.k_re2:] return score_fe, score_re, score_vc def hessian(self, params): """ Returns the model's Hessian matrix. Calculates the Hessian matrix for the linear mixed effects model with respect to the parameterization in which the covariance matrix is represented directly (without square-root transformation). Parameters ---------- params : MixedLMParams or array_like The model parameters at which the Hessian is calculated. If array-like, must contain the packed parameters in a form that is compatible with this model instance. Returns ------- hess : 2d ndarray The Hessian matrix, evaluated at `params`. """ if type(params) is not MixedLMParams: params = MixedLMParams.from_packed(params, self.k_fe, self.k_re, use_sqrt=self.use_sqrt, has_fe=True) fe_params = params.fe_params vcomp = params.vcomp cov_re = params.cov_re if self.k_re > 0: cov_re_inv = np.linalg.inv(cov_re) else: cov_re_inv = np.empty((0, 0)) # Blocks for the fixed and random effects parameters. hess_fe = 0. hess_re = np.zeros((self.k_re2 + self.k_vc, self.k_re2 + self.k_vc)) hess_fere = np.zeros((self.k_re2 + self.k_vc, self.k_fe)) fac = self.n_totobs if self.reml: fac -= self.exog.shape[1] rvir = 0. xtvix = 0. xtax = [0., ] * (self.k_re2 + self.k_vc) m = self.k_re2 + self.k_vc B = np.zeros(m) D = np.zeros((m, m)) F = [[0.] * m for k in range(m)] for group_ix, group in enumerate(self.group_labels): vc_var = self._expand_vcomp(vcomp, group_ix) exog = self.exog_li[group_ix] ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix] solver = _smw_solver(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var) # The residuals resid = self.endog_li[group_ix] if self.k_fe > 0: expval = np.dot(exog, fe_params) resid = resid - expval viexog = solver(exog) xtvix += np.dot(exog.T, viexog) vir = solver(resid) rvir += np.dot(resid, vir) for (jj1, matl1, matr1, vsl1, vsr1, sym1) in\ self._gen_dV_dPar(ex_r, solver, group_ix): ul = _dot(viexog.T, matl1) ur = _dot(matr1.T, vir) hess_fere[jj1, :] += np.dot(ul, ur) if not sym1: ul = _dot(viexog.T, matr1) ur = _dot(matl1.T, vir) hess_fere[jj1, :] += np.dot(ul, ur) if self.reml: ul = _dot(viexog.T, matl1) ur = ul if sym1 else np.dot(viexog.T, matr1) ulr = _dot(ul, ur.T) xtax[jj1] += ulr if not sym1: xtax[jj1] += ulr.T ul = _dot(vir, matl1) ur = ul if sym1 else _dot(vir, matr1) B[jj1] += np.dot(ul, ur) * (1 if sym1 else 2) # V^{-1} * dV/d_theta E = [(vsl1, matr1)] if not sym1: E.append((vsr1, matl1)) for (jj2, matl2, matr2, vsl2, vsr2, sym2) in\ self._gen_dV_dPar(ex_r, solver, group_ix, jj1): re = sum([_multi_dot_three(matr2.T, x[0], x[1].T) for x in E]) vt = 2 * _dot(_multi_dot_three(vir[None, :], matl2, re), vir[:, None]) if not sym2: le = sum([_multi_dot_three(matl2.T, x[0], x[1].T) for x in E]) vt += 2 * _dot(_multi_dot_three( vir[None, :], matr2, le), vir[:, None]) D[jj1, jj2] += vt if jj1!= jj2: D[jj2, jj1] += vt rt = _dotsum(vsl2, re.T) / 2 if not sym2: rt += _dotsum(vsr2, le.T) / 2 hess_re[jj1, jj2] += rt if jj1!= jj2: hess_re[jj2, jj1] += rt if self.reml: ev = sum([_dot(x[0], _dot(x[1].T, viexog)) for x in E]) u1 = _dot(viexog.T, matl2) u2 = _dot(matr2.T, ev) um = np.dot(u1, u2) F[jj1][jj2] += um + um.T if not sym2: u1 = np.dot(viexog.T, matr2) u2 = np.dot(matl2.T, ev) um = np.dot(u1, u2) F[jj1][jj2] += um + um.T hess_fe -= fac * xtvix / rvir hess_re = hess_re - 0.5 * fac * (D/rvir - np.outer(B, B) / rvir**2) hess_fere = -fac * hess_fere / rvir if self.reml: QL = [np.linalg.solve(xtvix, x) for x in xtax] for j1 in range(self.k_re2 + self.k_vc): for j2 in range(j1 + 1): a = _dotsum(QL[j1].T, QL[j2]) a -= np.trace(np.linalg.solve(xtvix, F[j1][j2])) a *= 0.5 hess_re[j1, j2] += a if j1 > j2: hess_re[j2, j1] += a # Put the blocks together to get the Hessian. m = self.k_fe + self.k_re2 + self.k_vc hess = np.zeros((m, m)) hess[0:self.k_fe, 0:self.k_fe] = hess_fe hess[0:self.k_fe, self.k_fe:] = hess_fere.T hess[self.k_fe:, 0:self.k_fe] = hess_fere hess[self.k_fe:, self.k_fe:] = hess_re return hess def get_scale(self, fe_params, cov_re, vcomp): """ Returns the estimated error variance based on given estimates of the slopes and random effects covariance matrix. Parameters ---------- fe_params : array_like The regression slope estimates cov_re : 2d array_like Estimate of the random effects covariance matrix vcomp : array_like Estimate of the variance components Returns ------- scale : float The estimated error variance. """ try: cov_re_inv = np.linalg.inv(cov_re) except np.linalg.LinAlgError: cov_re_inv = None qf = 0. for group_ix, group in enumerate(self.group_labels): vc_var = self._expand_vcomp(vcomp, group_ix) exog = self.exog_li[group_ix] ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix] solver = _smw_solver(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var) # The residuals resid = self.endog_li[group_ix] if self.k_fe > 0: expval = np.dot(exog, fe_params) resid = resid - expval mat = solver(resid) qf += np.dot(resid, mat) if self.reml: qf /= (self.n_totobs - self.k_fe) else: qf /= self.n_totobs return qf def fit(self, start_params=None, reml=True, niter_sa=0, do_cg=True, fe_pen=None, cov_pen=None, free=None, full_output=False, method=None, **kwargs): """ Fit a linear mixed model to the data. Parameters ---------- start_params: array_like or MixedLMParams Starting values for the profile log-likelihood. If not a `MixedLMParams` instance, this should be an array containing the packed parameters for the profile log-likelihood, including the fixed effects parameters. reml : bool If true, fit according to the REML likelihood, else fit the standard likelihood using ML. niter_sa : Currently this argument is ignored and has no effect on the results. cov_pen : CovariancePenalty object A penalty for the random effects covariance matrix do_cg : boolean, defaults to True If False, the optimization is skipped and a results object at the given (or default) starting values is returned. fe_pen : Penalty object A penalty on the fixed effects free : MixedLMParams object If not `None`, this is a mask that allows parameters to be held fixed at specified values. A 1 indicates that the correspondinig parameter is estimated, a 0 indicates that it is fixed at its starting value. Setting the `cov_re` component to the identity matrix fits a model with independent random effects. Note that some optimization methods do not respect this constraint (bfgs and lbfgs both work). full_output : bool If true, attach iteration history to results method : string Optimization method. Can be a scipy.optimize method name, or a list of such names to be tried in sequence. Returns ------- A MixedLMResults instance. """ _allowed_kwargs = ['gtol','maxiter', 'eps','maxcor', 'ftol', 'tol', 'disp','maxls'] for x in kwargs.keys(): if x not in _allowed_kwargs: warnings.warn("Argument %s not used by MixedLM.fit" % x) if method is None: method = ['bfgs', 'lbfgs', 'cg'] elif isinstance(method, str): method = [method] for meth in method: if meth.lower() in ["newton", "ncg"]: raise ValueError( "method %s not available for MixedLM" % meth) self.reml = reml self.cov_pen = cov_pen self.fe_pen = fe_pen self._freepat = free if full_output: hist = [] else: hist = None if start_params is None: params = MixedLMParams(self.k_fe, self.k_re, self.k_vc) params.fe_params = np.zeros(self.k_fe) params.cov_re = np.eye(self.k_re) params.vcomp = np.ones(self.k_vc) else: if isinstance(start_params, MixedLMParams): params = start_params else: # It's a packed array if len(start_params) == self.k_fe + self.k_re2 + self.k_vc: params = MixedLMParams.from_packed( start_params, self.k_fe, self.k_re, self.use_sqrt, has_fe=True) elif len(start_params) == self.k_re2 + self.k_vc: params = MixedLMParams.from_packed( start_params, self.k_fe, self.k_re, self.use_sqrt, has_fe=False) else: raise ValueError("invalid start_params") if do_cg: kwargs["retall"] = hist is not None if "disp" not in kwargs: kwargs["disp"] = False packed = params.get_packed(use_sqrt=self.use_sqrt, has_fe=False) if niter_sa > 0: warnings.warn("niter_sa is currently ignored") # Try optimizing one or more times for j in range(len(method)): rslt = super(MixedLM, self).fit(start_params=packed, skip_hessian=True, method=method[j], **kwargs) if rslt.mle_retvals['converged']: break packed = rslt.params if j + 1 < len(method): next_method = method[j + 1] warnings.warn( "Retrying MixedLM optimization with %s" % next_method, ConvergenceWarning) else: msg = ("MixedLM optimization failed, " + "trying a different optimizer may help.") warnings.warn(msg, ConvergenceWarning) # The optimization succeeded params = np.atleast_1d(rslt.params) if hist is not None: hist.append(rslt.mle_retvals) converged = rslt.mle_retvals['converged'] if not converged: gn = self.score(rslt.params) gn = np.sqrt(np.sum(gn**2)) msg = "Gradient optimization failed, |grad| = %f" % gn warnings.warn(msg, ConvergenceWarning) # Convert to the final parameterization (i.e. undo the square # root transform of the covariance matrix, and the profiling # over the error variance). params = MixedLMParams.from_packed( params, self.k_fe, self.k_re, use_sqrt=self.use_sqrt, has_fe=False) cov_re_unscaled = params.cov_re vcomp_unscaled = params.vcomp fe_params = self.get_fe_params(cov_re_unscaled, vcomp_unscaled) params.fe_params = fe_params scale = self.get_scale(fe_params, cov_re_unscaled, vcomp_unscaled) cov_re = scale * cov_re_unscaled vcomp = scale * vcomp_unscaled f1 = (self.k_re > 0) and (np.min(np.abs(np.diag(cov_re))) < 0.01) f2 = (self.k_vc > 0) and (np.min(np.abs(vcomp)) < 0.01) if f1 or f2: msg = "The MLE may be on the boundary of the parameter space." warnings.warn(msg, ConvergenceWarning) # Compute the Hessian at the MLE. Note that this is the # Hessian with respect to the random effects covariance matrix # (not its square root). It is used for obtaining standard # errors, not for optimization. hess = self.hessian(params) hess_diag = np.diag(hess) if free is not None: pcov = np.zeros_like(hess) pat = self._freepat.get_packed(use_sqrt=False, has_fe=True) ii = np.flatnonzero(pat) hess_diag = hess_diag[ii] if len(ii) > 0: hess1 = hess[np.ix_(ii, ii)] pcov[np.ix_(ii, ii)] = np.linalg.inv(-hess1) else: pcov = np.linalg.inv(-hess) if np.any(hess_diag >= 0): msg = ("The Hessian matrix at the estimated parameter values " + "is not positive definite.") warnings.warn(msg, ConvergenceWarning) # Prepare a results class instance params_packed = params.get_packed(use_sqrt=False, has_fe=True) results = MixedLMResults(self, params_packed, pcov / scale) results.params_object = params results.fe_params = fe_params results.cov_re = cov_re results.vcomp = vcomp results.scale = scale results.cov_re_unscaled = cov_re_unscaled results.method = "REML" if self.reml else "ML" results.converged = converged results.hist = hist results.reml = self.reml results.cov_pen = self.cov_pen results.k_fe = self.k_fe results.k_re = self.k_re results.k_re2 = self.k_re2 results.k_vc = self.k_vc results.use_sqrt = self.use_sqrt results.freepat = self._freepat return MixedLMResultsWrapper(results) def get_distribution(self, params, scale, exog): return _mixedlm_distribution(self, params, scale, exog) class _mixedlm_distribution(object): """ A private class for simulating data from a given mixed linear model. Parameters ---------- model : MixedLM instance A mixed linear model params : array_like A parameter vector defining a mixed linear model. See notes for more information. scale : scalar The unexplained variance exog : array_like An array of fixed effect covariates. If None, model.exog is used. Notes ----- The params array is a vector containing fixed effects parameters, random effects parameters, and variance component parameters, in that order. The lower triangle of the random effects covariance matrix is stored. The random effects and variance components parameters are divided by the scale parameter. This class is used in Mediation, and possibly elsewhere. """ def __init__(self, model, params, scale, exog): self.model = model self.exog = exog if exog is not None else model.exog po = MixedLMParams.from_packed( params, model.k_fe, model.k_re, False, True) self.fe_params = po.fe_params self.cov_re = scale * po.cov_re self.vcomp = scale * po.vcomp self.scale = scale group_idx = np.zeros(model.nobs, dtype=np.int) for k, g in enumerate(model.group_labels): group_idx[model.row_indices[g]] = k self.group_idx = group_idx def rvs(self, n): """ Return a vector of simulated values from a mixed linear model. The parameter n is ignored, but required by the interface """ model = self.model # Fixed effects y = np.dot(self.exog, self.fe_params) # Random effects u = np.random.normal(size=(model.n_groups, model.k_re)) u = np.dot(u, np.linalg.cholesky(self.cov_re).T) y += (u[self.group_idx, :] * model.exog_re).sum(1) # Variance components for j, _ in enumerate(model.exog_vc.names): ex = model.exog_vc.mats[j] v = self.vcomp[j] for i, g in enumerate(model.group_labels): exg = ex[i] ii = model.row_indices[g] u = np.random.normal(size=exg.shape[1]) y[ii] += np.sqrt(v) * np.dot(exg, u) # Residual variance y += np.sqrt(self.scale) * np.random.normal(size=len(y)) return y class MixedLMResults(base.LikelihoodModelResults, base.ResultMixin): ''' Class to contain results of fitting a linear mixed effects model. MixedLMResults inherits from statsmodels.LikelihoodModelResults Parameters ---------- See statsmodels.LikelihoodModelResults Attributes ---------- model : class instance Pointer to MixedLM model instance that called fit. normalized_cov_params : array The sampling covariance matrix of the estimates params : array A packed parameter vector for the profile parameterization. The first `k_fe` elements are the estimated fixed effects coefficients. The remaining elements are the estimated variance parameters. The variance parameters are all divided by `scale` and are not the variance parameters shown in the summary. fe_params : array The fitted fixed-effects coefficients cov_re : array The fitted random-effects covariance matrix bse_fe : array The standard errors of the fitted fixed effects coefficients bse_re : array The standard errors of the fitted random effects covariance matrix and variance components. The first `k_re * (k_re + 1)` parameters are the standard errors for the lower triangle of `cov_re`, the remaining elements are the standard errors for the variance components. See Also -------- statsmodels.LikelihoodModelResults ''' def __init__(self, model, params, cov_params): super(MixedLMResults, self).__init__(model, params, normalized_cov_params=cov_params) self.nobs = self.model.nobs self.df_resid = self.nobs - np.linalg.matrix_rank(self.model.exog) @cache_readonly def fittedvalues(self): """ Returns the fitted values for the model. The fitted values reflect the mean structure specified by the fixed effects and the predicted random effects. """ fit = np.dot(self.model.exog, self.fe_params) re = self.random_effects for group_ix, group in enumerate(self.model.group_labels): ix = self.model.row_indices[group] mat = [] if self.model.exog_re_li is not None: mat.append(self.model.exog_re_li[group_ix]) for j in range(self.k_vc): mat.append(self.model.exog_vc.mats[j][group_ix]) mat = np.concatenate(mat, axis=1) fit[ix] += np.dot(mat, re[group]) return fit @cache_readonly def resid(self): """ Returns the residuals for the model. The residuals reflect the mean structure specified by the fixed effects and the predicted random effects. """ return self.model.endog - self.fittedvalues @cache_readonly def bse_fe(self): """ Returns the standard errors of the fixed effect regression coefficients. """ p = self.model.exog.shape[1] return np.sqrt(np.diag(self.cov_params())[0:p]) @cache_readonly def bse_re(self): """ Returns the standard errors of the variance parameters. The first `k_re x (k_re + 1)` elements of the returned array are the standard errors of the lower triangle of `cov_re`. The remaining elements are the standard errors of the variance components. Note that the sampling distribution of variance parameters is strongly skewed unless the sample size is large, so these standard errors may not give meaningful confidence intervals or p-values if used in the usual way. """ p = self.model.exog.shape[1] return np.sqrt(self.scale * np.diag(self.cov_params())[p:]) def _expand_re_names(self, group_ix): names = list(self.model.data.exog_re_names) for j, v in enumerate(self.model.exog_vc.names): vg = self.model.exog_vc.colnames[j][group_ix] na = ["%s[%s]" % (v, s) for s in vg] names.extend(na) return names @cache_readonly def random_effects(self): """ The conditional means of random effects given the data. Returns ------- random_effects : dict A dictionary mapping the distinct `group` values to the conditional means of the random effects for the group given the data. """ try: cov_re_inv = np.linalg.inv(self.cov_re) except np.linalg.LinAlgError: raise ValueError("Cannot predict random effects from " + "singular covariance structure.") vcomp = self.vcomp k_re = self.k_re ranef_dict = {} for group_ix, group in enumerate(self.model.group_labels): endog = self.model.endog_li[group_ix] exog = self.model.exog_li[group_ix] ex_r = self.model._aex_r[group_ix] ex2_r = self.model._aex_r2[group_ix] vc_var = self.model._expand_vcomp(vcomp, group_ix) # Get the residuals relative to fixed effects resid = endog if self.k_fe > 0: expval = np.dot(exog, self.fe_params) resid = resid - expval solver = _smw_solver(self.scale, ex_r, ex2_r, cov_re_inv, 1 / vc_var) vir = solver(resid) xtvir = _dot(ex_r.T, vir) xtvir[0:k_re] = np.dot(self.cov_re, xtvir[0:k_re]) xtvir[k_re:] *= vc_var ranef_dict[group] = pd.Series( xtvir, index=self._expand_re_names(group_ix)) return ranef_dict @cache_readonly def random_effects_cov(self): """ Returns the conditional covariance matrix of the random effects for each group given the data. Returns ------- random_effects_cov : dict A dictionary mapping the distinct values of the `group` variable to the conditional covariance matrix of the random effects given the data. """ try: cov_re_inv = np.linalg.inv(self.cov_re) except np.linalg.LinAlgError: cov_re_inv = None vcomp = self.vcomp ranef_dict = {} for group_ix in range(self.model.n_groups): ex_r = self.model._aex_r[group_ix] ex2_r = self.model._aex_r2[group_ix] label = self.model.group_labels[group_ix] vc_var = self.model._expand_vcomp(vcomp, group_ix) solver = _smw_solver(self.scale, ex_r, ex2_r, cov_re_inv, 1 / vc_var) n = ex_r.shape[0] m = self.cov_re.shape[0] mat1 = np.empty((n, m + len(vc_var))) mat1[:, 0:m] = np.dot(ex_r[:, 0:m], self.cov_re) mat1[:, m:] = np.dot(ex_r[:, m:], np.diag(vc_var)) mat2 = solver(mat1) mat2 = np.dot(mat1.T, mat2) v = -mat2 v[0:m, 0:m] += self.cov_re ix = np.arange(m, v.shape[0]) v[ix, ix] += vc_var na = self._expand_re_names(group_ix) v = pd.DataFrame(v, index=na, columns=na) ranef_dict[label] = v return ranef_dict # Need to override since t-tests are only used for fixed effects # parameters. def t_test(self, r_matrix, scale=None, use_t=None): """ Compute a t-test for a each linear hypothesis of the form Rb = q Parameters ---------- r_matrix : array_like If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. scale : float, optional An optional `scale` to use. Default is the scale specified by the model fit. use_t : bool, optional If use_t is None, then the default of the model is used. If use_t is True, then the p-values are based on the t distribution. If use_t is False, then the p-values are based on the normal distribution. Returns ------- res : ContrastResults instance The results for the test are attributes of this results instance. The available results have the same elements as the parameter table in `summary()`. """ if scale is not None: import warnings warnings.warn('scale is has no effect and is deprecated. It will' 'be removed in the next version.', DeprecationWarning) if r_matrix.shape[1]!= self.k_fe: raise ValueError("r_matrix for t-test should have %d columns" % self.k_fe) d = self.k_re2 + self.k_vc z0 = np.zeros((r_matrix.shape[0], d)) r_matrix = np.concatenate((r_matrix, z0), axis=1) tst_rslt = super(MixedLMResults, self).t_test(r_matrix, use_t=use_t) return tst_rslt def summary(self, yname=None, xname_fe=None, xname_re=None, title=None, alpha=.05): """ Summarize the mixed model regression results. Parameters ---------- yname : str, optional Default is `y` xname_fe : list of strings, optional Fixed effects covariate names xname_re : list of strings, optional Random effects covariate names title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary2.Summary : class to hold summary results """ from statsmodels.iolib import summary2 smry = summary2.Summary() info = OrderedDict() info["Model:"] = "MixedLM" if yname is None: yname = self.model.endog_names param_names = self.model.data.param_names[:] k_fe_params = len(self.fe_params) k_re_params = len(param_names) - len(self.fe_params) if xname_fe is not None: if len(xname_fe)!= k_fe_params: msg = "xname_fe should be a list of length %d" % k_fe_params raise ValueError(msg) param_names[:k_fe_params] = xname_fe if xname_re is not None: if len(xname_re)!= k_re_params: msg = "xname_re should be a list of length %d" % k_re_params raise ValueError(msg) param_names[k_fe_params:] = xname_re info["No. Observations:"] = str(self.model.n_totobs) info["No. Groups:"] = str(self.model.n_groups) gs = np.array([len(x) for x in self.model.endog_li]) info["Min. group size:"] = "%.0f" % min(gs) info["Max. group size:"] = "%.0f" % max(gs) info["Mean group size:"] = "%.1f" % np.mean(gs) info["Dependent Variable:"] = yname info["Method:"] = self.method info["Scale:"] = self.scale info["Likelihood:"] = self.llf info["Converged:"] = "Yes" if self.converged else "No" smry.add_dict(info) smry.add_title("Mixed Linear Model Regression Results") float_fmt = "%.3f" sdf = np.nan * np.ones((self.k_fe + self.k_re2 + self.k_vc, 6)) # Coefficient estimates sdf[0:self.k_fe, 0] = self.fe_params # Standard errors sdf[0:self.k_fe, 1] = np.sqrt(np.diag(self.cov_params()[0:self.k_fe])) # Z-scores sdf[0:self.k_fe, 2] = sdf[0:self.k_fe, 0] / sdf[0:self.k_fe, 1] # p-values sdf[0:self.k_fe, 3] = 2 * norm.cdf(-np.abs(sdf[0:self.k_fe, 2])) # Confidence intervals qm = -norm.ppf(alpha / 2) sdf[0:self.k_fe, 4] = sdf[0:self.k_fe, 0] - qm * sdf[0:self.k_fe, 1] sdf[0:self.k_fe, 5] = sdf[0:self.k_fe, 0] + qm * sdf[0:self.k_fe, 1] # All random effects variances and covariances jj = self.k_fe for i in range(self.k_re): for j in range(i + 1): sdf[jj, 0] = self.cov_re[i, j] sdf[jj, 1] = np.sqrt(self.scale) * self.bse[jj] jj += 1 # Variance components for i in range(self.k_vc): sdf[jj, 0] = self.vcomp[i] sdf[jj, 1] = np.sqrt(self.scale) * self.bse[jj] jj += 1 sdf = pd.DataFrame(index=param_names, data=sdf) sdf.columns = ['Coef.', 'Std.Err.', 'z', 'P>|z|', '[' + str(alpha/2), str(1-alpha/2) + ']'] for col in sdf.columns: sdf[col] = [float_fmt % x if np.isfinite(x) else "" for x in sdf[col]] smry.add_df(sdf, align='r') return smry @cache_readonly def llf(self): return self.model.loglike(self.params_object, profile_fe=False) @cache_readonly def aic(self): """Akaike information criterion""" if self.reml: return np.nan if self.freepat is not None: df = self.freepat.get_packed(use_sqrt=False, has_fe=True).sum() + 1 else: df = self.params.size + 1 return -2 * (self.llf - df) @cache_readonly def bic(self): """Bayesian information criterion""" if self.reml: return np.nan if self.freepat is not None: df = self.freepat.get_packed(use_sqrt=False, has_fe=True).sum() + 1 else: df = self.params.size + 1 return -2 * self.llf + np.log(self.nobs) * df def profile_re(self, re_ix, vtype, num_low=5, dist_low=1., num_high=5, dist_high=1.): """ Profile-likelihood inference for variance parameters. Parameters ---------- re_ix : integer If vtype is `re`, this value is the index of the variance parameter for which to construct a profile likelihood. If `vtype` is 'vc' then `re_ix` is the name of the variance parameter to be profiled. vtype : string Either're' or 'vc', depending on whether the profile analysis is for a random effect or a variance component. num_low : integer The number of points at which to calculate the likelihood below the MLE of the parameter of interest. dist_low : float The distance below the MLE of the parameter of interest to begin calculating points on the profile likelihood. num_high : integer The number of points at which to calculate the likelihood abov the MLE of the parameter of interest. dist_high : float The distance above the MLE of the parameter of interest to begin calculating points on the profile likelihood. Returns ------- An array with two columns. The first column contains the values to which the parameter of interest is constrained. The second column contains the corresponding likelihood values. Notes ----- Only variance parameters can be profiled. """ pmodel = self.model k_fe = pmodel.k_fe k_re = pmodel.k_re k_vc = pmodel.k_vc endog, exog = pmodel.endog, pmodel.exog # Need to permute the columns of the random effects design # matrix so that the profiled variable is in the first column. if vtype =='re': ix = np.arange(k_re) ix[0] = re_ix ix[re_ix] = 0 exog_re = pmodel.exog_re.copy()[:, ix] # Permute the covariance structure to match the permuted # design matrix. params = self.params_object.copy() cov_re_unscaled = params.cov_re cov_re_unscaled = cov_re_unscaled[np.ix_(ix, ix)] params.cov_re = cov_re_unscaled ru0 = cov_re_unscaled[0, 0] # Convert dist_low and dist_high to the profile # parameterization cov_re = self.scale * cov_re_unscaled low = (cov_re[0, 0] - dist_low) / self.scale high = (cov_re[0, 0] + dist_high) / self.scale elif vtype == 'vc': re_ix = self.model.exog_vc.names.index(re_ix) params = self.params_object.copy() vcomp = self.vcomp low = (vcomp[re_ix] - dist_low) / self.scale high = (vcomp[re_ix] + dist_high) / self.scale ru0 = vcomp[re_ix] / self.scale # Define the sequence of values to which the parameter of # interest will be constrained. if low <= 0: raise ValueError("dist_low is too large and would result in a " "negative variance. Try a smaller value.") left = np.linspace(low, ru0, num_low + 1) right = np.linspace(ru0, high, num_high+1)[1:] rvalues = np.concatenate((left, right)) # Indicators of which parameters are free and fixed. free = MixedLMParams(k_fe, k_re, k_vc) if self.freepat is None: free.fe_params = np.ones(k_fe) vcomp = np.ones(k_vc) mat = np.ones((k_re, k_re)) else: # If a freepat already has been specified, we add the # constraint to it. free.fe_params = self.freepat.fe_params vcomp = self.freepat.vcomp mat = self.freepat.cov_re if vtype =='re': mat = mat[np.ix_(ix, ix)] if vtype =='re': mat[0, 0] = 0 else: vcomp[re_ix] = 0 free.cov_re = mat free.vcomp = vcomp klass = self.model.__class__ init_kwargs = pmodel._get_init_kwds() if vtype =='re': init_kwargs['exog_re'] = exog_re likev = [] for x in rvalues: model = klass(endog, exog, **init_kwargs) if vtype =='re': cov_re = params.cov_re.copy() cov_re[0, 0] = x params.cov_re = cov_re else: params.vcomp[re_ix] = x # TODO should use fit_kwargs rslt = model.fit(start_params=params, free=free, reml=self.reml, cov_pen=self.cov_pen)._results likev.append([x * rslt.scale, rslt.llf]) likev = np.asarray(likev) return likev class MixedLMResultsWrapper(base.LikelihoodResultsWrapper): _attrs = {'bse_re': ('generic_columns', 'exog_re_names_full'), 'fe_params': ('generic_columns', 'xnames'), 'bse_fe': ('generic_columns', 'xnames'), 'cov_re': ('generic_columns_2d', 'exog_re_names'), 'cov_re_unscaled': ('generic_columns_2d', 'exog_re_names'), } _upstream_attrs = base.LikelihoodResultsWrapper._wrap_attrs _wrap_attrs = base.wrap.union_dicts(_attrs, _upstream_attrs) _methods = {} _upstream_methods = base.LikelihoodResultsWrapper._wrap_methods _wrap_methods = base.wrap.union_dicts(_methods, _upstream_methods) def _handle_missing(data, groups, formula, re_formula, vc_formula): tokens = set([]) forms = [formula] if re_formula is not None: forms.append(re_formula) if vc_formula is not None: forms.extend(vc_formula.values()) import tokenize from statsmodels.compat.python import StringIO, asunicode skiptoks = {"(", ")", "*", ":", "+", "-", "**", "/"} for fml in forms: # Unicode conversion is for Py2 compatability rl = StringIO(fml) def rlu(): line = rl.readline() return asunicode(line, 'ascii') g = tokenize.generate_tokens(rlu) for tok in g: if tok not in skiptoks: tokens.add(tok.string) tokens = sorted(tokens & set(data.columns)) data = data[tokens] ii = pd.notnull(data).all(1) if type(groups)!= "str": ii &= pd.notnull(groups) return data.loc[ii, :], groups[np.asarray(ii)]

statsmodels__statsmodels

optimization.rst

Description / Module doc

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statsmodels__statsmodels/docs/source/optimization.rst

Optimization statsmodels uses three types of algorithms for the estimation of the parameters of a model. 1. Basic linear models such as WLS and OLS <regression> are directly estimated using appropriate linear algebra. 2. RLM <rlm> and GLM <glm>, use iteratively re-weighted least squares. However, you can optionally select one of the scipy optimizers discussed below. 3. For all other models, we use optimizers from scipy. Where practical, certain models allow for the optional selection of a scipy optimizer. A particular scipy optimizer might be default or an option. Depending on the model and the data, choosing an appropriate scipy optimizer enables avoidance of a local minima, fitting models in less time, or fitting a model with less memory. statsmodels supports the following optimizers along with keyword arguments associated with that specific optimizer: - newton - Newton-Raphson iteration. While not directly from scipy, we consider it an optimizer because only the score and hessian are required. tol : float Relative error in params acceptable for convergence. - nm - scipy's fmin_nm xtol : float Relative error in params acceptable for convergence ftol : float Relative error in loglike(params) acceptable for convergence maxfun : int Maximum number of function evaluations to make. - bfgs - Broyden–Fletcher–Goldfarb–Shanno optimization, scipy's fmin_bfgs. gtol : float Stop when norm of gradient is less than gtol. norm : float Order of norm (np.Inf is max, -np.Inf is min) epsilon If fprime is approximated, use this value for the step size. Only relevant if LikelihoodModel.score is None. - lbfgs - A more memory-efficient (limited memory) implementation of bfgs. Scipy's fmin_l_bfgs_b. m : int The maximum number of variable metric corrections used to define the limited memory matrix. (The limited memory BFGS method does not store the full hessian but uses this many terms in an approximation to it.) pgtol : float The iteration will stop when max{|proj g_i | i = 1, ..., n} <= pgtol where pg_i is the i-th component of the projected gradient. factr : float The iteration stops when (f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= factr * eps, where eps is the machine precision, which is automatically generated by the code. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See Notes for relationship to ftol, which is exposed (instead of factr) by the scipy.optimize.minimize interface to L-BFGS-B. maxfun : int Maximum number of iterations. epsilon : float Step size used when approx_grad is True, for numerically calculating the gradient approx_grad : bool Whether to approximate the gradient numerically (in which case func returns only the function value). - cg - Conjugate gradient optimization. Scipy's fmin_cg. gtol : float Stop when norm of gradient is less than gtol. norm : float Order of norm (np.Inf is max, -np.Inf is min) epsilon : float If fprime is approximated, use this value for the step size. Can be scalar or vector. Only relevant if Likelihoodmodel.score is None. - ncg - Newton conjugate gradient. Scipy's fmin_ncg. fhess_p : callable f'(x, *args) Function which computes the Hessian of f times an arbitrary vector, p. Should only be supplied if LikelihoodModel.hessian is None. avextol : float Stop when the average relative error in the minimizer falls below this amount. epsilon : float or ndarray If fhess is approximated, use this value for the step size. Only relevant if Likelihoodmodel.hessian is None. - powell - Powell's method. Scipy's fmin_powell. xtol : float Line-search error tolerance ftol : float Relative error in loglike(params) for acceptable for convergence. maxfun : int Maximum number of function evaluations to make. start_direc : ndarray Initial direction set. - basinhopping - Basin hopping. This is part of scipy's basinhopping tools. niter : integer The number of basin hopping iterations. niter_success : integer Stop the run if the global minimum candidate remains the same for this number of iterations. T : float The "temperature" parameter for the accept or reject criterion. Higher "temperatures" mean that larger jumps in function value will be accepted. For best results T should be comparable to the separation (in function value) between local minima. stepsize : float Initial step size for use in the random displacement. interval : integer The interval for how often to update the stepsize. minimizer : dict Extra keyword arguments to be passed to the minimizer scipy.optimize.minimize(), for example 'method' - the minimization method (e.g. 'L-BFGS-B'), or 'tol' - the tolerance for termination. Other arguments are mapped from explicit argument of `fit`: - args <- fargs - jac <- score - hess <- hess - minimize - Allows the use of any scipy optimizer. min_method : str, optional Name of minimization method to use. Any method specific arguments can be passed directly. For a list of methods and their arguments, see documentation of scipy.optimize.minimize. If no method is specified, then BFGS is used. Model Class Generally, there is no need for an end-user to directly call these functions and classes. However, we provide the class because the different optimization techniques have unique keyword arguments that may be useful to the user.

""" Functions that are general enough to use for any model fitting. The idea is to untie these from LikelihoodModel so that they may be re-used generally. """ import numpy as np from scipy import optimize def _check_method(method, methods): if method not in methods: message = "Unknown fit method %s" % method raise ValueError(message) class Optimizer(object): def _fit(self, objective, gradient, start_params, fargs, kwargs, hessian=None, method='newton', maxiter=100, full_output=True, disp=True, callback=None, retall=False): """ Fit function for any model with an objective function. Parameters ---------- start_params : array_like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str {'newton','nm','bfgs','powell','cg','ncg','basinhopping', 'minimize'} Method can be 'newton' for Newton-Raphson, 'nm' for Nelder-Mead, 'bfgs' for Broyden-Fletcher-Goldfarb-Shanno, 'powell' for modified Powell's method, 'cg' for conjugate gradient, 'ncg' for Newton- conjugate gradient, 'basinhopping' for global basin-hopping solver, if available or a generic'minimize' which is a wrapper for scipy.optimize.minimize. `method` determines which solver from scipy.optimize is used. The explicit arguments in `fit` are passed to the solver, with the exception of the basin-hopping solver. Each solver has several optional arguments that are not the same across solvers. See the notes section below (or scipy.optimize) for the available arguments and for the list of explicit arguments that the basin-hopping solver supports.. maxiter : int The maximum number of iterations to perform. full_output : bool Set to True to have all available output in the Results object's mle_retvals attribute. The output is dependent on the solver. See LikelihoodModelResults notes section for more information. disp : bool Set to True to print convergence messages. fargs : tuple Extra arguments passed to the likelihood function, i.e., loglike(x,*args) callback : callable callback(xk) Called after each iteration, as callback(xk), where xk is the current parameter vector. retall : bool Set to True to return list of solutions at each iteration. Available in Results object's mle_retvals attribute. Returns ------- xopt : array The solution to the objective function retvals : dict, None If `full_output` is True then this is a dictionary which holds information returned from the solver used. If it is False, this is None. optim_settings : dict A dictionary that contains the parameters passed to the solver. Notes ----- The 'basinhopping' solver ignores `maxiter`, `retall`, `full_output` explicit arguments. Optional arguments for the solvers (available in Results.mle_settings):: 'newton' tol : float Relative error in params acceptable for convergence. 'nm' -- Nelder Mead xtol : float Relative error in params acceptable for convergence ftol : float Relative error in loglike(params) acceptable for convergence maxfun : int Maximum number of function evaluations to make. 'bfgs' gtol : float Stop when norm of gradient is less than gtol. norm : float Order of norm (np.Inf is max, -np.Inf is min) epsilon If fprime is approximated, use this value for the step size. Only relevant if LikelihoodModel.score is None. 'lbfgs' m : int The maximum number of variable metric corrections used to define the limited memory matrix. (The limited memory BFGS method does not store the full hessian but uses this many terms in an approximation to it.) pgtol : float The iteration will stop when ``max{|proj g_i | i = 1,..., n} <= pgtol`` where pg_i is the i-th component of the projected gradient. factr : float The iteration stops when ``(f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= factr * eps``, where eps is the machine precision, which is automatically generated by the code. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See Notes for relationship to ftol, which is exposed (instead of factr) by the scipy.optimize.minimize interface to L-BFGS-B. maxfun : int Maximum number of iterations. epsilon : float Step size used when approx_grad is True, for numerically calculating the gradient approx_grad : bool Whether to approximate the gradient numerically (in which case func returns only the function value). 'cg' gtol : float Stop when norm of gradient is less than gtol. norm : float Order of norm (np.Inf is max, -np.Inf is min) epsilon : float If fprime is approximated, use this value for the step size. Can be scalar or vector. Only relevant if Likelihoodmodel.score is None. 'ncg' fhess_p : callable f'(x,*args) Function which computes the Hessian of f times an arbitrary vector, p. Should only be supplied if LikelihoodModel.hessian is None. avextol : float Stop when the average relative error in the minimizer falls below this amount. epsilon : float or ndarray If fhess is approximated, use this value for the step size. Only relevant if Likelihoodmodel.hessian is None. 'powell' xtol : float Line-search error tolerance ftol : float Relative error in loglike(params) for acceptable for convergence. maxfun : int Maximum number of function evaluations to make. start_direc : ndarray Initial direction set. 'basinhopping' niter : integer The number of basin hopping iterations. niter_success : integer Stop the run if the global minimum candidate remains the same for this number of iterations. T : float The "temperature" parameter for the accept or reject criterion. Higher "temperatures" mean that larger jumps in function value will be accepted. For best results `T` should be comparable to the separation (in function value) between local minima. stepsize : float Initial step size for use in the random displacement. interval : integer The interval for how often to update the `stepsize`. minimizer : dict Extra keyword arguments to be passed to the minimizer `scipy.optimize.minimize()`, for example'method' - the minimization method (e.g. 'L-BFGS-B'), or 'tol' - the tolerance for termination. Other arguments are mapped from explicit argument of `fit`: - `args` <- `fargs` - `jac` <- `score` - `hess` <- `hess` 'minimize' min_method : str, optional Name of minimization method to use. Any method specific arguments can be passed directly. For a list of methods and their arguments, see documentation of `scipy.optimize.minimize`. If no method is specified, then BFGS is used. """ #TODO: generalize the regularization stuff # Extract kwargs specific to fit_regularized calling fit extra_fit_funcs = kwargs.setdefault('extra_fit_funcs', dict()) methods = ['newton', 'nm', 'bfgs', 'lbfgs', 'powell', 'cg', 'ncg', 'basinhopping','minimize'] methods += extra_fit_funcs.keys() method = method.lower() _check_method(method, methods) fit_funcs = { 'newton': _fit_newton, 'nm': _fit_nm, # Nelder-Mead 'bfgs': _fit_bfgs, 'lbfgs': _fit_lbfgs, 'cg': _fit_cg, 'ncg': _fit_ncg, 'powell': _fit_powell, 'basinhopping': _fit_basinhopping, 'minimize': _fit_minimize # wrapper for scipy.optimize.minimize } #NOTE: fit_regularized checks the methods for these but it should be # moved up probably if extra_fit_funcs: fit_funcs.update(extra_fit_funcs) func = fit_funcs[method] xopt, retvals = func(objective, gradient, start_params, fargs, kwargs, disp=disp, maxiter=maxiter, callback=callback, retall=retall, full_output=full_output, hess=hessian) optim_settings = {'optimizer': method,'start_params': start_params, 'maxiter': maxiter, 'full_output': full_output, 'disp': disp, 'fargs': fargs, 'callback': callback, 'retall': retall} optim_settings.update(kwargs) # set as attributes or return? return xopt, retvals, optim_settings def _fit_constrained(self, params): """ TODO: how to add constraints? Something like sm.add_constraint(Model, func) or model_instance.add_constraint(func) model_instance.add_constraint("x1 + x2 = 2") result = model_instance.fit() """ raise NotImplementedError def _fit_regularized(self, params): # TODO: code won't necessarily be general here. 3 options. # 1) setup for scipy.optimize.fmin_sqlsqp # 2) setup for cvxopt # 3) setup for openopt raise NotImplementedError ######################################## # Helper functions to fit def _fit_minimize(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): kwargs.setdefault('min_method', 'BFGS') # prepare options dict for minimize filter_opts = ['extra_fit_funcs', 'niter','min_method', 'tol'] options = dict((k,v) for k,v in kwargs.items() if k not in filter_opts) options['disp'] = disp options['maxiter'] = maxiter # Use Hessian/Jacobian only if they're required by the method no_hess = ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'COBYLA', 'SLSQP'] no_jac = ['Nelder-Mead', 'Powell', 'COBYLA'] if kwargs['min_method'] in no_hess: hess = None if kwargs['min_method'] in no_jac: score = None res = optimize.minimize(f, start_params, args=fargs, method=kwargs['min_method'], jac=score, hess=hess, callback=callback, options=options) xopt = res.x retvals = None if full_output: nit = getattr(res, 'nit', np.nan) # scipy 0.14 compat retvals = {'fopt': res.fun, 'iterations': nit, 'fcalls': res.nfev, 'warnflag': res.status, 'converged': res.success} if retall: retvals.update({'allvecs': res.values()}) return xopt, retvals def _fit_newton(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None, ridge_factor=1e-10): tol = kwargs.setdefault('tol', 1e-8) iterations = 0 oldparams = np.inf newparams = np.asarray(start_params) if retall: history = [oldparams, newparams] while (iterations < maxiter and np.any(np.abs(newparams - oldparams) > tol)): H = np.asarray(hess(newparams)) # regularize Hessian, not clear what ridge factor should be # keyword option with absolute default 1e-10, see #1847 if not np.all(ridge_factor == 0): H[np.diag_indices(H.shape[0])] += ridge_factor oldparams = newparams newparams = oldparams - np.dot(np.linalg.inv(H), score(oldparams)) if retall: history.append(newparams) if callback is not None: callback(newparams) iterations += 1 fval = f(newparams, *fargs) # this is the negative likelihood if iterations == maxiter: warnflag = 1 if disp: print("Warning: Maximum number of iterations has been " "exceeded.") print(" Current function value: %f" % fval) print(" Iterations: %d" % iterations) else: warnflag = 0 if disp: print("Optimization terminated successfully.") print(" Current function value: %f" % fval) print(" Iterations %d" % iterations) if full_output: (xopt, fopt, niter, gopt, hopt) = (newparams, f(newparams, *fargs), iterations, score(newparams), hess(newparams)) converged = not warnflag retvals = {'fopt': fopt, 'iterations': niter,'score': gopt, 'Hessian': hopt, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': history}) else: xopt = newparams retvals = None return xopt, retvals def _fit_bfgs(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): gtol = kwargs.setdefault('gtol', 1.0000000000000001e-05) norm = kwargs.setdefault('norm', np.Inf) epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08) retvals = optimize.fmin_bfgs(f, start_params, score, args=fargs, gtol=gtol, norm=norm, epsilon=epsilon, maxiter=maxiter, full_output=full_output, disp=disp, retall=retall, callback=callback) if full_output: if not retall: xopt, fopt, gopt, Hinv, fcalls, gcalls, warnflag = retvals else: (xopt, fopt, gopt, Hinv, fcalls, gcalls, warnflag, allvecs) = retvals converged = not warnflag retvals = {'fopt': fopt, 'gopt': gopt, 'Hinv': Hinv, 'fcalls': fcalls, 'gcalls': gcalls, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': allvecs}) else: xopt = retvals retvals = None return xopt, retvals def _fit_lbfgs(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): """ Fit model using L-BFGS algorithm Parameters ---------- f : function Returns negative log likelihood given parameters. score : function Returns gradient of negative log likelihood with respect to params. Notes ----- Within the mle part of statsmodels, the log likelihood function and its gradient with respect to the parameters do not have notationally consistent sign. """ # Use unconstrained optimization by default. bounds = kwargs.setdefault('bounds', [(None, None)] * len(start_params)) kwargs.setdefault('iprint', 0) # Pass the following keyword argument names through to fmin_l_bfgs_b # if they are present in kwargs, otherwise use the fmin_l_bfgs_b # default values. names = ('m', 'pgtol', 'factr','maxfun', 'epsilon', 'approx_grad') extra_kwargs = dict((x, kwargs[x]) for x in names if x in kwargs) # Extract values for the options related to the gradient. approx_grad = kwargs.get('approx_grad', False) loglike_and_score = kwargs.get('loglike_and_score', None) epsilon = kwargs.get('epsilon', None) # The approx_grad flag has superpowers nullifying the score function arg. if approx_grad: score = None # Choose among three options for dealing with the gradient (the gradient # of a log likelihood function with respect to its parameters # is more specifically called the score in statistics terminology). # The first option is to use the finite-differences # approximation that is built into the fmin_l_bfgs_b optimizer. # The second option is to use the provided score function. # The third option is to use the score component of a provided # function that simultaneously evaluates the log likelihood and score. if epsilon and not approx_grad: raise ValueError('a finite-differences epsilon was provided ' 'even though we are not using approx_grad') if approx_grad and loglike_and_score: raise ValueError('gradient approximation was requested ' 'even though an analytic loglike_and_score function ' 'was given') if loglike_and_score: func = lambda p, *a : tuple(-x for x in loglike_and_score(p, *a)) elif score: func = f extra_kwargs['fprime'] = score elif approx_grad: func = f retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter, callback=callback, args=fargs, bounds=bounds, disp=disp, **extra_kwargs) if full_output: xopt, fopt, d = retvals # The warnflag is # 0 if converged # 1 if too many function evaluations or too many iterations # 2 if stopped for another reason, given in d['task'] warnflag = d['warnflag'] converged = (warnflag == 0) gopt = d['grad'] fcalls = d['funcalls'] iterations = d['nit'] retvals = {'fopt': fopt, 'gopt': gopt, 'fcalls': fcalls, 'warnflag': warnflag, 'converged': converged, 'iterations': iterations} else: xopt = retvals[0] retvals = None return xopt, retvals def _fit_nm(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): xtol = kwargs.setdefault('xtol', 0.0001) ftol = kwargs.setdefault('ftol', 0.0001) maxfun = kwargs.setdefault('maxfun', None) retvals = optimize.fmin(f, start_params, args=fargs, xtol=xtol, ftol=ftol, maxiter=maxiter, maxfun=maxfun, full_output=full_output, disp=disp, retall=retall, callback=callback) if full_output: if not retall: xopt, fopt, niter, fcalls, warnflag = retvals else: xopt, fopt, niter, fcalls, warnflag, allvecs = retvals converged = not warnflag retvals = {'fopt': fopt, 'iterations': niter, 'fcalls': fcalls, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': allvecs}) else: xopt = retvals retvals = None return xopt, retvals def _fit_cg(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): gtol = kwargs.setdefault('gtol', 1.0000000000000001e-05) norm = kwargs.setdefault('norm', np.Inf) epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08) retvals = optimize.fmin_cg(f, start_params, score, gtol=gtol, norm=norm, epsilon=epsilon, maxiter=maxiter, full_output=full_output, disp=disp, retall=retall, callback=callback) if full_output: if not retall: xopt, fopt, fcalls, gcalls, warnflag = retvals else: xopt, fopt, fcalls, gcalls, warnflag, allvecs = retvals converged = not warnflag retvals = {'fopt': fopt, 'fcalls': fcalls, 'gcalls': gcalls, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': allvecs}) else: xopt = retvals retvals = None return xopt, retvals def _fit_ncg(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): fhess_p = kwargs.setdefault('fhess_p', None) avextol = kwargs.setdefault('avextol', 1.0000000000000001e-05) epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08) retvals = optimize.fmin_ncg(f, start_params, score, fhess_p=fhess_p, fhess=hess, args=fargs, avextol=avextol, epsilon=epsilon, maxiter=maxiter, full_output=full_output, disp=disp, retall=retall, callback=callback) if full_output: if not retall: xopt, fopt, fcalls, gcalls, hcalls, warnflag = retvals else: xopt, fopt, fcalls, gcalls, hcalls, warnflag, allvecs =\ retvals converged = not warnflag retvals = {'fopt': fopt, 'fcalls': fcalls, 'gcalls': gcalls, 'hcalls': hcalls, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': allvecs}) else: xopt = retvals retvals = None return xopt, retvals def _fit_powell(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): xtol = kwargs.setdefault('xtol', 0.0001) ftol = kwargs.setdefault('ftol', 0.0001) maxfun = kwargs.setdefault('maxfun', None) start_direc = kwargs.setdefault('start_direc', None) retvals = optimize.fmin_powell(f, start_params, args=fargs, xtol=xtol, ftol=ftol, maxiter=maxiter, maxfun=maxfun, full_output=full_output, disp=disp, retall=retall, callback=callback, direc=start_direc) if full_output: if not retall: xopt, fopt, direc, niter, fcalls, warnflag = retvals else: xopt, fopt, direc, niter, fcalls, warnflag, allvecs =\ retvals converged = not warnflag retvals = {'fopt': fopt, 'direc': direc, 'iterations': niter, 'fcalls': fcalls, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': allvecs}) else: xopt = retvals retvals = None return xopt, retvals def _fit_basinhopping(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): from copy import copy kwargs = copy(kwargs) niter = kwargs.setdefault('niter', 100) niter_success = kwargs.setdefault('niter_success', None) T = kwargs.setdefault('T', 1.0) stepsize = kwargs.setdefault('stepsize', 0.5) interval = kwargs.setdefault('interval', 50) minimizer_kwargs = kwargs.get('minimizer', {}) minimizer_kwargs['args'] = fargs minimizer_kwargs['jac'] = score method = minimizer_kwargs.get('method', None) if method and method!= 'L-BFGS-B': # l_bfgs_b doesn't take a hessian minimizer_kwargs['hess'] = hess retvals = optimize.basinhopping(f, start_params, minimizer_kwargs=minimizer_kwargs, niter=niter, niter_success=niter_success, T=T, stepsize=stepsize, disp=disp, callback=callback, interval=interval) if full_output: xopt, fopt, niter, fcalls = map(lambda x : getattr(retvals, x), ['x', 'fun', 'nit', 'nfev']) converged = 'completed successfully' in retvals.message[0] retvals = {'fopt': fopt, 'iterations': niter, 'fcalls': fcalls, 'converged': converged} else: xopt = retvals.x retvals = None return xopt, retvals

statsmodels__statsmodels

regression.rst

Description / Module doc

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statsmodels__statsmodels/docs/source/regression.rst

Linear Regression Linear models with independently and identically distributed errors, and for errors with heteroscedasticity or autocorrelation. This module allows estimation by ordinary least squares (OLS), weighted least squares (WLS), generalized least squares (GLS), and feasible generalized least squares with autocorrelated AR(p) errors. Examples # Load modules and data import numpy as np import statsmodels.api as sm spector_data = sm.datasets.spector.load(as_pandas=False) spector_data.exog = sm.add_constant(spector_data.exog, prepend=False) # Fit and summarize OLS model mod = sm.OLS(spector_data.endog, spector_data.exog) res = mod.fit() print(res.summary()) Detailed examples can be found here: - OLS - WLS - GLS - Recursive LS Technical Documentation The statistical model is assumed to be Y = Xβ + μ, where μ ∼ N(0,Σ). Depending on the properties of Σ, we have currently four classes available: - GLS : generalized least squares for arbitrary covariance Σ - OLS : ordinary least squares for i.i.d. errors Σ = I - WLS : weighted least squares for heteroskedastic errors diag(Σ) - GLSAR : feasible generalized least squares with autocorrelated AR(p) errors Σ = Σ(ρ) All regression models define the same methods and follow the same structure, and can be used in a similar fashion. Some of them contain additional model specific methods and attributes. GLS is the superclass of the other regression classes except for RecursiveLS.

#!/usr/bin/env python ''' Quantile regression model Model parameters are estimated using iterated reweighted least squares. The asymptotic covariance matrix estimated using kernel density estimation. Author: Vincent Arel-Bundock License: BSD-3 Created: 2013-03-19 The original IRLS function was written for Matlab by Shapour Mohammadi, University of Tehran, 2008 (shmohammadi@gmail.com), with some lines based on code written by James P. Lesage in Applied Econometrics Using MATLAB(1999).PP. 73-4. Translated to python with permission from original author by Christian Prinoth (christian at prinoth dot name). ''' from statsmodels.compat.python import range import numpy as np import warnings import scipy.stats as stats from scipy.linalg import pinv from scipy.stats import norm from statsmodels.tools.tools import chain_dot from statsmodels.tools.decorators import cache_readonly from statsmodels.regression.linear_model import (RegressionModel, RegressionResults, RegressionResultsWrapper) from statsmodels.tools.sm_exceptions import (ConvergenceWarning, IterationLimitWarning) class QuantReg(RegressionModel): '''Quantile Regression Estimate a quantile regression model using iterative reweighted least squares. Parameters ---------- endog : array or dataframe endogenous/response variable exog : array or dataframe exogenous/explanatory variable(s) Notes ----- The Least Absolute Deviation (LAD) estimator is a special case where quantile is set to 0.5 (q argument of the fit method). The asymptotic covariance matrix is estimated following the procedure in Greene (2008, p.407-408), using either the logistic or gaussian kernels (kernel argument of the fit method). References ---------- General: * Birkes, D. and Y. Dodge(1993). Alternative Methods of Regression, John Wiley and Sons. * Green,W. H. (2008). Econometric Analysis. Sixth Edition. International Student Edition. * Koenker, R. (2005). Quantile Regression. New York: Cambridge University Press. * LeSage, J. P.(1999). Applied Econometrics Using MATLAB, Kernels (used by the fit method): * Green (2008) Table 14.2 Bandwidth selection (used by the fit method): * Bofinger, E. (1975). Estimation of a density function using order statistics. Australian Journal of Statistics 17: 1-17. * Chamberlain, G. (1994). Quantile regression, censoring, and the structure of wages. In Advances in Econometrics, Vol. 1: Sixth World Congress, ed. C. A. Sims, 171-209. Cambridge: Cambridge University Press. * Hall, P., and S. Sheather. (1988). On the distribution of the Studentized quantile. Journal of the Royal Statistical Society, Series B 50: 381-391. Keywords: Least Absolute Deviation(LAD) Regression, Quantile Regression, Regression, Robust Estimation. ''' def __init__(self, endog, exog, **kwargs): super(QuantReg, self).__init__(endog, exog, **kwargs) def whiten(self, data): """ QuantReg model whitener does nothing: returns data. """ return data def fit(self, q=.5, vcov='robust', kernel='epa', bandwidth='hsheather', max_iter=1000, p_tol=1e-6, **kwargs): '''Solve by Iterative Weighted Least Squares Parameters ---------- q : float Quantile must be between 0 and 1 vcov : string, method used to calculate the variance-covariance matrix of the parameters. Default is ``robust``: - robust : heteroskedasticity robust standard errors (as suggested in Greene 6th edition) - iid : iid errors (as in Stata 12) kernel : string, kernel to use in the kernel density estimation for the asymptotic covariance matrix: - epa: Epanechnikov - cos: Cosine - gau: Gaussian - par: Parzene bandwidth: string, Bandwidth selection method in kernel density estimation for asymptotic covariance estimate (full references in QuantReg docstring): - hsheather: Hall-Sheather (1988) - bofinger: Bofinger (1975) - chamberlain: Chamberlain (1994) ''' if q < 0 or q > 1: raise Exception('p must be between 0 and 1') kern_names = ['biw', 'cos', 'epa', 'gau', 'par'] if kernel not in kern_names: raise Exception("kernel must be one of " + ', '.join(kern_names)) else: kernel = kernels[kernel] if bandwidth == 'hsheather': bandwidth = hall_sheather elif bandwidth == 'bofinger': bandwidth = bofinger elif bandwidth == 'chamberlain': bandwidth = chamberlain else: raise Exception("bandwidth must be in 'hsheather', 'bofinger', 'chamberlain'") endog = self.endog exog = self.exog nobs = self.nobs exog_rank = np.linalg.matrix_rank(self.exog) self.rank = exog_rank self.df_model = float(self.rank - self.k_constant) self.df_resid = self.nobs - self.rank n_iter = 0 xstar = exog beta = np.ones(exog_rank) # TODO: better start, initial beta is used only for convergence check # Note the following doesn't work yet, # the iteration loop always starts with OLS as initial beta # if start_params is not None: # if len(start_params)!= rank: # raise ValueError('start_params has wrong length') # beta = start_params # else: # # start with OLS # beta = np.dot(np.linalg.pinv(exog), endog) diff = 10 cycle = False history = dict(params = [], mse=[]) while n_iter < max_iter and diff > p_tol and not cycle: n_iter += 1 beta0 = beta xtx = np.dot(xstar.T, exog) xty = np.dot(xstar.T, endog) beta = np.dot(pinv(xtx), xty) resid = endog - np.dot(exog, beta) mask = np.abs(resid) <.000001 resid[mask] = ((resid[mask] >= 0) * 2 - 1) *.000001 resid = np.where(resid < 0, q * resid, (1-q) * resid) resid = np.abs(resid) xstar = exog / resid[:, np.newaxis] diff = np.max(np.abs(beta - beta0)) history['params'].append(beta) history['mse'].append(np.mean(resid*resid)) if (n_iter >= 300) and (n_iter % 100 == 0): # check for convergence circle, shouldn't happen for ii in range(2, 10): if np.all(beta == history['params'][-ii]): cycle = True warnings.warn("Convergence cycle detected", ConvergenceWarning) break if n_iter == max_iter: warnings.warn("Maximum number of iterations (" + str(max_iter) + ") reached.", IterationLimitWarning) e = endog - np.dot(exog, beta) # Greene (2008, p.407) writes that Stata 6 uses this bandwidth: # h = 0.9 * np.std(e) / (nobs**0.2) # Instead, we calculate bandwidth as in Stata 12 iqre = stats.scoreatpercentile(e, 75) - stats.scoreatpercentile(e, 25) h = bandwidth(nobs, q) h = min(np.std(endog), iqre / 1.34) * (norm.ppf(q + h) - norm.ppf(q - h)) fhat0 = 1. / (nobs * h) * np.sum(kernel(e / h)) if vcov == 'robust': d = np.where(e > 0, (q/fhat0)**2, ((1-q)/fhat0)**2) xtxi = pinv(np.dot(exog.T, exog)) xtdx = np.dot(exog.T * d[np.newaxis, :], exog) vcov = chain_dot(xtxi, xtdx, xtxi) elif vcov == 'iid': vcov = (1. / fhat0)**2 * q * (1 - q) * pinv(np.dot(exog.T, exog)) else: raise Exception("vcov must be 'robust' or 'iid'") lfit = QuantRegResults(self, beta, normalized_cov_params=vcov) lfit.q = q lfit.iterations = n_iter lfit.sparsity = 1. / fhat0 lfit.bandwidth = h lfit.history = history return RegressionResultsWrapper(lfit) def _parzen(u): z = np.where(np.abs(u) <=.5, 4./3 - 8. * u**2 + 8. * np.abs(u)**3, 8. * (1 - np.abs(u))**3 / 3.) z[np.abs(u) > 1] = 0 return z kernels = {} kernels['biw'] = lambda u: 15. / 16 * (1 - u**2)**2 * np.where(np.abs(u) <= 1, 1, 0) kernels['cos'] = lambda u: np.where(np.abs(u) <=.5, 1 + np.cos(2 * np.pi * u), 0) kernels['epa'] = lambda u: 3. / 4 * (1-u**2) * np.where(np.abs(u) <= 1, 1, 0) kernels['gau'] = lambda u: norm.pdf(u) kernels['par'] = _parzen #kernels['bet'] = lambda u: np.where(np.abs(u) <= 1,.75 * (1 - u) * (1 + u), 0) #kernels['log'] = lambda u: logistic.pdf(u) * (1 - logistic.pdf(u)) #kernels['tri'] = lambda u: np.where(np.abs(u) <= 1, 1 - np.abs(u), 0) #kernels['trw'] = lambda u: 35. / 32 * (1 - u**2)**3 * np.where(np.abs(u) <= 1, 1, 0) #kernels['uni'] = lambda u: 1. / 2 * np.where(np.abs(u) <= 1, 1, 0) def hall_sheather(n, q, alpha=.05): z = norm.ppf(q) num = 1.5 * norm.pdf(z)**2. den = 2. * z**2. + 1. h = n**(-1. / 3) * norm.ppf(1. - alpha / 2.)**(2./3) * (num / den)**(1./3) return h def bofinger(n, q): num = 9. / 2 * norm.pdf(2 * norm.ppf(q))**4 den = (2 * norm.ppf(q)**2 + 1)**2 h = n**(-1. / 5) * (num / den)**(1. / 5) return h def chamberlain(n, q, alpha=.05): return norm.ppf(1 - alpha / 2) * np.sqrt(q*(1 - q) / n) class QuantRegResults(RegressionResults): '''Results instance for the QuantReg model''' @cache_readonly def prsquared(self): q = self.q endog = self.model.endog e = self.resid e = np.where(e < 0, (1 - q) * e, q * e) e = np.abs(e) ered = endog - stats.scoreatpercentile(endog, q * 100) ered = np.where(ered < 0, (1 - q) * ered, q * ered) ered = np.abs(ered) return 1 - np.sum(e) / np.sum(ered) #@cache_readonly def scale(self): return 1. @cache_readonly def bic(self): return np.nan @cache_readonly def aic(self): return np.nan @cache_readonly def llf(self): return np.nan @cache_readonly def rsquared(self): return np.nan @cache_readonly def rsquared_adj(self): return np.nan @cache_readonly def mse(self): return np.nan @cache_readonly def mse_model(self): return np.nan @cache_readonly def mse_total(self): return np.nan @cache_readonly def centered_tss(self): return np.nan @cache_readonly def uncentered_tss(self): return np.nan @cache_readonly def HC0_se(self): raise NotImplementedError @cache_readonly def HC1_se(self): raise NotImplementedError @cache_readonly def HC2_se(self): raise NotImplementedError @cache_readonly def HC3_se(self): raise NotImplementedError def summary(self, yname=None, xname=None, title=None, alpha=.05): """Summarize the Regression Results Parameters ---------- yname : str, optional Default is `y` xname : list[str], optional Names for the exogenous variables. Default is `var_##` for ## in the number of regressors. Must match the number of parameters in the model title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ eigvals = self.eigenvals condno = self.condition_number top_left = [('Dep. Variable:', None), ('Model:', None), ('Method:', ['Least Squares']), ('Date:', None), ('Time:', None) ] top_right = [('Pseudo R-squared:', ["%#8.4g" % self.prsquared]), ('Bandwidth:', ["%#8.4g" % self.bandwidth]), ('Sparsity:', ["%#8.4g" % self.sparsity]), ('No. Observations:', None), ('Df Residuals:', None), ('Df Model:', None) ] if title is None: title = self.model.__class__.__name__ +'' + "Regression Results" # create summary table instance from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=self.use_t) # add warnings/notes, added to text format only etext = [] if eigvals[-1] < 1e-10: wstr = "The smallest eigenvalue is %6.3g. This might indicate " wstr += "that there are\n" wstr += "strong multicollinearity problems or that the design " wstr += "matrix is singular." wstr = wstr % eigvals[-1] etext.append(wstr) elif condno > 1000: # TODO: what is recommended wstr = "The condition number is large, %6.3g. This might " wstr += "indicate that there are\n" wstr += "strong multicollinearity or other numerical " wstr += "problems." wstr = wstr % condno etext.append(wstr) if etext: smry.add_extra_txt(etext) return smry # TODO: Determine which tests are valid for GLSAR, and under what conditions # TODO: Fix issue with constant and GLS # TODO: GLS: add options Iterative GLS, for iterative fgls if sigma is None # TODO: GLS: default if sigma is none should be two-step GLS # TODO: Check nesting when performing model based tests, lr, wald, lm """ This module implements standard regression models: Generalized Least Squares (GLS) Ordinary Least Squares (OLS) Weighted Least Squares (WLS) Generalized Least Squares with autoregressive error terms GLSAR(p) Models are specified with an endogenous response variable and an exogenous design matrix and are fit using their `fit` method. Subclasses that have more complicated covariance matrices should write over the 'whiten' method as the fit method prewhitens the response by calling 'whiten'. General reference for regression models: D. C. Montgomery and E.A. Peck. "Introduction to Linear Regression Analysis." 2nd. Ed., Wiley, 1992. Econometrics references for regression models: R. Davidson and J.G. MacKinnon. "Econometric Theory and Methods," Oxford, 2004. W. Green. "Econometric Analysis," 5th ed., Pearson, 2003. """ from statsmodels.compat.python import lrange, lzip, range import numpy as np from scipy.linalg import toeplitz from scipy import stats from scipy import optimize from statsmodels.tools.tools import chain_dot, pinv_extended from statsmodels.tools.decorators import (cache_readonly, cache_writable) import statsmodels.base.model as base import statsmodels.base.wrapper as wrap from statsmodels.emplike.elregress import _ELRegOpts import warnings from statsmodels.tools.sm_exceptions import InvalidTestWarning # need import in module instead of lazily to copy `__doc__` from statsmodels.regression._prediction import PredictionResults from. import _prediction as pred __docformat__ ='restructuredtext en' __all__ = ['GLS', 'WLS', 'OLS', 'GLSAR', 'PredictionResults'] _fit_regularized_doc =\ r""" Return a regularized fit to a linear regression model. Parameters ---------- method : string 'elastic_net' and'sqrt_lasso' are currently implemented. alpha : scalar or array_like The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as `params`, and contains a penalty weight for each coefficient. L1_wt: scalar The fraction of the penalty given to the L1 penalty term. Must be between 0 and 1 (inclusive). If 0, the fit is a ridge fit, if 1 it is a lasso fit. start_params : array_like Starting values for ``params``. profile_scale : bool If True the penalized fit is computed using the profile (concentrated) log-likelihood for the Gaussian model. Otherwise the fit uses the residual sum of squares. refit : bool If True, the model is refit using only the variables that have non-zero coefficients in the regularized fit. The refitted model is not regularized. distributed : bool If True, the model uses distributed methods for fitting, will raise an error if True and partitions is None. generator : function generator used to partition the model, allows for handling of out of memory/parallel computing. partitions : scalar The number of partitions desired for the distributed estimation. threshold : scalar or array_like The threshold below which coefficients are zeroed out, only used for distributed estimation Returns ------- A RegularizedResults instance. Notes ----- The elastic net uses a combination of L1 and L2 penalties. The implementation closely follows the glmnet package in R. The function that is minimized is: .. math:: 0.5*RSS/n + alpha*((1-L1\_wt)*|params|_2^2/2 + L1\_wt*|params|_1) where RSS is the usual regression sum of squares, n is the sample size, and :math:`|*|_1` and :math:`|*|_2` are the L1 and L2 norms. For WLS and GLS, the RSS is calculated using the whitened endog and exog data. Post-estimation results are based on the same data used to select variables, hence may be subject to overfitting biases. The elastic_net method uses the following keyword arguments: maxiter : int Maximum number of iterations cnvrg_tol : float Convergence threshold for line searches zero_tol : float Coefficients below this threshold are treated as zero. The square root lasso approach is a variation of the Lasso that is largely self-tuning (the optimal tuning parameter does not depend on the standard deviation of the regression errors). If the errors are Gaussian, the tuning parameter can be taken to be alpha = 1.1 * np.sqrt(n) * norm.ppf(1 - 0.05 / (2 * p)) where n is the sample size and p is the number of predictors. The square root lasso uses the following keyword arguments: zero_tol : float Coefficients below this threshold are treated as zero. References ---------- Friedman, Hastie, Tibshirani (2008). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33(1), 1-22 Feb 2010. A Belloni, V Chernozhukov, L Wang (2011). Square-root Lasso: pivotal recovery of sparse signals via conic programming. Biometrika 98(4), 791-806. https://arxiv.org/pdf/1009.5689.pdf """ def _get_sigma(sigma, nobs): """ Returns sigma (matrix, nobs by nobs) for GLS and the inverse of its Cholesky decomposition. Handles dimensions and checks integrity. If sigma is None, returns None, None. Otherwise returns sigma, cholsigmainv. """ if sigma is None: return None, None sigma = np.asarray(sigma).squeeze() if sigma.ndim == 0: sigma = np.repeat(sigma, nobs) if sigma.ndim == 1: if sigma.shape!= (nobs,): raise ValueError("Sigma must be a scalar, 1d of length %s or a 2d " "array of shape %s x %s" % (nobs, nobs, nobs)) cholsigmainv = 1/np.sqrt(sigma) else: if sigma.shape!= (nobs, nobs): raise ValueError("Sigma must be a scalar, 1d of length %s or a 2d " "array of shape %s x %s" % (nobs, nobs, nobs)) cholsigmainv = np.linalg.cholesky(np.linalg.inv(sigma)).T return sigma, cholsigmainv class RegressionModel(base.LikelihoodModel): """ Base class for linear regression models. Should not be directly called. Intended for subclassing. """ def __init__(self, endog, exog, **kwargs): super(RegressionModel, self).__init__(endog, exog, **kwargs) self._data_attr.extend(['pinv_wexog', 'wendog', 'wexog', 'weights']) def initialize(self): self.wexog = self.whiten(self.exog) self.wendog = self.whiten(self.endog) # overwrite nobs from class Model: self.nobs = float(self.wexog.shape[0]) self._df_model = None self._df_resid = None self.rank = None @property def df_model(self): """ The model degree of freedom, defined as the rank of the regressor matrix minus 1 if a constant is included. """ if self._df_model is None: if self.rank is None: self.rank = np.linalg.matrix_rank(self.exog) self._df_model = float(self.rank - self.k_constant) return self._df_model @df_model.setter def df_model(self, value): self._df_model = value @property def df_resid(self): """ The residual degree of freedom, defined as the number of observations minus the rank of the regressor matrix. """ if self._df_resid is None: if self.rank is None: self.rank = np.linalg.matrix_rank(self.exog) self._df_resid = self.nobs - self.rank return self._df_resid @df_resid.setter def df_resid(self, value): self._df_resid = value def whiten(self, X): raise NotImplementedError("Subclasses should implement.") def fit(self, method="pinv", cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs): """ Full fit of the model. The results include an estimate of covariance matrix, (whitened) residuals and an estimate of scale. Parameters ---------- method : str, optional Can be "pinv", "qr". "pinv" uses the Moore-Penrose pseudoinverse to solve the least squares problem. "qr" uses the QR factorization. cov_type : str, optional See `regression.linear_model.RegressionResults` for a description of the available covariance estimators cov_kwds : list or None, optional See `linear_model.RegressionResults.get_robustcov_results` for a description required keywords for alternative covariance estimators use_t : bool, optional Flag indicating to use the Student's t distribution when computing p-values. Default behavior depends on cov_type. See `linear_model.RegressionResults.get_robustcov_results` for implementation details. Returns ------- A RegressionResults class instance. See Also -------- regression.linear_model.RegressionResults regression.linear_model.RegressionResults.get_robustcov_results Notes ----- The fit method uses the pseudoinverse of the design/exogenous variables to solve the least squares minimization. """ if method == "pinv": if not (hasattr(self, 'pinv_wexog') and hasattr(self, 'normalized_cov_params') and hasattr(self, 'rank')): self.pinv_wexog, singular_values = pinv_extended(self.wexog) self.normalized_cov_params = np.dot( self.pinv_wexog, np.transpose(self.pinv_wexog)) # Cache these singular values for use later. self.wexog_singular_values = singular_values self.rank = np.linalg.matrix_rank(np.diag(singular_values)) beta = np.dot(self.pinv_wexog, self.wendog) elif method == "qr": if not (hasattr(self, 'exog_Q') and hasattr(self, 'exog_R') and hasattr(self, 'normalized_cov_params') and hasattr(self, 'rank')): Q, R = np.linalg.qr(self.wexog) self.exog_Q, self.exog_R = Q, R self.normalized_cov_params = np.linalg.inv(np.dot(R.T, R)) # Cache singular values from R. self.wexog_singular_values = np.linalg.svd(R, 0, 0) self.rank = np.linalg.matrix_rank(R) else: Q, R = self.exog_Q, self.exog_R # used in ANOVA self.effects = effects = np.dot(Q.T, self.wendog) beta = np.linalg.solve(R, effects) else: raise ValueError('method has to be "pinv" or "qr"') if self._df_model is None: self._df_model = float(self.rank - self.k_constant) if self._df_resid is None: self.df_resid = self.nobs - self.rank if isinstance(self, OLS): lfit = OLSResults( self, beta, normalized_cov_params=self.normalized_cov_params, cov_type=cov_type, cov_kwds=cov_kwds, use_t=use_t) else: lfit = RegressionResults( self, beta, normalized_cov_params=self.normalized_cov_params, cov_type=cov_type, cov_kwds=cov_kwds, use_t=use_t, **kwargs) return RegressionResultsWrapper(lfit) def predict(self, params, exog=None): """ Return linear predicted values from a design matrix. Parameters ---------- params : array_like Parameters of a linear model exog : array_like, optional. Design / exogenous data. Model exog is used if None. Returns ------- An array of fitted values Notes ----- If the model has not yet been fit, params is not optional. """ # JP: this doesn't look correct for GLMAR # SS: it needs its own predict method if exog is None: exog = self.exog return np.dot(exog, params) def get_distribution(self, params, scale, exog=None, dist_class=None): """ Returns a random number generator for the predictive distribution. Parameters ---------- params : array_like The model parameters (regression coefficients). scale : scalar The variance parameter. exog : array_like The predictor variable matrix. dist_class : class A random number generator class. Must take 'loc' and'scale' as arguments and return a random number generator implementing an ``rvs`` method for simulating random values. Defaults to Gaussian. Returns ------- gen Frozen random number generator object with mean and variance determined by the fitted linear model. Use the ``rvs`` method to generate random values. Notes ----- Due to the behavior of ``scipy.stats.distributions objects``, the returned random number generator must be called with ``gen.rvs(n)`` where ``n`` is the number of observations in the data set used to fit the model. If any other value is used for ``n``, misleading results will be produced. """ fit = self.predict(params, exog) if dist_class is None: from scipy.stats.distributions import norm dist_class = norm gen = dist_class(loc=fit, scale=np.sqrt(scale)) return gen class GLS(RegressionModel): __doc__ = r""" Generalized least squares model with a general covariance structure. %(params)s sigma : scalar or array `sigma` is the weighting matrix of the covariance. The default is None for no scaling. If `sigma` is a scalar, it is assumed that `sigma` is an n x n diagonal matrix with the given scalar, `sigma` as the value of each diagonal element. If `sigma` is an n-length vector, then `sigma` is assumed to be a diagonal matrix with the given `sigma` on the diagonal. This should be the same as WLS. %(extra_params)s Attributes ---------- pinv_wexog : array `pinv_wexog` is the p x n Moore-Penrose pseudoinverse of `wexog`. cholsimgainv : array The transpose of the Cholesky decomposition of the pseudoinverse. df_model : float p - 1, where p is the number of regressors including the intercept. of freedom. df_resid : float Number of observations n less the number of parameters p. llf : float The value of the likelihood function of the fitted model. nobs : float The number of observations n. normalized_cov_params : array p x p array :math:`(X^{T}\Sigma^{-1}X)^{-1}` results : RegressionResults instance A property that returns the RegressionResults class if fit. sigma : array `sigma` is the n x n covariance structure of the error terms. wexog : array Design matrix whitened by `cholsigmainv` wendog : array Response variable whitened by `cholsigmainv` Notes ----- If sigma is a function of the data making one of the regressors a constant, then the current postestimation statistics will not be correct. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm >>> data = sm.datasets.longley.load(as_pandas=False) >>> data.exog = sm.add_constant(data.exog) >>> ols_resid = sm.OLS(data.endog, data.exog).fit().resid >>> res_fit = sm.OLS(ols_resid[1:], ols_resid[:-1]).fit() >>> rho = res_fit.params `rho` is a consistent estimator of the correlation of the residuals from an OLS fit of the longley data. It is assumed that this is the true rho of the AR process data. >>> from scipy.linalg import toeplitz >>> order = toeplitz(np.arange(16)) >>> sigma = rho**order `sigma` is an n x n matrix of the autocorrelation structure of the data. >>> gls_model = sm.GLS(data.endog, data.exog, sigma=sigma) >>> gls_results = gls_model.fit() >>> print(gls_results.summary()) """ % {'params': base._model_params_doc, 'extra_params': base._missing_param_doc + base._extra_param_doc} def __init__(self, endog, exog, sigma=None, missing='none', hasconst=None, **kwargs): # TODO: add options igls, for iterative fgls if sigma is None # TODO: default if sigma is none should be two-step GLS sigma, cholsigmainv = _get_sigma(sigma, len(endog)) super(GLS, self).__init__(endog, exog, missing=missing, hasconst=hasconst, sigma=sigma, cholsigmainv=cholsigmainv, **kwargs) # store attribute names for data arrays self._data_attr.extend(['sigma', 'cholsigmainv']) def whiten(self, X): """ GLS whiten method. Parameters ---------- X : array_like Data to be whitened. Returns ------- np.dot(cholsigmainv,X) See Also -------- regression.GLS """ X = np.asarray(X) if self.sigma is None or self.sigma.shape == (): return X elif self.sigma.ndim == 1: if X.ndim == 1: return X * self.cholsigmainv else: return X * self.cholsigmainv[:, None] else: return np.dot(self.cholsigmainv, X) def loglike(self, params): """ Returns the value of the Gaussian log-likelihood function at params. Given the whitened design matrix, the log-likelihood is evaluated at the parameter vector `params` for the dependent variable `endog`. Parameters ---------- params : array_like The parameter estimates Returns ------- loglike : float The value of the log-likelihood function for a GLS Model. Notes ----- The log-likelihood function for the normal distribution is .. math:: -\\frac{n}{2}\\log\\left(\\left(Y-\\hat{Y}\\right)^{\\prime}\\left(Y-\\hat{Y}\\right)\\right)-\\frac{n}{2}\\left(1+\\log\\left(\\frac{2\\pi}{n}\\right)\\right)-\\frac{1}{2}\\log\\left(\\left|\\Sigma\\right|\\right) Y and Y-hat are whitened. """ # TODO: combine this with OLS/WLS loglike and add _det_sigma argument nobs2 = self.nobs / 2.0 SSR = np.sum((self.wendog - np.dot(self.wexog, params))**2, axis=0) llf = -np.log(SSR) * nobs2 # concentrated likelihood llf -= (1+np.log(np.pi/nobs2))*nobs2 # with likelihood constant if np.any(self.sigma): # FIXME: robust-enough check? unneeded if _det_sigma gets defined if self.sigma.ndim == 2: det = np.linalg.slogdet(self.sigma) llf -=.5*det[1] else: llf -= 0.5*np.sum(np.log(self.sigma)) # with error covariance matrix return llf def hessian_factor(self, params, scale=None, observed=True): """Weights for calculating Hessian Parameters ---------- params : ndarray parameter at which Hessian is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by `self.scaletype` and set in fit. If scale is not None, then it is used as a fixed scale. observed : bool If True, then the observed Hessian is returned. If false then the expected information matrix is returned. Returns ------- hessian_factor : ndarray, 1d A 1d weight vector used in the calculation of the Hessian. The hessian is obtained by `(exog.T * hessian_factor).dot(exog)` """ if self.sigma is None or self.sigma.shape == (): return np.ones(self.exog.shape[0]) elif self.sigma.ndim == 1: return self.cholsigmainv else: return np.diag(self.cholsigmainv) def fit_regularized(self, method="elastic_net", alpha=0., L1_wt=1., start_params=None, profile_scale=False, refit=False, **kwargs): # Docstring attached below # Need to adjust since RSS/n term in elastic net uses nominal # n in denominator if self.sigma is not None: alpha = alpha * np.sum(1 / np.diag(self.sigma)) / len(self.endog) rslt = OLS(self.wendog, self.wexog).fit_regularized( method=method, alpha=alpha, L1_wt=L1_wt, start_params=start_params, profile_scale=profile_scale, refit=refit, **kwargs) from statsmodels.base.elastic_net import ( RegularizedResults, RegularizedResultsWrapper) rrslt = RegularizedResults(self, rslt.params) return RegularizedResultsWrapper(rrslt) fit_regularized.__doc__ = _fit_regularized_doc class WLS(RegressionModel): __doc__ = """ A regression model with diagonal but non-identity covariance structure. The weights are presumed to be (proportional to) the inverse of the variance of the observations. That is, if the variables are to be transformed by 1/sqrt(W) you must supply weights = 1/W. %(params)s weights : array_like, optional 1d array of weights. If you supply 1/W then the variables are pre- multiplied by 1/sqrt(W). If no weights are supplied the default value is 1 and WLS results are the same as OLS. %(extra_params)s Attributes ---------- weights : array The stored weights supplied as an argument. See Also -------- regression.GLS Examples -------- >>> import numpy as np >>> import statsmodels.api as sm >>> Y = [1,3,4,5,2,3,4] >>> X = range(1,8) >>> X = sm.add_constant(X) >>> wls_model = sm.WLS(Y,X, weights=list(range(1,8))) >>> results = wls_model.fit() >>> results.params array([ 2.91666667, 0.0952381 ]) >>> results.tvalues array([ 2.0652652, 0.35684428]) >>> print(results.t_test([1, 0])) <T test: effect=array([ 2.91666667]), sd=array([[ 1.41224801]]), t=array([[ 2.0652652]]), p=array([[ 0.04690139]]), df_denom=5> >>> print(results.f_test([0, 1])) <F test: F=array([[ 0.12733784]]), p=[[ 0.73577409]], df_denom=5, df_num=1> Notes ----- If the weights are a function of the data, then the post estimation statistics such as fvalue and mse_model might not be correct, as the package does not yet support no-constant regression. """ % {'params': base._model_params_doc, 'extra_params': base._missing_param_doc + base._extra_param_doc} def __init__(self, endog, exog, weights=1., missing='none', hasconst=None, **kwargs): weights = np.array(weights) if weights.shape == (): if (missing == 'drop' and'missing_idx' in kwargs and kwargs['missing_idx'] is not None): # patsy may have truncated endog weights = np.repeat(weights, len(kwargs['missing_idx'])) else: weights = np.repeat(weights, len(endog)) # handle case that endog might be of len == 1 if len(weights) == 1: weights = np.array([weights.squeeze()]) else: weights = weights.squeeze() super(WLS, self).__init__(endog, exog, missing=missing, weights=weights, hasconst=hasconst, **kwargs) nobs = self.exog.shape[0] weights = self.weights # Experimental normalization of weights weights = weights / np.sum(weights) * nobs if weights.size!= nobs and weights.shape[0]!= nobs: raise ValueError('Weights must be scalar or same length as design') def whiten(self, X): """ Whitener for WLS model, multiplies each column by sqrt(self.weights) Parameters ---------- X : array_like Data to be whitened Returns ------- whitened : array_like sqrt(weights)*X """ X = np.asarray(X) if X.ndim == 1: return X * np.sqrt(self.weights) elif X.ndim == 2: return np.sqrt(self.weights)[:, None]*X def loglike(self, params): r""" Returns the value of the gaussian log-likelihood function at params. Given the whitened design matrix, the log-likelihood is evaluated at the parameter vector `params` for the dependent variable `Y`. Parameters ---------- params : array_like The parameter estimates. Returns ------- llf : float The value of the log-likelihood function for a WLS Model. Notes -------- .. math:: -\frac{n}{2}\log SSR -\frac{n}{2}\left(1+\log\left(\frac{2\pi}{n}\right)\right)-\frac{1}{2}\log\left(\left|W\right|\right) where :math:`W` is a diagonal weight matrix matrix and :math:`SSR=\left(Y-\hat{Y}\right)^\prime W \left(Y-\hat{Y}\right)` is the sum of the squared weighted residuals. """ nobs2 = self.nobs / 2.0 SSR = np.sum((self.wendog - np.dot(self.wexog, params))**2, axis=0) llf = -np.log(SSR) * nobs2 # concentrated likelihood llf -= (1+np.log(np.pi/nobs2))*nobs2 # with constant llf += 0.5 * np.sum(np.log(self.weights)) return llf def hessian_factor(self, params, scale=None, observed=True): """Weights for calculating Hessian Parameters ---------- params : ndarray parameter at which Hessian is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by `self.scaletype` and set in fit. If scale is not None, then it is used as a fixed scale. observed : bool If True, then the observed Hessian is returned. If false then the expected information matrix is returned. Returns ------- hessian_factor : ndarray, 1d A 1d weight vector used in the calculation of the Hessian. The hessian is obtained by `(exog.T * hessian_factor).dot(exog)` """ return self.weights def fit_regularized(self, method="elastic_net", alpha=0., L1_wt=1., start_params=None, profile_scale=False, refit=False, **kwargs): # Docstring attached below # Need to adjust since RSS/n in elastic net uses nominal n in # denominator alpha = alpha * np.sum(self.weights) / len(self.weights) rslt = OLS(self.wendog, self.wexog).fit_regularized( method=method, alpha=alpha, L1_wt=L1_wt, start_params=start_params, profile_scale=profile_scale, refit=refit, **kwargs) from statsmodels.base.elastic_net import ( RegularizedResults, RegularizedResultsWrapper) rrslt = RegularizedResults(self, rslt.params) return RegularizedResultsWrapper(rrslt) fit_regularized.__doc__ = _fit_regularized_doc class OLS(WLS): __doc__ = """ A simple ordinary least squares model. %(params)s %(extra_params)s Attributes ---------- weights : scalar Has an attribute weights = array(1.0) due to inheritance from WLS. See Also -------- GLS Examples -------- >>> import numpy as np >>> >>> import statsmodels.api as sm >>> >>> Y = [1,3,4,5,2,3,4] >>> X = range(1,8) >>> X = sm.add_constant(X) >>> >>> model = sm.OLS(Y,X) >>> results = model.fit() >>> results.params array([ 2.14285714, 0.25 ]) >>> results.tvalues array([ 1.87867287, 0.98019606]) >>> print(results.t_test([1, 0])) <T test: effect=array([ 2.14285714]), sd=array([[ 1.14062282]]), t=array([[ 1.87867287]]), p=array([[ 0.05953974]]), df_denom=5> >>> print(results.f_test(np.identity(2))) <F test: F=array([[ 19.46078431]]), p=[[ 0.00437251]], df_denom=5, df_num=2> Notes ----- No constant is added by the model unless you are using formulas. """ % {'params': base._model_params_doc, 'extra_params': base._missing_param_doc + base._extra_param_doc} # TODO: change example to use datasets. This was the point of datasets! def __init__(self, endog, exog=None, missing='none', hasconst=None, **kwargs): super(OLS, self).__init__(endog, exog, missing=missing, hasconst=hasconst, **kwargs) if "weights" in self._init_keys: self._init_keys.remove("weights") def loglike(self, params, scale=None): """ The likelihood function for the OLS model. Parameters ---------- params : array_like The coefficients with which to estimate the log-likelihood. scale : float or None If None, return the profile (concentrated) log likelihood (profiled over the scale parameter), else return the log-likelihood using the given scale value. Returns ------- The likelihood function evaluated at params. """ nobs2 = self.nobs / 2.0 nobs = float(self.nobs) resid = self.endog - np.dot(self.exog, params) if hasattr(self, 'offset'): resid -= self.offset ssr = np.sum(resid**2) if scale is None: # profile log likelihood llf = -nobs2*np.log(2*np.pi) - nobs2*np.log(ssr / nobs) - nobs2 else: # log-likelihood llf = -nobs2 * np.log(2 * np.pi * scale) - ssr / (2*scale) return llf def whiten(self, Y): """ OLS model whitener does nothing: returns Y. """ return Y def score(self, params, scale=None): """ Evaluate the score function at a given point. The score corresponds to the profile (concentrated) log-likelihood in which the scale parameter has been profiled out. Parameters ---------- params : array_like The parameter vector at which the score function is computed. scale : float or None If None, return the profile (concentrated) log likelihood (profiled over the scale parameter), else return the log-likelihood using the given scale value. Returns ------- The score vector. """ if not hasattr(self, "_wexog_xprod"): self._setup_score_hess() xtxb = np.dot(self._wexog_xprod, params) sdr = -self._wexog_x_wendog + xtxb if scale is None: ssr = self._wendog_xprod - 2 * np.dot(self._wexog_x_wendog.T, params) ssr += np.dot(params, xtxb) return -self.nobs * sdr / ssr else: return -sdr / scale def _setup_score_hess(self): y = self.wendog if hasattr(self, 'offset'): y = y - self.offset self._wendog_xprod = np.sum(y * y) self._wexog_xprod = np.dot(self.wexog.T, self.wexog) self._wexog_x_wendog = np.dot(self.wexog.T, y) def hessian(self, params, scale=None): """ Evaluate the Hessian function at a given point. Parameters ---------- params : array_like The parameter vector at which the Hessian is computed. scale : float or None If None, return the profile (concentrated) log likelihood (profiled over the scale parameter), else return the log-likelihood using the given scale value. Returns ------- The Hessian matrix. """ if not hasattr(self, "_wexog_xprod"): self._setup_score_hess() xtxb = np.dot(self._wexog_xprod, params) if scale is None: ssr = self._wendog_xprod - 2 * np.dot(self._wexog_x_wendog.T, params) ssr += np.dot(params, xtxb) ssrp = -2*self._wexog_x_wendog + 2*xtxb hm = self._wexog_xprod / ssr - np.outer(ssrp, ssrp) / ssr**2 return -self.nobs * hm / 2 else: return -self._wexog_xprod / scale def hessian_factor(self, params, scale=None, observed=True): """Weights for calculating Hessian Parameters ---------- params : ndarray parameter at which Hessian is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by `self.scaletype` and set in fit. If scale is not None, then it is used as a fixed scale. observed : bool If True, then the observed Hessian is returned. If false then the expected information matrix is returned. Returns ------- hessian_factor : ndarray, 1d A 1d weight vector used in the calculation of the Hessian. The hessian is obtained by `(exog.T * hessian_factor).dot(exog)` """ return np.ones(self.exog.shape[0]) def fit_regularized(self, method="elastic_net", alpha=0., L1_wt=1., start_params=None, profile_scale=False, refit=False, **kwargs): # Docstring attached below # In the future we could add support for other penalties, e.g. SCAD. if method not in ("elastic_net", "sqrt_lasso"): msg = "Unknown method '%s' for fit_regularized" % method raise ValueError(msg) # Set default parameters. defaults = {"maxiter": 50, "cnvrg_tol": 1e-10, "zero_tol": 1e-8} defaults.update(kwargs) if method == "sqrt_lasso": from statsmodels.base.elastic_net import ( RegularizedResults, RegularizedResultsWrapper ) params = self._sqrt_lasso(alpha, refit, defaults["zero_tol"]) results = RegularizedResults(self, params) return RegularizedResultsWrapper(results) from statsmodels.base.elastic_net import fit_elasticnet if L1_wt == 0: return self._fit_ridge(alpha) # If a scale parameter is passed in, the non-profile # likelihood (residual sum of squares divided by -2) is used, # otherwise the profile likelihood is used. if profile_scale: loglike_kwds = {} score_kwds = {} hess_kwds = {} else: loglike_kwds = {"scale": 1} score_kwds = {"scale": 1} hess_kwds = {"scale": 1} return fit_elasticnet(self, method=method, alpha=alpha, L1_wt=L1_wt, start_params=start_params, loglike_kwds=loglike_kwds, score_kwds=score_kwds, hess_kwds=hess_kwds, refit=refit, check_step=False, **defaults) fit_regularized.__doc__ = _fit_regularized_doc def _sqrt_lasso(self, alpha, refit, zero_tol): try: import cvxopt except ImportError: msg = "sqrt_lasso fitting requires the cvxopt module to be installed" raise ValueError(msg) n = len(self.endog) p = self.exog.shape[1] h0 = cvxopt.matrix(0., (2*p+1, 1)) h1 = cvxopt.matrix(0., (n+1, 1)) h1[1:, 0] = cvxopt.matrix(self.endog, (n, 1)) G0 = cvxopt.spmatrix([], [], [], (2*p+1, 2*p+1)) for i in range(1, 2*p+1): G0[i, i] = -1 G1 = cvxopt.matrix(0., (n+1, 2*p+1)) G1[0, 0] = -1 G1[1:, 1:p+1] = self.exog G1[1:, p+1:] = -self.exog c = cvxopt.matrix(alpha / n, (2*p + 1, 1)) c[0] = 1 / np.sqrt(n) from cvxopt import solvers solvers.options["show_progress"] = False rslt = solvers.socp(c, Gl=G0, hl=h0, Gq=[G1], hq=[h1]) x = np.asarray(rslt['x']).flat bp = x[1:p+1] bn = x[p+1:] params = bp - bn if not refit: return params ii = np.flatnonzero(np.abs(params) > zero_tol) rfr = OLS(self.endog, self.exog[:, ii]).fit() params *= 0 params[ii] = rfr.params return params def _fit_ridge(self, alpha): """ Fit a linear model using ridge regression. Parameters ---------- alpha : scalar or array_like The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as `params`, and contains a penalty weight for each coefficient. Notes ----- Equivalent to fit_regularized with L1_wt = 0 (but implemented more efficiently). """ u, s, vt = np.linalg.svd(self.exog, 0) v = vt.T q = np.dot(u.T, self.endog) * s s2 = s * s if np.isscalar(alpha): sd = s2 + alpha * self.nobs params = q / sd params = np.dot(v, params) else: vtav = self.nobs * np.dot(vt, alpha[:, None] * v) d = np.diag(vtav) + s2 np.fill_diagonal(vtav, d) r = np.linalg.solve(vtav, q) params = np.dot(v, r) from statsmodels.base.elastic_net import RegularizedResults return RegularizedResults(self, params) class GLSAR(GLS): __doc__ = """ A regression model with an AR(p) covariance structure. %(params)s rho : int Order of the autoregressive covariance %(extra_params)s Examples -------- >>> import statsmodels.api as sm >>> X = range(1,8) >>> X = sm.add_constant(X) >>> Y = [1,3,4,5,8,10,9] >>> model = sm.GLSAR(Y, X, rho=2) >>> for i in range(6): ... results = model.fit() ... print("AR coefficients: {0}".format(model.rho)) ... rho, sigma = sm.regression.yule_walker(results.resid, ... order=model.order) ... model = sm.GLSAR(Y, X, rho) ... AR coefficients: [ 0. 0.] AR coefficients: [-0.52571491 -0.84496178] AR coefficients: [-0.6104153 -0.86656458] AR coefficients: [-0.60439494 -0.857867 ] AR coefficients: [-0.6048218 -0.85846157] AR coefficients: [-0.60479146 -0.85841922] >>> results.params array([-0.66661205, 1.60850853]) >>> results.tvalues array([ -2.10304127, 21.8047269 ]) >>> print(results.t_test([1, 0])) <T test: effect=array([-0.66661205]), sd=array([[ 0.31697526]]), t=array([[-2.10304127]]), p=array([[ 0.06309969]]), df_denom=3> >>> print(results.f_test(np.identity(2))) <F test: F=array([[ 1815.23061844]]), p=[[ 0.00002372]], df_denom=3, df_num=2> Or, equivalently >>> model2 = sm.GLSAR(Y, X, rho=2) >>> res = model2.iterative_fit(maxiter=6) >>> model2.rho array([-0.60479146, -0.85841922]) Notes ----- GLSAR is considered to be experimental. The linear autoregressive process of order p--AR(p)--is defined as: TODO """ % {'params': base._model_params_doc, 'extra_params': base._missing_param_doc + base._extra_param_doc} def __init__(self, endog, exog=None, rho=1, missing='none', **kwargs): # this looks strange, interpreting rho as order if it is int if isinstance(rho, np.int): self.order = rho self.rho = np.zeros(self.order, np.float64) else: self.rho = np.squeeze(np.asarray(rho)) if len(self.rho.shape) not in [0, 1]: raise ValueError("AR parameters must be a scalar or a vector") if self.rho.shape == (): self.rho.shape = (1,) self.order = self.rho.shape[0] if exog is None: # JP this looks wrong, should be a regression on constant # results for rho estimate now identical to yule-walker on y # super(AR, self).__init__(endog, add_constant(endog)) super(GLSAR, self).__init__(endog, np.ones((endog.shape[0], 1)), missing=missing, **kwargs) else: super(GLSAR, self).__init__(endog, exog, missing=missing, **kwargs) def iterative_fit(self, maxiter=3, rtol=1e-4, **kwds): """ Perform an iterative two-stage procedure to estimate a GLS model. The model is assumed to have AR(p) errors, AR(p) parameters and regression coefficients are estimated iteratively. Parameters ---------- maxiter : integer, optional the number of iterations rtol : float, optional Relative tolerance between estimated coefficients to stop the estimation. Stops if max(abs(last - current) / abs(last)) < rtol """ # TODO: update this after going through example. converged = False i = -1 # need to initialize for maxiter < 1 (skip loop) history = {'params': [], 'rho': [self.rho]} for i in range(maxiter - 1): if hasattr(self, 'pinv_wexog'): del self.pinv_wexog self.initialize() results = self.fit() history['params'].append(results.params) if i == 0: last = results.params else: diff = np.max(np.abs(last - results.params) / np.abs(last)) if diff < rtol: converged = True break last = results.params self.rho, _ = yule_walker(results.resid, order=self.order, df=None) history['rho'].append(self.rho) # why not another call to self.initialize # Use kwarg to insert history if not converged and maxiter > 0: # maxiter <= 0 just does OLS if hasattr(self, 'pinv_wexog'): del self.pinv_wexog self.initialize() # if converged then this is a duplicate fit, because we didn't # update rho results = self.fit(history=history, **kwds) results.iter = i + 1 # add last fit to history, not if duplicate fit if not converged: results.history['params'].append(results.params) results.iter += 1 results.converged = converged return results def whiten(self, X): """ Whiten a series of columns according to an AR(p) covariance structure. This drops initial p observations. Parameters ---------- X : array_like The data to be whitened, Returns ------- whitened array """ # TODO: notation for AR process X = np.asarray(X, np.float64) _X = X.copy() # the following loops over the first axis, works for 1d and nd for i in range(self.order): _X[(i+1):] = _X[(i+1):] - self.rho[i] * X[0:-(i+1)] return _X[self.order:] def yule_walker(X, order=1, method="unbiased", df=None, inv=False, demean=True): """ Estimate AR(p) parameters from a sequence X using Yule-Walker equation. Unbiased or maximum-likelihood estimator (mle) See, for example: https://en.wikipedia.org/wiki/Autoregressive_moving_average_model Parameters ---------- X : array_like 1d array order : integer, optional The order of the autoregressive process. Default is 1. method : string, optional Method can be 'unbiased' or'mle' and this determines denominator in estimate of autocorrelation function (ACF) at lag k. If'mle', the denominator is n=X.shape[0], if 'unbiased' the denominator is n-k. The default is unbiased. df : integer, optional Specifies the degrees of freedom. If `df` is supplied, then it is assumed the X has `df` degrees of freedom rather than `n`. Default is None. inv : bool If inv is True the inverse of R is also returned. Default is False. demean : bool True, the mean is subtracted from `X` before estimation. Returns ------- rho The autoregressive coefficients sigma TODO Examples -------- >>> import statsmodels.api as sm >>> from statsmodels.datasets.sunspots import load >>> data = load(as_pandas=False) >>> rho, sigma = sm.regression.yule_walker(data.endog, ... order=4, method="mle") >>> rho array([ 1.28310031, -0.45240924, -0.20770299, 0.04794365]) >>> sigma 16.808022730464351 """ # TODO: define R better, look back at notes and technical notes on YW. # First link here is useful # http://www-stat.wharton.upenn.edu/~steele/Courses/956/ResourceDetails/YuleWalkerAndMore.htm method = str(method).lower() if method not in ["unbiased", "mle"]: raise ValueError("ACF estimation method must be 'unbiased' or 'MLE'") X = np.array(X, dtype=np.float64) if demean: X -= X.mean() # automatically demean's X n = df or X.shape[0] if method == "unbiased": # this is df_resid ie., n - p denom = lambda k: n - k else: denom = lambda k: n if X.ndim > 1 and X.shape[1]!= 1: raise ValueError("expecting a vector to estimate AR parameters") r = np.zeros(order+1, np.float64) r[0] = (X**2).sum() / denom(0) for k in range(1, order+1): r[k] = (X[0:-k] * X[k:]).sum() / denom(k) R = toeplitz(r[:-1]) rho = np.linalg.solve(R, r[1:]) sigmasq = r[0] - (r[1:]*rho).sum() if inv: return rho, np.sqrt(sigmasq), np.linalg.inv(R) else: return rho, np.sqrt(sigmasq) def burg(endog, order=1, demean=True): """ Burg's AP(p) parameter estimator Parameters ---------- endog : array_like The endogenous variable order : int, optional Order of the AR. Default is 1. demean : bool, optional Flag indicating to subtract the mean from endog before estimation Returns ------- rho : ndarray AR(p) coefficients computed using Burg's algorithm sigma2 : float Estimate of the residual variance Notes ----- AR model estimated includes a constant estimated using the sample mean. This value is not reported. References ---------- .. [1] Brockwell, P.J. and Davis, R.A., 2016. Introduction to time series and forecasting. Springer. """ # Avoid circular imports from statsmodels.tsa.stattools import levinson_durbin_pacf, pacf_burg endog = np.squeeze(np.asarray(endog)) if endog.ndim!= 1: raise ValueError('endog must be 1-d or squeezable to 1-d.') order = int(order) if order < 1: raise ValueError('order must be an integer larger than 1') if demean: endog = endog - endog.mean() pacf, sigma = pacf_burg(endog, order, demean=demean) ar, _ = levinson_durbin_pacf(pacf) return ar, sigma[-1] class RegressionResults(base.LikelihoodModelResults): r""" This class summarizes the fit of a linear regression model. It handles the output of contrasts, estimates of covariance, etc. Attributes ---------- pinv_wexog See specific model class docstring cov_HC0 Heteroscedasticity robust covariance matrix. See HC0_se below. cov_HC1 Heteroscedasticity robust covariance matrix. See HC1_se below. cov_HC2 Heteroscedasticity robust covariance matrix. See HC2_se below. cov_HC3 Heteroscedasticity robust covariance matrix. See HC3_se below. cov_type Parameter covariance estimator used for standard errors and t-stats df_model Model degrees of freedom. The number of regressors `p`. Does not include the constant if one is present df_resid Residual degrees of freedom. `n - p - 1`, if a constant is present. `n - p` if a constant is not included. het_scale adjusted squared residuals for heteroscedasticity robust standard errors. Is only available after `HC#_se` or `cov_HC#` is called. See HC#_se for more information. history Estimation history for iterative estimators HC0_se White's (1980) heteroskedasticity robust standard errors. Defined as sqrt(diag(X.T X)^(-1)X.T diag(e_i^(2)) X(X.T X)^(-1) where e_i = resid[i] HC0_se is a cached property. When HC0_se or cov_HC0 is called the RegressionResults instance will then have another attribute `het_scale`, which is in this case is just resid**2. HC1_se MacKinnon and White's (1985) alternative heteroskedasticity robust standard errors. Defined as sqrt(diag(n/(n-p)*HC_0) HC1_see is a cached property. When HC1_se or cov_HC1 is called the RegressionResults instance will then have another attribute `het_scale`, which is in this case is n/(n-p)*resid**2. HC2_se MacKinnon and White's (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC2_see is a cached property. When HC2_se or cov_HC2 is called the RegressionResults instance will then have another attribute `het_scale`, which is in this case is resid^(2)/(1-h_ii). HC3_se MacKinnon and White's (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)^(2)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC3_see is a cached property. When HC3_se or cov_HC3 is called the RegressionResults instance will then have another attribute `het_scale`, which is in this case is resid^(2)/(1-h_ii)^(2). model A pointer to the model instance that called fit() or results. params The linear coefficients that minimize the least squares criterion. This is usually called Beta for the classical linear model. resid_pearson `wresid` normalized to have unit variance. """ _cache = {} # needs to be a class attribute for scale setter? def __init__(self, model, params, normalized_cov_params=None, scale=1., cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs): super(RegressionResults, self).__init__( model, params, normalized_cov_params, scale) self._cache = {} if hasattr(model, 'wexog_singular_values'): self._wexog_singular_values = model.wexog_singular_values else: self._wexog_singular_values = None self.df_model = model.df_model self.df_resid = model.df_resid if cov_type == 'nonrobust': self.cov_type = 'nonrobust' self.cov_kwds = { 'description': 'Standard Errors assume that the'+ 'covariance matrix of the errors is correctly'+ 'specified.'} if use_t is None: use_t = True # TODO: class default self.use_t = use_t else: if cov_kwds is None: cov_kwds = {} if 'use_t' in cov_kwds: # TODO: we want to get rid of 'use_t' in cov_kwds use_t_2 = cov_kwds.pop('use_t') if use_t is None: use_t = use_t_2 # TODO: warn or not? self.get_robustcov_results(cov_type=cov_type, use_self=True, use_t=use_t, **cov_kwds) for key in kwargs: setattr(self, key, kwargs[key]) def __str__(self): self.summary() def conf_int(self, alpha=.05, cols=None): """ Returns the confidence interval of the fitted parameters. Parameters ---------- alpha : float, optional The `alpha` level for the confidence interval. ie., The default `alpha` =.05 returns a 95% confidence interval. cols : array_like, optional `cols` specifies which confidence intervals to return Notes ----- The confidence interval is based on Student's t-distribution. """ # keep method for docstring for now ci = super(RegressionResults, self).conf_int(alpha=alpha, cols=cols) return ci @cache_readonly def nobs(self): """Number of observations n.""" return float(self.model.wexog.shape[0]) @cache_readonly def fittedvalues(self): """The predicted values for the original (unwhitened) design.""" return self.model.predict(self.params, self.model.exog) @cache_readonly def wresid(self): """ The residuals of the transformed/whitened regressand and regressor(s) """ return self.model.wendog - self.model.predict( self.params, self.model.wexog) @cache_readonly def resid(self): """The residuals of the model.""" return self.model.endog - self.model.predict( self.params, self.model.exog) # TODO: fix writable example @cache_writable() def scale(self): """ A scale factor for the covariance matrix. Default value is ssr/(n-p). Note that the square root of `scale` is often called the standard error of the regression. """ wresid = self.wresid return np.dot(wresid, wresid) / self.df_resid @cache_readonly def ssr(self): """Sum of squared (whitened) residuals.""" wresid = self.wresid return np.dot(wresid, wresid) @cache_readonly def centered_tss(self): """The total (weighted) sum of squares centered about the mean.""" model = self.model weights = getattr(model, 'weights', None) sigma = getattr(model,'sigma', None) if weights is not None: mean = np.average(model.endog, weights=weights) return np.sum(weights * (model.endog - mean)**2) elif sigma is not None: # Exactly matches WLS when sigma is diagonal iota = np.ones_like(model.endog) iota = model.whiten(iota) mean = model.wendog.dot(iota) / iota.dot(iota) err = model.endog - mean err = model.whiten(err) return np.sum(err**2) else: centered_endog = model.wendog - model.wendog.mean() return np.dot(centered_endog, centered_endog) @cache_readonly def uncentered_tss(self): """ Uncentered sum of squares. Sum of the squared values of the (whitened) endogenous response variable. """ wendog = self.model.wendog return np.dot(wendog, wendog) @cache_readonly def ess(self): """Explained sum of squares. If a constant is present, the centered total sum of squares minus the sum of squared residuals. If there is no constant, the uncentered total sum of squares is used.""" if self.k_constant: return self.centered_tss - self.ssr else: return self.uncentered_tss - self.ssr @cache_readonly def rsquared(self): """ R-squared of a model with an intercept. This is defined here as 1 - `ssr`/`centered_tss` if the constant is included in the model and 1 - `ssr`/`uncentered_tss` if the constant is omitted. """ if self.k_constant: return 1 - self.ssr/self.centered_tss else: return 1 - self.ssr/self.uncentered_tss @cache_readonly def rsquared_adj(self): """ Adjusted R-squared. This is defined here as 1 - (`nobs`-1)/`df_resid` * (1-`rsquared`) if a constant is included and 1 - `nobs`/`df_resid` * (1-`rsquared`) if no constant is included. """ return 1 - (np.divide(self.nobs - self.k_constant, self.df_resid) * (1 - self.rsquared)) @cache_readonly def mse_model(self): """ Mean squared error the model. This is the explained sum of squares divided by the model degrees of freedom. """ return self.ess/self.df_model @cache_readonly def mse_resid(self): """ Mean squared error of the residuals. The sum of squared residuals divided by the residual degrees of freedom. """ return self.ssr/self.df_resid @cache_readonly def mse_total(self): """ Total mean squared error. Defined as the uncentered total sum of squares divided by n the number of observations. """ if self.k_constant: return self.centered_tss / (self.df_resid + self.df_model) else: return self.uncentered_tss / (self.df_resid + self.df_model) @cache_readonly def fvalue(self): """F-statistic of the fully specified model. Calculated as the mean squared error of the model divided by the mean squared error of the residuals.""" if hasattr(self, 'cov_type') and self.cov_type!= 'nonrobust': # with heteroscedasticity or correlation robustness k_params = self.normalized_cov_params.shape[0] mat = np.eye(k_params) const_idx = self.model.data.const_idx # TODO: What if model includes implicit constant, e.g. all # dummies but no constant regressor? # TODO: Restats as LM test by projecting orthogonalizing # to constant? if self.model.data.k_constant == 1: # if constant is implicit, return nan see #2444 if const_idx is None: return np.nan idx = lrange(k_params) idx.pop(const_idx) mat = mat[idx] # remove constant if mat.size == 0: # see #3642 return np.nan ft = self.f_test(mat) # using backdoor to set another attribute that we already have self._cache['f_pvalue'] = ft.pvalue return ft.fvalue else: # for standard homoscedastic case return self.mse_model/self.mse_resid @cache_readonly def f_pvalue(self): """p-value of the F-statistic""" return stats.f.sf(self.fvalue, self.df_model, self.df_resid) @cache_readonly def bse(self): """The standard errors of the parameter estimates.""" return np.sqrt(np.diag(self.cov_params())) @cache_readonly def aic(self): r"""Akaike's information criteria. For a model with a constant :math:`-2llf + 2(df\_model + 1)`. For a model without a constant :math:`-2llf + 2(df\_model)`.""" return -2 * self.llf + 2 * (self.df_model + self.k_constant) @cache_readonly def bic(self): r"""Bayes' information criteria. For a model with a constant :math:`-2llf + \log(n)(df\_model+1)`. For a model without a constant :math:`-2llf + \log(n)(df\_model)`""" return (-2 * self.llf + np.log(self.nobs) * (self.df_model + self.k_constant)) @cache_readonly def eigenvals(self): """ Return eigenvalues sorted in decreasing order. """ if self._wexog_singular_values is not None: eigvals = self._wexog_singular_values ** 2 else: eigvals = np.linalg.linalg.eigvalsh(np.dot(self.model.wexog.T, self.model.wexog)) return np.sort(eigvals)[::-1] @cache_readonly def condition_number(self): """ Return condition number of exogenous matrix. Calculated as ratio of largest to smallest eigenvalue. """ eigvals = self.eigenvals return np.sqrt(eigvals[0]/eigvals[-1]) # TODO: make these properties reset bse def _HCCM(self, scale): H = np.dot(self.model.pinv_wexog, scale[:, None] * self.model.pinv_wexog.T) return H @cache_readonly def cov_HC0(self): """ See statsmodels.RegressionResults """ self.het_scale = self.wresid**2 cov_HC0 = self._HCCM(self.het_scale) return cov_HC0 @cache_readonly def cov_HC1(self): """ See statsmodels.RegressionResults """ self.het_scale = self.nobs/(self.df_resid)*(self.wresid**2) cov_HC1 = self._HCCM(self.het_scale) return cov_HC1 @cache_readonly def cov_HC2(self): """ See statsmodels.RegressionResults """ # probably could be optimized h = np.diag(chain_dot(self.model.wexog, self.normalized_cov_params, self.model.wexog.T)) self.het_scale = self.wresid**2/(1-h) cov_HC2 = self._HCCM(self.het_scale) return cov_HC2 @cache_readonly def cov_HC3(self): """ See statsmodels.RegressionResults """ h = np.diag(chain_dot( self.model.wexog, self.normalized_cov_params, self.model.wexog.T)) self.het_scale = (self.wresid / (1 - h))**2 cov_HC3 = self._HCCM(self.het_scale) return cov_HC3 @cache_readonly def HC0_se(self): """ See statsmodels.RegressionResults """ return np.sqrt(np.diag(self.cov_HC0)) @cache_readonly def HC1_se(self): """ See statsmodels.RegressionResults """ return np.sqrt(np.diag(self.cov_HC1)) @cache_readonly def HC2_se(self): """ See statsmodels.RegressionResults """ return np.sqrt(np.diag(self.cov_HC2)) @cache_readonly def HC3_se(self): """ See statsmodels.RegressionResults """ return np.sqrt(np.diag(self.cov_HC3)) @cache_readonly def resid_pearson(self): """ Residuals, normalized to have unit variance. Returns ------- An array wresid standardized by the sqrt if scale """ if not hasattr(self,'resid'): raise ValueError('Method requires residuals.') eps = np.finfo(self.wresid.dtype).eps if np.sqrt(self.scale) < 10 * eps * self.model.endog.mean(): # don't divide if scale is zero close to numerical precision from warnings import warn warn("All residuals are 0, cannot compute normed residuals.", RuntimeWarning) return self.wresid else: return self.wresid / np.sqrt(self.scale) def _is_nested(self, restricted): """ Parameters ---------- restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, `ssr`, residual degrees of freedom, `df_resid`. Returns ------- nested : bool True if nested, otherwise false Notes ----- A most nests another model if the regressors in the smaller model are spanned by the regressors in the larger model and the regressand is identical. """ if self.model.nobs!= restricted.model.nobs: return False full_rank = self.model.rank restricted_rank = restricted.model.rank if full_rank <= restricted_rank: return False restricted_exog = restricted.model.wexog full_wresid = self.wresid scores = restricted_exog * full_wresid[:, None] score_l2 = np.sqrt(np.mean(scores.mean(0) ** 2)) # TODO: Could be improved, and may fail depending on scale of # regressors return np.allclose(score_l2, 0) def compare_lm_test(self, restricted, demean=True, use_lr=False): """Use Lagrange Multiplier test to test whether restricted model is correct Parameters ---------- restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, `ssr`, residual degrees of freedom, `df_resid`. demean : bool Flag indicating whether the demean the scores based on the residuals from the restricted model. If True, the covariance of the scores are used and the LM test is identical to the large sample version of the LR test. Returns ------- lm_value : float test statistic, chi2 distributed p_value : float p-value of the test statistic df_diff : int degrees of freedom of the restriction, i.e. difference in df between models Notes ----- TODO: explain LM text """ import statsmodels.stats.sandwich_covariance as sw from numpy.linalg import inv if not self._is_nested(restricted): raise ValueError("Restricted model is not nested by full model.") wresid = restricted.wresid wexog = self.model.wexog scores = wexog * wresid[:, None] n = self.nobs df_full = self.df_resid df_restr = restricted.df_resid df_diff = (df_restr - df_full) s = scores.mean(axis=0) if use_lr: scores = wexog * self.wresid[:, None] demean = False if demean: scores = scores - scores.mean(0)[None, :] # Form matters here. If homoskedastics can be sigma^2 (X'X)^-1 # If Heteroskedastic then the form below is fine # If HAC then need to use HAC # If Cluster, shoudl use cluster cov_type = getattr(self, 'cov_type', 'nonrobust') if cov_type == 'nonrobust': sigma2 = np.mean(wresid**2) XpX = np.dot(wexog.T, wexog) / n Sinv = inv(sigma2 * XpX) elif cov_type in ('HC0', 'HC1', 'HC2', 'HC3'): Sinv = inv(np.dot(scores.T, scores) / n) elif cov_type == 'HAC': maxlags = self.cov_kwds['maxlags'] Sinv = inv(sw.S_hac_simple(scores, maxlags) / n) elif cov_type == 'cluster': # cluster robust standard errors groups = self.cov_kwds['groups'] # TODO: Might need demean option in S_crosssection by group? Sinv = inv(sw.S_crosssection(scores, groups)) else: raise ValueError('Only nonrobust, HC, HAC and cluster are'+ 'currently connected') lm_value = n * chain_dot(s, Sinv, s.T) p_value = stats.chi2.sf(lm_value, df_diff) return lm_value, p_value, df_diff def compare_f_test(self, restricted): """use F test to test whether restricted model is correct Parameters ---------- restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, `ssr`, residual degrees of freedom, `df_resid`. Returns ------- f_value : float test statistic, F distributed p_value : float p-value of the test statistic df_diff : int degrees of freedom of the restriction, i.e. difference in df between models Notes ----- See mailing list discussion October 17, This test compares the residual sum of squares of the two models. This is not a valid test, if there is unspecified heteroscedasticity or correlation. This method will issue a warning if this is detected but still return the results under the assumption of homoscedasticity and no autocorrelation (sphericity). """ has_robust1 = getattr(self, 'cov_type', 'nonrobust')!= 'nonrobust' has_robust2 = (getattr(restricted, 'cov_type', 'nonrobust')!= 'nonrobust') if has_robust1 or has_robust2: warnings.warn('F test for comparison is likely invalid with'+ 'robust covariance, proceeding anyway', InvalidTestWarning) ssr_full = self.ssr ssr_restr = restricted.ssr df_full = self.df_resid df_restr = restricted.df_resid df_diff = (df_restr - df_full) f_value = (ssr_restr - ssr_full) / df_diff / ssr_full * df_full p_value = stats.f.sf(f_value, df_diff, df_full) return f_value, p_value, df_diff def compare_lr_test(self, restricted, large_sample=False): """ Likelihood ratio test to test whether restricted model is correct Parameters ---------- restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, `ssr`, residual degrees of freedom, `df_resid`. large_sample : bool Flag indicating whether to use a heteroskedasticity robust version of the LR test, which is a modified LM test. Returns ------- lr_stat : float likelihood ratio, chisquare distributed with df_diff degrees of freedom p_value : float p-value of the test statistic df_diff : int degrees of freedom of the restriction, i.e. difference in df between models Notes ----- The exact likelihood ratio is valid for homoskedastic data, and is defined as .. math:: D=-2\\log\\left(\\frac{\\mathcal{L}_{null}} {\\mathcal{L}_{alternative}}\\right) where :math:`\\mathcal{L}` is the likelihood of the model. With :math:`D` distributed as chisquare with df equal to difference in number of parameters or equivalently difference in residual degrees of freedom. The large sample version of the likelihood ratio is defined as .. math:: D=n s^{\\prime}S^{-1}s where :math:`s=n^{-1}\\sum_{i=1}^{n} s_{i}` .. math:: s_{i} = x_{i,alternative} \\epsilon_{i,null} is the average score of the model evaluated using the residuals from null model and the regressors from the alternative model and :math:`S` is the covariance of the scores, :math:`s_{i}`. The covariance of the scores is estimated using the same estimator as in the alternative model. This test compares the loglikelihood of the two models. This may not be a valid test, if there is unspecified heteroscedasticity or correlation. This method will issue a warning if this is detected but still return the results without taking unspecified heteroscedasticity or correlation into account. This test compares the loglikelihood of the two models. This may not be a valid test, if there is unspecified heteroscedasticity or correlation. This method will issue a warning if this is detected but still return the results without taking unspecified heteroscedasticity or correlation into account. is the average score of the model evaluated using the residuals from null model and the regressors from the alternative model and :math:`S` is the covariance of the scores, :math:`s_{i}`. The covariance of the scores is estimated using the same estimator as in the alternative model. TODO: put into separate function, needs tests """ # See mailing list discussion October 17, if large_sample: return self.compare_lm_test(restricted, use_lr=True) has_robust1 = (getattr(self, 'cov_type', 'nonrobust')!= 'nonrobust') has_robust2 = ( getattr(restricted, 'cov_type', 'nonrobust')!= 'nonrobust') if has_robust1 or has_robust2: warnings.warn('Likelihood Ratio test is likely invalid with'+ 'robust covariance, proceeding anyway', InvalidTestWarning) llf_full = self.llf llf_restr = restricted.llf df_full = self.df_resid df_restr = restricted.df_resid lrdf = (df_restr - df_full) lrstat = -2*(llf_restr - llf_full) lr_pvalue = stats.chi2.sf(lrstat, lrdf) return lrstat, lr_pvalue, lrdf def get_robustcov_results(self, cov_type='HC1', use_t=None, **kwds): """create new results instance with robust covariance as default Parameters ---------- cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. If `use_t` is None, then an appropriate default is used, which is `true` if the cov_type is nonrobust, and `false` in all other cases. kwds : depends on cov_type Required or optional arguments for robust covariance calculation. see Notes below Returns ------- results : results instance This method creates a new results instance with the requested robust covariance as the default covariance of the parameters. Inferential statistics like p-values and hypothesis tests will be based on this covariance matrix. Notes ----- The following covariance types and required or optional arguments are currently available: - 'fixed scale' and optional keyword argument'scale' which uses a predefined scale estimate with default equal to one. - 'HC0', 'HC1', 'HC2', 'HC3' and no keyword arguments: heteroscedasticity robust covariance - 'HAC' and keywords - `maxlag` integer (required) : number of lags to use - `kernel` callable or str (optional) : kernel currently available kernels are ['bartlett', 'uniform'], default is Bartlett - `use_correction` bool (optional) : If true, use small sample correction - 'cluster' and required keyword `groups`, integer group indicator - `groups` array_like, integer (required) : index of clusters or groups - `use_correction` bool (optional) : If True the sandwich covariance is calculated with a small sample correction. If False the sandwich covariance is calculated without small sample correction. - `df_correction` bool (optional) If True (default), then the degrees of freedom for the inferential statistics and hypothesis tests, such as pvalues, f_pvalue, conf_int, and t_test and f_test, are based on the number of groups minus one instead of the total number of observations minus the number of explanatory variables. `df_resid` of the results instance is adjusted. If False, then `df_resid` of the results instance is not adjusted. - 'hac-groupsum' Driscoll and Kraay, heteroscedasticity and autocorrelation robust standard errors in panel data keywords - `time` array_like (required) : index of time periods - `maxlag` integer (required) : number of lags to use - `kernel` callable or str (optional) : kernel currently available kernels are ['bartlett', 'uniform'], default is Bartlett - `use_correction` False or string in ['hac', 'cluster'] (optional) : If False the the sandwich covariance is calulated without small sample correction. If `use_correction = 'cluster'` (default), then the same small sample correction as in the case of 'covtype='cluster'' is used. - `df_correction` bool (optional) adjustment to df_resid, see cov_type 'cluster' above # TODO: we need more options here - 'hac-panel' heteroscedasticity and autocorrelation robust standard errors in panel data. The data needs to be sorted in this case, the time series for each panel unit or cluster need to be stacked. The membership to a timeseries of an individual or group can be either specified by group indicators or by increasing time periods. keywords - either `groups` or `time` : array_like (required) `groups` : indicator for groups `time` : index of time periods - `maxlag` integer (required) : number of lags to use - `kernel` callable or str (optional) : kernel currently available kernels are ['bartlett', 'uniform'], default is Bartlett - `use_correction` False or string in ['hac', 'cluster'] (optional) : If False the sandwich covariance is calculated without small sample correction. - `df_correction` bool (optional) adjustment to df_resid, see cov_type 'cluster' above # TODO: we need more options here Reminder: `use_correction` in "hac-groupsum" and "hac-panel" is not bool, needs to be in [False, 'hac', 'cluster'] TODO: Currently there is no check for extra or misspelled keywords, except in the case of cov_type `HCx` """ import statsmodels.stats.sandwich_covariance as sw from statsmodels.base.covtype import normalize_cov_type, descriptions cov_type = normalize_cov_type(cov_type) if 'kernel' in kwds: kwds['weights_func'] = kwds.pop('kernel') if 'weights_func' in kwds and not callable(kwds['weights_func']): kwds['weights_func'] = sw.kernel_dict[kwds['weights_func']] # TODO: make separate function that returns a robust cov plus info use_self = kwds.pop('use_self', False) if use_self: res = self else: res = self.__class__( self.model, self.params, normalized_cov_params=self.normalized_cov_params, scale=self.scale) res.cov_type = cov_type # use_t might already be defined by the class, and already set if use_t is None: use_t = self.use_t res.cov_kwds = {'use_t': use_t} # store for information res.use_t = use_t adjust_df = False if cov_type in ['cluster', 'hac-panel', 'hac-groupsum']: df_correction = kwds.get('df_correction', None) # TODO: check also use_correction, do I need all combinations? if df_correction is not False: # i.e. in [None, True]: # user didn't explicitely set it to False adjust_df = True res.cov_kwds['adjust_df'] = adjust_df # verify and set kwds, and calculate cov # TODO: this should be outsourced in a function so we can reuse it in # other models # TODO: make it DRYer repeated code for checking kwds if cov_type in ['fixed scale', 'fixed_scale']: res.cov_kwds['description'] = descriptions['fixed_scale'] res.cov_kwds['scale'] = scale = kwds.get('scale', 1.) res.cov_params_default = scale * res.normalized_cov_params elif cov_type.upper() in ('HC0', 'HC1', 'HC2', 'HC3'): if kwds: raise ValueError('heteroscedasticity robust covariance ' 'does not use keywords') res.cov_kwds['description'] = descriptions[cov_type.upper()] res.cov_params_default = getattr(self, 'cov_' + cov_type.upper()) elif cov_type.lower() == 'hac': maxlags = kwds['maxlags'] # required?, default in cov_hac_simple res.cov_kwds['maxlags'] = maxlags weights_func = kwds.get('weights_func', sw.weights_bartlett) res.cov_kwds['weights_func'] = weights_func use_correction = kwds.get('use_correction', False) res.cov_kwds['use_correction'] = use_correction res.cov_kwds['description'] = descriptions['HAC'].format( maxlags=maxlags, correction=['without', 'with'][use_correction]) res.cov_params_default = sw.cov_hac_simple( self, nlags=maxlags, weights_func=weights_func, use_correction=use_correction) elif cov_type.lower() == 'cluster': # cluster robust standard errors, one- or two-way groups = kwds['groups'] if not hasattr(groups,'shape'): groups = np.asarray(groups).T if groups.ndim >= 2: groups = groups.squeeze() res.cov_kwds['groups'] = groups use_correction = kwds.get('use_correction', True) res.cov_kwds['use_correction'] = use_correction if groups.ndim == 1: if adjust_df: # need to find number of groups # duplicate work self.n_groups = n_groups = len(np.unique(groups)) res.cov_params_default = sw.cov_cluster( self, groups, use_correction=use_correction) elif groups.ndim == 2: if hasattr(groups, 'values'): groups = groups.values if adjust_df: # need to find number of groups # duplicate work n_groups0 = len(np.unique(groups[:, 0])) n_groups1 = len(np.unique(groups[:, 1])) self.n_groups = (n_groups0, n_groups1) n_groups = min(n_groups0, n_groups1) # use for adjust_df # Note: sw.cov_cluster_2groups has 3 returns res.cov_params_default = sw.cov_cluster_2groups( self, groups, use_correction=use_correction)[0] else: raise ValueError('only two groups are supported') res.cov_kwds['description'] = descriptions['cluster'] elif cov_type.lower() == 'hac-panel': # cluster robust standard errors res.cov_kwds['time'] = time = kwds.get('time', None) res.cov_kwds['groups'] = groups = kwds.get('groups', None) # TODO: nlags is currently required # nlags = kwds.get('nlags', True) # res.cov_kwds['nlags'] = nlags # TODO: `nlags` or `maxlags` res.cov_kwds['maxlags'] = maxlags = kwds['maxlags'] use_correction = kwds.get('use_correction', 'hac') res.cov_kwds['use_correction'] = use_correction weights_func = kwds.get('weights_func', sw.weights_bartlett) res.cov_kwds['weights_func'] = weights_func if groups is not None: groups = np.asarray(groups) tt = (np.nonzero(groups[:-1]!= groups[1:])[0] + 1).tolist() nobs_ = len(groups) elif time is not None: time = np.asarray(time) # TODO: clumsy time index in cov_nw_panel tt = (np.nonzero(time[1:] < time[:-1])[0] + 1).tolist() nobs_ = len(time) else: raise ValueError('either time or groups needs to be given') groupidx = lzip([0] + tt, tt + [nobs_]) self.n_groups = n_groups = len(groupidx) res.cov_params_default = sw.cov_nw_panel(self, maxlags, groupidx, weights_func=weights_func, use_correction=use_correction) res.cov_kwds['description'] = descriptions['HAC-Panel'] elif cov_type.lower() == 'hac-groupsum': # Driscoll-Kraay standard errors res.cov_kwds['time'] = time = kwds['time'] # TODO: nlags is currently required # nlags = kwds.get('nlags', True) # res.cov_kwds['nlags'] = nlags # TODO: `nlags` or `maxlags` res.cov_kwds['maxlags'] = maxlags = kwds['maxlags'] use_correction = kwds.get('use_correction', 'cluster') res.cov_kwds['use_correction'] = use_correction weights_func = kwds.get('weights_func', sw.weights_bartlett) res.cov_kwds['weights_func'] = weights_func if adjust_df: # need to find number of groups tt = (np.nonzero(time[1:] < time[:-1])[0] + 1) self.n_groups = n_groups = len(tt) + 1 res.cov_params_default = sw.cov_nw_groupsum( self, maxlags, time, weights_func=weights_func, use_correction=use_correction) res.cov_kwds['description'] = descriptions['HAC-Groupsum'] else: raise ValueError('cov_type not recognized. See docstring for'+ 'available options and spelling') if adjust_df: # Note: df_resid is used for scale and others, add new attribute res.df_resid_inference = n_groups - 1 return res def get_prediction(self, exog=None, transform=True, weights=None, row_labels=None, **kwds): return pred.get_prediction( self, exog=exog, transform=transform, weights=weights, row_labels=row_labels, **kwds) get_prediction.__doc__ = pred.get_prediction.__doc__ def summary(self, yname=None, xname=None, title=None, alpha=.05): """Summarize the Regression Results Parameters ---------- yname : str, optional Default is `y` xname : list[str], optional Names for the exogenous variables. Default is `var_##` for ## in the number of regressors. Must match the number of parameters in the model title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ from statsmodels.stats.stattools import ( jarque_bera, omni_normtest, durbin_watson) jb, jbpv, skew, kurtosis = jarque_bera(self.wresid) omni, omnipv = omni_normtest(self.wresid) eigvals = self.eigenvals condno = self.condition_number # TODO: Avoid adding attributes in non-__init__ self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis, omni=omni, omnipv=omnipv, condno=condno, mineigval=eigvals[-1]) # TODO not used yet # diagn_left_header = ['Models stats'] # diagn_right_header = ['Residual stats'] # TODO: requiring list/iterable is a bit annoying # need more control over formatting # TODO: default don't work if it's not identically spelled top_left = [('Dep. Variable:', None), ('Model:', None), ('Method:', ['Least Squares']), ('Date:', None), ('Time:', None), ('No. Observations:', None), ('Df Residuals:', None), ('Df Model:', None), ] if hasattr(self, 'cov_type'): top_left.append(('Covariance Type:', [self.cov_type])) rsquared_type = '' if self.k_constant else'(uncentered)' top_right = [('R-squared' + rsquared_type + ':', ["%#8.3f" % self.rsquared]), ('Adj. R-squared' + rsquared_type + ':', ["%#8.3f" % self.rsquared_adj]), ('F-statistic:', ["%#8.4g" % self.fvalue]), ('Prob (F-statistic):', ["%#6.3g" % self.f_pvalue]), ('Log-Likelihood:', None), ('AIC:', ["%#8.4g" % self.aic]), ('BIC:', ["%#8.4g" % self.bic]) ] diagn_left = [('Omnibus:', ["%#6.3f" % omni]), ('Prob(Omnibus):', ["%#6.3f" % omnipv]), ('Skew:', ["%#6.3f" % skew]), ('Kurtosis:', ["%#6.3f" % kurtosis]) ] diagn_right = [('Durbin-Watson:', ["%#8.3f" % durbin_watson(self.wresid)] ), ('Jarque-Bera (JB):', ["%#8.3f" % jb]), ('Prob(JB):', ["%#8.3g" % jbpv]), ('Cond. No.', ["%#8.3g" % condno]) ] if title is None: title = self.model.__class__.__name__ +'' + "Regression Results" # create summary table instance from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=self.use_t) smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right, yname=yname, xname=xname, title="") # add warnings/notes, added to text format only etext = [] if hasattr(self, 'cov_type'): etext.append(self.cov_kwds['description']) if self.model.exog.shape[0] < self.model.exog.shape[1]: wstr = "The input rank is higher than the number of observations." etext.append(wstr) if eigvals[-1] < 1e-10: wstr = "The smallest eigenvalue is %6.3g. This might indicate " wstr += "that there are\n" wstr += "strong multicollinearity problems or that the design " wstr += "matrix is singular." wstr = wstr % eigvals[-1] etext.append(wstr) elif condno > 1000: # TODO: what is recommended? wstr = "The condition number is large, %6.3g. This might " wstr += "indicate that there are\n" wstr += "strong multicollinearity or other numerical " wstr += "problems." wstr = wstr % condno etext.append(wstr) if etext: etext = ["[{0}] {1}".format(i + 1, text) for i, text in enumerate(etext)] etext.insert(0, "Warnings:") smry.add_extra_txt(etext) return smry def summary2(self, yname=None, xname=None, title=None, alpha=.05, float_format="%.4f"): """Experimental summary function to summarize the regression results Parameters ---------- yname : str Name of the dependent variable (optional) xname : list[str], optional Names for the exogenous variables. Default is `var_##` for ## in the number of regressors. Must match the number of parameters in the model title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format : str print format for floats in parameters summary Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary2.Summary : class to hold summary results """ # Diagnostics from statsmodels.stats.stattools import (jarque_bera, omni_normtest, durbin_watson) from collections import OrderedDict jb, jbpv, skew, kurtosis = jarque_bera(self.wresid) omni, omnipv = omni_normtest(self.wresid) dw = durbin_watson(self.wresid) eigvals = self.eigenvals condno = self.condition_number eigvals = np.sort(eigvals) # in increasing order diagnostic = OrderedDict([ ('Omnibus:', "%.3f" % omni), ('Prob(Omnibus):', "%.3f" % omnipv), ('Skew:', "%.3f" % skew), ('Kurtosis:', "%.3f" % kurtosis), ('Durbin-Watson:', "%.3f" % dw), ('Jarque-Bera (JB):', "%.3f" % jb), ('Prob(JB):', "%.3f" % jbpv), ('Condition No.:', "%.0f" % condno) ]) # Summary from statsmodels.iolib import summary2 smry = summary2.Summary() smry.add_base(results=self, alpha=alpha, float_format=float_format, xname=xname, yname=yname, title=title) smry.add_dict(diagnostic) # Warnings if eigvals[-1] < 1e-10: warn = "The smallest eigenvalue is %6.3g. This might indicate that\ there are strong multicollinearity problems or that the design\ matrix is singular." % eigvals[-1] smry.add_text(warn) if condno > 1000: warn = "* The condition number is large (%.g). This might indicate \ strong multicollinearity or other numerical problems." % condno smry.add_text(warn) return smry class OLSResults(RegressionResults): """ Results class for for an OLS model. Most of the methods and attributes are inherited from RegressionResults. The special methods that are only available for OLS are: - get_influence - outlier_test - el_test - conf_int_el See Also -------- RegressionResults """ def get_influence(self): """ get an instance of Influence with influence and outlier measures Returns ------- infl : Influence instance the instance has methods to calculate the main influence and outlier measures for the OLS regression See Also -------- statsmodels.stats.outliers_influence.OLSInfluence """ from statsmodels.stats.outliers_influence import OLSInfluence return OLSInfluence(self) def outlier_test(self, method='bonf', alpha=.05, labels=None, order=False, cutoff=None): """ Test observations for outliers according to method Parameters ---------- method : str - `bonferroni` : one-step correction - `sidak` : one-step correction - `holm-sidak` : - `holm` : - `simes-hochberg` : - `hommel` : - `fdr_bh` : Benjamini/Hochberg - `fdr_by` : Benjamini/Yekutieli See `statsmodels.stats.multitest.multipletests` for details. alpha : float familywise error rate labels : None or array_like If `labels` is not None, then it will be used as index to the returned pandas DataFrame. See also Returns below order : bool Whether or not to order the results by the absolute value of the studentized residuals. If labels are provided they will also be sorted. cutoff : None or float in [0, 1] If cutoff is not None, then the return only includes observations with multiple testing corrected p-values strictly below the cutoff. The returned array or dataframe can be empty if t Returns ------- table : ndarray or DataFrame Returns either an ndarray or a DataFrame if labels is not None. Will attempt to get labels from model_results if available. The columns are the Studentized residuals, the unadjusted p-value, and the corrected p-value according to method. Notes ----- The unadjusted p-value is stats.t.sf(abs(resid), df) where df = df_resid - 1. """ from statsmodels.stats.outliers_influence import outlier_test return outlier_test(self, method, alpha, labels=labels, order=order, cutoff=cutoff) def el_test(self, b0_vals, param_nums, return_weights=0, ret_params=0, method='nm', stochastic_exog=1, return_params=0): """ Tests single or joint hypotheses of the regression parameters using Empirical Likelihood. Parameters ---------- b0_vals : 1darray The hypothesized value of the parameter to be tested param_nums : 1darray The parameter number to be tested print_weights : bool If true, returns the weights that optimize the likelihood ratio at b0_vals. Default is False ret_params : bool If true, returns the parameter vector that maximizes the likelihood ratio at b0_vals. Also returns the weights. Default is False method : string Can either be 'nm' for Nelder-Mead or 'powell' for Powell. The optimization method that optimizes over nuisance parameters. Default is 'nm' stochastic_exog : bool When TRUE, the exogenous variables are assumed to be stochastic. When the regressors are nonstochastic, moment conditions are placed on the exogenous variables. Confidence intervals for stochastic regressors are at least as large as non-stochastic regressors. Default = TRUE Returns ------- res : tuple The p-value and -2 times the log-likelihood ratio for the hypothesized values. Examples -------- >>> import statsmodels.api as sm >>> data = sm.datasets.stackloss.load(as_pandas=False) >>> endog = data.endog >>> exog = sm.add_constant(data.exog) >>> model = sm.OLS(endog, exog) >>> fitted = model.fit() >>> fitted.params >>> array([-39.91967442, 0.7156402, 1.29528612, -0.15212252]) >>> fitted.rsquared >>> 0.91357690446068196 >>> # Test that the slope on the first variable is 0 >>> fitted.el_test([0], [1]) >>> (27.248146353888796, 1.7894660442330235e-07) """ params = np.copy(self.params) opt_fun_inst = _ELRegOpts() # to store weights if len(param_nums) == len(params): llr = opt_fun_inst._opt_nuis_regress( [], param_nums=param_nums, endog=self.model.endog, exog=self.model.exog, nobs=self.model.nobs, nvar=self.model.exog.shape[1], params=params, b0_vals=b0_vals, stochastic_exog=stochastic_exog) pval = 1 - stats.chi2.cdf(llr, len(param_nums)) if return_weights: return llr, pval, opt_fun_inst.new_weights else: return llr, pval x0 = np.delete(params, param_nums) args = (param_nums, self.model.endog, self.model.exog, self.model.nobs, self.model.exog.shape[1], params, b0_vals, stochastic_exog) if method == 'nm': llr = optimize.fmin(opt_fun_inst._opt_nuis_regress, x0, maxfun=10000, maxiter=10000, full_output=1, disp=0, args=args)[1] if method == 'powell': llr = optimize.fmin_powell(opt_fun_inst._opt_nuis_regress, x0, full_output=1, disp=0, args=args)[1] pval = 1 - stats.chi2.cdf(llr, len(param_nums)) if ret_params: return llr, pval, opt_fun_inst.new_weights, opt_fun_inst.new_params elif return_weights: return llr, pval, opt_fun_inst.new_weights else: return llr, pval def conf_int_el(self, param_num, sig=.05, upper_bound=None, lower_bound=None, method='nm', stochastic_exog=1): """ Computes the confidence interval for the parameter given by param_num using Empirical Likelihood Parameters ---------- param_num : float The parameter for which the confidence interval is desired sig : float The significance level. Default is.05 upper_bound : float The maximum value the upper limit can be. Default is the 99.9% confidence value under OLS assumptions. lower_bound : float The minimum value the lower limit can be. Default is the 99.9% confidence value under OLS assumptions. method : string Can either be 'nm' for Nelder-Mead or 'powell' for Powell. The optimization method that optimizes over nuisance parameters. Default is 'nm' Returns ------- ci : tuple The confidence interval See Also -------- el_test Notes ----- This function uses brentq to find the value of beta where test_beta([beta], param_num)[1] is equal to the critical value. The function returns the results of each iteration of brentq at each value of beta. The current function value of the last printed optimization should be the critical value at the desired significance level. For alpha=.05, the value is 3.841459. To ensure optimization terminated successfully, it is suggested to do el_test([lower_limit], [param_num]) If the optimization does not terminate successfully, consider switching optimization algorithms. If optimization is still not successful, try changing the values of start_int_params. If the current function value repeatedly jumps from a number between 0 and the critical value and a very large number (>50), the starting parameters of the interior minimization need to be changed. """ r0 = stats.chi2.ppf(1 - sig, 1) if upper_bound is None: upper_bound = self.conf_int(.01)[param_num][1] if lower_bound is None: lower_bound = self.conf_int(.01)[param_num][0] f = lambda b0: self.el_test(np.array([b0]), np.array([param_num]), method=method, stochastic_exog=stochastic_exog)[0]-r0 lowerl = optimize.brenth(f, lower_bound, self.params[param_num]) upperl = optimize.brenth(f, self.params[param_num], upper_bound) # ^ Seems to be faster than brentq in most cases return (lowerl, upperl) class RegressionResultsWrapper(wrap.ResultsWrapper): _attrs = { 'chisq': 'columns', 'sresid': 'rows', 'weights': 'rows', 'wresid': 'rows', 'bcov_unscaled': 'cov', 'bcov_scaled': 'cov', 'HC0_se': 'columns', 'HC1_se': 'columns', 'HC2_se': 'columns', 'HC3_se': 'columns', 'norm_resid': 'rows', } _wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._attrs, _attrs) _methods = {} _wrap_methods = wrap.union_dicts( base.LikelihoodResultsWrapper._wrap_methods, _methods) wrap.populate_wrapper(RegressionResultsWrapper, RegressionResults) import warnings import numpy as np import pandas as pd from statsmodels.base import model import statsmodels.base.wrapper as wrap class _DimReductionRegression(model.Model): """ A base class for dimension reduction regression methods. """ def __init__(self, endog, exog, **kwargs): super(_DimReductionRegression, self).__init__(endog, exog, **kwargs) def _prep(self, n_slice): # Sort the data by endog ii = np.argsort(self.endog) x = self.exog[ii, :] # Whiten the data x -= x.mean(0) covx = np.cov(x.T) covxr = np.linalg.cholesky(covx) x = np.linalg.solve(covxr, x.T).T self.wexog = x self._covxr = covxr # Split the data into slices self._split_wexog = np.array_split(x, n_slice) class SlicedInverseReg(_DimReductionRegression): """ Sliced Inverse Regression (SIR) Parameters ---------- endog : array_like (1d) The dependent variable exog : array_like (2d) The covariates References ---------- KC Li (1991). Sliced inverse regression for dimension reduction. JASA 86, 316-342. """ def fit(self, **kwargs): """ Estimate the EDR space. Parameters ---------- slice_n : int, optional Number of observations per slice """ # Sample size per slice slice_n = kwargs.get("slice_n", 20) # Number of slices n_slice = self.exog.shape[0] // slice_n self._prep(n_slice) mn = [z.mean(0) for z in self._split_wexog] n = [z.shape[0] for z in self._split_wexog] mn = np.asarray(mn) n = np.asarray(n) mnc = np.cov(mn.T, fweights=n) a, b = np.linalg.eigh(mnc) jj = np.argsort(-a) a = a[jj] b = b[:, jj] params = np.linalg.solve(self._covxr.T, b) results = DimReductionResults(self, params, eigs=a) return DimReductionResultsWrapper(results) class PrincipalHessianDirections(_DimReductionRegression): """ Principal Hessian Directions Parameters ---------- endog : array_like (1d) The dependent variable exog : array_like (2d) The covariates References ---------- KC Li (1992). On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another application of Stein's lemma. JASA 87:420. """ def fit(self, **kwargs): """ Estimate the EDR space using PHD. Parameters ---------- resid : bool, optional If True, use least squares regression to remove the linear relationship between each covariate and the response, before conducting PHD. """ resid = kwargs.get("resid", False) y = self.endog - self.endog.mean() x = self.exog - self.exog.mean(0) if resid: from statsmodels.regression.linear_model import OLS r = OLS(y, x).fit() y = r.resid cm = np.einsum('i,ij,ik->jk', y, x, x) cm /= len(y) cx = np.cov(x.T) cb = np.linalg.solve(cx, cm) a, b = np.linalg.eig(cb) jj = np.argsort(-np.abs(a)) a = a[jj] params = b[:, jj] results = DimReductionResults(self, params, eigs=a) return DimReductionResultsWrapper(results) class SlicedAverageVarianceEstimation(_DimReductionRegression): """ Sliced Average Variance Estimation (SAVE) Parameters ---------- endog : array_like (1d) The dependent variable exog : array_like (2d) The covariates bc : bool, optional If True, use the bias-correctedCSAVE method of Li and Zhu. References ---------- RD Cook. SAVE: A method for dimension reduction and graphics in regression. http://www.stat.umn.edu/RegGraph/RecentDev/save.pdf Y Li, L-X Zhu (2007). Asymptotics for sliced average variance estimation. The Annals of Statistics. https://arxiv.org/pdf/0708.0462.pdf """ def __init__(self, endog, exog, **kwargs): super(SAVE, self).__init__(endog, exog, **kwargs) self.bc = False if "bc" in kwargs and kwargs["bc"] is True: self.bc = True def fit(self, **kwargs): """ Estimate the EDR space. Parameters ---------- slice_n : int Number of observations per slice """ # Sample size per slice slice_n = kwargs.get("slice_n", 50) # Number of slices n_slice = self.exog.shape[0] // slice_n self._prep(n_slice) cv = [np.cov(z.T) for z in self._split_wexog] ns = [z.shape[0] for z in self._split_wexog] p = self.wexog.shape[1] if not self.bc: # Cook's original approach vm = 0 for w, cvx in zip(ns, cv): icv = np.eye(p) - cvx vm += w * np.dot(icv, icv) vm /= len(cv) else: # The bias-corrected approach of Li and Zhu # \Lambda_n in Li, Zhu av = 0 for c in cv: av += np.dot(c, c) av /= len(cv) # V_n in Li, Zhu vn = 0 for x in self._split_wexog: r = x - x.mean(0) for i in range(r.shape[0]): u = r[i, :] m = np.outer(u, u) vn += np.dot(m, m) vn /= self.exog.shape[0] c = np.mean(ns) k1 = c * (c - 1) / ((c - 1)**2 + 1) k2 = (c - 1) / ((c - 1)**2 + 1) av2 = k1 * av - k2 * vn vm = np.eye(p) - 2 * sum(cv) / len(cv) + av2 a, b = np.linalg.eigh(vm) jj = np.argsort(-a) a = a[jj] b = b[:, jj] params = np.linalg.solve(self._covxr.T, b) results = DimReductionResults(self, params, eigs=a) return DimReductionResultsWrapper(results) class DimReductionResults(model.Results): """ Results class for a dimension reduction regression. """ def __init__(self, model, params, eigs): super(DimReductionResults, self).__init__( model, params) self.eigs = eigs class DimReductionResultsWrapper(wrap.ResultsWrapper): _attrs = { 'params': 'columns', } _wrap_attrs = _attrs wrap.populate_wrapper(DimReductionResultsWrapper, # noqa:E305 DimReductionResults) class CovarianceReduction(_DimReductionRegression): """ Dimension reduction for covariance matrices (CORE). Parameters ---------- endog : array_like The dependent variable, treated as group labels exog : array_like The independent variables. dim : integer The dimension of the subspace onto which the covariance matrices are projected. Returns ------- An orthogonal matrix P such that replacing each group's covariance matrix C with P'CP optimally preserves the differences among these matrices. Notes ----- This is a likelihood-based dimension reduction procedure based on Wishart models for sample covariance matrices. The goal is to find a projection matrix P so that C_i | P'C_iP and C_j | P'C_jP are equal in distribution for all i, j, where the C_i are the within-group covariance matrices. The model and methodology are as described in Cook and Forzani, but the optimization method follows Edelman et. al. References ---------- DR Cook, L Forzani (2008). Covariance reducing models: an alternative to spectral modeling of covariance matrices. Biometrika 95:4. A Edelman, TA Arias, ST Smith (1998). The geometry of algorithms with orthogonality constraints. SIAM J Matrix Anal Appl. http://math.mit.edu/~edelman/publications/geometry_of_algorithms.pdf """ def __init__(self, endog, exog, dim): super(CovarianceReduction, self).__init__(endog, exog) covs, ns = [], [] df = pd.DataFrame(self.exog, index=self.endog) for _, v in df.groupby(df.index): covs.append(v.cov().values) ns.append(v.shape[0]) self.nobs = len(endog) # The marginal covariance covm = 0 for i, _ in enumerate(covs): covm += covs[i] * ns[i] covm /= self.nobs self.covm = covm self.covs = covs self.ns = ns self.dim = dim def loglike(self, params): """ Evaluate the log-likelihood Parameters ---------- params : array_like The projection matrix used to reduce the covariances, flattened to 1d. Returns the log-likelihood. """ p = self.covm.shape[0] proj = params.reshape((p, self.dim)) c = np.dot(proj.T, np.dot(self.covm, proj)) _, ldet = np.linalg.slogdet(c) f = self.nobs * ldet / 2 for j, c in enumerate(self.covs): c = np.dot(proj.T, np.dot(c, proj)) _, ldet = np.linalg.slogdet(c) f -= self.ns[j] * ldet / 2 return f def score(self, params): """ Evaluate the score function. Parameters ---------- params : array_like The projection matrix used to reduce the covariances, flattened to 1d. Returns the score function evaluated at 'params'. """ p = self.covm.shape[0] proj = params.reshape((p, self.dim)) c0 = np.dot(proj.T, np.dot(self.covm, proj)) cP = np.dot(self.covm, proj) g = self.nobs * np.linalg.solve(c0, cP.T).T for j, c in enumerate(self.covs): c0 = np.dot(proj.T, np.dot(c, proj)) cP = np.dot(c, proj) g -= self.ns[j] * np.linalg.solve(c0, cP.T).T return g.ravel() def fit(self, start_params=None, maxiter=100, gtol=1e-4): """ Fit the covariance reduction model. Parameters ---------- start_params : array_like Starting value for the projection matrix. May be rectangular, or flattened. maxiter : integer The maximum number of gradient steps to take. gtol : float Convergence criterion for the gradient norm. Returns ------- An orthogonal p x d matrix P that optimizes the likelihood. """ p = self.covm.shape[0] d = self.dim # Starting value for params if start_params is None: params = np.zeros((p, d)) params[0:d, 0:d] = np.eye(d) params = params.ravel() else: params = start_params.ravel() llf = self.loglike(params) for _ in range(maxiter): g = self.score(params) g -= np.dot(g, params) * params / np.dot(params, params) if np.sqrt(np.sum(g * g)) < gtol: break gm = g.reshape((p, d)) u, s, vt = np.linalg.svd(gm, 0) paramsm = params.reshape((p, d)) pa0 = np.dot(paramsm, vt.T) def geo(t): # Parameterize the geodesic path in the direction # of the gradient as a function of t (real). pa = pa0 * np.cos(s * t) + u * np.sin(s * t) return np.dot(pa, vt).ravel() # Try to find an uphill step along the geodesic path. step = 2. while step > 1e-10: pa = geo(step) llf1 = self.loglike(pa) if llf1 > llf: params = pa llf = llf1 break step /= 2 if step <= 1e-10: msg = "CovReduce optimization did not converge" warnings.warn(msg) break params = params.reshape((p, d)) results = DimReductionResults(self, params, eigs=None) results.llf = llf return DimReductionResultsWrapper(results) # aliases for expert users SIR = SlicedInverseReg PHD = PrincipalHessianDirections SAVE = SlicedAverageVarianceEstimation CORE = CovarianceReduction # -*- coding: utf-8 -*- """ This module implements maximum likelihood-based estimation (MLE) of Gaussian models for finite-dimensional observations made on infinite-dimensional processes. The ProcessMLE class supports regression analyses on grouped data, where the observations within a group are dependent (they are made on the same underlying process). The main application is repeated measures regression for temporal (longitudinal) data, in which the repeated measures occur at arbitrary real-valued time points. The mean structure is specified as a linear model. The covariance parameters depend on covariates via a link function. """ import numpy as np import pandas as pd import patsy import statsmodels.base.model as base import statsmodels.api as sm import collections from statsmodels.compat.python import string_types from scipy.optimize import minimize from statsmodels.iolib import summary2 from statsmodels.tools.numdiff import approx_fprime import warnings class ProcessCovariance(object): r""" A covariance model for a process indexed by a real parameter. An implementation of this class is based on a positive definite correlation function h that maps real numbers to the interval [0, 1], such as the Gaussian (squared exponential) correlation function :math:`\exp(-x^2)`. It also depends on a positive scaling function `s` and a positive smoothness function `u`. """ def get_cov(self, time, sc, sm): """ Returns the covariance matrix for given time values. Parameters ---------- time : array_like The time points for the observations. If len(time) = p, a pxp covariance matrix is returned. sc : array_like The scaling parameters for the observations. sm : array_like The smoothness parameters for the observation. See class docstring for details. """ raise NotImplementedError def jac(self, time, sc, sm): """ The Jacobian of the covariance respect to the parameters. See get_cov for parameters. Returns ------- jsc : list-like jsc[i] is the derivative of the covariance matrix with respect to the i^th scaling parameter. jsm : list-like jsm[i] is the derivative of the covariance matrix with respect to the i^th smoothness parameter. """ raise NotImplementedError class GaussianCovariance(ProcessCovariance): r""" An implementation of ProcessCovariance using the Gaussian kernel. This class represents a parametric covariance model for a Gaussian process as described in the work of Paciorek et al. cited below. Following Paciorek et al [1]_, the covariance between observations with index `i` and `j` is given by: .. math:: s[i] \cdot s[j] \cdot h(|time[i] - time[j]| / \sqrt{(u[i] + u[j]) / 2}) \cdot \frac{u[i]^{1/4}u[j]^{1/4}}{\sqrt{(u[i] + u[j])/2}} The ProcessMLE class allows linear models with this covariance structure to be fit using maximum likelihood (ML), which is equivalent to generalized least squares (GLS) in this setting. The mean and covariance parameters of the model are fit jointly. The mean, scaling, and smoothing parameters can be linked to covariates. The mean parameters are linked linearly, and the scaling and smoothing parameters use an exponential link to preserve positivity. The reference of Paciorek et al. below provides more details. Note that here we only implement the 1-dimensional version of their approach. References ---------- .. [1] Paciorek, C. J. and Schervish, M. J. (2006). Spatial modeling using a new class of nonstationary covariance functions. Environmetrics, 17:483–506. https://papers.nips.cc/paper/2350-nonstationary-covariance-functions-for-gaussian-process-regression.pdf """ def get_cov(self, time, sc, sm): da = np.subtract.outer(time, time) ds = np.add.outer(sm, sm) / 2 qmat = da * da / ds cm = np.exp(-qmat / 2) / np.sqrt(ds) cm *= np.outer(sm, sm)**0.25 cm *= np.outer(sc, sc) return cm def jac(self, time, sc, sm): da = np.subtract.outer(time, time) ds = np.add.outer(sm, sm) / 2 sds = np.sqrt(ds) daa = da * da qmat = daa / ds p = len(time) eqm = np.exp(-qmat / 2) sm4 = np.outer(sm, sm)**0.25 cmx = eqm * sm4 / sds dq0 = -daa / ds**2 di = np.zeros((p, p)) fi = np.zeros((p, p)) scc = np.outer(sc, sc) # Derivatives with respect to the smoothing parameters. jsm = [] for i, _ in enumerate(sm): di *= 0 di[i, :] += 0.5 di[:, i] += 0.5 dbottom = 0.5 * di / sds dtop = -0.5 * eqm * dq0 * di b = dtop / sds - eqm * dbottom / ds c = eqm / sds v = 0.25 * sm**0.25 / sm[i]**0.75 fi *= 0 fi[i, :] = v fi[:, i] = v fi[i, i] = 0.5 / sm[i]**0.5 b = c * fi + b * sm4 b *= scc jsm.append(b) # Derivatives with respect to the scaling parameters. jsc = [] for i in range(0, len(sc)): b = np.zeros((p, p)) b[i, :] = cmx[i, :] * sc b[:, i] += cmx[:, i] * sc jsc.append(b) return jsc, jsm def _check_args(endog, exog, exog_scale, exog_smooth, exog_noise, time, groups): v = [ len(endog), exog.shape[0], exog_scale.shape[0], exog_smooth.shape[0], exog_noise.shape[0], len(time), len(groups) ] if min(v)!= max(v): msg = ("The leading dimensions of all array arguments " + "must be equal.") raise ValueError(msg) class ProcessMLE(base.LikelihoodModel): """ Fit a Gaussian mean/variance regression model. This class fits a one-dimensional Gaussian process model with parameterized mean and covariance structures to grouped data. For each group, there is an independent realization of a latent Gaussian process indexed by an observed real-valued time variable.. The data consist of the Gaussian process observed at a finite number of `time` values. The process mean and variance can be lined to covariates. The mean structure is linear in the covariates. The covariance structure is non-stationary, and is defined parametrically through 'scaling', and'smoothing' parameters. The covariance of the process between two observations in the same group is a function of the distance between the time values of the two observations. The scaling and smoothing parameters can be linked to covariates. The observed data are modeled as the sum of the Gaussian process realization and independent white noise. The standard deviation of the white noise can be linked to covariates. The data should be provided in 'long form', with a group label to indicate which observations belong to the same group. Observations in different groups are always independent. Parameters ---------- endog : array_like The dependent variable. exog : array_like The design matrix for the mean structure exog_scale : array_like The design matrix for the scaling structure exog_smooth : array_like The design matrix for the smoothness structure exog_noise : array_like The design matrix for the white noise structure. The linear predictor is the log of the white noise standard deviation. time : array_like (1-dimensional) The univariate index values, used to calculate distances between observations in the same group, which determines their correlations. groups : array_like (1-dimensional) The group values. cov : a ProcessCovariance instance Defaults to GaussianCovariance. """ def __init__(self, endog, exog, exog_scale, exog_smooth, exog_noise, time, groups, cov=None, **kwargs): super(ProcessMLE, self).__init__( endog, exog, exog_scale=exog_scale, exog_smooth=exog_smooth, exog_noise=exog_noise, time=time, groups=groups, **kwargs) # Create parameter names xnames = [] if hasattr(exog, "columns"): xnames = list(exog.columns) else: xnames = ["Mean%d" % j for j in range(exog.shape[1])] if hasattr(exog_scale, "columns"): xnames += list(exog_scale.columns) else: xnames += ["Scale%d" % j for j in range(exog_scale.shape[1])] if hasattr(exog_smooth, "columns"): xnames += list(exog_smooth.columns) else: xnames += ["Smooth%d" % j for j in range(exog_smooth.shape[1])] if hasattr(exog_noise, "columns"): xnames += list(exog_noise.columns) else: xnames += ["Noise%d" % j for j in range(exog_noise.shape[1])] self.data.param_names = xnames if cov is None: cov = GaussianCovariance() self.cov = cov _check_args(endog, exog, exog_scale, exog_smooth, exog_noise, time, groups) groups_ix = collections.defaultdict(lambda: []) for i, g in enumerate(groups): groups_ix[g].append(i) self._groups_ix = groups_ix # Default, can be set in call to fit. self.verbose = False self.k_exog = self.exog.shape[1] self.k_scale = self.exog_scale.shape[1] self.k_smooth = self.exog_smooth.shape[1] self.k_noise = self.exog_noise.shape[1] def _split_param_names(self): xnames = self.data.param_names q = 0 mean_names = xnames[q:q+self.k_exog] q += self.k_exog scale_names = xnames[q:q+self.k_scale] q += self.k_scale smooth_names = xnames[q:q+self.k_smooth] q += self.k_noise noise_names = xnames[q:q+self.k_noise] return mean_names, scale_names, smooth_names, noise_names @classmethod def from_formula(cls, formula, data, subset=None, drop_cols=None, *args, **kwargs): if "scale_formula" in kwargs: scale_formula = kwargs["scale_formula"] else: raise ValueError("scale_formula is a required argument") if "smooth_formula" in kwargs: smooth_formula = kwargs["smooth_formula"] else: raise ValueError("smooth_formula is a required argument") if "noise_formula" in kwargs: noise_formula = kwargs["noise_formula"] else: raise ValueError("noise_formula is a required argument") if "time" in kwargs: time = kwargs["time"] else: raise ValueError("time is a required argument") if "groups" in kwargs: groups = kwargs["groups"] else: raise ValueError("groups is a required argument") if subset is not None: warnings.warn("'subset' is ignored") if drop_cols is not None: warnings.warn("'drop_cols' is ignored") if isinstance(time, string_types): time = np.asarray(data[time]) if isinstance(groups, string_types): groups = np.asarray(data[groups]) exog_scale = patsy.dmatrix(scale_formula, data) scale_design_info = exog_scale.design_info scale_names = scale_design_info.column_names exog_scale = np.asarray(exog_scale) exog_smooth = patsy.dmatrix(smooth_formula, data) smooth_design_info = exog_smooth.design_info smooth_names = smooth_design_info.column_names exog_smooth = np.asarray(exog_smooth) exog_noise = patsy.dmatrix(noise_formula, data) noise_design_info = exog_noise.design_info noise_names = noise_design_info.column_names exog_noise = np.asarray(exog_noise) mod = super(ProcessMLE, cls).from_formula( formula, data=data, subset=None, exog_scale=exog_scale, exog_smooth=exog_smooth, exog_noise=exog_noise, time=time, groups=groups) mod.data.scale_design_info = scale_design_info mod.data.smooth_design_info = smooth_design_info mod.data.noise_design_info = noise_design_info mod.data.param_names = (mod.exog_names + scale_names + smooth_names + noise_names) return mod def unpack(self, z): """ Split the packed parameter vector into blocks. """ # Mean parameters pm = self.exog.shape[1] mnpar = z[0:pm] # Standard deviation parameters pv = self.exog_scale.shape[1] scpar = z[pm:pm + pv] # Smoothness parameters ps = self.exog_smooth.shape[1] smpar = z[pm + pv:pm + pv + ps] # Observation white noise standard deviation nopar = z[pm + pv + ps:] return mnpar, scpar, smpar, nopar def _get_start(self): # Use OLS to get starting values for mean structure parameters model = sm.OLS(self.endog, self.exog) result = model.fit() m = self.exog_scale.shape[1] + self.exog_smooth.shape[1] m += self.exog_noise.shape[1] return np.concatenate((result.params, np.zeros(m))) def loglike(self, params): """ Calculate the log-likelihood function for the model. Parameters ---------- params : array_like The packed parameters for the model. Returns ------- The log-likelihood value at the given parameter point. Notes ----- The mean, scaling, and smoothing parameters are packed into a vector. Use `unpack` to access the component vectors. """ mnpar, scpar, smpar, nopar = self.unpack(params) # Residuals resid = self.endog - np.dot(self.exog, mnpar) # Scaling parameters sc = np.exp(np.dot(self.exog_scale, scpar)) # Smoothness parameters sm = np.exp(np.dot(self.exog_smooth, smpar)) # White noise standard deviation no = np.exp(np.dot(self.exog_noise, nopar)) # Get the log-likelihood ll = 0. for _, ix in self._groups_ix.items(): # Get the covariance matrix for this person. cm = self.cov.get_cov(self.time[ix], sc[ix], sm[ix]) cm.flat[::cm.shape[0] + 1] += no[ix]**2 re = resid[ix] ll -= 0.5 * np.linalg.slogdet(cm)[1] ll -= 0.5 * np.dot(re, np.linalg.solve(cm, re)) if self.verbose: print("L=", ll) return ll def score(self, params): """ Calculate the score function for the model. Parameters ---------- params : array_like The packed parameters for the model. Returns ------- The score vector at the given parameter point. Notes ----- The mean, scaling, and smoothing parameters are packed into a vector. Use `unpack` to access the component vectors. """ mnpar, scpar, smpar, nopar = self.unpack(params) pm, pv, ps = len(mnpar), len(scpar), len(smpar) # Residuals resid = self.endog - np.dot(self.exog, mnpar) # Scaling sc = np.exp(np.dot(self.exog_scale, scpar)) # Smoothness sm = np.exp(np.dot(self.exog_smooth, smpar)) # White noise standard deviation no = np.exp(np.dot(self.exog_noise, nopar)) # Get the log-likelihood score = np.zeros(len(mnpar) + len(scpar) + len(smpar) + len(nopar)) for _, ix in self._groups_ix.items(): sc_i = sc[ix] sm_i = sm[ix] no_i = no[ix] resid_i = resid[ix] time_i = self.time[ix] exog_i = self.exog[ix, :] exog_scale_i = self.exog_scale[ix, :] exog_smooth_i = self.exog_smooth[ix, :] exog_noise_i = self.exog_noise[ix, :] # Get the covariance matrix for this person. cm = self.cov.get_cov(time_i, sc_i, sm_i) cm.flat[::cm.shape[0] + 1] += no[ix]**2 cmi = np.linalg.inv(cm) jacv, jacs = self.cov.jac(time_i, sc_i, sm_i) # The derivatives for the mean parameters. dcr = np.linalg.solve(cm, resid_i) score[0:pm] += np.dot(exog_i.T, dcr) # The derivatives for the scaling parameters. rx = np.outer(resid_i, resid_i) qm = np.linalg.solve(cm, rx) qm = 0.5 * np.linalg.solve(cm, qm.T) scx = sc_i[:, None] * exog_scale_i for i, _ in enumerate(ix): jq = np.sum(jacv[i] * qm) score[pm:pm + pv] += jq * scx[i, :] score[pm:pm + pv] -= 0.5 * np.sum(jacv[i] * cmi) * scx[i, :] # The derivatives for the smoothness parameters. smx = sm_i[:, None] * exog_smooth_i for i, _ in enumerate(ix): jq = np.sum(jacs[i] * qm) score[pm + pv:pm + pv + ps] += jq * smx[i, :] score[pm + pv:pm + pv + ps] -= ( 0.5 * np.sum(jacs[i] * cmi) * smx[i, :]) # The derivatives with respect to the standard deviation parameters sno = no_i[:, None]**2 * exog_noise_i score[pm + pv + ps:] -= np.dot(cmi.flat[::cm.shape[0] + 1], sno) bm = np.dot(cmi, np.dot(rx, cmi)) score[pm + pv + ps:] += np.dot(bm.flat[::bm.shape[0] + 1], sno) if self.verbose: print("|G|=", np.sqrt(np.sum(score * score))) return score def hessian(self, params): hess = approx_fprime(params, self.score) return hess def fit(self, start_params=None, method=None, maxiter=None, **kwargs): """ Fit a grouped Gaussian process regression using MLE. Parameters ---------- start_params : array_like Optional starting values. method : string or array of strings Method or sequence of methods for scipy optimize. maxiter : int The maximum number of iterations in the optimization. Returns ------- An instance of ProcessMLEResults. """ if "verbose" in kwargs: self.verbose = kwargs["verbose"] minim_opts = {} if "minim_opts" in kwargs: minim_opts = kwargs["minim_opts"] if start_params is None: start_params = self._get_start() if isinstance(method, str): method = [method] elif method is None: method = ["powell", "bfgs"] for j, meth in enumerate(method): if meth not in ("powell",): def jac(x): return -self.score(x) else: jac = None if maxiter is not None: if np.isscalar(maxiter): minim_opts["maxiter"] = maxiter else: minim_opts["maxiter"] = maxiter[j % len(maxiter)] f = minimize( lambda x: -self.loglike(x), method=meth, x0=start_params, jac=jac, options=minim_opts) if not f.success: msg = "Fitting did not converge" if jac is not None: msg += ", |gradient|=%.6f" % np.sqrt(np.sum(f.jac**2)) if j < len(method) - 1: msg += ", trying %s next..." % method[j+1] warnings.warn(msg) if np.isfinite(f.x).all(): start_params = f.x hess = self.hessian(f.x) try: cov_params = -np.linalg.inv(hess) except Exception: cov_params = None class rslt: pass r = rslt() r.params = f.x r.normalized_cov_params = cov_params r.optim_retvals = f r.scale = 1 rslt = ProcessMLEResults(self, r) return rslt def covariance(self, time, scale_params, smooth_params, scale_data, smooth_data): """ Returns a Gaussian process covariance matrix. Parameters ---------- time : array_like The time points at which the fitted covariance matrix is calculated. scale_params : array_like The regression parameters for the scaling part of the covariance structure. smooth_params : array_like The regression parameters for the smoothing part of the covariance structure. scale_data : Dataframe The data used to determine the scale parameter, must have len(time) rows. smooth_data: Dataframe The data used to determine the smoothness parameter, must have len(time) rows. Returns ------- A covariance matrix. Notes ----- If the model was fit using formulas, `scale` and `smooth` should be Dataframes, containing all variables that were present in the respective scaling and smoothing formulas used to fit the model. Otherwise, `scale` and `smooth` should contain data arrays whose columns align with the fitted scaling and smoothing parameters. The covariance is only for the Gaussian process and does not include the white noise variance. """ if not hasattr(self.data, "scale_design_info"): sca = np.dot(scale_data, scale_params) smo = np.dot(smooth_data, smooth_params) else: sc = patsy.dmatrix(self.data.scale_design_info, scale_data) sm = patsy.dmatrix(self.data.smooth_design_info, smooth_data) sca = np.exp(np.dot(sc, scale_params)) smo = np.exp(np.dot(sm, smooth_params)) return self.cov.get_cov(time, sca, smo) def predict(self, params, exog=None, *args, **kwargs): """ Obtain predictions of the mean structure. Parameters ---------- params : array_like The model parameters, may be truncated to include only mean parameters. exog : array_like The design matrix for the mean structure. If not provided, the model's design matrix is used. """ if exog is None: exog = self.exog elif hasattr(self.data, "design_info"): # Run the provided data through the formula if present exog = patsy.dmatrix(self.data.design_info, exog) if len(params) > exog.shape[1]: params = params[0:exog.shape[1]] return np.dot(exog, params) class ProcessMLEResults(base.GenericLikelihoodModelResults): """ Results class for Gaussian process regression models. """ def __init__(self, model, mlefit): super(ProcessMLEResults, self).__init__( model, mlefit) pa = model.unpack(mlefit.params) self.mean_params = pa[0] self.scale_params = pa[1] self.smooth_params = pa[2] self.no_params = pa[3] self.df_resid = model.endog.shape[0] - len(mlefit.params) self.k_exog = self.model.exog.shape[1] self.k_scale = self.model.exog_scale.shape[1] self.k_smooth = self.model.exog_smooth.shape[1] self.k_noise = self.model.exog_noise.shape[1] def predict(self, exog=None, transform=True, *args, **kwargs): if not transform: warnings.warn("'transform=False' is ignored in predict") if len(args) > 0 or len(kwargs) > 0: warnings.warn("extra arguments ignored in 'predict'") return self.model.predict(self.params, exog) def covariance(self, time, scale, smooth): """ Returns a fitted covariance matrix. Parameters ---------- time : array_like The time points at which the fitted covariance matrix is calculated. scale : array_like The data used to determine the scale parameter, must have len(time) rows. smooth: array_like The data used to determine the smoothness parameter, must have len(time) rows. Returns ------- A covariance matrix. Notes ----- If the model was fit using formulas, `scale` and `smooth` should be Dataframes, containing all variables that were present in the respective scaling and smoothing formulas used to fit the model. Otherwise, `scale` and `smooth` should be data arrays whose columns align with the fitted scaling and smoothing parameters. """ return self.model.covariance(time, self.scale_params, self.smooth_params, scale, smooth) def covariance_group(self, group): # Check if the group exists, since _groups_ix is a # DefaultDict use len instead of catching a KeyError. ix = self.model._groups_ix[group] if len(ix) == 0: msg = "Group '%s' does not exist" % str(group) raise ValueError(msg) scale_data = self.model.exog_scale[ix, :] smooth_data = self.model.exog_smooth[ix, :] _, scale_names, smooth_names, _ = self.model._split_param_names() scale_data = pd.DataFrame(scale_data, columns=scale_names) smooth_data = pd.DataFrame(smooth_data, columns=smooth_names) time = self.model.time[ix] return self.model.covariance(time, self.scale_params, self.smooth_params, scale_data, smooth_data) def summary(self, yname=None, xname=None, title=None, alpha=0.05): df = pd.DataFrame() df["Type"] = (["Mean"] * self.k_exog + ["Scale"] * self.k_scale + ["Smooth"] * self.k_smooth + ["SD"] * self.k_noise) df["coef"] = self.params try: df["std err"] = np.sqrt(np.diag(self.cov_params())) except Exception: df["std err"] = np.nan from scipy.stats.distributions import norm df["tvalues"] = df.coef / df["std err"] df["P>|t|"] = 2 * norm.sf(np.abs(df.tvalues)) f = norm.ppf(1 - alpha / 2) df["[%.3f" % (alpha / 2)] = df.coef - f * df["std err"] df["%.3f]" % (1 - alpha / 2)] = df.coef + f * df["std err"] df.index = self.model.data.param_names summ = summary2.Summary() if title is None: title = "Gaussian process regression results" summ.add_title(title) summ.add_df(df) return summ """ Recursive least squares model Author: Chad Fulton License: Simplified-BSD """ import numpy as np import pandas as pd from statsmodels.compat import unicode from statsmodels.tools.data import _is_using_pandas from statsmodels.tsa.statespace.mlemodel import ( MLEModel, MLEResults, MLEResultsWrapper, PredictionResults, PredictionResultsWrapper) from statsmodels.tsa.statespace.tools import concat from statsmodels.tools.tools import Bunch from statsmodels.tools.decorators import cache_readonly import statsmodels.base.wrapper as wrap # Columns are alpha = 0.1, 0.05, 0.025, 0.01, 0.005 _cusum_squares_scalars = np.array([ [1.0729830, 1.2238734, 1.3581015, 1.5174271, 1.6276236], [-0.6698868, -0.6700069, -0.6701218, -0.6702672, -0.6703724], [-0.5816458, -0.7351697, -0.8858694, -1.0847745, -1.2365861] ]) class RecursiveLS(MLEModel): r""" Recursive least squares Parameters ---------- endog : array_like The observed time-series process :math:`y` exog : array_like Array of exogenous regressors, shaped nobs x k. constraints : array_like, str, or tuple - array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. - str : The full hypotheses to test can be given as a string. See the examples. - tuple : A tuple of arrays in the form (R, q), ``q`` can be either a scalar or a length p row vector. Notes ----- Recursive least squares (RLS) corresponds to expanding window ordinary least squares (OLS). This model applies the Kalman filter to compute recursive estimates of the coefficients and recursive residuals. References ---------- .. [*] Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press. """ def __init__(self, endog, exog, constraints=None, **kwargs): # Standardize data endog_using_pandas = _is_using_pandas(endog, None) if not endog_using_pandas: endog = np.asanyarray(endog) exog_is_using_pandas = _is_using_pandas(exog, None) if not exog_is_using_pandas: exog = np.asarray(exog) # Make sure we have 2-dimensional array if exog.ndim == 1: if not exog_is_using_pandas: exog = exog[:, None] else: exog = pd.DataFrame(exog) self.k_exog = exog.shape[1] # Handle constraints self.k_constraints = 0 self._r_matrix = self._q_matrix = None if constraints is not None: from patsy import DesignInfo from statsmodels.base.data import handle_data data = handle_data(endog, exog, **kwargs) names = data.param_names LC = DesignInfo(names).linear_constraint(constraints) self._r_matrix, self._q_matrix = LC.coefs, LC.constants self.k_constraints = self._r_matrix.shape[0] constraint_endog = np.zeros((len(endog), len(self._r_matrix))) if endog_using_pandas: constraint_endog = pd.DataFrame(constraint_endog, index=endog.index) endog = concat([endog, constraint_endog], axis=1) endog.values[:, 1:] = self._q_matrix[:, 0] else: endog[:, 1:] = self._q_matrix[:, 0] # Handle coefficient initialization kwargs.setdefault('initialization', 'diffuse') # Initialize the state space representation super(RecursiveLS, self).__init__( endog, k_states=self.k_exog, exog=exog, **kwargs) # Use univariate filtering by default self.ssm.filter_univariate = True # Concentrate the scale out of the likelihood function self.ssm.filter_concentrated = True # Setup the state space representation self['design'] = np.zeros((self.k_endog, self.k_states, self.nobs)) self['design', 0] = self.exog[:, :, None].T if self._r_matrix is not None: self['design', 1:, :] = self._r_matrix[:, :, None] self['transition'] = np.eye(self.k_states) # Notice that the filter output does not depend on the measurement # variance, so we set it here to 1 self['obs_cov', 0, 0] = 1. self['transition'] = np.eye(self.k_states) # Linear constraints are technically imposed by adding "fake" endog # variables that are used during filtering, but for all model- and # results-based purposes we want k_endog = 1. if self._r_matrix is not None: self.k_endog = 1 @classmethod def from_formula(cls, formula, data, subset=None, constraints=None): return super(MLEModel, cls).from_formula(formula, data, subset, constraints=constraints) def fit(self): """ Fits the model by application of the Kalman filter Returns ------- RecursiveLSResults """ smoother_results = self.smooth(return_ssm=True) with self.ssm.fixed_scale(smoother_results.scale): res = self.smooth() return res def filter(self, return_ssm=False, **kwargs): # Get the state space output result = super(RecursiveLS, self).filter([], transformed=True, cov_type='none', return_ssm=True, **kwargs) # Wrap in a results object if not return_ssm: params = result.filtered_state[:, -1] cov_kwds = { 'custom_cov_type': 'nonrobust', 'custom_cov_params': result.filtered_state_cov[:, :, -1], 'custom_description': ('Parameters and covariance matrix' 'estimates are RLS estimates' 'conditional on the entire sample.') } result = RecursiveLSResultsWrapper( RecursiveLSResults(self, params, result, cov_type='custom', cov_kwds=cov_kwds) ) return result def smooth(self, return_ssm=False, **kwargs): # Get the state space output result = super(RecursiveLS, self).smooth([], transformed=True, cov_type='none', return_ssm=True, **kwargs) # Wrap in a results object if not return_ssm: params = result.filtered_state[:, -1] cov_kwds = { 'custom_cov_type': 'nonrobust', 'custom_cov_params': result.filtered_state_cov[:, :, -1], 'custom_description': ('Parameters and covariance matrix' 'estimates are RLS estimates' 'conditional on the entire sample.') } result = RecursiveLSResultsWrapper( RecursiveLSResults(self, params, result, cov_type='custom', cov_kwds=cov_kwds) ) return result @property def endog_names(self): endog_names = super(RecursiveLS, self).endog_names return endog_names[0] if isinstance(endog_names, list) else endog_names @property def param_names(self): return self.exog_names @property def start_params(self): # Only parameter is the measurement disturbance standard deviation return np.zeros(0) def update(self, params, **kwargs): """ Update the parameters of the model Updates the representation matrices to fill in the new parameter values. Parameters ---------- params : array_like Array of new parameters. transformed : boolean, optional Whether or not `params` is already transformed. If set to False, `transform_params` is called. Default is True.. Returns ------- params : array_like Array of parameters. """ pass class RecursiveLSResults(MLEResults): """ Class to hold results from fitting a recursive least squares model. Parameters ---------- model : RecursiveLS instance The fitted model instance Attributes ---------- specification : dictionary Dictionary including all attributes from the recursive least squares model instance. See Also -------- statsmodels.tsa.statespace.kalman_filter.FilterResults statsmodels.tsa.statespace.mlemodel.MLEResults """ def __init__(self, model, params, filter_results, cov_type='opg', **kwargs): super(RecursiveLSResults, self).__init__( model, params, filter_results, cov_type, **kwargs) # Since we are overriding params with things that aren't MLE params, # need to adjust df's q = max(self.loglikelihood_burn, self.k_diffuse_states) self.df_model = q - self.model.k_constraints self.df_resid = self.nobs_effective - self.df_model # Save _init_kwds self._init_kwds = self.model._get_init_kwds() # Save the model specification self.specification = Bunch(**{ 'k_exog': self.model.k_exog, 'k_constraints': self.model.k_constraints}) # Adjust results to remove "faux" endog from the constraints if self.model._r_matrix is not None: for name in ['forecasts', 'forecasts_error', 'forecasts_error_cov','standardized_forecasts_error', 'forecasts_error_diffuse_cov']: setattr(self, name, getattr(self, name)[0:1]) @property def recursive_coefficients(self): """ Estimates of regression coefficients, recursively estimated Returns ------- out: Bunch Has the following attributes: - `filtered`: a time series array with the filtered estimate of the component - `filtered_cov`: a time series array with the filtered estimate of the variance/covariance of the component - `smoothed`: a time series array with the smoothed estimate of the component - `smoothed_cov`: a time series array with the smoothed estimate of the variance/covariance of the component - `offset`: an integer giving the offset in the state vector where this component begins """ out = None spec = self.specification start = offset = 0 end = offset + spec.k_exog out = Bunch( filtered=self.filtered_state[start:end], filtered_cov=self.filtered_state_cov[start:end, start:end], smoothed=None, smoothed_cov=None, offset=offset ) if self.smoothed_state is not None: out.smoothed = self.smoothed_state[start:end] if self.smoothed_state_cov is not None: out.smoothed_cov = ( self.smoothed_state_cov[start:end, start:end]) return out @cache_readonly def resid_recursive(self): r""" Recursive residuals Returns ------- resid_recursive : array_like An array of length `nobs` holding the recursive residuals. Notes ----- These quantities are defined in, for example, Harvey (1989) section 5.4. In fact, there he defines the standardized innovations in equation 5.4.1, but in his version they have non-unit variance, whereas the standardized forecast errors computed by the Kalman filter here assume unit variance. To convert to Harvey's definition, we need to multiply by the standard deviation. Harvey notes that in smaller samples, "although the second moment of the :math:`\tilde \sigma_*^{-1} \tilde v_t`'s is unity, the variance is not necessarily equal to unity as the mean need not be equal to zero", and he defines an alternative version (which are not provided here). """ return (self.filter_results.standardized_forecasts_error[0] * self.scale**0.5) @cache_readonly def cusum(self): r""" Cumulative sum of standardized recursive residuals statistics Returns ------- cusum : array_like An array of length `nobs - k_exog` holding the CUSUM statistics. Notes ----- The CUSUM statistic takes the form: .. math:: W_t = \frac{1}{\hat \sigma} \sum_{j=k+1}^t w_j where :math:`w_j` is the recursive residual at time :math:`j` and :math:`\hat \sigma` is the estimate of the standard deviation from the full sample. Excludes the first `k_exog` datapoints. Due to differences in the way :math:`\hat \sigma` is calculated, the output of this function differs slightly from the output in the R package strucchange and the Stata contributed.ado file cusum6. The calculation in this package is consistent with the description of Brown et al. (1975) References ---------- .. [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975. "Techniques for Testing the Constancy of Regression Relationships over Time." Journal of the Royal Statistical Society. Series B (Methodological) 37 (2): 149-92. """ d = max(self.nobs_diffuse, self.loglikelihood_burn) return (np.cumsum(self.resid_recursive[d:]) / np.std(self.resid_recursive[d:], ddof=1)) @cache_readonly def cusum_squares(self): r""" Cumulative sum of squares of standardized recursive residuals statistics Returns ------- cusum_squares : array_like An array of length `nobs - k_exog` holding the CUSUM of squares statistics. Notes ----- The CUSUM of squares statistic takes the form: .. math:: s_t = \left ( \sum_{j=k+1}^t w_j^2 \right ) \Bigg / \left ( \sum_{j=k+1}^T w_j^2 \right ) where :math:`w_j` is the recursive residual at time :math:`j`. Excludes the first `k_exog` datapoints. References ---------- .. [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975. "Techniques for Testing the Constancy of Regression Relationships over Time." Journal of the Royal Statistical Society. Series B (Methodological) 37 (2): 149-92. """ d = max(self.nobs_diffuse, self.loglikelihood_burn) numer = np.cumsum(self.resid_recursive[d:]**2) denom = numer[-1] return numer / denom @cache_readonly def llf_recursive_obs(self): """ (float) Loglikelihood at observation, computed from recursive residuals """ from scipy.stats import norm return np.log(norm.pdf(self.resid_recursive, loc=0, scale=self.scale**0.5)) @cache_readonly def llf_recursive(self): """ (float) Loglikelihood defined by recursive residuals, equivalent to OLS """ return np.sum(self.llf_recursive_obs) @cache_readonly def ssr(self): """ssr""" d = max(self.nobs_diffuse, self.loglikelihood_burn) return (self.nobs - d) * self.filter_results.obs_cov[0, 0, 0] @cache_readonly def centered_tss(self): """Centered tss""" return np.sum((self.filter_results.endog[0] - np.mean(self.filter_results.endog))**2) @cache_readonly def uncentered_tss(self): """uncentered tss""" return np.sum((self.filter_results.endog[0])**2) @cache_readonly def ess(self): """esss""" if self.k_constant: return self.centered_tss - self.ssr else: return self.uncentered_tss - self.ssr @cache_readonly def rsquared(self): """rsquared""" if self.k_constant: return 1 - self.ssr / self.centered_tss else: return 1 - self.ssr / self.uncentered_tss @cache_readonly def mse_model(self): """mse_model""" return self.ess / self.df_model @cache_readonly def mse_resid(self): """mse_resid""" return self.ssr / self.df_resid @cache_readonly def mse_total(self): """mse_total""" if self.k_constant: return self.centered_tss / (self.df_resid + self.df_model) else: return self.uncentered_tss / (self.df_resid + self.df_model) def get_prediction(self, start=None, end=None, dynamic=False, index=None, **kwargs): # Note: need to override this, because we currently don't support # dynamic prediction or forecasts when there are constraints. if start is None: start = self.model._index[0] # Handle start, end, dynamic start, end, out_of_sample, prediction_index = ( self.model._get_prediction_index(start, end, index)) # Handle `dynamic` if isinstance(dynamic, (bytes, unicode)): dynamic, _, _ = self.model._get_index_loc(dynamic) if self.model._r_matrix is not None and (out_of_sample or dynamic): raise NotImplementedError('Cannot yet perform out-of-sample or' 'dynamic prediction in models with' 'constraints.') # Perform the prediction # This is a (k_endog x npredictions) array; don't want to squeeze in # case of npredictions = 1 prediction_results = self.filter_results.predict( start, end + out_of_sample + 1, dynamic, **kwargs) # Return a new mlemodel.PredictionResults object return PredictionResultsWrapper(PredictionResults( self, prediction_results, row_labels=prediction_index)) get_prediction.__doc__ = MLEResults.get_prediction.__doc__ def plot_recursive_coefficient(self, variables=0, alpha=0.05, legend_loc='upper left', fig=None, figsize=None): r""" Plot the recursively estimated coefficients on a given variable Parameters ---------- variables : int or str or iterable of int or string, optional Integer index or string name of the variable whose coefficient will be plotted. Can also be an iterable of integers or strings. Default is the first variable. alpha : float, optional The confidence intervals for the coefficient are (1 - alpha) % legend_loc : string, optional The location of the legend in the plot. Default is upper left. fig : Matplotlib Figure instance, optional If given, subplots are created in this figure instead of in a new figure. Note that the grid will be created in the provided figure using `fig.add_subplot()`. figsize : tuple, optional If a figure is created, this argument allows specifying a size. The tuple is (width, height). Notes ----- All plots contain (1 - `alpha`) % confidence intervals. """ # Get variables if isinstance(variables, (int, str)): variables = [variables] k_variables = len(variables) # If a string was given for `variable`, try to get it from exog names exog_names = self.model.exog_names for i in range(k_variables): variable = variables[i] if isinstance(variable, str): variables[i] = exog_names.index(variable) # Create the plot from scipy.stats import norm from statsmodels.graphics.utils import _import_mpl, create_mpl_fig plt = _import_mpl() fig = create_mpl_fig(fig, figsize) for i in range(k_variables): variable = variables[i] ax = fig.add_subplot(k_variables, 1, i + 1) # Get dates, if applicable if hasattr(self.data, 'dates') and self.data.dates is not None: dates = self.data.dates._mpl_repr() else: dates = np.arange(self.nobs) d = max(self.nobs_diffuse, self.loglikelihood_burn) # Plot the coefficient coef = self.recursive_coefficients ax.plot(dates[d:], coef.filtered[variable, d:], label='Recursive estimates: %s' % exog_names[variable]) # Legend handles, labels = ax.get_legend_handles_labels() # Get the critical value for confidence intervals if alpha is not None: critical_value = norm.ppf(1 - alpha / 2.) # Plot confidence intervals std_errors = np.sqrt(coef.filtered_cov[variable, variable, :]) ci_lower = ( coef.filtered[variable] - critical_value * std_errors) ci_upper = ( coef.filtered[variable] + critical_value * std_errors) ci_poly = ax.fill_between( dates[d:], ci_lower[d:], ci_upper[d:], alpha=0.2 ) ci_label = ('$%.3g \\%%$ confidence interval' % ((1 - alpha)*100)) # Only add CI to legend for the first plot if i == 0: # Proxy artist for fill_between legend entry # See https://matplotlib.org/1.3.1/users/legend_guide.html p = plt.Rectangle((0, 0), 1, 1, fc=ci_poly.get_facecolor()[0]) handles.append(p) labels.append(ci_label) ax.legend(handles, labels, loc=legend_loc) # Remove xticks for all but the last plot if i < k_variables - 1: ax.xaxis.set_ticklabels([]) fig.tight_layout() return fig def _cusum_significance_bounds(self, alpha, ddof=0, points=None): """ Parameters ---------- alpha : float, optional The significance bound is alpha %. ddof : int, optional The number of periods additional to `k_exog` to exclude in constructing the bounds. Default is zero. This is usually used only for testing purposes. points : iterable, optional The points at which to evaluate the significance bounds. Default is two points, beginning and end of the sample. Notes ----- Comparing against the cusum6 package for Stata, this does not produce exactly the same confidence bands (which are produced in cusum6 by lw, uw) because they burn the first k_exog + 1 periods instead of the first k_exog. If this change is performed (so that `tmp = (self.nobs - d - 1)**0.5`), then the output here matches cusum6. The cusum6 behavior does not seem to be consistent with Brown et al. (1975); it is likely they did that because they needed three initial observations to get the initial OLS estimates, whereas we do not need to do that. """ # Get the constant associated with the significance level if alpha == 0.01: scalar = 1.143 elif alpha == 0.05: scalar = 0.948 elif alpha == 0.10: scalar = 0.950 else: raise ValueError('Invalid significance level.') # Get the points for the significance bound lines d = max(self.nobs_diffuse, self.loglikelihood_burn) tmp = (self.nobs - d - ddof)**0.5 def upper_line(x): return scalar * tmp + 2 * scalar * (x - d) / tmp if points is None: points = np.array([d, self.nobs]) return -upper_line(points), upper_line(points) def plot_cusum(self, alpha=0.05, legend_loc='upper left', fig=None, figsize=None): r""" Plot the CUSUM statistic and significance bounds. Parameters ---------- alpha : float, optional The plotted significance bounds are alpha %. legend_loc : string, optional The location of the legend in the plot. Default is upper left. fig : Matplotlib Figure instance, optional If given, subplots are created in this figure instead of in a new figure. Note that the grid will be created in the provided figure using `fig.add_subplot()`. figsize : tuple, optional If a figure is created, this argument allows specifying a size. The tuple is (width, height). Notes ----- Evidence of parameter instability may be found if the CUSUM statistic moves out of the significance bounds. References ---------- .. [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975. "Techniques for Testing the Constancy of Regression Relationships over Time." Journal of the Royal Statistical Society. Series B (Methodological) 37 (2): 149-92. """ # Create the plot from statsmodels.graphics.utils import _import_mpl, create_mpl_fig _import_mpl() fig = create_mpl_fig(fig, figsize) ax = fig.add_subplot(1, 1, 1) # Get dates, if applicable if hasattr(self.data, 'dates') and self.data.dates is not None: dates = self.data.dates._mpl_repr() else: dates = np.arange(self.nobs) d = max(self.nobs_diffuse, self.loglikelihood_burn) # Plot cusum series and reference line ax.plot(dates[d:], self.cusum, label='CUSUM') ax.hlines(0, dates[d], dates[-1], color='k', alpha=0.3) # Plot significance bounds lower_line, upper_line = self._cusum_significance_bounds(alpha) ax.plot([dates[d], dates[-1]], upper_line, 'k--', label='%d%% significance' % (alpha * 100)) ax.plot([dates[d], dates[-1]], lower_line, 'k--') ax.legend(loc=legend_loc) return fig def _cusum_squares_significance_bounds(self, alpha, points=None): """ Notes ----- Comparing against the cusum6 package for Stata, this does not produce exactly the same confidence bands (which are produced in cusum6 by lww, uww) because they use a different method for computing the critical value; in particular, they use tabled values from Table C, pp. 364-365 of "The Econometric Analysis of Time Series" Harvey, (1990), and use the value given to 99 observations for any larger number of observations. In contrast, we use the approximating critical values suggested in Edgerton and Wells (1994) which allows computing relatively good approximations for any number of observations. """ # Get the approximate critical value associated with the significance # level d = max(self.nobs_diffuse, self.loglikelihood_burn) n = 0.5 * (self.nobs - d) - 1 try: ix = [0.1, 0.05, 0.025, 0.01, 0.005].index(alpha / 2) except ValueError: raise ValueError('Invalid significance level.') scalars = _cusum_squares_scalars[:, ix] crit = scalars[0] / n**0.5 + scalars[1] / n + scalars[2] / n**1.5 # Get the points for the significance bound lines if points is None: points = np.array([d, self.nobs]) line = (points - d) / (self.nobs - d) return line - crit, line + crit def plot_cusum_squares(self, alpha=0.05, legend_loc='upper left', fig=None, figsize=None): r""" Plot the CUSUM of squares statistic and significance bounds. Parameters ---------- alpha : float, optional The plotted significance bounds are alpha %. legend_loc : string, optional The location of the legend in the plot. Default is upper left. fig : Matplotlib Figure instance, optional If given, subplots are created in this figure instead of in a new figure. Note that the grid will be created in the provided figure using `fig.add_subplot()`. figsize : tuple, optional If a figure is created, this argument allows specifying a size. The tuple is (width, height). Notes ----- Evidence of parameter instability may be found if the CUSUM of squares statistic moves out of the significance bounds. Critical values used in creating the significance bounds are computed using the approximate formula of [1]_. References ---------- .. [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975. "Techniques for Testing the Constancy of Regression Relationships over Time." Journal of the Royal Statistical Society. Series B (Methodological) 37 (2): 149-92. .. [1] Edgerton, David, and Curt Wells. 1994. "Critical Values for the Cusumsq Statistic in Medium and Large Sized Samples." Oxford Bulletin of Economics and Statistics 56 (3): 355-65. """ # Create the plot from statsmodels.graphics.utils import _import_mpl, create_mpl_fig _import_mpl() fig = create_mpl_fig(fig, figsize) ax = fig.add_subplot(1, 1, 1) # Get dates, if applicable if hasattr(self.data, 'dates') and self.data.dates is not None: dates = self.data.dates._mpl_repr() else: dates = np.arange(self.nobs) d = max(self.nobs_diffuse, self.loglikelihood_burn) # Plot cusum series and reference line ax.plot(dates[d:], self.cusum_squares, label='CUSUM of squares') ref_line = (np.arange(d, self.nobs) - d) / (self.nobs - d) ax.plot(dates[d:], ref_line, 'k', alpha=0.3) # Plot significance bounds lower_line, upper_line = self._cusum_squares_significance_bounds(alpha) ax.plot([dates[d], dates[-1]], upper_line, 'k--', label='%d%% significance' % (alpha * 100)) ax.plot([dates[d], dates[-1]], lower_line, 'k--') ax.legend(loc=legend_loc) return fig class RecursiveLSResultsWrapper(MLEResultsWrapper): _attrs = {} _wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs, _attrs) _methods = {} _wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods, _methods) wrap.populate_wrapper(RecursiveLSResultsWrapper, # noqa:E305 RecursiveLSResults)

sqlalchemy__sqlalchemy

collection_api.rst

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sqlalchemy__sqlalchemy/doc/build/orm/collection_api.rst

Collection Customization and API Details The _orm.relationship function defines a linkage between two classes. When the linkage defines a one-to-many or many-to-many relationship, it's represented as a Python collection when objects are loaded and manipulated. This section presents additional information about collection configuration and techniques. Customizing Collection Access Mapping a one-to-many or many-to-many relationship results in a collection of values accessible through an attribute on the parent instance. The two common collection types for these are list and set, which in Declarative <orm_declarative_styles_toplevel> mappings that use _orm.Mapped is established by using the collection type within the _orm.Mapped container, as demonstrated in the Parent.children collection below where list is used: from sqlalchemy import ForeignKey from sqlalchemy.orm import DeclarativeBase from sqlalchemy.orm import Mapped from sqlalchemy.orm import mapped_column from sqlalchemy.orm import relationship class Base(DeclarativeBase): pass class Parent(Base): __tablename__ = "parent" parent_id: Mapped[int] = mapped_column(primary_key=True) # use a list children: Mapped[List["Child"]] = relationship() class Child(Base): __tablename__ = "child" child_id: Mapped[int] = mapped_column(primary_key=True) parent_id: Mapped[int] = mapped_column(ForeignKey("parent.id")) Or for a set, illustrated in the same Parent.children collection: from typing import Set from sqlalchemy import ForeignKey from sqlalchemy.orm import DeclarativeBase from sqlalchemy.orm import Mapped from sqlalchemy.orm import mapped_column from sqlalchemy.orm import relationship class Base(DeclarativeBase): pass class Parent(Base): __tablename__ = "parent" parent_id: Mapped[int] = mapped_column(primary_key=True) # use a set children: Mapped[Set["Child"]] = relationship() class Child(Base): __tablename__ = "child" child_id: Mapped[int] = mapped_column(primary_key=True) parent_id: Mapped[int] = mapped_column(ForeignKey("parent.id")) Note If using Python 3.7 or 3.8, annotations for collections need to use typing.List or typing.Set, e.g. Mapped[List["Child"]] or Mapped[Set["Child"]]; the list and set Python built-ins don't yet support generic annotation in these Python versions, such as: from typing import List class Parent(Base): __tablename__ = "parent" parent_id: Mapped[int] = mapped_column(primary_key=True) # use a List, Python 3.8 and earlier children: Mapped[List["Child"]] = relationship() When using mappings without the _orm.Mapped annotation, such as when using imperative mappings <orm_imperative_mapping> or untyped Python code, as well as in a few special cases, the collection class for a _orm.relationship can always be specified directly using the _orm.relationship.collection_class parameter: # non-annotated mapping class Parent(Base): __tablename__ = "parent" parent_id = mapped_column(Integer, primary_key=True) children = relationship("Child", collection_class=set) class Child(Base): __tablename__ = "child" child_id = mapped_column(Integer, primary_key=True) parent_id = mapped_column(ForeignKey("parent.id")) In the absence of _orm.relationship.collection_class or _orm.Mapped, the default collection type is list. Beyond list and set builtins, there is also support for two varities of dictionary, described below at orm_dictionary_collection. There is also support for any arbitrary mutable sequence type can be set up as the target collection, with some additional configuration steps; this is described in the section orm_custom_collection. Dictionary Collections A little extra detail is needed when using a dictionary as a collection. This because objects are always loaded from the database as lists, and a key-generation strategy must be available to populate the dictionary correctly. The .attribute_keyed_dict function is by far the most common way to achieve a simple dictionary collection. It produces a dictionary class that will apply a particular attribute of the mapped class as a key. Below we map an Item class containing a dictionary of Note items keyed to the Note.keyword attribute. When using .attribute_keyed_dict, the _orm.Mapped annotation may be typed using the _orm.KeyFuncDict or just plain dict as illustrated in the following example. However, the _orm.relationship.collection_class parameter is required in this case so that the .attribute_keyed_dict may be appropriately parametrized: from typing import Dict from typing import Optional from sqlalchemy import ForeignKey from sqlalchemy.orm import attribute_keyed_dict from sqlalchemy.orm import DeclarativeBase from sqlalchemy.orm import Mapped from sqlalchemy.orm import mapped_column from sqlalchemy.orm import relationship class Base(DeclarativeBase): pass class Item(Base): __tablename__ = "item" id: Mapped[int] = mapped_column(primary_key=True) notes: Mapped[Dict[str, "Note"]] = relationship( collection_class=attribute_keyed_dict("keyword"), cascade="all, delete-orphan", ) class Note(Base): __tablename__ = "note" id: Mapped[int] = mapped_column(primary_key=True) item_id: Mapped[int] = mapped_column(ForeignKey("item.id")) keyword: Mapped[str] text: Mapped[Optional[str]] def __init__(self, keyword: str, text: str): self.keyword = keyword self.text = text Item.notes is then a dictionary: >>> item = Item() >>> item.notes["a"] = Note("a", "atext") >>> item.notes.items() {'a': <__main__.Note object at 0x2eaaf0>} .attribute_keyed_dict will ensure that the .keyword attribute of each Note complies with the key in the dictionary. Such as, when assigning to Item.notes, the dictionary key we supply must match that of the actual Note object: item = Item() item.notes = { "a": Note("a", "atext"), "b": Note("b", "btext"), } The attribute which .attribute_keyed_dict uses as a key does not need to be mapped at all! Using a regular Python @property allows virtually any detail or combination of details about the object to be used as the key, as below when we establish it as a tuple of Note.keyword and the first ten letters of the Note.text field: class Item(Base): __tablename__ = "item" id: Mapped[int] = mapped_column(primary_key=True) notes: Mapped[Dict[str, "Note"]] = relationship( collection_class=attribute_keyed_dict("note_key"), back_populates="item", cascade="all, delete-orphan", ) class Note(Base): __tablename__ = "note" id: Mapped[int] = mapped_column(primary_key=True) item_id: Mapped[int] = mapped_column(ForeignKey("item.id")) keyword: Mapped[str] text: Mapped[str] item: Mapped["Item"] = relationship() @property def note_key(self): return (self.keyword, self.text[0:10]) def __init__(self, keyword: str, text: str): self.keyword = keyword self.text = text Above we added a Note.item relationship, with a bi-directional _orm.relationship.back_populates configuration. Assigning to this reverse relationship, the Note is added to the Item.notes dictionary and the key is generated for us automatically: >>> item = Item() >>> n1 = Note("a", "atext") >>> n1.item = item >>> item.notes {('a', 'atext'): <__main__.Note object at 0x2eaaf0>} Other built-in dictionary types include .column_keyed_dict, which is almost like .attribute_keyed_dict except given the _schema.Column object directly: from sqlalchemy.orm import column_keyed_dict class Item(Base): __tablename__ = "item" id: Mapped[int] = mapped_column(primary_key=True) notes: Mapped[Dict[str, "Note"]] = relationship( collection_class=column_keyed_dict(Note.__table__.c.keyword), cascade="all, delete-orphan", ) as well as .mapped_collection which is passed any callable function. Note that it's usually easier to use .attribute_keyed_dict along with a @property as mentioned earlier: from sqlalchemy.orm import mapped_collection class Item(Base): __tablename__ = "item" id: Mapped[int] = mapped_column(primary_key=True) notes: Mapped[Dict[str, "Note"]] = relationship( collection_class=mapped_collection(lambda note: note.text[0:10]), cascade="all, delete-orphan", ) Dictionary mappings are often combined with the "Association Proxy" extension to produce streamlined dictionary views. See proxying_dictionaries and composite_association_proxy for examples. Dealing with Key Mutations and back-populating for Dictionary collections When using .attribute_keyed_dict, the "key" for the dictionary is taken from an attribute on the target object. Changes to this key are not tracked. This means that the key must be assigned towards when it is first used, and if the key changes, the collection will not be mutated. A typical example where this might be an issue is when relying upon backrefs to populate an attribute mapped collection. Given the following: class A(Base): __tablename__ = "a" id: Mapped[int] = mapped_column(primary_key=True) bs: Mapped[Dict[str, "B"]] = relationship( collection_class=attribute_keyed_dict("data"), back_populates="a", ) class B(Base): __tablename__ = "b" id: Mapped[int] = mapped_column(primary_key=True) a_id: Mapped[int] = mapped_column(ForeignKey("a.id")) data: Mapped[str] a: Mapped["A"] = relationship(back_populates="bs") Above, if we create a B() that refers to a specific A(), the back populates will then add the B() to the A.bs collection, however if the value of B.data is not set yet, the key will be None: >>> a1 = A() >>> b1 = B(a=a1) >>> a1.bs {None: <test3.B object at 0x7f7b1023ef70>} Setting b1.data after the fact does not update the collection: >>> b1.data = "the key" >>> a1.bs {None: <test3.B object at 0x7f7b1023ef70>} This can also be seen if one attempts to set up B() in the constructor. The order of arguments changes the result: >>> B(a=a1, data="the key") <test3.B object at 0x7f7b10114280> >>> a1.bs {None: <test3.B object at 0x7f7b10114280>} vs: >>> B(data="the key", a=a1) <test3.B object at 0x7f7b10114340> >>> a1.bs {'the key': <test3.B object at 0x7f7b10114340>} If backrefs are being used in this way, ensure that attributes are populated in the correct order using an __init__ method. An event handler such as the following may also be used to track changes in the collection as well: from sqlalchemy import event from sqlalchemy.orm import attributes @event.listens_for(B.data, "set") def set_item(obj, value, previous, initiator): if obj.a is not None: previous = None if previous == attributes.NO_VALUE else previous obj.a.bs[value] = obj obj.a.bs.pop(previous) Custom Collection Implementations You can use your own types for collections as well. In simple cases, inheriting from list or set, adding custom behavior, is all that's needed. In other cases, special decorators are needed to tell SQLAlchemy more detail about how the collection operates. Do I need a custom collection implementation? In most cases not at all! The most common use cases for a "custom" collection is one that validates or marshals incoming values into a new form, such as a string that becomes a class instance, or one which goes a step beyond and represents the data internally in some fashion, presenting a "view" of that data on the outside of a different form. For the first use case, the _orm.validates decorator is by far the simplest way to intercept incoming values in all cases for the purposes of validation and simple marshaling. See simple_validators for an example of this. For the second use case, the associationproxy_toplevel extension is a well-tested, widely used system that provides a read/write "view" of a collection in terms of some attribute present on the target object. As the target attribute can be a @property that returns virtually anything, a wide array of "alternative" views of a collection can be constructed with just a few functions. This approach leaves the underlying mapped collection unaffected and avoids the need to carefully tailor collection behavior on a method-by-method basis. Customized collections are useful when the collection needs to have special behaviors upon access or mutation operations that can't otherwise be modeled externally to the collection. They can of course be combined with the above two approaches. Collections in SQLAlchemy are transparently instrumented. Instrumentation means that normal operations on the collection are tracked and result in changes being written to the database at flush time. Additionally, collection operations can fire events which indicate some secondary operation must take place. Examples of a secondary operation include saving the child item in the parent's ~sqlalchemy.orm.session.Session (i.e. the save-update cascade), as well as synchronizing the state of a bi-directional relationship (i.e. a .backref). The collections package understands the basic interface of lists, sets and dicts and will automatically apply instrumentation to those built-in types and their subclasses. Object-derived types that implement a basic collection interface are detected and instrumented via duck-typing: class ListLike: def __init__(self): self.data = [] def append(self, item): self.data.append(item) def remove(self, item): self.data.remove(item) def extend(self, items): self.data.extend(items) def __iter__(self): return iter(self.data) def foo(self): return "foo" append, remove, and extend are known members of list, and will be instrumented automatically. __iter__ is not a mutator method and won't be instrumented, and foo won't be either. Duck-typing (i.e. guesswork) isn't rock-solid, of course, so you can be explicit about the interface you are implementing by providing an __emulates__ class attribute: class SetLike: __emulates__ = set def __init__(self): self.data = set() def append(self, item): self.data.add(item) def remove(self, item): self.data.remove(item) def __iter__(self): return iter(self.data) This class looks similar to a Python list (i.e. "list-like") as it has an append method, but the __emulates__ attribute forces it to be treated as a set. remove is known to be part of the set interface and will be instrumented. But this class won't work quite yet: a little glue is needed to adapt it for use by SQLAlchemy. The ORM needs to know which methods to use to append, remove and iterate over members of the collection. When using a type like list or set, the appropriate methods are well-known and used automatically when present. However the class above, which only roughly resembles a set, does not provide the expected add method, so we must indicate to the ORM the method that will instead take the place of the add method, in this case using a decorator @collection.appender; this is illustrated in the next section. Annotating Custom Collections via Decorators Decorators can be used to tag the individual methods the ORM needs to manage collections. Use them when your class doesn't quite meet the regular interface for its container type, or when you otherwise would like to use a different method to get the job done. from sqlalchemy.orm.collections import collection class SetLike: __emulates__ = set def __init__(self): self.data = set() @collection.appender def append(self, item): self.data.add(item) def remove(self, item): self.data.remove(item) def __iter__(self): return iter(self.data) And that's all that's needed to complete the example. SQLAlchemy will add instances via the append method. remove and __iter__ are the default methods for sets and will be used for removing and iteration. Default methods can be changed as well: from sqlalchemy.orm.collections import collection class MyList(list): @collection.remover def zark(self, item): # do something special... ... @collection.iterator def hey_use_this_instead_for_iteration(self): ... There is no requirement to be "list-like" or "set-like" at all. Collection classes can be any shape, so long as they have the append, remove and iterate interface marked for SQLAlchemy's use. Append and remove methods will be called with a mapped entity as the single argument, and iterator methods are called with no arguments and must return an iterator. Custom Dictionary-Based Collections The .KeyFuncDict class can be used as a base class for your custom types or as a mix-in to quickly add dict collection support to other classes. It uses a keying function to delegate to __setitem__ and __delitem__: from sqlalchemy.orm.collections import KeyFuncDict class MyNodeMap(KeyFuncDict): """Holds 'Node' objects, keyed by the 'name' attribute.""" def __init__(self, *args, **kw): super().__init__(keyfunc=lambda node: node.name) dict.__init__(self, *args, **kw) When subclassing .KeyFuncDict, user-defined versions of __setitem__() or __delitem__() should be decorated with .collection.internally_instrumented, if they call down to those same methods on .KeyFuncDict. This because the methods on .KeyFuncDict are already instrumented - calling them from within an already instrumented call can cause events to be fired off repeatedly, or inappropriately, leading to internal state corruption in rare cases: from sqlalchemy.orm.collections import KeyFuncDict, collection class MyKeyFuncDict(KeyFuncDict): """Use @internally_instrumented when your methods call down to already-instrumented methods. """ @collection.internally_instrumented def __setitem__(self, key, value, _sa_initiator=None): # do something with key, value super(MyKeyFuncDict, self).__setitem__(key, value, _sa_initiator) @collection.internally_instrumented def __delitem__(self, key, _sa_initiator=None): # do something with key super(MyKeyFuncDict, self).__delitem__(key, _sa_initiator) The ORM understands the dict interface just like lists and sets, and will automatically instrument all "dict-like" methods if you choose to subclass dict or provide dict-like collection behavior in a duck-typed class. You must decorate appender and remover methods, however- there are no compatible methods in the basic dictionary interface for SQLAlchemy to use by default. Iteration will go through values() unless otherwise decorated. Instrumentation and Custom Types Many custom types and existing library classes can be used as a entity collection type as-is without further ado. However, it is important to note that the instrumentation process will modify the type, adding decorators around methods automatically. The decorations are lightweight and no-op outside of relationships, but they do add unneeded overhead when triggered elsewhere. When using a library class as a collection, it can be good practice to use the "trivial subclass" trick to restrict the decorations to just your usage in relationships. For example: class MyAwesomeList(some.great.library.AwesomeList): pass # ... relationship(..., collection_class=MyAwesomeList) The ORM uses this approach for built-ins, quietly substituting a trivial subclass when a list, set or dict is used directly.

# orm/collections.py # Copyright (C) 2005-2023 the SQLAlchemy authors and contributors # <see AUTHORS file> # # This module is part of SQLAlchemy and is released under # the MIT License: https://www.opensource.org/licenses/mit-license.php # mypy: allow-untyped-defs, allow-untyped-calls """Support for collections of mapped entities. The collections package supplies the machinery used to inform the ORM of collection membership changes. An instrumentation via decoration approach is used, allowing arbitrary types (including built-ins) to be used as entity collections without requiring inheritance from a base class. Instrumentation decoration relays membership change events to the :class:`.CollectionAttributeImpl` that is currently managing the collection. The decorators observe function call arguments and return values, tracking entities entering or leaving the collection. Two decorator approaches are provided. One is a bundle of generic decorators that map function arguments and return values to events:: from sqlalchemy.orm.collections import collection class MyClass: #... @collection.adds(1) def store(self, item): self.data.append(item) @collection.removes_return() def pop(self): return self.data.pop() The second approach is a bundle of targeted decorators that wrap appropriate append and remove notifiers around the mutation methods present in the standard Python ``list``, ``set`` and ``dict`` interfaces. These could be specified in terms of generic decorator recipes, but are instead hand-tooled for increased efficiency. The targeted decorators occasionally implement adapter-like behavior, such as mapping bulk-set methods (``extend``, ``update``, ``__setslice__``, etc.) into the series of atomic mutation events that the ORM requires. The targeted decorators are used internally for automatic instrumentation of entity collection classes. Every collection class goes through a transformation process roughly like so: 1. If the class is a built-in, substitute a trivial sub-class 2. Is this class already instrumented? 3. Add in generic decorators 4. Sniff out the collection interface through duck-typing 5. Add targeted decoration to any undecorated interface method This process modifies the class at runtime, decorating methods and adding some bookkeeping properties. This isn't possible (or desirable) for built-in classes like ``list``, so trivial sub-classes are substituted to hold decoration:: class InstrumentedList(list): pass Collection classes can be specified in ``relationship(collection_class=)`` as types or a function that returns an instance. Collection classes are inspected and instrumented during the mapper compilation phase. The collection_class callable will be executed once to produce a specimen instance, and the type of that specimen will be instrumented. Functions that return built-in types like ``lists`` will be adapted to produce instrumented instances. When extending a known type like ``list``, additional decorations are not generally not needed. Odds are, the extension method will delegate to a method that's already instrumented. For example:: class QueueIsh(list): def push(self, item): self.append(item) def shift(self): return self.pop(0) There's no need to decorate these methods. ``append`` and ``pop`` are already instrumented as part of the ``list`` interface. Decorating them would fire duplicate events, which should be avoided. The targeted decoration tries not to rely on other methods in the underlying collection class, but some are unavoidable. Many depend on'read' methods being present to properly instrument a 'write', for example, ``__setitem__`` needs ``__getitem__``. "Bulk" methods like ``update`` and ``extend`` may also reimplemented in terms of atomic appends and removes, so the ``extend`` decoration will actually perform many ``append`` operations and not call the underlying method at all. Tight control over bulk operation and the firing of events is also possible by implementing the instrumentation internally in your methods. The basic instrumentation package works under the general assumption that collection mutation will not raise unusual exceptions. If you want to closely orchestrate append and remove events with exception management, internal instrumentation may be the answer. Within your method, ``collection_adapter(self)`` will retrieve an object that you can use for explicit control over triggering append and remove events. The owning object and :class:`.CollectionAttributeImpl` are also reachable through the adapter, allowing for some very sophisticated behavior. """ from __future__ import annotations import operator import threading import typing from typing import Any from typing import Callable from typing import cast from typing import Collection from typing import Dict from typing import Iterable from typing import List from typing import NoReturn from typing import Optional from typing import Set from typing import Tuple from typing import Type from typing import TYPE_CHECKING from typing import TypeVar from typing import Union import weakref from.base import NO_KEY from.. import exc as sa_exc from.. import util from..sql.base import NO_ARG from..util.compat import inspect_getfullargspec from..util.typing import Protocol if typing.TYPE_CHECKING: from.attributes import AttributeEventToken from.attributes import CollectionAttributeImpl from.mapped_collection import attribute_keyed_dict from.mapped_collection import column_keyed_dict from.mapped_collection import keyfunc_mapping from.mapped_collection import KeyFuncDict # noqa: F401 from.state import InstanceState __all__ = [ "collection", "collection_adapter", "keyfunc_mapping", "column_keyed_dict", "attribute_keyed_dict", "column_keyed_dict", "attribute_keyed_dict", "MappedCollection", "KeyFuncDict", ] __instrumentation_mutex = threading.Lock() _CollectionFactoryType = Callable[[], "_AdaptedCollectionProtocol"] _T = TypeVar("_T", bound=Any) _KT = TypeVar("_KT", bound=Any) _VT = TypeVar("_VT", bound=Any) _COL = TypeVar("_COL", bound="Collection[Any]") _FN = TypeVar("_FN", bound="Callable[..., Any]") class _CollectionConverterProtocol(Protocol): def __call__(self, collection: _COL) -> _COL: ... class _AdaptedCollectionProtocol(Protocol): _sa_adapter: CollectionAdapter _sa_appender: Callable[..., Any] _sa_remover: Callable[..., Any] _sa_iterator: Callable[..., Iterable[Any]] _sa_converter: _CollectionConverterProtocol class collection: """Decorators for entity collection classes. The decorators fall into two groups: annotations and interception recipes. The annotating decorators (appender, remover, iterator, converter, internally_instrumented) indicate the method's purpose and take no arguments. They are not written with parens:: @collection.appender def append(self, append):... The recipe decorators all require parens, even those that take no arguments:: @collection.adds('entity') def insert(self, position, entity):... @collection.removes_return() def popitem(self):... """ # Bundled as a class solely for ease of use: packaging, doc strings, # importability. @staticmethod def appender(fn): """Tag the method as the collection appender. The appender method is called with one positional argument: the value to append. The method will be automatically decorated with 'adds(1)' if not already decorated:: @collection.appender def add(self, append):... # or, equivalently @collection.appender @collection.adds(1) def add(self, append):... # for mapping type, an 'append' may kick out a previous value # that occupies that slot. consider d['a'] = 'foo'- any previous # value in d['a'] is discarded. @collection.appender @collection.replaces(1) def add(self, entity): key = some_key_func(entity) previous = None if key in self: previous = self[key] self[key] = entity return previous If the value to append is not allowed in the collection, you may raise an exception. Something to remember is that the appender will be called for each object mapped by a database query. If the database contains rows that violate your collection semantics, you will need to get creative to fix the problem, as access via the collection will not work. If the appender method is internally instrumented, you must also receive the keyword argument '_sa_initiator' and ensure its promulgation to collection events. """ fn._sa_instrument_role = "appender" return fn @staticmethod def remover(fn): """Tag the method as the collection remover. The remover method is called with one positional argument: the value to remove. The method will be automatically decorated with :meth:`removes_return` if not already decorated:: @collection.remover def zap(self, entity):... # or, equivalently @collection.remover @collection.removes_return() def zap(self, ):... If the value to remove is not present in the collection, you may raise an exception or return None to ignore the error. If the remove method is internally instrumented, you must also receive the keyword argument '_sa_initiator' and ensure its promulgation to collection events. """ fn._sa_instrument_role = "remover" return fn @staticmethod def iterator(fn): """Tag the method as the collection remover. The iterator method is called with no arguments. It is expected to return an iterator over all collection members:: @collection.iterator def __iter__(self):... """ fn._sa_instrument_role = "iterator" return fn @staticmethod def internally_instrumented(fn): """Tag the method as instrumented. This tag will prevent any decoration from being applied to the method. Use this if you are orchestrating your own calls to :func:`.collection_adapter` in one of the basic SQLAlchemy interface methods, or to prevent an automatic ABC method decoration from wrapping your implementation:: # normally an 'extend' method on a list-like class would be # automatically intercepted and re-implemented in terms of # SQLAlchemy events and append(). your implementation will # never be called, unless: @collection.internally_instrumented def extend(self, items):... """ fn._sa_instrumented = True return fn @staticmethod @util.deprecated( "1.3", "The :meth:`.collection.converter` handler is deprecated and will " "be removed in a future release. Please refer to the " ":class:`.AttributeEvents.bulk_replace` listener interface in " "conjunction with the :func:`.event.listen` function.", ) def converter(fn): """Tag the method as the collection converter. This optional method will be called when a collection is being replaced entirely, as in:: myobj.acollection = [newvalue1, newvalue2] The converter method will receive the object being assigned and should return an iterable of values suitable for use by the ``appender`` method. A converter must not assign values or mutate the collection, its sole job is to adapt the value the user provides into an iterable of values for the ORM's use. The default converter implementation will use duck-typing to do the conversion. A dict-like collection will be convert into an iterable of dictionary values, and other types will simply be iterated:: @collection.converter def convert(self, other):... If the duck-typing of the object does not match the type of this collection, a TypeError is raised. Supply an implementation of this method if you want to expand the range of possible types that can be assigned in bulk or perform validation on the values about to be assigned. """ fn._sa_instrument_role = "converter" return fn @staticmethod def adds(arg): """Mark the method as adding an entity to the collection. Adds "add to collection" handling to the method. The decorator argument indicates which method argument holds the SQLAlchemy-relevant value. Arguments can be specified positionally (i.e. integer) or by name:: @collection.adds(1) def push(self, item):... @collection.adds('entity') def do_stuff(self, thing, entity=None):... """ def decorator(fn): fn._sa_instrument_before = ("fire_append_event", arg) return fn return decorator @staticmethod def replaces(arg): """Mark the method as replacing an entity in the collection. Adds "add to collection" and "remove from collection" handling to the method. The decorator argument indicates which method argument holds the SQLAlchemy-relevant value to be added, and return value, if any will be considered the value to remove. Arguments can be specified positionally (i.e. integer) or by name:: @collection.replaces(2) def __setitem__(self, index, item):... """ def decorator(fn): fn._sa_instrument_before = ("fire_append_event", arg) fn._sa_instrument_after = "fire_remove_event" return fn return decorator @staticmethod def removes(arg): """Mark the method as removing an entity in the collection. Adds "remove from collection" handling to the method. The decorator argument indicates which method argument holds the SQLAlchemy-relevant value to be removed. Arguments can be specified positionally (i.e. integer) or by name:: @collection.removes(1) def zap(self, item):... For methods where the value to remove is not known at call-time, use collection.removes_return. """ def decorator(fn): fn._sa_instrument_before = ("fire_remove_event", arg) return fn return decorator @staticmethod def removes_return(): """Mark the method as removing an entity in the collection. Adds "remove from collection" handling to the method. The return value of the method, if any, is considered the value to remove. The method arguments are not inspected:: @collection.removes_return() def pop(self):... For methods where the value to remove is known at call-time, use collection.remove. """ def decorator(fn): fn._sa_instrument_after = "fire_remove_event" return fn return decorator if TYPE_CHECKING: def collection_adapter(collection: Collection[Any]) -> CollectionAdapter: """Fetch the :class:`.CollectionAdapter` for a collection.""" else: collection_adapter = operator.attrgetter("_sa_adapter") class CollectionAdapter: """Bridges between the ORM and arbitrary Python collections. Proxies base-level collection operations (append, remove, iterate) to the underlying Python collection, and emits add/remove events for entities entering or leaving the collection. The ORM uses :class:`.CollectionAdapter` exclusively for interaction with entity collections. """ __slots__ = ( "attr", "_key", "_data", "owner_state", "_converter", "invalidated", "empty", ) attr: CollectionAttributeImpl _key: str # this is actually a weakref; see note in constructor _data: Callable[..., _AdaptedCollectionProtocol] owner_state: InstanceState[Any] _converter: _CollectionConverterProtocol invalidated: bool empty: bool def __init__( self, attr: CollectionAttributeImpl, owner_state: InstanceState[Any], data: _AdaptedCollectionProtocol, ): self.attr = attr self._key = attr.key # this weakref stays referenced throughout the lifespan of # CollectionAdapter. so while the weakref can return None, this # is realistically only during garbage collection of this object, so # we type this as a callable that returns _AdaptedCollectionProtocol # in all cases. self._data = weakref.ref(data) # type: ignore self.owner_state = owner_state data._sa_adapter = self self._converter = data._sa_converter self.invalidated = False self.empty = False def _warn_invalidated(self) -> None: util.warn("This collection has been invalidated.") @property def data(self) -> _AdaptedCollectionProtocol: "The entity collection being adapted." return self._data() @property def _referenced_by_owner(self) -> bool: """return True if the owner state still refers to this collection. This will return False within a bulk replace operation, where this collection is the one being replaced. """ return self.owner_state.dict[self._key] is self._data() def bulk_appender(self): return self._data()._sa_appender def append_with_event( self, item: Any, initiator: Optional[AttributeEventToken] = None ) -> None: """Add an entity to the collection, firing mutation events.""" self._data()._sa_appender(item, _sa_initiator=initiator) def _set_empty(self, user_data): assert ( not self.empty ), "This collection adapter is already in the 'empty' state" self.empty = True self.owner_state._empty_collections[self._key] = user_data def _reset_empty(self) -> None: assert ( self.empty ), "This collection adapter is not in the 'empty' state" self.empty = False self.owner_state.dict[ self._key ] = self.owner_state._empty_collections.pop(self._key) def _refuse_empty(self) -> NoReturn: raise sa_exc.InvalidRequestError( "This is a special 'empty' collection which cannot accommodate " "internal mutation operations" ) def append_without_event(self, item: Any) -> None: """Add or restore an entity to the collection, firing no events.""" if self.empty: self._refuse_empty() self._data()._sa_appender(item, _sa_initiator=False) def append_multiple_without_event(self, items: Iterable[Any]) -> None: """Add or restore an entity to the collection, firing no events.""" if self.empty: self._refuse_empty() appender = self._data()._sa_appender for item in items: appender(item, _sa_initiator=False) def bulk_remover(self): return self._data()._sa_remover def remove_with_event( self, item: Any, initiator: Optional[AttributeEventToken] = None ) -> None: """Remove an entity from the collection, firing mutation events.""" self._data()._sa_remover(item, _sa_initiator=initiator) def remove_without_event(self, item: Any) -> None: """Remove an entity from the collection, firing no events.""" if self.empty: self._refuse_empty() self._data()._sa_remover(item, _sa_initiator=False) def clear_with_event( self, initiator: Optional[AttributeEventToken] = None ) -> None: """Empty the collection, firing a mutation event for each entity.""" if self.empty: self._refuse_empty() remover = self._data()._sa_remover for item in list(self): remover(item, _sa_initiator=initiator) def clear_without_event(self) -> None: """Empty the collection, firing no events.""" if self.empty: self._refuse_empty() remover = self._data()._sa_remover for item in list(self): remover(item, _sa_initiator=False) def __iter__(self): """Iterate over entities in the collection.""" return iter(self._data()._sa_iterator()) def __len__(self): """Count entities in the collection.""" return len(list(self._data()._sa_iterator())) def __bool__(self): return True def _fire_append_wo_mutation_event_bulk( self, items, initiator=None, key=NO_KEY ): if not items: return if initiator is not False: if self.invalidated: self._warn_invalidated() if self.empty: self._reset_empty() for item in items: self.attr.fire_append_wo_mutation_event( self.owner_state, self.owner_state.dict, item, initiator, key, ) def fire_append_wo_mutation_event(self, item, initiator=None, key=NO_KEY): """Notify that a entity is entering the collection but is already present. Initiator is a token owned by the InstrumentedAttribute that initiated the membership mutation, and should be left as None unless you are passing along an initiator value from a chained operation. .. versionadded:: 1.4.15 """ if initiator is not False: if self.invalidated: self._warn_invalidated() if self.empty: self._reset_empty() return self.attr.fire_append_wo_mutation_event( self.owner_state, self.owner_state.dict, item, initiator, key ) else: return item def fire_append_event(self, item, initiator=None, key=NO_KEY): """Notify that a entity has entered the collection. Initiator is a token owned by the InstrumentedAttribute that initiated the membership mutation, and should be left as None unless you are passing along an initiator value from a chained operation. """ if initiator is not False: if self.invalidated: self._warn_invalidated() if self.empty: self._reset_empty() return self.attr.fire_append_event( self.owner_state, self.owner_state.dict, item, initiator, key ) else: return item def _fire_remove_event_bulk(self, items, initiator=None, key=NO_KEY): if not items: return if initiator is not False: if self.invalidated: self._warn_invalidated() if self.empty: self._reset_empty() for item in items: self.attr.fire_remove_event( self.owner_state, self.owner_state.dict, item, initiator, key, ) def fire_remove_event(self, item, initiator=None, key=NO_KEY): """Notify that a entity has been removed from the collection. Initiator is the InstrumentedAttribute that initiated the membership mutation, and should be left as None unless you are passing along an initiator value from a chained operation. """ if initiator is not False: if self.invalidated: self._warn_invalidated() if self.empty: self._reset_empty() self.attr.fire_remove_event( self.owner_state, self.owner_state.dict, item, initiator, key ) def fire_pre_remove_event(self, initiator=None, key=NO_KEY): """Notify that an entity is about to be removed from the collection. Only called if the entity cannot be removed after calling fire_remove_event(). """ if self.invalidated: self._warn_invalidated() self.attr.fire_pre_remove_event( self.owner_state, self.owner_state.dict, initiator=initiator, key=key, ) def __getstate__(self): return { "key": self._key, "owner_state": self.owner_state, "owner_cls": self.owner_state.class_, "data": self.data, "invalidated": self.invalidated, "empty": self.empty, } def __setstate__(self, d): self._key = d["key"] self.owner_state = d["owner_state"] # see note in constructor regarding this type: ignore self._data = weakref.ref(d["data"]) # type: ignore self._converter = d["data"]._sa_converter d["data"]._sa_adapter = self self.invalidated = d["invalidated"] self.attr = getattr(d["owner_cls"], self._key).impl self.empty = d.get("empty", False) def bulk_replace(values, existing_adapter, new_adapter, initiator=None): """Load a new collection, firing events based on prior like membership. Appends instances in ``values`` onto the ``new_adapter``. Events will be fired for any instance not present in the ``existing_adapter``. Any instances in ``existing_adapter`` not present in ``values`` will have remove events fired upon them. :param values: An iterable of collection member instances :param existing_adapter: A :class:`.CollectionAdapter` of instances to be replaced :param new_adapter: An empty :class:`.CollectionAdapter` to load with ``values`` """ assert isinstance(values, list) idset = util.IdentitySet existing_idset = idset(existing_adapter or ()) constants = existing_idset.intersection(values or ()) additions = idset(values or ()).difference(constants) removals = existing_idset.difference(constants) appender = new_adapter.bulk_appender() for member in values or (): if member in additions: appender(member, _sa_initiator=initiator) elif member in constants: appender(member, _sa_initiator=False) if existing_adapter: existing_adapter._fire_append_wo_mutation_event_bulk( constants, initiator=initiator ) existing_adapter._fire_remove_event_bulk(removals, initiator=initiator) def prepare_instrumentation( factory: Union[Type[Collection[Any]], _CollectionFactoryType], ) -> _CollectionFactoryType: """Prepare a callable for future use as a collection class factory. Given a collection class factory (either a type or no-arg callable), return another factory that will produce compatible instances when called. This function is responsible for converting collection_class=list into the run-time behavior of collection_class=InstrumentedList. """ impl_factory: _CollectionFactoryType # Convert a builtin to 'Instrumented*' if factory in __canned_instrumentation: impl_factory = __canned_instrumentation[factory] else: impl_factory = cast(_CollectionFactoryType, factory) cls: Union[_CollectionFactoryType, Type[Collection[Any]]] # Create a specimen cls = type(impl_factory()) # Did factory callable return a builtin? if cls in __canned_instrumentation: # if so, just convert. # in previous major releases, this codepath wasn't working and was # not covered by tests. prior to that it supplied a "wrapper" # function that would return the class, though the rationale for this # case is not known impl_factory = __canned_instrumentation[cls] cls = type(impl_factory()) # Instrument the class if needed. if __instrumentation_mutex.acquire(): try: if getattr(cls, "_sa_instrumented", None)!= id(cls): _instrument_class(cls) finally: __instrumentation_mutex.release() return impl_factory def _instrument_class(cls): """Modify methods in a class and install instrumentation.""" # In the normal call flow, a request for any of the 3 basic collection # types is transformed into one of our trivial subclasses # (e.g. InstrumentedList). Catch anything else that sneaks in here... if cls.__module__ == "__builtin__": raise sa_exc.ArgumentError( "Can not instrument a built-in type. Use a " "subclass, even a trivial one." ) roles, methods = _locate_roles_and_methods(cls) _setup_canned_roles(cls, roles, methods) _assert_required_roles(cls, roles, methods) _set_collection_attributes(cls, roles, methods) def _locate_roles_and_methods(cls): """search for _sa_instrument_role-decorated methods in method resolution order, assign to roles. """ roles: Dict[str, str] = {} methods: Dict[str, Tuple[Optional[str], Optional[int], Optional[str]]] = {} for supercls in cls.__mro__: for name, method in vars(supercls).items(): if not callable(method): continue # note role declarations if hasattr(method, "_sa_instrument_role"): role = method._sa_instrument_role assert role in ( "appender", "remover", "iterator", "converter", ) roles.setdefault(role, name) # transfer instrumentation requests from decorated function # to the combined queue before: Optional[Tuple[str, int]] = None after: Optional[str] = None if hasattr(method, "_sa_instrument_before"): op, argument = method._sa_instrument_before assert op in ("fire_append_event", "fire_remove_event") before = op, argument if hasattr(method, "_sa_instrument_after"): op = method._sa_instrument_after assert op in ("fire_append_event", "fire_remove_event") after = op if before: methods[name] = before + (after,) elif after: methods[name] = None, None, after return roles, methods def _setup_canned_roles(cls, roles, methods): """see if this class has "canned" roles based on a known collection type (dict, set, list). Apply those roles as needed to the "roles" dictionary, and also prepare "decorator" methods """ collection_type = util.duck_type_collection(cls) if collection_type in __interfaces: assert collection_type is not None canned_roles, decorators = __interfaces[collection_type] for role, name in canned_roles.items(): roles.setdefault(role, name) # apply ABC auto-decoration to methods that need it for method, decorator in decorators.items(): fn = getattr(cls, method, None) if ( fn and method not in methods and not hasattr(fn, "_sa_instrumented") ): setattr(cls, method, decorator(fn)) def _assert_required_roles(cls, roles, methods): """ensure all roles are present, and apply implicit instrumentation if needed """ if "appender" not in roles or not hasattr(cls, roles["appender"]): raise sa_exc.ArgumentError( "Type %s must elect an appender method to be " "a collection class" % cls.__name__ ) elif roles["appender"] not in methods and not hasattr( getattr(cls, roles["appender"]), "_sa_instrumented" ): methods[roles["appender"]] = ("fire_append_event", 1, None) if "remover" not in roles or not hasattr(cls, roles["remover"]): raise sa_exc.ArgumentError( "Type %s must elect a remover method to be " "a collection class" % cls.__name__ ) elif roles["remover"] not in methods and not hasattr( getattr(cls, roles["remover"]), "_sa_instrumented" ): methods[roles["remover"]] = ("fire_remove_event", 1, None) if "iterator" not in roles or not hasattr(cls, roles["iterator"]): raise sa_exc.ArgumentError( "Type %s must elect an iterator method to be " "a collection class" % cls.__name__ ) def _set_collection_attributes(cls, roles, methods): """apply ad-hoc instrumentation from decorators, class-level defaults and implicit role declarations """ for method_name, (before, argument, after) in methods.items(): setattr( cls, method_name, _instrument_membership_mutator( getattr(cls, method_name), before, argument, after ), ) # intern the role map for role, method_name in roles.items(): setattr(cls, "_sa_%s" % role, getattr(cls, method_name)) cls._sa_adapter = None if not hasattr(cls, "_sa_converter"): cls._sa_converter = None cls._sa_instrumented = id(cls) def _instrument_membership_mutator(method, before, argument, after): """Route method args and/or return value through the collection adapter.""" # This isn't smart enough to handle @adds(1) for 'def fn(self, (a, b))' if before: fn_args = list( util.flatten_iterator(inspect_getfullargspec(method)[0]) ) if isinstance(argument, int): pos_arg = argument named_arg = len(fn_args) > argument and fn_args[argument] or None else: if argument in fn_args: pos_arg = fn_args.index(argument) else: pos_arg = None named_arg = argument del fn_args def wrapper(*args, **kw): if before: if pos_arg is None: if named_arg not in kw: raise sa_exc.ArgumentError( "Missing argument %s" % argument ) value = kw[named_arg] else: if len(args) > pos_arg: value = args[pos_arg] elif named_arg in kw: value = kw[named_arg] else: raise sa_exc.ArgumentError( "Missing argument %s" % argument ) initiator = kw.pop("_sa_initiator", None) if initiator is False: executor = None else: executor = args[0]._sa_adapter if before and executor: getattr(executor, before)(value, initiator) if not after or not executor: return method(*args, **kw) else: res = method(*args, **kw) if res is not None: getattr(executor, after)(res, initiator) return res wrapper._sa_instrumented = True # type: ignore[attr-defined] if hasattr(method, "_sa_instrument_role"): wrapper._sa_instrument_role = method._sa_instrument_role # type: ignore[attr-defined] # noqa: E501 wrapper.__name__ = method.__name__ wrapper.__doc__ = method.__doc__ return wrapper def __set_wo_mutation(collection, item, _sa_initiator=None): """Run set wo mutation events. The collection is not mutated. """ if _sa_initiator is not False: executor = collection._sa_adapter if executor: executor.fire_append_wo_mutation_event( item, _sa_initiator, key=None ) def __set(collection, item, _sa_initiator, key): """Run set events. This event always occurs before the collection is actually mutated. """ if _sa_initiator is not False: executor = collection._sa_adapter if executor: item = executor.fire_append_event(item, _sa_initiator, key=key) return item def __del(collection, item, _sa_initiator, key): """Run del events. This event occurs before the collection is actually mutated, *except* in the case of a pop operation, in which case it occurs afterwards. For pop operations, the __before_pop hook is called before the operation occurs. """ if _sa_initiator is not False: executor = collection._sa_adapter if executor: executor.fire_remove_event(item, _sa_initiator, key=key) def __before_pop(collection, _sa_initiator=None): """An event which occurs on a before a pop() operation occurs.""" executor = collection._sa_adapter if executor: executor.fire_pre_remove_event(_sa_initiator) def _list_decorators() -> Dict[str, Callable[[_FN], _FN]]: """Tailored instrumentation wrappers for any list-like class.""" def _tidy(fn): fn._sa_instrumented = True fn.__doc__ = getattr(list, fn.__name__).__doc__ def append(fn): def append(self, item, _sa_initiator=None): item = __set(self, item, _sa_initiator, NO_KEY) fn(self, item) _tidy(append) return append def remove(fn): def remove(self, value, _sa_initiator=None): __del(self, value, _sa_initiator, NO_KEY) # testlib.pragma exempt:__eq__ fn(self, value) _tidy(remove) return remove def insert(fn): def insert(self, index, value): value = __set(self, value, None, index) fn(self, index, value) _tidy(insert) return insert def __setitem__(fn): def __setitem__(self, index, value): if not isinstance(index, slice): existing = self[index] if existing is not None: __del(self, existing, None, index) value = __set(self, value, None, index) fn(self, index, value) else: # slice assignment requires __delitem__, insert, __len__ step = index.step or 1 start = index.start or 0 if start < 0: start += len(self) if index.stop is not None: stop = index.stop else: stop = len(self) if stop < 0: stop += len(self) if step == 1: if value is self: return for i in range(start, stop, step): if len(self) > start: del self[start] for i, item in enumerate(value): self.insert(i + start, item) else: rng = list(range(start, stop, step)) if len(value)!= len(rng): raise ValueError( "attempt to assign sequence of size %s to " "extended slice of size %s" % (len(value), len(rng)) ) for i, item in zip(rng, value): self.__setitem__(i, item) _tidy(__setitem__) return __setitem__ def __delitem__(fn): def __delitem__(self, index): if not isinstance(index, slice): item = self[index] __del(self, item, None, index) fn(self, index) else: # slice deletion requires __getslice__ and a slice-groking # __getitem__ for stepped deletion # note: not breaking this into atomic dels for item in self[index]: __del(self, item, None, index) fn(self, index) _tidy(__delitem__) return __delitem__ def extend(fn): def extend(self, iterable): for value in list(iterable): self.append(value) _tidy(extend) return extend def __iadd__(fn): def __iadd__(self, iterable): # list.__iadd__ takes any iterable and seems to let TypeError # raise as-is instead of returning NotImplemented for value in list(iterable): self.append(value) return self _tidy(__iadd__) return __iadd__ def pop(fn): def pop(self, index=-1): __before_pop(self) item = fn(self, index) __del(self, item, None, index) return item _tidy(pop) return pop def clear(fn): def clear(self, index=-1): for item in self: __del(self, item, None, index) fn(self) _tidy(clear) return clear # __imul__ : not wrapping this. all members of the collection are already # present, so no need to fire appends... wrapping it with an explicit # decorator is still possible, so events on *= can be had if they're # desired. hard to imagine a use case for __imul__, though. l = locals().copy() l.pop("_tidy") return l def _dict_decorators() -> Dict[str, Callable[[_FN], _FN]]: """Tailored instrumentation wrappers for any dict-like mapping class.""" def _tidy(fn): fn._sa_instrumented = True fn.__doc__ = getattr(dict, fn.__name__).__doc__ def __setitem__(fn): def __setitem__(self, key, value, _sa_initiator=None): if key in self: __del(self, self[key], _sa_initiator, key) value = __set(self, value, _sa_initiator, key) fn(self, key, value) _tidy(__setitem__) return __setitem__ def __delitem__(fn): def __delitem__(self, key, _sa_initiator=None): if key in self: __del(self, self[key], _sa_initiator, key) fn(self, key) _tidy(__delitem__) return __delitem__ def clear(fn): def clear(self): for key in self: __del(self, self[key], None, key) fn(self) _tidy(clear) return clear def pop(fn): def pop(self, key, default=NO_ARG): __before_pop(self) _to_del = key in self if default is NO_ARG: item = fn(self, key) else: item = fn(self, key, default) if _to_del: __del(self, item, None, key) return item _tidy(pop) return pop def popitem(fn): def popitem(self): __before_pop(self) item = fn(self) __del(self, item[1], None, 1) return item _tidy(popitem) return popitem def setdefault(fn): def setdefault(self, key, default=None): if key not in self: self.__setitem__(key, default) return default else: value = self.__getitem__(key) if value is default: __set_wo_mutation(self, value, None) return value _tidy(setdefault) return setdefault def update(fn): def update(self, __other=NO_ARG, **kw): if __other is not NO_ARG: if hasattr(__other, "keys"): for key in list(__other): if key not in self or self[key] is not __other[key]: self[key] = __other[key] else: __set_wo_mutation(self, __other[key], None) else: for key, value in __other: if key not in self or self[key] is not value: self[key] = value else: __set_wo_mutation(self, value, None) for key in kw: if key not in self or self[key] is not kw[key]: self[key] = kw[key] else: __set_wo_mutation(self, kw[key], None) _tidy(update) return update l = locals().copy() l.pop("_tidy") return l _set_binop_bases = (set, frozenset) def _set_binops_check_strict(self: Any, obj: Any) -> bool: """Allow only set, frozenset and self.__class__-derived objects in binops.""" return isinstance(obj, _set_binop_bases + (self.__class__,)) def _set_binops_check_loose(self: Any, obj: Any) -> bool: """Allow anything set-like to participate in set binops.""" return ( isinstance(obj, _set_binop_bases + (self.__class__,)) or util.duck_type_collection(obj) == set ) def _set_decorators() -> Dict[str, Callable[[_FN], _FN]]: """Tailored instrumentation wrappers for any set-like class.""" def _tidy(fn): fn._sa_instrumented = True fn.__doc__ = getattr(set, fn.__name__).__doc__ def add(fn): def add(self, value, _sa_initiator=None): if value not in self: value = __set(self, value, _sa_initiator, NO_KEY) else: __set_wo_mutation(self, value, _sa_initiator) # testlib.pragma exempt:__hash__ fn(self, value) _tidy(add) return add def discard(fn): def discard(self, value, _sa_initiator=None): # testlib.pragma exempt:__hash__ if value in self: __del(self, value, _sa_initiator, NO_KEY) # testlib.pragma exempt:__hash__ fn(self, value) _tidy(discard) return discard def remove(fn): def remove(self, value, _sa_initiator=None): # testlib.pragma exempt:__hash__ if value in self: __del(self, value, _sa_initiator, NO_KEY) # testlib.pragma exempt:__hash__ fn(self, value) _tidy(remove) return remove def pop(fn): def pop(self): __before_pop(self) item = fn(self) # for set in particular, we have no way to access the item # that will be popped before pop is called. __del(self, item, None, NO_KEY) return item _tidy(pop) return pop def clear(fn): def clear(self): for item in list(self): self.remove(item) _tidy(clear) return clear def update(fn): def update(self, value): for item in value: self.add(item) _tidy(update) return update def __ior__(fn): def __ior__(self, value): if not _set_binops_check_strict(self, value): return NotImplemented for item in value: self.add(item) return self _tidy(__ior__) return __ior__ def difference_update(fn): def difference_update(self, value): for item in value: self.discard(item) _tidy(difference_update) return difference_update def __isub__(fn): def __isub__(self, value): if not _set_binops_check_strict(self, value): return NotImplemented for item in value: self.discard(item) return self _tidy(__isub__) return __isub__ def intersection_update(fn): def intersection_update(self, other): want, have = self.intersection(other), set(self) remove, add = have - want, want - have for item in remove: self.remove(item) for item in add: self.add(item) _tidy(intersection_update) return intersection_update def __iand__(fn): def __iand__(self, other): if not _set_binops_check_strict(self, other): return NotImplemented want, have = self.intersection(other), set(self) remove, add = have - want, want - have for item in remove: self.remove(item) for item in add: self.add(item) return self _tidy(__iand__) return __iand__ def symmetric_difference_update(fn): def symmetric_difference_update(self, other): want, have = self.symmetric_difference(other), set(self) remove, add = have - want, want - have for item in remove: self.remove(item) for item in add: self.add(item) _tidy(symmetric_difference_update) return symmetric_difference_update def __ixor__(fn): def __ixor__(self, other): if not _set_binops_check_strict(self, other): return NotImplemented want, have = self.symmetric_difference(other), set(self) remove, add = have - want, want - have for item in remove: self.remove(item) for item in add: self.add(item) return self _tidy(__ixor__) return __ixor__ l = locals().copy() l.pop("_tidy") return l class InstrumentedList(List[_T]): """An instrumented version of the built-in list.""" class InstrumentedSet(Set[_T]): """An instrumented version of the built-in set.""" class InstrumentedDict(Dict[_KT, _VT]): """An instrumented version of the built-in dict.""" __canned_instrumentation: util.immutabledict[ Any, _CollectionFactoryType ] = util.immutabledict( { list: InstrumentedList, set: InstrumentedSet, dict: InstrumentedDict, } ) __interfaces: util.immutabledict[ Any, Tuple[ Dict[str, str], Dict[str, Callable[..., Any]], ], ] = util.immutabledict( { list: ( { "appender": "append", "remover": "remove", "iterator": "__iter__", }, _list_decorators(), ), set: ( {"appender": "add", "remover": "remove", "iterator": "__iter__"}, _set_decorators(), ), # decorators are required for dicts and object collections. dict: ({"iterator": "values"}, _dict_decorators()), } ) def __go(lcls): global keyfunc_mapping, mapped_collection global column_keyed_dict, column_mapped_collection global MappedCollection, KeyFuncDict global attribute_keyed_dict, attribute_mapped_collection from.mapped_collection import keyfunc_mapping from.mapped_collection import column_keyed_dict from.mapped_collection import attribute_keyed_dict from.mapped_collection import KeyFuncDict from.mapped_collection import mapped_collection from.mapped_collection import column_mapped_collection from.mapped_collection import attribute_mapped_collection from.mapped_collection import MappedCollection # ensure instrumentation is associated with # these built-in classes; if a user-defined class # subclasses these and uses @internally_instrumented, # the superclass is otherwise not instrumented. # see [ticket:2406]. _instrument_class(InstrumentedList) _instrument_class(InstrumentedSet) _instrument_class(KeyFuncDict) __go(locals())

sqlalchemy__sqlalchemy

events.rst

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MIT License

sqlalchemy__sqlalchemy/doc/build/orm/events.rst

ORM Events The ORM includes a wide variety of hooks available for subscription. For an introduction to the most commonly used ORM events, see the section session_events_toplevel. The event system in general is discussed at event_toplevel. Non-ORM events such as those regarding connections and low-level statement execution are described in core_event_toplevel. Session Events The most basic event hooks are available at the level of the ORM _orm.Session object. The types of things that are intercepted here include: - Persistence Operations - the ORM flush process that sends changes to the database can be extended using events that fire off at different parts of the flush, to augment or modify the data being sent to the database or to allow other things to happen when persistence occurs. Read more about persistence events at session_persistence_events. - Object lifecycle events - hooks when objects are added, persisted, deleted from sessions. Read more about these at session_lifecycle_events. - Execution Events - Part of the 2.0 style execution model, all SELECT statements against ORM entities emitted, as well as bulk UPDATE and DELETE statements outside of the flush process, are intercepted from the _orm.Session.execute method using the _orm.SessionEvents.do_orm_execute method. Read more about this event at session_execute_events. Be sure to read the session_events_toplevel chapter for context on these events. Mapper Events Mapper event hooks encompass things that happen as related to individual or multiple _orm.Mapper objects, which are the central configurational object that maps a user-defined class to a _schema.Table object. Types of things which occur at the _orm.Mapper level include: - Per-object persistence operations - the most popular mapper hooks are the unit-of-work hooks such as _orm.MapperEvents.before_insert, _orm.MapperEvents.after_update, etc. These events are contrasted to the more coarse grained session-level events such as _orm.SessionEvents.before_flush in that they occur within the flush process on a per-object basis; while finer grained activity on an object is more straightforward, availability of _orm.Session features is limited. - Mapper configuration events - the other major class of mapper hooks are those which occur as a class is mapped, as a mapper is finalized, and when sets of mappers are configured to refer to each other. These events include _orm.MapperEvents.instrument_class, _orm.MapperEvents.before_mapper_configured and _orm.MapperEvents.mapper_configured at the individual _orm.Mapper level, and _orm.MapperEvents.before_configured and _orm.MapperEvents.after_configured at the level of collections of _orm.Mapper objects. Instance Events Instance events are focused on the construction of ORM mapped instances, including when they are instantiated as transient objects, when they are loaded from the database and become persistent objects, as well as when database refresh or expiration operations occur on the object. Attribute Events Attribute events are triggered as things occur on individual attributes of ORM mapped objects. These events form the basis for things like custom validation functions <simple_validators> as well as backref handlers <relationships_backref>.

# orm/instrumentation.py # Copyright (C) 2005-2023 the SQLAlchemy authors and contributors # <see AUTHORS file> # # This module is part of SQLAlchemy and is released under # the MIT License: https://www.opensource.org/licenses/mit-license.php # mypy: allow-untyped-defs, allow-untyped-calls """Defines SQLAlchemy's system of class instrumentation. This module is usually not directly visible to user applications, but defines a large part of the ORM's interactivity. instrumentation.py deals with registration of end-user classes for state tracking. It interacts closely with state.py and attributes.py which establish per-instance and per-class-attribute instrumentation, respectively. The class instrumentation system can be customized on a per-class or global basis using the :mod:`sqlalchemy.ext.instrumentation` module, which provides the means to build and specify alternate instrumentation forms. .. versionchanged: 0.8 The instrumentation extension system was moved out of the ORM and into the external :mod:`sqlalchemy.ext.instrumentation` package. When that package is imported, it installs itself within sqlalchemy.orm so that its more comprehensive resolution mechanics take effect. """ from __future__ import annotations from typing import Any from typing import Callable from typing import cast from typing import Collection from typing import Dict from typing import Generic from typing import Iterable from typing import List from typing import Optional from typing import Set from typing import Tuple from typing import Type from typing import TYPE_CHECKING from typing import TypeVar from typing import Union import weakref from. import base from. import collections from. import exc from. import interfaces from. import state from._typing import _O from.attributes import _is_collection_attribute_impl from.. import util from..event import EventTarget from..util import HasMemoized from..util.typing import Literal from..util.typing import Protocol if TYPE_CHECKING: from._typing import _RegistryType from.attributes import AttributeImpl from.attributes import QueryableAttribute from.collections import _AdaptedCollectionProtocol from.collections import _CollectionFactoryType from.decl_base import _MapperConfig from.events import InstanceEvents from.mapper import Mapper from.state import InstanceState from..event import dispatcher _T = TypeVar("_T", bound=Any) DEL_ATTR = util.symbol("DEL_ATTR") class _ExpiredAttributeLoaderProto(Protocol): def __call__( self, state: state.InstanceState[Any], toload: Set[str], passive: base.PassiveFlag, ) -> None: ... class _ManagerFactory(Protocol): def __call__(self, class_: Type[_O]) -> ClassManager[_O]: ... class ClassManager( HasMemoized, Dict[str, "QueryableAttribute[Any]"], Generic[_O], EventTarget, ): """Tracks state information at the class level.""" dispatch: dispatcher[ClassManager[_O]] MANAGER_ATTR = base.DEFAULT_MANAGER_ATTR STATE_ATTR = base.DEFAULT_STATE_ATTR _state_setter = staticmethod(util.attrsetter(STATE_ATTR)) expired_attribute_loader: _ExpiredAttributeLoaderProto "previously known as deferred_scalar_loader" init_method: Optional[Callable[..., None]] original_init: Optional[Callable[..., None]] = None factory: Optional[_ManagerFactory] declarative_scan: Optional[weakref.ref[_MapperConfig]] = None registry: _RegistryType if not TYPE_CHECKING: # starts as None during setup registry = None class_: Type[_O] _bases: List[ClassManager[Any]] @property @util.deprecated( "1.4", message="The ClassManager.deferred_scalar_loader attribute is now " "named expired_attribute_loader", ) def deferred_scalar_loader(self): return self.expired_attribute_loader @deferred_scalar_loader.setter # type: ignore[no-redef] @util.deprecated( "1.4", message="The ClassManager.deferred_scalar_loader attribute is now " "named expired_attribute_loader", ) def deferred_scalar_loader(self, obj): self.expired_attribute_loader = obj def __init__(self, class_): self.class_ = class_ self.info = {} self.new_init = None self.local_attrs = {} self.originals = {} self._finalized = False self.factory = None self.init_method = None self._bases = [ mgr for mgr in cast( "List[Optional[ClassManager[Any]]]", [ opt_manager_of_class(base) for base in self.class_.__bases__ if isinstance(base, type) ], ) if mgr is not None ] for base_ in self._bases: self.update(base_) cast( "InstanceEvents", self.dispatch._events )._new_classmanager_instance(class_, self) for basecls in class_.__mro__: mgr = opt_manager_of_class(basecls) if mgr is not None: self.dispatch._update(mgr.dispatch) self.manage() if "__del__" in class_.__dict__: util.warn( "__del__() method on class %s will " "cause unreachable cycles and memory leaks, " "as SQLAlchemy instrumentation often creates " "reference cycles. Please remove this method." % class_ ) def _update_state( self, finalize: bool = False, mapper: Optional[Mapper[_O]] = None, registry: Optional[_RegistryType] = None, declarative_scan: Optional[_MapperConfig] = None, expired_attribute_loader: Optional[ _ExpiredAttributeLoaderProto ] = None, init_method: Optional[Callable[..., None]] = None, ) -> None: if mapper: self.mapper = mapper # type: ignore[assignment] if registry: registry._add_manager(self) if declarative_scan: self.declarative_scan = weakref.ref(declarative_scan) if expired_attribute_loader: self.expired_attribute_loader = expired_attribute_loader if init_method: assert not self._finalized, ( "class is already instrumented, " "init_method %s can't be applied" % init_method ) self.init_method = init_method if not self._finalized: self.original_init = ( self.init_method if self.init_method is not None and self.class_.__init__ is object.__init__ else self.class_.__init__ ) if finalize and not self._finalized: self._finalize() def _finalize(self) -> None: if self._finalized: return self._finalized = True self._instrument_init() _instrumentation_factory.dispatch.class_instrument(self.class_) def __hash__(self) -> int: # type: ignore[override] return id(self) def __eq__(self, other: Any) -> bool: return other is self @property def is_mapped(self) -> bool: return "mapper" in self.__dict__ @HasMemoized.memoized_attribute def _all_key_set(self): return frozenset(self) @HasMemoized.memoized_attribute def _collection_impl_keys(self): return frozenset( [attr.key for attr in self.values() if attr.impl.collection] ) @HasMemoized.memoized_attribute def _scalar_loader_impls(self): return frozenset( [ attr.impl for attr in self.values() if attr.impl.accepts_scalar_loader ] ) @HasMemoized.memoized_attribute def _loader_impls(self): return frozenset([attr.impl for attr in self.values()]) @util.memoized_property def mapper(self) -> Mapper[_O]: # raises unless self.mapper has been assigned raise exc.UnmappedClassError(self.class_) def _all_sqla_attributes(self, exclude=None): """return an iterator of all classbound attributes that are implement :class:`.InspectionAttr`. This includes :class:`.QueryableAttribute` as well as extension types such as :class:`.hybrid_property` and :class:`.AssociationProxy`. """ found: Dict[str, Any] = {} # constraints: # 1. yield keys in cls.__dict__ order # 2. if a subclass has the same key as a superclass, include that # key as part of the ordering of the superclass, because an # overridden key is usually installed by the mapper which is going # on a different ordering # 3. don't use getattr() as this fires off descriptors for supercls in self.class_.__mro__[0:-1]: inherits = supercls.__mro__[1] for key in supercls.__dict__: found.setdefault(key, supercls) if key in inherits.__dict__: continue val = found[key].__dict__[key] if ( isinstance(val, interfaces.InspectionAttr) and val.is_attribute ): yield key, val def _get_class_attr_mro(self, key, default=None): """return an attribute on the class without tripping it.""" for supercls in self.class_.__mro__: if key in supercls.__dict__: return supercls.__dict__[key] else: return default def _attr_has_impl(self, key: str) -> bool: """Return True if the given attribute is fully initialized. i.e. has an impl. """ return key in self and self[key].impl is not None def _subclass_manager(self, cls: Type[_T]) -> ClassManager[_T]: """Create a new ClassManager for a subclass of this ClassManager's class. This is called automatically when attributes are instrumented so that the attributes can be propagated to subclasses against their own class-local manager, without the need for mappers etc. to have already pre-configured managers for the full class hierarchy. Mappers can post-configure the auto-generated ClassManager when needed. """ return register_class(cls, finalize=False) def _instrument_init(self): self.new_init = _generate_init(self.class_, self, self.original_init) self.install_member("__init__", self.new_init) @util.memoized_property def _state_constructor(self) -> Type[state.InstanceState[_O]]: self.dispatch.first_init(self, self.class_) return state.InstanceState def manage(self): """Mark this instance as the manager for its class.""" setattr(self.class_, self.MANAGER_ATTR, self) @util.hybridmethod def manager_getter(self): return _default_manager_getter @util.hybridmethod def state_getter(self): """Return a (instance) -> InstanceState callable. "state getter" callables should raise either KeyError or AttributeError if no InstanceState could be found for the instance. """ return _default_state_getter @util.hybridmethod def dict_getter(self): return _default_dict_getter def instrument_attribute( self, key: str, inst: QueryableAttribute[Any], propagated: bool = False, ) -> None: if propagated: if key in self.local_attrs: return # don't override local attr with inherited attr else: self.local_attrs[key] = inst self.install_descriptor(key, inst) self._reset_memoizations() self[key] = inst for cls in self.class_.__subclasses__(): manager = self._subclass_manager(cls) manager.instrument_attribute(key, inst, True) def subclass_managers(self, recursive): for cls in self.class_.__subclasses__(): mgr = opt_manager_of_class(cls) if mgr is not None and mgr is not self: yield mgr if recursive: yield from mgr.subclass_managers(True) def post_configure_attribute(self, key): _instrumentation_factory.dispatch.attribute_instrument( self.class_, key, self[key] ) def uninstrument_attribute(self, key, propagated=False): if key not in self: return if propagated: if key in self.local_attrs: return # don't get rid of local attr else: del self.local_attrs[key] self.uninstall_descriptor(key) self._reset_memoizations() del self[key] for cls in self.class_.__subclasses__(): manager = opt_manager_of_class(cls) if manager: manager.uninstrument_attribute(key, True) def unregister(self) -> None: """remove all instrumentation established by this ClassManager.""" for key in list(self.originals): self.uninstall_member(key) self.mapper = None # type: ignore self.dispatch = None # type: ignore self.new_init = None self.info.clear() for key in list(self): if key in self.local_attrs: self.uninstrument_attribute(key) if self.MANAGER_ATTR in self.class_.__dict__: delattr(self.class_, self.MANAGER_ATTR) def install_descriptor( self, key: str, inst: QueryableAttribute[Any] ) -> None: if key in (self.STATE_ATTR, self.MANAGER_ATTR): raise KeyError( "%r: requested attribute name conflicts with " "instrumentation attribute of the same name." % key ) setattr(self.class_, key, inst) def uninstall_descriptor(self, key: str) -> None: delattr(self.class_, key) def install_member(self, key: str, implementation: Any) -> None: if key in (self.STATE_ATTR, self.MANAGER_ATTR): raise KeyError( "%r: requested attribute name conflicts with " "instrumentation attribute of the same name." % key ) self.originals.setdefault(key, self.class_.__dict__.get(key, DEL_ATTR)) setattr(self.class_, key, implementation) def uninstall_member(self, key: str) -> None: original = self.originals.pop(key, None) if original is not DEL_ATTR: setattr(self.class_, key, original) else: delattr(self.class_, key) def instrument_collection_class( self, key: str, collection_class: Type[Collection[Any]] ) -> _CollectionFactoryType: return collections.prepare_instrumentation(collection_class) def initialize_collection( self, key: str, state: InstanceState[_O], factory: _CollectionFactoryType, ) -> Tuple[collections.CollectionAdapter, _AdaptedCollectionProtocol]: user_data = factory() impl = self.get_impl(key) assert _is_collection_attribute_impl(impl) adapter = collections.CollectionAdapter(impl, state, user_data) return adapter, user_data def is_instrumented(self, key: str, search: bool = False) -> bool: if search: return key in self else: return key in self.local_attrs def get_impl(self, key: str) -> AttributeImpl: return self[key].impl @property def attributes(self) -> Iterable[Any]: return iter(self.values()) # InstanceState management def new_instance(self, state: Optional[InstanceState[_O]] = None) -> _O: # here, we would prefer _O to be bound to "object" # so that mypy sees that __new__ is present. currently # it's bound to Any as there were other problems not having # it that way but these can be revisited instance = self.class_.__new__(self.class_) # type: ignore if state is None: state = self._state_constructor(instance, self) self._state_setter(instance, state) return instance # type: ignore[no-any-return] def setup_instance( self, instance: _O, state: Optional[InstanceState[_O]] = None ) -> None: if state is None: state = self._state_constructor(instance, self) self._state_setter(instance, state) def teardown_instance(self, instance: _O) -> None: delattr(instance, self.STATE_ATTR) def _serialize( self, state: InstanceState[_O], state_dict: Dict[str, Any] ) -> _SerializeManager: return _SerializeManager(state, state_dict) def _new_state_if_none( self, instance: _O ) -> Union[Literal[False], InstanceState[_O]]: """Install a default InstanceState if none is present. A private convenience method used by the __init__ decorator. """ if hasattr(instance, self.STATE_ATTR): return False elif self.class_ is not instance.__class__ and self.is_mapped: # this will create a new ClassManager for the # subclass, without a mapper. This is likely a # user error situation but allow the object # to be constructed, so that it is usable # in a non-ORM context at least. return self._subclass_manager( instance.__class__ )._new_state_if_none(instance) else: state = self._state_constructor(instance, self) self._state_setter(instance, state) return state def has_state(self, instance: _O) -> bool: return hasattr(instance, self.STATE_ATTR) def has_parent( self, state: InstanceState[_O], key: str, optimistic: bool = False ) -> bool: """TODO""" return self.get_impl(key).hasparent(state, optimistic=optimistic) def __bool__(self) -> bool: """All ClassManagers are non-zero regardless of attribute state.""" return True def __repr__(self) -> str: return "<%s of %r at %x>" % ( self.__class__.__name__, self.class_, id(self), ) class _SerializeManager: """Provide serialization of a :class:`.ClassManager`. The :class:`.InstanceState` uses ``__init__()`` on serialize and ``__call__()`` on deserialize. """ def __init__(self, state: state.InstanceState[Any], d: Dict[str, Any]): self.class_ = state.class_ manager = state.manager manager.dispatch.pickle(state, d) def __call__(self, state, inst, state_dict): state.manager = manager = opt_manager_of_class(self.class_) if manager is None: raise exc.UnmappedInstanceError( inst, "Cannot deserialize object of type %r - " "no mapper() has " "been configured for this class within the current " "Python process!" % self.class_, ) elif manager.is_mapped and not manager.mapper.configured: manager.mapper._check_configure() # setup _sa_instance_state ahead of time so that # unpickle events can access the object normally. # see [ticket:2362] if inst is not None: manager.setup_instance(inst, state) manager.dispatch.unpickle(state, state_dict) class InstrumentationFactory(EventTarget): """Factory for new ClassManager instances.""" dispatch: dispatcher[InstrumentationFactory] def create_manager_for_cls(self, class_: Type[_O]) -> ClassManager[_O]: assert class_ is not None assert opt_manager_of_class(class_) is None # give a more complicated subclass # a chance to do what it wants here manager, factory = self._locate_extended_factory(class_) if factory is None: factory = ClassManager manager = ClassManager(class_) else: assert manager is not None self._check_conflicts(class_, factory) manager.factory = factory return manager def _locate_extended_factory( self, class_: Type[_O] ) -> Tuple[Optional[ClassManager[_O]], Optional[_ManagerFactory]]: """Overridden by a subclass to do an extended lookup.""" return None, None def _check_conflicts( self, class_: Type[_O], factory: Callable[[Type[_O]], ClassManager[_O]] ) -> None: """Overridden by a subclass to test for conflicting factories.""" def unregister(self, class_: Type[_O]) -> None: manager = manager_of_class(class_) manager.unregister() self.dispatch.class_uninstrument(class_) # this attribute is replaced by sqlalchemy.ext.instrumentation # when imported. _instrumentation_factory = InstrumentationFactory() # these attributes are replaced by sqlalchemy.ext.instrumentation # when a non-standard InstrumentationManager class is first # used to instrument a class. instance_state = _default_state_getter = base.instance_state instance_dict = _default_dict_getter = base.instance_dict manager_of_class = _default_manager_getter = base.manager_of_class opt_manager_of_class = _default_opt_manager_getter = base.opt_manager_of_class def register_class( class_: Type[_O], finalize: bool = True, mapper: Optional[Mapper[_O]] = None, registry: Optional[_RegistryType] = None, declarative_scan: Optional[_MapperConfig] = None, expired_attribute_loader: Optional[_ExpiredAttributeLoaderProto] = None, init_method: Optional[Callable[..., None]] = None, ) -> ClassManager[_O]: """Register class instrumentation. Returns the existing or newly created class manager. """ manager = opt_manager_of_class(class_) if manager is None: manager = _instrumentation_factory.create_manager_for_cls(class_) manager._update_state( mapper=mapper, registry=registry, declarative_scan=declarative_scan, expired_attribute_loader=expired_attribute_loader, init_method=init_method, finalize=finalize, ) return manager def unregister_class(class_): """Unregister class instrumentation.""" _instrumentation_factory.unregister(class_) def is_instrumented(instance, key): """Return True if the given attribute on the given instance is instrumented by the attributes package. This function may be used regardless of instrumentation applied directly to the class, i.e. no descriptors are required. """ return manager_of_class(instance.__class__).is_instrumented( key, search=True ) def _generate_init(class_, class_manager, original_init): """Build an __init__ decorator that triggers ClassManager events.""" # TODO: we should use the ClassManager's notion of the # original '__init__' method, once ClassManager is fixed # to always reference that. if original_init is None: original_init = class_.__init__ # Go through some effort here and don't change the user's __init__ # calling signature, including the unlikely case that it has # a return value. # FIXME: need to juggle local names to avoid constructor argument # clashes. func_body = """\ def __init__(%(apply_pos)s): new_state = class_manager._new_state_if_none(%(self_arg)s) if new_state: return new_state._initialize_instance(%(apply_kw)s) else: return original_init(%(apply_kw)s) """ func_vars = util.format_argspec_init(original_init, grouped=False) func_text = func_body % func_vars func_defaults = getattr(original_init, "__defaults__", None) func_kw_defaults = getattr(original_init, "__kwdefaults__", None) env = locals().copy() env["__name__"] = __name__ exec(func_text, env) __init__ = env["__init__"] __init__.__doc__ = original_init.__doc__ __init__._sa_original_init = original_init if func_defaults: __init__.__defaults__ = func_defaults if func_kw_defaults: __init__.__kwdefaults__ = func_kw_defaults return __init__

sqlalchemy__sqlalchemy

visitors.rst

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sqlalchemy__sqlalchemy/doc/build/core/visitors.rst

Visitor and Traversal Utilities The sqlalchemy.sql.visitors module consists of classes and functions that serve the purpose of generically traversing a Core SQL expression structure. This is not unlike the Python ast module in that is presents a system by which a program can operate upon each component of a SQL expression. Common purposes this serves are locating various kinds of elements such as _schema.Table or .BindParameter objects, as well as altering the state of the structure such as replacing certain FROM clauses with others. Note the sqlalchemy.sql.visitors module is an internal API and is not fully public. It is subject to change and may additionally not function as expected for use patterns that aren't considered within SQLAlchemy's own internals. The sqlalchemy.sql.visitors module is part of the internals of SQLAlchemy and it is not usually used by calling application code. It is however used in certain edge cases such as when constructing caching routines as well as when building out custom SQL expressions using the Custom SQL Constructs and Compilation Extension <sqlalchemy.ext.compiler_toplevel>.

# sql/visitors.py # Copyright (C) 2005-2023 the SQLAlchemy authors and contributors # <see AUTHORS file> # # This module is part of SQLAlchemy and is released under # the MIT License: https://www.opensource.org/licenses/mit-license.php """Visitor/traversal interface and library functions. """ from __future__ import annotations from collections import deque from enum import Enum import itertools import operator import typing from typing import Any from typing import Callable from typing import cast from typing import ClassVar from typing import Dict from typing import Iterable from typing import Iterator from typing import List from typing import Mapping from typing import Optional from typing import overload from typing import Tuple from typing import Type from typing import TYPE_CHECKING from typing import TypeVar from typing import Union from.. import exc from.. import util from..util import langhelpers from..util._has_cy import HAS_CYEXTENSION from..util.typing import Literal from..util.typing import Protocol from..util.typing import Self if TYPE_CHECKING: from.annotation import _AnnotationDict from.elements import ColumnElement if typing.TYPE_CHECKING or not HAS_CYEXTENSION: from._py_util import prefix_anon_map as prefix_anon_map from._py_util import cache_anon_map as anon_map else: from sqlalchemy.cyextension.util import ( # noqa: F401,E501 prefix_anon_map as prefix_anon_map, ) from sqlalchemy.cyextension.util import ( # noqa: F401,E501 cache_anon_map as anon_map, ) __all__ = [ "iterate", "traverse_using", "traverse", "cloned_traverse", "replacement_traverse", "Visitable", "ExternalTraversal", "InternalTraversal", "anon_map", ] class _CompilerDispatchType(Protocol): def __call__(_self, self: Visitable, visitor: Any, **kw: Any) -> Any: ... class Visitable: """Base class for visitable objects. :class:`.Visitable` is used to implement the SQL compiler dispatch functions. Other forms of traversal such as for cache key generation are implemented separately using the :class:`.HasTraverseInternals` interface. .. versionchanged:: 2.0 The :class:`.Visitable` class was named :class:`.Traversible` in the 1.4 series; the name is changed back to :class:`.Visitable` in 2.0 which is what it was prior to 1.4. Both names remain importable in both 1.4 and 2.0 versions. """ __slots__ = () __visit_name__: str _original_compiler_dispatch: _CompilerDispatchType if typing.TYPE_CHECKING: def _compiler_dispatch(self, visitor: Any, **kw: Any) -> str: ... def __init_subclass__(cls) -> None: if "__visit_name__" in cls.__dict__: cls._generate_compiler_dispatch() super().__init_subclass__() @classmethod def _generate_compiler_dispatch(cls) -> None: visit_name = cls.__visit_name__ if "_compiler_dispatch" in cls.__dict__: # class has a fixed _compiler_dispatch() method. # copy it to "original" so that we can get it back if # sqlalchemy.ext.compiles overrides it. cls._original_compiler_dispatch = cls._compiler_dispatch return if not isinstance(visit_name, str): raise exc.InvalidRequestError( f"__visit_name__ on class {cls.__name__} must be a string " "at the class level" ) name = "visit_%s" % visit_name getter = operator.attrgetter(name) def _compiler_dispatch( self: Visitable, visitor: Any, **kw: Any ) -> str: """Look for an attribute named "visit_<visit_name>" on the visitor, and call it with the same kw params. """ try: meth = getter(visitor) except AttributeError as err: return visitor.visit_unsupported_compilation(self, err, **kw) # type: ignore # noqa: E501 else: return meth(self, **kw) # type: ignore # noqa: E501 cls._compiler_dispatch = ( # type: ignore cls._original_compiler_dispatch ) = _compiler_dispatch def __class_getitem__(cls, key: Any) -> Any: # allow generic classes in py3.9+ return cls class InternalTraversal(Enum): r"""Defines visitor symbols used for internal traversal. The :class:`.InternalTraversal` class is used in two ways. One is that it can serve as the superclass for an object that implements the various visit methods of the class. The other is that the symbols themselves of :class:`.InternalTraversal` are used within the ``_traverse_internals`` collection. Such as, the :class:`.Case` object defines ``_traverse_internals`` as :: _traverse_internals = [ ("value", InternalTraversal.dp_clauseelement), ("whens", InternalTraversal.dp_clauseelement_tuples), ("else_", InternalTraversal.dp_clauseelement), ] Above, the :class:`.Case` class indicates its internal state as the attributes named ``value``, ``whens``, and ``else_``. They each link to an :class:`.InternalTraversal` method which indicates the type of datastructure referred towards. Using the ``_traverse_internals`` structure, objects of type :class:`.InternalTraversible` will have the following methods automatically implemented: * :meth:`.HasTraverseInternals.get_children` * :meth:`.HasTraverseInternals._copy_internals` * :meth:`.HasCacheKey._gen_cache_key` Subclasses can also implement these methods directly, particularly for the :meth:`.HasTraverseInternals._copy_internals` method, when special steps are needed. .. versionadded:: 1.4 """ dp_has_cache_key = "HC" """Visit a :class:`.HasCacheKey` object.""" dp_has_cache_key_list = "HL" """Visit a list of :class:`.HasCacheKey` objects.""" dp_clauseelement = "CE" """Visit a :class:`_expression.ClauseElement` object.""" dp_fromclause_canonical_column_collection = "FC" """Visit a :class:`_expression.FromClause` object in the context of the ``columns`` attribute. The column collection is "canonical", meaning it is the originally defined location of the :class:`.ColumnClause` objects. Right now this means that the object being visited is a :class:`_expression.TableClause` or :class:`_schema.Table` object only. """ dp_clauseelement_tuples = "CTS" """Visit a list of tuples which contain :class:`_expression.ClauseElement` objects. """ dp_clauseelement_list = "CL" """Visit a list of :class:`_expression.ClauseElement` objects. """ dp_clauseelement_tuple = "CT" """Visit a tuple of :class:`_expression.ClauseElement` objects. """ dp_executable_options = "EO" dp_with_context_options = "WC" dp_fromclause_ordered_set = "CO" """Visit an ordered set of :class:`_expression.FromClause` objects. """ dp_string = "S" """Visit a plain string value. Examples include table and column names, bound parameter keys, special keywords such as "UNION", "UNION ALL". The string value is considered to be significant for cache key generation. """ dp_string_list = "SL" """Visit a list of strings.""" dp_anon_name = "AN" """Visit a potentially "anonymized" string value. The string value is considered to be significant for cache key generation. """ dp_boolean = "B" """Visit a boolean value. The boolean value is considered to be significant for cache key generation. """ dp_operator = "O" """Visit an operator. The operator is a function from the :mod:`sqlalchemy.sql.operators` module. The operator value is considered to be significant for cache key generation. """ dp_type = "T" """Visit a :class:`.TypeEngine` object The type object is considered to be significant for cache key generation. """ dp_plain_dict = "PD" """Visit a dictionary with string keys. The keys of the dictionary should be strings, the values should be immutable and hashable. The dictionary is considered to be significant for cache key generation. """ dp_dialect_options = "DO" """Visit a dialect options structure.""" dp_string_clauseelement_dict = "CD" """Visit a dictionary of string keys to :class:`_expression.ClauseElement` objects. """ dp_string_multi_dict = "MD" """Visit a dictionary of string keys to values which may either be plain immutable/hashable or :class:`.HasCacheKey` objects. """ dp_annotations_key = "AK" """Visit the _annotations_cache_key element. This is a dictionary of additional information about a ClauseElement that modifies its role. It should be included when comparing or caching objects, however generating this key is relatively expensive. Visitors should check the "_annotations" dict for non-None first before creating this key. """ dp_plain_obj = "PO" """Visit a plain python object. The value should be immutable and hashable, such as an integer. The value is considered to be significant for cache key generation. """ dp_named_ddl_element = "DD" """Visit a simple named DDL element. The current object used by this method is the :class:`.Sequence`. The object is only considered to be important for cache key generation as far as its name, but not any other aspects of it. """ dp_prefix_sequence = "PS" """Visit the sequence represented by :class:`_expression.HasPrefixes` or :class:`_expression.HasSuffixes`. """ dp_table_hint_list = "TH" """Visit the ``_hints`` collection of a :class:`_expression.Select` object. """ dp_setup_join_tuple = "SJ" dp_memoized_select_entities = "ME" dp_statement_hint_list = "SH" """Visit the ``_statement_hints`` collection of a :class:`_expression.Select` object. """ dp_unknown_structure = "UK" """Visit an unknown structure. """ dp_dml_ordered_values = "DML_OV" """Visit the values() ordered tuple list of an :class:`_expression.Update` object.""" dp_dml_values = "DML_V" """Visit the values() dictionary of a :class:`.ValuesBase` (e.g. Insert or Update) object. """ dp_dml_multi_values = "DML_MV" """Visit the values() multi-valued list of dictionaries of an :class:`_expression.Insert` object. """ dp_propagate_attrs = "PA" """Visit the propagate attrs dict. This hardcodes to the particular elements we care about right now.""" """Symbols that follow are additional symbols that are useful in caching applications. Traversals for :class:`_expression.ClauseElement` objects only need to use those symbols present in :class:`.InternalTraversal`. However, for additional caching use cases within the ORM, symbols dealing with the :class:`.HasCacheKey` class are added here. """ dp_ignore = "IG" """Specify an object that should be ignored entirely. This currently applies function call argument caching where some arguments should not be considered to be part of a cache key. """ dp_inspectable = "IS" """Visit an inspectable object where the return value is a :class:`.HasCacheKey` object.""" dp_multi = "M" """Visit an object that may be a :class:`.HasCacheKey` or may be a plain hashable object.""" dp_multi_list = "MT" """Visit a tuple containing elements that may be :class:`.HasCacheKey` or may be a plain hashable object.""" dp_has_cache_key_tuples = "HT" """Visit a list of tuples which contain :class:`.HasCacheKey` objects. """ dp_inspectable_list = "IL" """Visit a list of inspectable objects which upon inspection are HasCacheKey objects.""" _TraverseInternalsType = List[Tuple[str, InternalTraversal]] """a structure that defines how a HasTraverseInternals should be traversed. This structure consists of a list of (attributename, internaltraversal) tuples, where the "attributename" refers to the name of an attribute on an instance of the HasTraverseInternals object, and "internaltraversal" refers to an :class:`.InternalTraversal` enumeration symbol defining what kind of data this attribute stores, which indicates to the traverser how it should be handled. """ class HasTraverseInternals: """base for classes that have a "traverse internals" element, which defines all kinds of ways of traversing the elements of an object. Compared to :class:`.Visitable`, which relies upon an external visitor to define how the object is travered (i.e. the :class:`.SQLCompiler`), the :class:`.HasTraverseInternals` interface allows classes to define their own traversal, that is, what attributes are accessed and in what order. """ __slots__ = () _traverse_internals: _TraverseInternalsType _is_immutable: bool = False @util.preload_module("sqlalchemy.sql.traversals") def get_children( self, *, omit_attrs: Tuple[str,...] = (), **kw: Any ) -> Iterable[HasTraverseInternals]: r"""Return immediate child :class:`.visitors.HasTraverseInternals` elements of this :class:`.visitors.HasTraverseInternals`. This is used for visit traversal. \**kw may contain flags that change the collection that is returned, for example to return a subset of items in order to cut down on larger traversals, or to return child items from a different context (such as schema-level collections instead of clause-level). """ traversals = util.preloaded.sql_traversals try: traverse_internals = self._traverse_internals except AttributeError: # user-defined classes may not have a _traverse_internals return [] dispatch = traversals._get_children.run_generated_dispatch return itertools.chain.from_iterable( meth(obj, **kw) for attrname, obj, meth in dispatch( self, traverse_internals, "_generated_get_children_traversal" ) if attrname not in omit_attrs and obj is not None ) class _InternalTraversalDispatchType(Protocol): def __call__(s, self: object, visitor: HasTraversalDispatch) -> Any: ... class HasTraversalDispatch: r"""Define infrastructure for classes that perform internal traversals .. versionadded:: 2.0 """ __slots__ = () _dispatch_lookup: ClassVar[Dict[Union[InternalTraversal, str], str]] = {} def dispatch(self, visit_symbol: InternalTraversal) -> Callable[..., Any]: """Given a method from :class:`.HasTraversalDispatch`, return the corresponding method on a subclass. """ name = _dispatch_lookup[visit_symbol] return getattr(self, name, None) # type: ignore def run_generated_dispatch( self, target: object, internal_dispatch: _TraverseInternalsType, generate_dispatcher_name: str, ) -> Any: dispatcher: _InternalTraversalDispatchType try: dispatcher = target.__class__.__dict__[generate_dispatcher_name] except KeyError: # traversals.py -> _preconfigure_traversals() # may be used to run these ahead of time, but # is not enabled right now. # this block will generate any remaining dispatchers. dispatcher = self.generate_dispatch( target.__class__, internal_dispatch, generate_dispatcher_name ) return dispatcher(target, self) def generate_dispatch( self, target_cls: Type[object], internal_dispatch: _TraverseInternalsType, generate_dispatcher_name: str, ) -> _InternalTraversalDispatchType: dispatcher = self._generate_dispatcher( internal_dispatch, generate_dispatcher_name ) # assert isinstance(target_cls, type) setattr(target_cls, generate_dispatcher_name, dispatcher) return dispatcher def _generate_dispatcher( self, internal_dispatch: _TraverseInternalsType, method_name: str ) -> _InternalTraversalDispatchType: names = [] for attrname, visit_sym in internal_dispatch: meth = self.dispatch(visit_sym) if meth is not None: visit_name = _dispatch_lookup[visit_sym] names.append((attrname, visit_name)) code = ( (" return [\n") + ( ", \n".join( " (%r, self.%s, visitor.%s)" % (attrname, attrname, visit_name) for attrname, visit_name in names ) ) + ("\n ]\n") ) meth_text = ("def %s(self, visitor):\n" % method_name) + code + "\n" return cast( _InternalTraversalDispatchType, langhelpers._exec_code_in_env(meth_text, {}, method_name), ) ExtendedInternalTraversal = InternalTraversal def _generate_traversal_dispatch() -> None: lookup = _dispatch_lookup for sym in InternalTraversal: key = sym.name if key.startswith("dp_"): visit_key = key.replace("dp_", "visit_") sym_name = sym.value assert sym_name not in lookup, sym_name lookup[sym] = lookup[sym_name] = visit_key _dispatch_lookup = HasTraversalDispatch._dispatch_lookup _generate_traversal_dispatch() class ExternallyTraversible(HasTraverseInternals, Visitable): __slots__ = () _annotations: Mapping[Any, Any] = util.EMPTY_DICT if typing.TYPE_CHECKING: def _annotate(self, values: _AnnotationDict) -> Self: ... def get_children( self, *, omit_attrs: Tuple[str,...] = (), **kw: Any ) -> Iterable[ExternallyTraversible]: ... def _clone(self, **kw: Any) -> Self: """clone this element""" raise NotImplementedError() def _copy_internals( self, *, omit_attrs: Tuple[str,...] = (), **kw: Any ) -> None: """Reassign internal elements to be clones of themselves. Called during a copy-and-traverse operation on newly shallow-copied elements to create a deep copy. The given clone function should be used, which may be applying additional transformations to the element (i.e. replacement traversal, cloned traversal, annotations). """ raise NotImplementedError() _ET = TypeVar("_ET", bound=ExternallyTraversible) _CE = TypeVar("_CE", bound="ColumnElement[Any]") _TraverseCallableType = Callable[[_ET], None] class _CloneCallableType(Protocol): def __call__(self, element: _ET, **kw: Any) -> _ET: ... class _TraverseTransformCallableType(Protocol[_ET]): def __call__(self, element: _ET, **kw: Any) -> Optional[_ET]: ... _ExtT = TypeVar("_ExtT", bound="ExternalTraversal") class ExternalTraversal(util.MemoizedSlots): """Base class for visitor objects which can traverse externally using the :func:`.visitors.traverse` function. Direct usage of the :func:`.visitors.traverse` function is usually preferred. """ __slots__ = ("_visitor_dict", "_next") __traverse_options__: Dict[str, Any] = {} _next: Optional[ExternalTraversal] def traverse_single(self, obj: Visitable, **kw: Any) -> Any: for v in self.visitor_iterator: meth = getattr(v, "visit_%s" % obj.__visit_name__, None) if meth: return meth(obj, **kw) def iterate( self, obj: Optional[ExternallyTraversible] ) -> Iterator[ExternallyTraversible]: """Traverse the given expression structure, returning an iterator of all elements. """ return iterate(obj, self.__traverse_options__) @overload def traverse(self, obj: Literal[None]) -> None: ... @overload def traverse(self, obj: ExternallyTraversible) -> ExternallyTraversible: ... def traverse( self, obj: Optional[ExternallyTraversible] ) -> Optional[ExternallyTraversible]: """Traverse and visit the given expression structure.""" return traverse(obj, self.__traverse_options__, self._visitor_dict) def _memoized_attr__visitor_dict( self, ) -> Dict[str, _TraverseCallableType[Any]]: visitors = {} for name in dir(self): if name.startswith("visit_"): visitors[name[6:]] = getattr(self, name) return visitors @property def visitor_iterator(self) -> Iterator[ExternalTraversal]: """Iterate through this visitor and each 'chained' visitor.""" v: Optional[ExternalTraversal] = self while v: yield v v = getattr(v, "_next", None) def chain(self: _ExtT, visitor: ExternalTraversal) -> _ExtT: """'Chain' an additional ExternalTraversal onto this ExternalTraversal The chained visitor will receive all visit events after this one. """ tail = list(self.visitor_iterator)[-1] tail._next = visitor return self class CloningExternalTraversal(ExternalTraversal): """Base class for visitor objects which can traverse using the :func:`.visitors.cloned_traverse` function. Direct usage of the :func:`.visitors.cloned_traverse` function is usually preferred. """ __slots__ = () def copy_and_process( self, list_: List[ExternallyTraversible] ) -> List[ExternallyTraversible]: """Apply cloned traversal to the given list of elements, and return the new list. """ return [self.traverse(x) for x in list_] @overload def traverse(self, obj: Literal[None]) -> None: ... @overload def traverse(self, obj: ExternallyTraversible) -> ExternallyTraversible: ... def traverse( self, obj: Optional[ExternallyTraversible] ) -> Optional[ExternallyTraversible]: """Traverse and visit the given expression structure.""" return cloned_traverse( obj, self.__traverse_options__, self._visitor_dict ) class ReplacingExternalTraversal(CloningExternalTraversal): """Base class for visitor objects which can traverse using the :func:`.visitors.replacement_traverse` function. Direct usage of the :func:`.visitors.replacement_traverse` function is usually preferred. """ __slots__ = () def replace( self, elem: ExternallyTraversible ) -> Optional[ExternallyTraversible]: """Receive pre-copied elements during a cloning traversal. If the method returns a new element, the element is used instead of creating a simple copy of the element. Traversal will halt on the newly returned element if it is re-encountered. """ return None @overload def traverse(self, obj: Literal[None]) -> None: ... @overload def traverse(self, obj: ExternallyTraversible) -> ExternallyTraversible: ... def traverse( self, obj: Optional[ExternallyTraversible] ) -> Optional[ExternallyTraversible]: """Traverse and visit the given expression structure.""" def replace( element: ExternallyTraversible, **kw: Any, ) -> Optional[ExternallyTraversible]: for v in self.visitor_iterator: e = cast(ReplacingExternalTraversal, v).replace(element) if e is not None: return e return None return replacement_traverse(obj, self.__traverse_options__, replace) # backwards compatibility Traversible = Visitable ClauseVisitor = ExternalTraversal CloningVisitor = CloningExternalTraversal ReplacingCloningVisitor = ReplacingExternalTraversal def iterate( obj: Optional[ExternallyTraversible], opts: Mapping[str, Any] = util.EMPTY_DICT, ) -> Iterator[ExternallyTraversible]: r"""Traverse the given expression structure, returning an iterator. Traversal is configured to be breadth-first. The central API feature used by the :func:`.visitors.iterate` function is the :meth:`_expression.ClauseElement.get_children` method of :class:`_expression.ClauseElement` objects. This method should return all the :class:`_expression.ClauseElement` objects which are associated with a particular :class:`_expression.ClauseElement` object. For example, a :class:`.Case` structure will refer to a series of :class:`_expression.ColumnElement` objects within its "whens" and "else\_" member variables. :param obj: :class:`_expression.ClauseElement` structure to be traversed :param opts: dictionary of iteration options. This dictionary is usually empty in modern usage. """ if obj is None: return yield obj children = obj.get_children(**opts) if not children: return stack = deque([children]) while stack: t_iterator = stack.popleft() for t in t_iterator: yield t stack.append(t.get_children(**opts)) @overload def traverse_using( iterator: Iterable[ExternallyTraversible], obj: Literal[None], visitors: Mapping[str, _TraverseCallableType[Any]], ) -> None: ... @overload def traverse_using( iterator: Iterable[ExternallyTraversible], obj: ExternallyTraversible, visitors: Mapping[str, _TraverseCallableType[Any]], ) -> ExternallyTraversible: ... def traverse_using( iterator: Iterable[ExternallyTraversible], obj: Optional[ExternallyTraversible], visitors: Mapping[str, _TraverseCallableType[Any]], ) -> Optional[ExternallyTraversible]: """Visit the given expression structure using the given iterator of objects. :func:`.visitors.traverse_using` is usually called internally as the result of the :func:`.visitors.traverse` function. :param iterator: an iterable or sequence which will yield :class:`_expression.ClauseElement` structures; the iterator is assumed to be the product of the :func:`.visitors.iterate` function. :param obj: the :class:`_expression.ClauseElement` that was used as the target of the :func:`.iterate` function. :param visitors: dictionary of visit functions. See :func:`.traverse` for details on this dictionary. .. seealso:: :func:`.traverse` """ for target in iterator: meth = visitors.get(target.__visit_name__, None) if meth: meth(target) return obj @overload def traverse( obj: Literal[None], opts: Mapping[str, Any], visitors: Mapping[str, _TraverseCallableType[Any]], ) -> None: ... @overload def traverse( obj: ExternallyTraversible, opts: Mapping[str, Any], visitors: Mapping[str, _TraverseCallableType[Any]], ) -> ExternallyTraversible: ... def traverse( obj: Optional[ExternallyTraversible], opts: Mapping[str, Any], visitors: Mapping[str, _TraverseCallableType[Any]], ) -> Optional[ExternallyTraversible]: """Traverse and visit the given expression structure using the default iterator. e.g.:: from sqlalchemy.sql import visitors stmt = select(some_table).where(some_table.c.foo == 'bar') def visit_bindparam(bind_param): print("found bound value: %s" % bind_param.value) visitors.traverse(stmt, {}, {"bindparam": visit_bindparam}) The iteration of objects uses the :func:`.visitors.iterate` function, which does a breadth-first traversal using a stack. :param obj: :class:`_expression.ClauseElement` structure to be traversed :param opts: dictionary of iteration options. This dictionary is usually empty in modern usage. :param visitors: dictionary of visit functions. The dictionary should have strings as keys, each of which would correspond to the ``__visit_name__`` of a particular kind of SQL expression object, and callable functions as values, each of which represents a visitor function for that kind of object. """ return traverse_using(iterate(obj, opts), obj, visitors) @overload def cloned_traverse( obj: Literal[None], opts: Mapping[str, Any], visitors: Mapping[str, _TraverseCallableType[Any]], ) -> None: ... # a bit of controversy here, as the clone of the lead element # *could* in theory replace with an entirely different kind of element. # however this is really not how cloned_traverse is ever used internally # at least. @overload def cloned_traverse( obj: _ET, opts: Mapping[str, Any], visitors: Mapping[str, _TraverseCallableType[Any]], ) -> _ET: ... def cloned_traverse( obj: Optional[ExternallyTraversible], opts: Mapping[str, Any], visitors: Mapping[str, _TraverseCallableType[Any]], ) -> Optional[ExternallyTraversible]: """Clone the given expression structure, allowing modifications by visitors for mutable objects. Traversal usage is the same as that of :func:`.visitors.traverse`. The visitor functions present in the ``visitors`` dictionary may also modify the internals of the given structure as the traversal proceeds. The :func:`.cloned_traverse` function does **not** provide objects that are part of the :class:`.Immutable` interface to the visit methods (this primarily includes :class:`.ColumnClause`, :class:`.Column`, :class:`.TableClause` and :class:`.Table` objects). As this traversal is only intended to allow in-place mutation of objects, :class:`.Immutable` objects are skipped. The :meth:`.Immutable._clone` method is still called on each object to allow for objects to replace themselves with a different object based on a clone of their sub-internals (e.g. a :class:`.ColumnClause` that clones its subquery to return a new :class:`.ColumnClause`). .. versionchanged:: 2.0 The :func:`.cloned_traverse` function omits objects that are part of the :class:`.Immutable` interface. The central API feature used by the :func:`.visitors.cloned_traverse` and :func:`.visitors.replacement_traverse` functions, in addition to the :meth:`_expression.ClauseElement.get_children` function that is used to achieve the iteration, is the :meth:`_expression.ClauseElement._copy_internals` method. For a :class:`_expression.ClauseElement` structure to support cloning and replacement traversals correctly, it needs to be able to pass a cloning function into its internal members in order to make copies of them. .. seealso:: :func:`.visitors.traverse` :func:`.visitors.replacement_traverse` """ cloned: Dict[int, ExternallyTraversible] = {} stop_on = set(opts.get("stop_on", [])) def deferred_copy_internals( obj: ExternallyTraversible, ) -> ExternallyTraversible: return cloned_traverse(obj, opts, visitors) def clone(elem: ExternallyTraversible, **kw: Any) -> ExternallyTraversible: if elem in stop_on: return elem else: if id(elem) not in cloned: if "replace" in kw: newelem = cast( Optional[ExternallyTraversible], kw["replace"](elem) ) if newelem is not None: cloned[id(elem)] = newelem return newelem # the _clone method for immutable normally returns "self". # however, the method is still allowed to return a # different object altogether; ColumnClause._clone() will # based on options clone the subquery to which it is associated # and return the new corresponding column. cloned[id(elem)] = newelem = elem._clone(clone=clone, **kw) newelem._copy_internals(clone=clone, **kw) # however, visit methods which are tasked with in-place # mutation of the object should not get access to the immutable # object. if not elem._is_immutable: meth = visitors.get(newelem.__visit_name__, None) if meth: meth(newelem) return cloned[id(elem)] if obj is not None: obj = clone( obj, deferred_copy_internals=deferred_copy_internals, **opts ) clone = None # type: ignore[assignment] # remove gc cycles return obj @overload def replacement_traverse( obj: Literal[None], opts: Mapping[str, Any], replace: _TraverseTransformCallableType[Any], ) -> None: ... @overload def replacement_traverse( obj: _CE, opts: Mapping[str, Any], replace: _TraverseTransformCallableType[Any], ) -> _CE: ... @overload def replacement_traverse( obj: ExternallyTraversible, opts: Mapping[str, Any], replace: _TraverseTransformCallableType[Any], ) -> ExternallyTraversible: ... def replacement_traverse( obj: Optional[ExternallyTraversible], opts: Mapping[str, Any], replace: _TraverseTransformCallableType[Any], ) -> Optional[ExternallyTraversible]: """Clone the given expression structure, allowing element replacement by a given replacement function. This function is very similar to the :func:`.visitors.cloned_traverse` function, except instead of being passed a dictionary of visitors, all elements are unconditionally passed into the given replace function. The replace function then has the option to return an entirely new object which will replace the one given. If it returns ``None``, then the object is kept in place. The difference in usage between :func:`.visitors.cloned_traverse` and :func:`.visitors.replacement_traverse` is that in the former case, an already-cloned object is passed to the visitor function, and the visitor function can then manipulate the internal state of the object. In the case of the latter, the visitor function should only return an entirely different object, or do nothing. The use case for :func:`.visitors.replacement_traverse` is that of replacing a FROM clause inside of a SQL structure with a different one, as is a common use case within the ORM. """ cloned = {} stop_on = {id(x) for x in opts.get("stop_on", [])} def deferred_copy_internals( obj: ExternallyTraversible, ) -> ExternallyTraversible: return replacement_traverse(obj, opts, replace) def clone(elem: ExternallyTraversible, **kw: Any) -> ExternallyTraversible: if ( id(elem) in stop_on or "no_replacement_traverse" in elem._annotations ): return elem else: newelem = replace(elem) if newelem is not None: stop_on.add(id(newelem)) return newelem # type: ignore else: # base "already seen" on id(), not hash, so that we don't # replace an Annotated element with its non-annotated one, and # vice versa id_elem = id(elem) if id_elem not in cloned: if "replace" in kw: newelem = kw["replace"](elem) if newelem is not None: cloned[id_elem] = newelem return newelem # type: ignore cloned[id_elem] = newelem = elem._clone(**kw) newelem._copy_internals(clone=clone, **kw) return cloned[id_elem] # type: ignore if obj is not None: obj = clone( obj, deferred_copy_internals=deferred_copy_internals, **opts ) clone = None # type: ignore[assignment] # remove gc cycles return obj

sqlalchemy__alembic

commands.rst

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sqlalchemy__alembic/docs/build/api/commands.rst

Commands Note this section discusses the internal API of Alembic as regards its command invocation system. This section is only useful for developers who wish to extend the capabilities of Alembic. For documentation on using Alembic commands, please see /tutorial. Alembic commands are all represented by functions in the alembic.command.toplevel package. They all accept the same style of usage, being sent the .Config object as the first argument. Commands can be run programmatically, by first constructing a .Config object, as in: from alembic.config import Config from alembic import command alembic_cfg = Config("/path/to/yourapp/alembic.ini") command.upgrade(alembic_cfg, "head") In many cases, and perhaps more often than not, an application will wish to call upon a series of Alembic commands and/or other features. It is usually a good idea to link multiple commands along a single connection and transaction, if feasible. This can be achieved using the .Config.attributes dictionary in order to share a connection: with engine.begin() as connection: alembic_cfg.attributes['connection'] = connection command.upgrade(alembic_cfg, "head") This recipe requires that env.py consumes this connection argument; see the example in connection_sharing for details. To write small API functions that make direct use of database and script directory information, rather than just running one of the built-in commands, use the .ScriptDirectory and .MigrationContext classes directly.

from __future__ import annotations import os from typing import List from typing import Optional from typing import TYPE_CHECKING from typing import Union from. import autogenerate as autogen from. import util from.runtime.environment import EnvironmentContext from.script import ScriptDirectory if TYPE_CHECKING: from alembic.config import Config from alembic.script.base import Script from.runtime.environment import ProcessRevisionDirectiveFn def list_templates(config: Config): """List available templates. :param config: a :class:`.Config` object. """ config.print_stdout("Available templates:\n") for tempname in os.listdir(config.get_template_directory()): with open( os.path.join(config.get_template_directory(), tempname, "README") ) as readme: synopsis = next(readme).rstrip() config.print_stdout("%s - %s", tempname, synopsis) config.print_stdout("\nTemplates are used via the 'init' command, e.g.:") config.print_stdout("\n alembic init --template generic./scripts") def init( config: Config, directory: str, template: str = "generic", package: bool = False, ) -> None: """Initialize a new scripts directory. :param config: a :class:`.Config` object. :param directory: string path of the target directory :param template: string name of the migration environment template to use. :param package: when True, write ``__init__.py`` files into the environment location as well as the versions/ location. """ if os.access(directory, os.F_OK) and os.listdir(directory): raise util.CommandError( "Directory %s already exists and is not empty" % directory ) template_dir = os.path.join(config.get_template_directory(), template) if not os.access(template_dir, os.F_OK): raise util.CommandError("No such template %r" % template) if not os.access(directory, os.F_OK): with util.status( f"Creating directory {os.path.abspath(directory)!r}", **config.messaging_opts, ): os.makedirs(directory) versions = os.path.join(directory, "versions") with util.status( f"Creating directory {os.path.abspath(versions)!r}", **config.messaging_opts, ): os.makedirs(versions) script = ScriptDirectory(directory) config_file: str | None = None for file_ in os.listdir(template_dir): file_path = os.path.join(template_dir, file_) if file_ == "alembic.ini.mako": assert config.config_file_name is not None config_file = os.path.abspath(config.config_file_name) if os.access(config_file, os.F_OK): util.msg( f"File {config_file!r} already exists, skipping", **config.messaging_opts, ) else: script._generate_template( file_path, config_file, script_location=directory ) elif os.path.isfile(file_path): output_file = os.path.join(directory, file_) script._copy_file(file_path, output_file) if package: for path in [ os.path.join(os.path.abspath(directory), "__init__.py"), os.path.join(os.path.abspath(versions), "__init__.py"), ]: with util.status("Adding {path!r}", **config.messaging_opts): with open(path, "w"): pass assert config_file is not None util.msg( "Please edit configuration/connection/logging " f"settings in {config_file!r} before proceeding.", **config.messaging_opts, ) def revision( config: Config, message: Optional[str] = None, autogenerate: bool = False, sql: bool = False, head: str = "head", splice: bool = False, branch_label: Optional[str] = None, version_path: Optional[str] = None, rev_id: Optional[str] = None, depends_on: Optional[str] = None, process_revision_directives: Optional[ProcessRevisionDirectiveFn] = None, ) -> Union[Optional[Script], List[Optional[Script]]]: """Create a new revision file. :param config: a :class:`.Config` object. :param message: string message to apply to the revision; this is the ``-m`` option to ``alembic revision``. :param autogenerate: whether or not to autogenerate the script from the database; this is the ``--autogenerate`` option to ``alembic revision``. :param sql: whether to dump the script out as a SQL string; when specified, the script is dumped to stdout. This is the ``--sql`` option to ``alembic revision``. :param head: head revision to build the new revision upon as a parent; this is the ``--head`` option to ``alembic revision``. :param splice: whether or not the new revision should be made into a new head of its own; is required when the given ``head`` is not itself a head. This is the ``--splice`` option to ``alembic revision``. :param branch_label: string label to apply to the branch; this is the ``--branch-label`` option to ``alembic revision``. :param version_path: string symbol identifying a specific version path from the configuration; this is the ``--version-path`` option to ``alembic revision``. :param rev_id: optional revision identifier to use instead of having one generated; this is the ``--rev-id`` option to ``alembic revision``. :param depends_on: optional list of "depends on" identifiers; this is the ``--depends-on`` option to ``alembic revision``. :param process_revision_directives: this is a callable that takes the same form as the callable described at :paramref:`.EnvironmentContext.configure.process_revision_directives`; will be applied to the structure generated by the revision process where it can be altered programmatically. Note that unlike all the other parameters, this option is only available via programmatic use of :func:`.command.revision` """ script_directory = ScriptDirectory.from_config(config) command_args = dict( message=message, autogenerate=autogenerate, sql=sql, head=head, splice=splice, branch_label=branch_label, version_path=version_path, rev_id=rev_id, depends_on=depends_on, ) revision_context = autogen.RevisionContext( config, script_directory, command_args, process_revision_directives=process_revision_directives, ) environment = util.asbool(config.get_main_option("revision_environment")) if autogenerate: environment = True if sql: raise util.CommandError( "Using --sql with --autogenerate does not make any sense" ) def retrieve_migrations(rev, context): revision_context.run_autogenerate(rev, context) return [] elif environment: def retrieve_migrations(rev, context): revision_context.run_no_autogenerate(rev, context) return [] elif sql: raise util.CommandError( "Using --sql with the revision command when " "revision_environment is not configured does not make any sense" ) if environment: with EnvironmentContext( config, script_directory, fn=retrieve_migrations, as_sql=sql, template_args=revision_context.template_args, revision_context=revision_context, ): script_directory.run_env() # the revision_context now has MigrationScript structure(s) present. # these could theoretically be further processed / rewritten *here*, # in addition to the hooks present within each run_migrations() call, # or at the end of env.py run_migrations_online(). scripts = [script for script in revision_context.generate_scripts()] if len(scripts) == 1: return scripts[0] else: return scripts def check( config: "Config", ) -> None: """Check if revision command with autogenerate has pending upgrade ops. :param config: a :class:`.Config` object. .. versionadded:: 1.9.0 """ script_directory = ScriptDirectory.from_config(config) command_args = dict( message=None, autogenerate=True, sql=False, head="head", splice=False, branch_label=None, version_path=None, rev_id=None, depends_on=None, ) revision_context = autogen.RevisionContext( config, script_directory, command_args, ) def retrieve_migrations(rev, context): revision_context.run_autogenerate(rev, context) return [] with EnvironmentContext( config, script_directory, fn=retrieve_migrations, as_sql=False, template_args=revision_context.template_args, revision_context=revision_context, ): script_directory.run_env() # the revision_context now has MigrationScript structure(s) present. migration_script = revision_context.generated_revisions[-1] diffs = migration_script.upgrade_ops.as_diffs() if diffs: raise util.AutogenerateDiffsDetected( f"New upgrade operations detected: {diffs}" ) else: config.print_stdout("No new upgrade operations detected.") def merge( config: Config, revisions: str, message: Optional[str] = None, branch_label: Optional[str] = None, rev_id: Optional[str] = None, ) -> Optional[Script]: """Merge two revisions together. Creates a new migration file. :param config: a :class:`.Config` instance :param message: string message to apply to the revision :param branch_label: string label name to apply to the new revision :param rev_id: hardcoded revision identifier instead of generating a new one. .. seealso:: :ref:`branches` """ script = ScriptDirectory.from_config(config) template_args = { "config": config # Let templates use config for # e.g. multiple databases } environment = util.asbool(config.get_main_option("revision_environment")) if environment: def nothing(rev, context): return [] with EnvironmentContext( config, script, fn=nothing, as_sql=False, template_args=template_args, ): script.run_env() return script.generate_revision( rev_id or util.rev_id(), message, refresh=True, head=revisions, branch_labels=branch_label, **template_args, # type:ignore[arg-type] ) def upgrade( config: Config, revision: str, sql: bool = False, tag: Optional[str] = None, ) -> None: """Upgrade to a later version. :param config: a :class:`.Config` instance. :param revision: string revision target or range for --sql mode :param sql: if True, use ``--sql`` mode :param tag: an arbitrary "tag" that can be intercepted by custom ``env.py`` scripts via the :meth:`.EnvironmentContext.get_tag_argument` method. """ script = ScriptDirectory.from_config(config) starting_rev = None if ":" in revision: if not sql: raise util.CommandError("Range revision not allowed") starting_rev, revision = revision.split(":", 2) def upgrade(rev, context): return script._upgrade_revs(revision, rev) with EnvironmentContext( config, script, fn=upgrade, as_sql=sql, starting_rev=starting_rev, destination_rev=revision, tag=tag, ): script.run_env() def downgrade( config: Config, revision: str, sql: bool = False, tag: Optional[str] = None, ) -> None: """Revert to a previous version. :param config: a :class:`.Config` instance. :param revision: string revision target or range for --sql mode :param sql: if True, use ``--sql`` mode :param tag: an arbitrary "tag" that can be intercepted by custom ``env.py`` scripts via the :meth:`.EnvironmentContext.get_tag_argument` method. """ script = ScriptDirectory.from_config(config) starting_rev = None if ":" in revision: if not sql: raise util.CommandError("Range revision not allowed") starting_rev, revision = revision.split(":", 2) elif sql: raise util.CommandError( "downgrade with --sql requires <fromrev>:<torev>" ) def downgrade(rev, context): return script._downgrade_revs(revision, rev) with EnvironmentContext( config, script, fn=downgrade, as_sql=sql, starting_rev=starting_rev, destination_rev=revision, tag=tag, ): script.run_env() def show(config, rev): """Show the revision(s) denoted by the given symbol. :param config: a :class:`.Config` instance. :param revision: string revision target """ script = ScriptDirectory.from_config(config) if rev == "current": def show_current(rev, context): for sc in script.get_revisions(rev): config.print_stdout(sc.log_entry) return [] with EnvironmentContext(config, script, fn=show_current): script.run_env() else: for sc in script.get_revisions(rev): config.print_stdout(sc.log_entry) def history( config: Config, rev_range: Optional[str] = None, verbose: bool = False, indicate_current: bool = False, ) -> None: """List changeset scripts in chronological order. :param config: a :class:`.Config` instance. :param rev_range: string revision range :param verbose: output in verbose mode. :param indicate_current: indicate current revision. """ base: Optional[str] head: Optional[str] script = ScriptDirectory.from_config(config) if rev_range is not None: if ":" not in rev_range: raise util.CommandError( "History range requires [start]:[end], " "[start]:, or :[end]" ) base, head = rev_range.strip().split(":") else: base = head = None environment = ( util.asbool(config.get_main_option("revision_environment")) or indicate_current ) def _display_history(config, script, base, head, currents=()): for sc in script.walk_revisions( base=base or "base", head=head or "heads" ): if indicate_current: sc._db_current_indicator = sc.revision in currents config.print_stdout( sc.cmd_format( verbose=verbose, include_branches=True, include_doc=True, include_parents=True, ) ) def _display_history_w_current(config, script, base, head): def _display_current_history(rev, context): if head == "current": _display_history(config, script, base, rev, rev) elif base == "current": _display_history(config, script, rev, head, rev) else: _display_history(config, script, base, head, rev) return [] with EnvironmentContext(config, script, fn=_display_current_history): script.run_env() if base == "current" or head == "current" or environment: _display_history_w_current(config, script, base, head) else: _display_history(config, script, base, head) def heads(config, verbose=False, resolve_dependencies=False): """Show current available heads in the script directory. :param config: a :class:`.Config` instance. :param verbose: output in verbose mode. :param resolve_dependencies: treat dependency version as down revisions. """ script = ScriptDirectory.from_config(config) if resolve_dependencies: heads = script.get_revisions("heads") else: heads = script.get_revisions(script.get_heads()) for rev in heads: config.print_stdout( rev.cmd_format( verbose, include_branches=True, tree_indicators=False ) ) def branches(config, verbose=False): """Show current branch points. :param config: a :class:`.Config` instance. :param verbose: output in verbose mode. """ script = ScriptDirectory.from_config(config) for sc in script.walk_revisions(): if sc.is_branch_point: config.print_stdout( "%s\n%s\n", sc.cmd_format(verbose, include_branches=True), "\n".join( "%s -> %s" % ( " " * len(str(sc.revision)), rev_obj.cmd_format( False, include_branches=True, include_doc=verbose ), ) for rev_obj in ( script.get_revision(rev) for rev in sc.nextrev ) ), ) def current(config: Config, verbose: bool = False) -> None: """Display the current revision for a database. :param config: a :class:`.Config` instance. :param verbose: output in verbose mode. """ script = ScriptDirectory.from_config(config) def display_version(rev, context): if verbose: config.print_stdout( "Current revision(s) for %s:", util.obfuscate_url_pw(context.connection.engine.url), ) for rev in script.get_all_current(rev): config.print_stdout(rev.cmd_format(verbose)) return [] with EnvironmentContext( config, script, fn=display_version, dont_mutate=True ): script.run_env() def stamp( config: Config, revision: str, sql: bool = False, tag: Optional[str] = None, purge: bool = False, ) -> None: """'stamp' the revision table with the given revision; don't run any migrations. :param config: a :class:`.Config` instance. :param revision: target revision or list of revisions. May be a list to indicate stamping of multiple branch heads. .. note:: this parameter is called "revisions" in the command line interface. :param sql: use ``--sql`` mode :param tag: an arbitrary "tag" that can be intercepted by custom ``env.py`` scripts via the :class:`.EnvironmentContext.get_tag_argument` method. :param purge: delete all entries in the version table before stamping. """ script = ScriptDirectory.from_config(config) if sql: destination_revs = [] starting_rev = None for _revision in util.to_list(revision): if ":" in _revision: srev, _revision = _revision.split(":", 2) if starting_rev!= srev: if starting_rev is None: starting_rev = srev else: raise util.CommandError( "Stamp operation with --sql only supports a " "single starting revision at a time" ) destination_revs.append(_revision) else: destination_revs = util.to_list(revision) def do_stamp(rev, context): return script._stamp_revs(util.to_tuple(destination_revs), rev) with EnvironmentContext( config, script, fn=do_stamp, as_sql=sql, starting_rev=starting_rev if sql else None, destination_rev=util.to_tuple(destination_revs), tag=tag, purge=purge, ): script.run_env() def edit(config: Config, rev: str) -> None: """Edit revision script(s) using $EDITOR. :param config: a :class:`.Config` instance. :param rev: target revision. """ script = ScriptDirectory.from_config(config) if rev == "current": def edit_current(rev, context): if not rev: raise util.CommandError("No current revisions") for sc in script.get_revisions(rev): util.open_in_editor(sc.path) return [] with EnvironmentContext(config, script, fn=edit_current): script.run_env() else: revs = script.get_revisions(rev) if not revs: raise util.CommandError( "No revision files indicated by symbol '%s'" % rev ) for sc in revs: assert sc util.open_in_editor(sc.path) def ensure_version(config: Config, sql: bool = False) -> None: """Create the alembic version table if it doesn't exist already. :param config: a :class:`.Config` instance. :param sql: use ``--sql`` mode .. versionadded:: 1.7.6 """ script = ScriptDirectory.from_config(config) def do_ensure_version(rev, context): context._ensure_version_table() return [] with EnvironmentContext( config, script, fn=do_ensure_version, as_sql=sql, ): script.run_env()

sqlalchemy__alembic

operations.rst

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sqlalchemy__alembic/docs/build/api/operations.rst

Operation Directives Within migration scripts, actual database migration operations are handled via an instance of .Operations. The .Operations class lists out available migration operations that are linked to a .MigrationContext, which communicates instructions originated by the .Operations object into SQL that is sent to a database or SQL output stream. Most methods on the .Operations class are generated dynamically using a "plugin" system, described in the next section operation_plugins. Additionally, when Alembic migration scripts actually run, the methods on the current .Operations object are proxied out to the alembic.op module, so that they are available using module-style access. For an overview of how to use an .Operations object directly in programs, as well as for reference to the standard operation methods as well as "batch" methods, see ops. Operation Plugins The Operations object is extensible using a plugin system. This system allows one to add new op.<some_operation> methods at runtime. The steps to use this system are to first create a subclass of .MigrateOperation, register it using the .Operations.register_operation class decorator, then build a default "implementation" function which is established using the .Operations.implementation_for decorator. Below we illustrate a very simple operation CreateSequenceOp which will implement a new method op.create_sequence() for use in migration scripts: from alembic.operations import Operations, MigrateOperation @Operations.register_operation("create_sequence") class CreateSequenceOp(MigrateOperation): """Create a SEQUENCE.""" def __init__(self, sequence_name, schema=None): self.sequence_name = sequence_name self.schema = schema @classmethod def create_sequence(cls, operations, sequence_name, **kw): """Issue a "CREATE SEQUENCE" instruction.""" op = CreateSequenceOp(sequence_name, **kw) return operations.invoke(op) def reverse(self): # only needed to support autogenerate return DropSequenceOp(self.sequence_name, schema=self.schema) @Operations.register_operation("drop_sequence") class DropSequenceOp(MigrateOperation): """Drop a SEQUENCE.""" def __init__(self, sequence_name, schema=None): self.sequence_name = sequence_name self.schema = schema @classmethod def drop_sequence(cls, operations, sequence_name, **kw): """Issue a "DROP SEQUENCE" instruction.""" op = DropSequenceOp(sequence_name, **kw) return operations.invoke(op) def reverse(self): # only needed to support autogenerate return CreateSequenceOp(self.sequence_name, schema=self.schema) Above, the CreateSequenceOp and DropSequenceOp classes represent new operations that will be available as op.create_sequence() and op.drop_sequence(). The reason the operations are represented as stateful classes is so that an operation and a specific set of arguments can be represented generically; the state can then correspond to different kinds of operations, such as invoking the instruction against a database, or autogenerating Python code for the operation into a script. In order to establish the migrate-script behavior of the new operations, we use the .Operations.implementation_for decorator: @Operations.implementation_for(CreateSequenceOp) def create_sequence(operations, operation): if operation.schema is not None: name = "%s.%s" % (operation.schema, operation.sequence_name) else: name = operation.sequence_name operations.execute("CREATE SEQUENCE %s" % name) @Operations.implementation_for(DropSequenceOp) def drop_sequence(operations, operation): if operation.schema is not None: name = "%s.%s" % (operation.schema, operation.sequence_name) else: name = operation.sequence_name operations.execute("DROP SEQUENCE %s" % name) Above, we use the simplest possible technique of invoking our DDL, which is just to call .Operations.execute with literal SQL. If this is all a custom operation needs, then this is fine. However, options for more comprehensive support include building out a custom SQL construct, as documented at sqlalchemy.ext.compiler_toplevel. With the above two steps, a migration script can now use new methods op.create_sequence() and op.drop_sequence() that will proxy to our object as a classmethod: def upgrade(): op.create_sequence("my_sequence") def downgrade(): op.drop_sequence("my_sequence") The registration of new operations only needs to occur in time for the env.py script to invoke .MigrationContext.run_migrations; within the module level of the env.py script is sufficient. autogen_custom_ops - how to add autogenerate support to custom operations. Built-in Operation Objects The migration operations present on .Operations are themselves delivered via operation objects that represent an operation and its arguments. All operations descend from the .MigrateOperation class, and are registered with the .Operations class using the .Operations.register_operation class decorator. The .MigrateOperation objects also serve as the basis for how the autogenerate system renders new migration scripts.

from __future__ import annotations from abc import abstractmethod import re from typing import Any from typing import Callable from typing import cast from typing import FrozenSet from typing import Iterator from typing import List from typing import MutableMapping from typing import Optional from typing import Sequence from typing import Set from typing import Tuple from typing import Type from typing import TYPE_CHECKING from typing import Union from sqlalchemy.types import NULLTYPE from. import schemaobj from.base import BatchOperations from.base import Operations from.. import util from..util import sqla_compat if TYPE_CHECKING: from typing import Literal from sqlalchemy.sql.dml import Insert from sqlalchemy.sql.dml import Update from sqlalchemy.sql.elements import BinaryExpression from sqlalchemy.sql.elements import ColumnElement from sqlalchemy.sql.elements import conv from sqlalchemy.sql.elements import quoted_name from sqlalchemy.sql.elements import TextClause from sqlalchemy.sql.functions import Function from sqlalchemy.sql.schema import CheckConstraint from sqlalchemy.sql.schema import Column from sqlalchemy.sql.schema import Computed from sqlalchemy.sql.schema import Constraint from sqlalchemy.sql.schema import ForeignKeyConstraint from sqlalchemy.sql.schema import Identity from sqlalchemy.sql.schema import Index from sqlalchemy.sql.schema import MetaData from sqlalchemy.sql.schema import PrimaryKeyConstraint from sqlalchemy.sql.schema import SchemaItem from sqlalchemy.sql.schema import Table from sqlalchemy.sql.schema import UniqueConstraint from sqlalchemy.sql.selectable import TableClause from sqlalchemy.sql.type_api import TypeEngine from..autogenerate.rewriter import Rewriter from..runtime.migration import MigrationContext from..script.revision import _RevIdType class MigrateOperation: """base class for migration command and organization objects. This system is part of the operation extensibility API. .. seealso:: :ref:`operation_objects` :ref:`operation_plugins` :ref:`customizing_revision` """ @util.memoized_property def info(self): """A dictionary that may be used to store arbitrary information along with this :class:`.MigrateOperation` object. """ return {} _mutations: FrozenSet[Rewriter] = frozenset() def reverse(self) -> MigrateOperation: raise NotImplementedError def to_diff_tuple(self) -> Tuple[Any,...]: raise NotImplementedError class AddConstraintOp(MigrateOperation): """Represent an add constraint operation.""" add_constraint_ops = util.Dispatcher() @property def constraint_type(self): raise NotImplementedError() @classmethod def register_add_constraint(cls, type_: str) -> Callable: def go(klass): cls.add_constraint_ops.dispatch_for(type_)(klass.from_constraint) return klass return go @classmethod def from_constraint(cls, constraint: Constraint) -> AddConstraintOp: return cls.add_constraint_ops.dispatch(constraint.__visit_name__)( constraint ) @abstractmethod def to_constraint( self, migration_context: Optional[MigrationContext] = None ) -> Constraint: pass def reverse(self) -> DropConstraintOp: return DropConstraintOp.from_constraint(self.to_constraint()) def to_diff_tuple(self) -> Tuple[str, Constraint]: return ("add_constraint", self.to_constraint()) @Operations.register_operation("drop_constraint") @BatchOperations.register_operation("drop_constraint", "batch_drop_constraint") class DropConstraintOp(MigrateOperation): """Represent a drop constraint operation.""" def __init__( self, constraint_name: Optional[sqla_compat._ConstraintNameDefined], table_name: str, type_: Optional[str] = None, *, schema: Optional[str] = None, _reverse: Optional[AddConstraintOp] = None, ) -> None: self.constraint_name = constraint_name self.table_name = table_name self.constraint_type = type_ self.schema = schema self._reverse = _reverse def reverse(self) -> AddConstraintOp: return AddConstraintOp.from_constraint(self.to_constraint()) def to_diff_tuple( self, ) -> Tuple[str, SchemaItem]: if self.constraint_type == "foreignkey": return ("remove_fk", self.to_constraint()) else: return ("remove_constraint", self.to_constraint()) @classmethod def from_constraint(cls, constraint: Constraint) -> DropConstraintOp: types = { "unique_constraint": "unique", "foreign_key_constraint": "foreignkey", "primary_key_constraint": "primary", "check_constraint": "check", "column_check_constraint": "check", "table_or_column_check_constraint": "check", } constraint_table = sqla_compat._table_for_constraint(constraint) return cls( sqla_compat.constraint_name_or_none(constraint.name), constraint_table.name, schema=constraint_table.schema, type_=types.get(constraint.__visit_name__), _reverse=AddConstraintOp.from_constraint(constraint), ) def to_constraint(self) -> Constraint: if self._reverse is not None: constraint = self._reverse.to_constraint() constraint.name = self.constraint_name constraint_table = sqla_compat._table_for_constraint(constraint) constraint_table.name = self.table_name constraint_table.schema = self.schema return constraint else: raise ValueError( "constraint cannot be produced; " "original constraint is not present" ) @classmethod def drop_constraint( cls, operations: Operations, constraint_name: str, table_name: str, type_: Optional[str] = None, *, schema: Optional[str] = None, ) -> None: r"""Drop a constraint of the given name, typically via DROP CONSTRAINT. :param constraint_name: name of the constraint. :param table_name: table name. :param type\_: optional, required on MySQL. can be 'foreignkey', 'primary', 'unique', or 'check'. :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. """ op = cls(constraint_name, table_name, type_=type_, schema=schema) return operations.invoke(op) @classmethod def batch_drop_constraint( cls, operations: BatchOperations, constraint_name: str, type_: Optional[str] = None, ) -> None: """Issue a "drop constraint" instruction using the current batch migration context. The batch form of this call omits the ``table_name`` and ``schema`` arguments from the call. .. seealso:: :meth:`.Operations.drop_constraint` """ op = cls( constraint_name, operations.impl.table_name, type_=type_, schema=operations.impl.schema, ) return operations.invoke(op) @Operations.register_operation("create_primary_key") @BatchOperations.register_operation( "create_primary_key", "batch_create_primary_key" ) @AddConstraintOp.register_add_constraint("primary_key_constraint") class CreatePrimaryKeyOp(AddConstraintOp): """Represent a create primary key operation.""" constraint_type = "primarykey" def __init__( self, constraint_name: Optional[sqla_compat._ConstraintNameDefined], table_name: str, columns: Sequence[str], *, schema: Optional[str] = None, **kw: Any, ) -> None: self.constraint_name = constraint_name self.table_name = table_name self.columns = columns self.schema = schema self.kw = kw @classmethod def from_constraint(cls, constraint: Constraint) -> CreatePrimaryKeyOp: constraint_table = sqla_compat._table_for_constraint(constraint) pk_constraint = cast("PrimaryKeyConstraint", constraint) return cls( sqla_compat.constraint_name_or_none(pk_constraint.name), constraint_table.name, pk_constraint.columns.keys(), schema=constraint_table.schema, **pk_constraint.dialect_kwargs, ) def to_constraint( self, migration_context: Optional[MigrationContext] = None ) -> PrimaryKeyConstraint: schema_obj = schemaobj.SchemaObjects(migration_context) return schema_obj.primary_key_constraint( self.constraint_name, self.table_name, self.columns, schema=self.schema, **self.kw, ) @classmethod def create_primary_key( cls, operations: Operations, constraint_name: Optional[str], table_name: str, columns: List[str], *, schema: Optional[str] = None, ) -> None: """Issue a "create primary key" instruction using the current migration context. e.g.:: from alembic import op op.create_primary_key("pk_my_table", "my_table", ["id", "version"]) This internally generates a :class:`~sqlalchemy.schema.Table` object containing the necessary columns, then generates a new :class:`~sqlalchemy.schema.PrimaryKeyConstraint` object which it then associates with the :class:`~sqlalchemy.schema.Table`. Any event listeners associated with this action will be fired off normally. The :class:`~sqlalchemy.schema.AddConstraint` construct is ultimately used to generate the ALTER statement. :param constraint_name: Name of the primary key constraint. The name is necessary so that an ALTER statement can be emitted. For setups that use an automated naming scheme such as that described at :ref:`sqla:constraint_naming_conventions` ``name`` here can be ``None``, as the event listener will apply the name to the constraint object when it is associated with the table. :param table_name: String name of the target table. :param columns: a list of string column names to be applied to the primary key constraint. :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. """ op = cls(constraint_name, table_name, columns, schema=schema) return operations.invoke(op) @classmethod def batch_create_primary_key( cls, operations: BatchOperations, constraint_name: str, columns: List[str], ) -> None: """Issue a "create primary key" instruction using the current batch migration context. The batch form of this call omits the ``table_name`` and ``schema`` arguments from the call. .. seealso:: :meth:`.Operations.create_primary_key` """ op = cls( constraint_name, operations.impl.table_name, columns, schema=operations.impl.schema, ) return operations.invoke(op) @Operations.register_operation("create_unique_constraint") @BatchOperations.register_operation( "create_unique_constraint", "batch_create_unique_constraint" ) @AddConstraintOp.register_add_constraint("unique_constraint") class CreateUniqueConstraintOp(AddConstraintOp): """Represent a create unique constraint operation.""" constraint_type = "unique" def __init__( self, constraint_name: Optional[sqla_compat._ConstraintNameDefined], table_name: str, columns: Sequence[str], *, schema: Optional[str] = None, **kw: Any, ) -> None: self.constraint_name = constraint_name self.table_name = table_name self.columns = columns self.schema = schema self.kw = kw @classmethod def from_constraint( cls, constraint: Constraint ) -> CreateUniqueConstraintOp: constraint_table = sqla_compat._table_for_constraint(constraint) uq_constraint = cast("UniqueConstraint", constraint) kw: dict = {} if uq_constraint.deferrable: kw["deferrable"] = uq_constraint.deferrable if uq_constraint.initially: kw["initially"] = uq_constraint.initially kw.update(uq_constraint.dialect_kwargs) return cls( sqla_compat.constraint_name_or_none(uq_constraint.name), constraint_table.name, [c.name for c in uq_constraint.columns], schema=constraint_table.schema, **kw, ) def to_constraint( self, migration_context: Optional[MigrationContext] = None ) -> UniqueConstraint: schema_obj = schemaobj.SchemaObjects(migration_context) return schema_obj.unique_constraint( self.constraint_name, self.table_name, self.columns, schema=self.schema, **self.kw, ) @classmethod def create_unique_constraint( cls, operations: Operations, constraint_name: Optional[str], table_name: str, columns: Sequence[str], *, schema: Optional[str] = None, **kw: Any, ) -> Any: """Issue a "create unique constraint" instruction using the current migration context. e.g.:: from alembic import op op.create_unique_constraint("uq_user_name", "user", ["name"]) This internally generates a :class:`~sqlalchemy.schema.Table` object containing the necessary columns, then generates a new :class:`~sqlalchemy.schema.UniqueConstraint` object which it then associates with the :class:`~sqlalchemy.schema.Table`. Any event listeners associated with this action will be fired off normally. The :class:`~sqlalchemy.schema.AddConstraint` construct is ultimately used to generate the ALTER statement. :param name: Name of the unique constraint. The name is necessary so that an ALTER statement can be emitted. For setups that use an automated naming scheme such as that described at :ref:`sqla:constraint_naming_conventions`, ``name`` here can be ``None``, as the event listener will apply the name to the constraint object when it is associated with the table. :param table_name: String name of the source table. :param columns: a list of string column names in the source table. :param deferrable: optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint. :param initially: optional string. If set, emit INITIALLY <value> when issuing DDL for this constraint. :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. """ op = cls(constraint_name, table_name, columns, schema=schema, **kw) return operations.invoke(op) @classmethod def batch_create_unique_constraint( cls, operations: BatchOperations, constraint_name: str, columns: Sequence[str], **kw: Any, ) -> Any: """Issue a "create unique constraint" instruction using the current batch migration context. The batch form of this call omits the ``source`` and ``schema`` arguments from the call. .. seealso:: :meth:`.Operations.create_unique_constraint` """ kw["schema"] = operations.impl.schema op = cls(constraint_name, operations.impl.table_name, columns, **kw) return operations.invoke(op) @Operations.register_operation("create_foreign_key") @BatchOperations.register_operation( "create_foreign_key", "batch_create_foreign_key" ) @AddConstraintOp.register_add_constraint("foreign_key_constraint") class CreateForeignKeyOp(AddConstraintOp): """Represent a create foreign key constraint operation.""" constraint_type = "foreignkey" def __init__( self, constraint_name: Optional[sqla_compat._ConstraintNameDefined], source_table: str, referent_table: str, local_cols: List[str], remote_cols: List[str], **kw: Any, ) -> None: self.constraint_name = constraint_name self.source_table = source_table self.referent_table = referent_table self.local_cols = local_cols self.remote_cols = remote_cols self.kw = kw def to_diff_tuple(self) -> Tuple[str, ForeignKeyConstraint]: return ("add_fk", self.to_constraint()) @classmethod def from_constraint(cls, constraint: Constraint) -> CreateForeignKeyOp: fk_constraint = cast("ForeignKeyConstraint", constraint) kw: dict = {} if fk_constraint.onupdate: kw["onupdate"] = fk_constraint.onupdate if fk_constraint.ondelete: kw["ondelete"] = fk_constraint.ondelete if fk_constraint.initially: kw["initially"] = fk_constraint.initially if fk_constraint.deferrable: kw["deferrable"] = fk_constraint.deferrable if fk_constraint.use_alter: kw["use_alter"] = fk_constraint.use_alter if fk_constraint.match: kw["match"] = fk_constraint.match ( source_schema, source_table, source_columns, target_schema, target_table, target_columns, onupdate, ondelete, deferrable, initially, ) = sqla_compat._fk_spec(fk_constraint) kw["source_schema"] = source_schema kw["referent_schema"] = target_schema kw.update(fk_constraint.dialect_kwargs) return cls( sqla_compat.constraint_name_or_none(fk_constraint.name), source_table, target_table, source_columns, target_columns, **kw, ) def to_constraint( self, migration_context: Optional[MigrationContext] = None ) -> ForeignKeyConstraint: schema_obj = schemaobj.SchemaObjects(migration_context) return schema_obj.foreign_key_constraint( self.constraint_name, self.source_table, self.referent_table, self.local_cols, self.remote_cols, **self.kw, ) @classmethod def create_foreign_key( cls, operations: Operations, constraint_name: Optional[str], source_table: str, referent_table: str, local_cols: List[str], remote_cols: List[str], *, onupdate: Optional[str] = None, ondelete: Optional[str] = None, deferrable: Optional[bool] = None, initially: Optional[str] = None, match: Optional[str] = None, source_schema: Optional[str] = None, referent_schema: Optional[str] = None, **dialect_kw: Any, ) -> None: """Issue a "create foreign key" instruction using the current migration context. e.g.:: from alembic import op op.create_foreign_key( "fk_user_address", "address", "user", ["user_id"], ["id"], ) This internally generates a :class:`~sqlalchemy.schema.Table` object containing the necessary columns, then generates a new :class:`~sqlalchemy.schema.ForeignKeyConstraint` object which it then associates with the :class:`~sqlalchemy.schema.Table`. Any event listeners associated with this action will be fired off normally. The :class:`~sqlalchemy.schema.AddConstraint` construct is ultimately used to generate the ALTER statement. :param constraint_name: Name of the foreign key constraint. The name is necessary so that an ALTER statement can be emitted. For setups that use an automated naming scheme such as that described at :ref:`sqla:constraint_naming_conventions`, ``name`` here can be ``None``, as the event listener will apply the name to the constraint object when it is associated with the table. :param source_table: String name of the source table. :param referent_table: String name of the destination table. :param local_cols: a list of string column names in the source table. :param remote_cols: a list of string column names in the remote table. :param onupdate: Optional string. If set, emit ON UPDATE <value> when issuing DDL for this constraint. Typical values include CASCADE, DELETE and RESTRICT. :param ondelete: Optional string. If set, emit ON DELETE <value> when issuing DDL for this constraint. Typical values include CASCADE, DELETE and RESTRICT. :param deferrable: optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint. :param source_schema: Optional schema name of the source table. :param referent_schema: Optional schema name of the destination table. """ op = cls( constraint_name, source_table, referent_table, local_cols, remote_cols, onupdate=onupdate, ondelete=ondelete, deferrable=deferrable, source_schema=source_schema, referent_schema=referent_schema, initially=initially, match=match, **dialect_kw, ) return operations.invoke(op) @classmethod def batch_create_foreign_key( cls, operations: BatchOperations, constraint_name: str, referent_table: str, local_cols: List[str], remote_cols: List[str], *, referent_schema: Optional[str] = None, onupdate: Optional[str] = None, ondelete: Optional[str] = None, deferrable: Optional[bool] = None, initially: Optional[str] = None, match: Optional[str] = None, **dialect_kw: Any, ) -> None: """Issue a "create foreign key" instruction using the current batch migration context. The batch form of this call omits the ``source`` and ``source_schema`` arguments from the call. e.g.:: with batch_alter_table("address") as batch_op: batch_op.create_foreign_key( "fk_user_address", "user", ["user_id"], ["id"], ) .. seealso:: :meth:`.Operations.create_foreign_key` """ op = cls( constraint_name, operations.impl.table_name, referent_table, local_cols, remote_cols, onupdate=onupdate, ondelete=ondelete, deferrable=deferrable, source_schema=operations.impl.schema, referent_schema=referent_schema, initially=initially, match=match, **dialect_kw, ) return operations.invoke(op) @Operations.register_operation("create_check_constraint") @BatchOperations.register_operation( "create_check_constraint", "batch_create_check_constraint" ) @AddConstraintOp.register_add_constraint("check_constraint") @AddConstraintOp.register_add_constraint("table_or_column_check_constraint") @AddConstraintOp.register_add_constraint("column_check_constraint") class CreateCheckConstraintOp(AddConstraintOp): """Represent a create check constraint operation.""" constraint_type = "check" def __init__( self, constraint_name: Optional[sqla_compat._ConstraintNameDefined], table_name: str, condition: Union[str, TextClause, ColumnElement[Any]], *, schema: Optional[str] = None, **kw: Any, ) -> None: self.constraint_name = constraint_name self.table_name = table_name self.condition = condition self.schema = schema self.kw = kw @classmethod def from_constraint( cls, constraint: Constraint ) -> CreateCheckConstraintOp: constraint_table = sqla_compat._table_for_constraint(constraint) ck_constraint = cast("CheckConstraint", constraint) return cls( sqla_compat.constraint_name_or_none(ck_constraint.name), constraint_table.name, cast("ColumnElement[Any]", ck_constraint.sqltext), schema=constraint_table.schema, **ck_constraint.dialect_kwargs, ) def to_constraint( self, migration_context: Optional[MigrationContext] = None ) -> CheckConstraint: schema_obj = schemaobj.SchemaObjects(migration_context) return schema_obj.check_constraint( self.constraint_name, self.table_name, self.condition, schema=self.schema, **self.kw, ) @classmethod def create_check_constraint( cls, operations: Operations, constraint_name: Optional[str], table_name: str, condition: Union[str, BinaryExpression, TextClause], *, schema: Optional[str] = None, **kw: Any, ) -> None: """Issue a "create check constraint" instruction using the current migration context. e.g.:: from alembic import op from sqlalchemy.sql import column, func op.create_check_constraint( "ck_user_name_len", "user", func.len(column("name")) > 5, ) CHECK constraints are usually against a SQL expression, so ad-hoc table metadata is usually needed. The function will convert the given arguments into a :class:`sqlalchemy.schema.CheckConstraint` bound to an anonymous table in order to emit the CREATE statement. :param name: Name of the check constraint. The name is necessary so that an ALTER statement can be emitted. For setups that use an automated naming scheme such as that described at :ref:`sqla:constraint_naming_conventions`, ``name`` here can be ``None``, as the event listener will apply the name to the constraint object when it is associated with the table. :param table_name: String name of the source table. :param condition: SQL expression that's the condition of the constraint. Can be a string or SQLAlchemy expression language structure. :param deferrable: optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint. :param initially: optional string. If set, emit INITIALLY <value> when issuing DDL for this constraint. :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. """ op = cls(constraint_name, table_name, condition, schema=schema, **kw) return operations.invoke(op) @classmethod def batch_create_check_constraint( cls, operations: BatchOperations, constraint_name: str, condition: Union[str, BinaryExpression, TextClause], **kw: Any, ) -> None: """Issue a "create check constraint" instruction using the current batch migration context. The batch form of this call omits the ``source`` and ``schema`` arguments from the call. .. seealso:: :meth:`.Operations.create_check_constraint` """ op = cls( constraint_name, operations.impl.table_name, condition, schema=operations.impl.schema, **kw, ) return operations.invoke(op) @Operations.register_operation("create_index") @BatchOperations.register_operation("create_index", "batch_create_index") class CreateIndexOp(MigrateOperation): """Represent a create index operation.""" def __init__( self, index_name: Optional[str], table_name: str, columns: Sequence[Union[str, TextClause, ColumnElement[Any]]], *, schema: Optional[str] = None, unique: bool = False, if_not_exists: Optional[bool] = None, **kw: Any, ) -> None: self.index_name = index_name self.table_name = table_name self.columns = columns self.schema = schema self.unique = unique self.if_not_exists = if_not_exists self.kw = kw def reverse(self) -> DropIndexOp: return DropIndexOp.from_index(self.to_index()) def to_diff_tuple(self) -> Tuple[str, Index]: return ("add_index", self.to_index()) @classmethod def from_index(cls, index: Index) -> CreateIndexOp: assert index.table is not None return cls( index.name, # type: ignore[arg-type] index.table.name, sqla_compat._get_index_expressions(index), schema=index.table.schema, unique=index.unique, **index.kwargs, ) def to_index( self, migration_context: Optional[MigrationContext] = None ) -> Index: schema_obj = schemaobj.SchemaObjects(migration_context) idx = schema_obj.index( self.index_name, self.table_name, self.columns, schema=self.schema, unique=self.unique, **self.kw, ) return idx @classmethod def create_index( cls, operations: Operations, index_name: Optional[str], table_name: str, columns: Sequence[Union[str, TextClause, Function[Any]]], *, schema: Optional[str] = None, unique: bool = False, if_not_exists: Optional[bool] = None, **kw: Any, ) -> None: r"""Issue a "create index" instruction using the current migration context. e.g.:: from alembic import op op.create_index("ik_test", "t1", ["foo", "bar"]) Functional indexes can be produced by using the :func:`sqlalchemy.sql.expression.text` construct:: from alembic import op from sqlalchemy import text op.create_index("ik_test", "t1", [text("lower(foo)")]) :param index_name: name of the index. :param table_name: name of the owning table. :param columns: a list consisting of string column names and/or :func:`~sqlalchemy.sql.expression.text` constructs. :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. :param unique: If True, create a unique index. :param quote: Force quoting of this column's name on or off, corresponding to ``True`` or ``False``. When left at its default of ``None``, the column identifier will be quoted according to whether the name is case sensitive (identifiers with at least one upper case character are treated as case sensitive), or if it's a reserved word. This flag is only needed to force quoting of a reserved word which is not known by the SQLAlchemy dialect. :param if_not_exists: If True, adds IF NOT EXISTS operator when creating the new index. .. versionadded:: 1.12.0 :param \**kw: Additional keyword arguments not mentioned above are dialect specific, and passed in the form ``<dialectname>_<argname>``. See the documentation regarding an individual dialect at :ref:`dialect_toplevel` for detail on documented arguments. """ op = cls( index_name, table_name, columns, schema=schema, unique=unique, if_not_exists=if_not_exists, **kw, ) return operations.invoke(op) @classmethod def batch_create_index( cls, operations: BatchOperations, index_name: str, columns: List[str], **kw: Any, ) -> None: """Issue a "create index" instruction using the current batch migration context. .. seealso:: :meth:`.Operations.create_index` """ op = cls( index_name, operations.impl.table_name, columns, schema=operations.impl.schema, **kw, ) return operations.invoke(op) @Operations.register_operation("drop_index") @BatchOperations.register_operation("drop_index", "batch_drop_index") class DropIndexOp(MigrateOperation): """Represent a drop index operation.""" def __init__( self, index_name: Union[quoted_name, str, conv], table_name: Optional[str] = None, *, schema: Optional[str] = None, if_exists: Optional[bool] = None, _reverse: Optional[CreateIndexOp] = None, **kw: Any, ) -> None: self.index_name = index_name self.table_name = table_name self.schema = schema self.if_exists = if_exists self._reverse = _reverse self.kw = kw def to_diff_tuple(self) -> Tuple[str, Index]: return ("remove_index", self.to_index()) def reverse(self) -> CreateIndexOp: return CreateIndexOp.from_index(self.to_index()) @classmethod def from_index(cls, index: Index) -> DropIndexOp: assert index.table is not None return cls( index.name, # type: ignore[arg-type] table_name=index.table.name, schema=index.table.schema, _reverse=CreateIndexOp.from_index(index), **index.kwargs, ) def to_index( self, migration_context: Optional[MigrationContext] = None ) -> Index: schema_obj = schemaobj.SchemaObjects(migration_context) # need a dummy column name here since SQLAlchemy # 0.7.6 and further raises on Index with no columns return schema_obj.index( self.index_name, self.table_name, self._reverse.columns if self._reverse else ["x"], schema=self.schema, **self.kw, ) @classmethod def drop_index( cls, operations: Operations, index_name: str, table_name: Optional[str] = None, *, schema: Optional[str] = None, if_exists: Optional[bool] = None, **kw: Any, ) -> None: r"""Issue a "drop index" instruction using the current migration context. e.g.:: drop_index("accounts") :param index_name: name of the index. :param table_name: name of the owning table. Some backends such as Microsoft SQL Server require this. :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. :param if_exists: If True, adds IF EXISTS operator when dropping the index. .. versionadded:: 1.12.0 :param \**kw: Additional keyword arguments not mentioned above are dialect specific, and passed in the form ``<dialectname>_<argname>``. See the documentation regarding an individual dialect at :ref:`dialect_toplevel` for detail on documented arguments. """ op = cls( index_name, table_name=table_name, schema=schema, if_exists=if_exists, **kw, ) return operations.invoke(op) @classmethod def batch_drop_index( cls, operations: BatchOperations, index_name: str, **kw: Any ) -> None: """Issue a "drop index" instruction using the current batch migration context. .. seealso:: :meth:`.Operations.drop_index` """ op = cls( index_name, table_name=operations.impl.table_name, schema=operations.impl.schema, **kw, ) return operations.invoke(op) @Operations.register_operation("create_table") class CreateTableOp(MigrateOperation): """Represent a create table operation.""" def __init__( self, table_name: str, columns: Sequence[SchemaItem], *, schema: Optional[str] = None, _namespace_metadata: Optional[MetaData] = None, _constraints_included: bool = False, **kw: Any, ) -> None: self.table_name = table_name self.columns = columns self.schema = schema self.info = kw.pop("info", {}) self.comment = kw.pop("comment", None) self.prefixes = kw.pop("prefixes", None) self.kw = kw self._namespace_metadata = _namespace_metadata self._constraints_included = _constraints_included def reverse(self) -> DropTableOp: return DropTableOp.from_table( self.to_table(), _namespace_metadata=self._namespace_metadata ) def to_diff_tuple(self) -> Tuple[str, Table]: return ("add_table", self.to_table()) @classmethod def from_table( cls, table: Table, *, _namespace_metadata: Optional[MetaData] = None ) -> CreateTableOp: if _namespace_metadata is None: _namespace_metadata = table.metadata return cls( table.name, list(table.c) + list(table.constraints), # type:ignore[arg-type] schema=table.schema, _namespace_metadata=_namespace_metadata, # given a Table() object, this Table will contain full Index() # and UniqueConstraint objects already constructed in response to # each unique=True / index=True flag on a Column. Carry this # state along so that when we re-convert back into a Table, we # skip unique=True/index=True so that these constraints are # not doubled up. see #844 #848 _constraints_included=True, comment=table.comment, info=dict(table.info), prefixes=list(table._prefixes), **table.kwargs, ) def to_table( self, migration_context: Optional[MigrationContext] = None ) -> Table: schema_obj = schemaobj.SchemaObjects(migration_context) return schema_obj.table( self.table_name, *self.columns, schema=self.schema, prefixes=list(self.prefixes) if self.prefixes else [], comment=self.comment, info=self.info.copy() if self.info else {}, _constraints_included=self._constraints_included, **self.kw, ) @classmethod def create_table( cls, operations: Operations, table_name: str, *columns: SchemaItem, **kw: Any, ) -> Table: r"""Issue a "create table" instruction using the current migration context. This directive receives an argument list similar to that of the traditional :class:`sqlalchemy.schema.Table` construct, but without the metadata:: from sqlalchemy import INTEGER, VARCHAR, NVARCHAR, Column from alembic import op op.create_table( "account", Column("id", INTEGER, primary_key=True), Column("name", VARCHAR(50), nullable=False), Column("description", NVARCHAR(200)), Column("timestamp", TIMESTAMP, server_default=func.now()), ) Note that :meth:`.create_table` accepts :class:`~sqlalchemy.schema.Column` constructs directly from the SQLAlchemy library. In particular, default values to be created on the database side are specified using the ``server_default`` parameter, and not ``default`` which only specifies Python-side defaults:: from alembic import op from sqlalchemy import Column, TIMESTAMP, func # specify "DEFAULT NOW" along with the "timestamp" column op.create_table( "account", Column("id", INTEGER, primary_key=True), Column("timestamp", TIMESTAMP, server_default=func.now()), ) The function also returns a newly created :class:`~sqlalchemy.schema.Table` object, corresponding to the table specification given, which is suitable for immediate SQL operations, in particular :meth:`.Operations.bulk_insert`:: from sqlalchemy import INTEGER, VARCHAR, NVARCHAR, Column from alembic import op account_table = op.create_table( "account", Column("id", INTEGER, primary_key=True), Column("name", VARCHAR(50), nullable=False), Column("description", NVARCHAR(200)), Column("timestamp", TIMESTAMP, server_default=func.now()), ) op.bulk_insert( account_table, [ {"name": "A1", "description": "account 1"}, {"name": "A2", "description": "account 2"}, ], ) :param table_name: Name of the table :param \*columns: collection of :class:`~sqlalchemy.schema.Column` objects within the table, as well as optional :class:`~sqlalchemy.schema.Constraint` objects and :class:`~.sqlalchemy.schema.Index` objects. :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. :param \**kw: Other keyword arguments are passed to the underlying :class:`sqlalchemy.schema.Table` object created for the command. :return: the :class:`~sqlalchemy.schema.Table` object corresponding to the parameters given. """ op = cls(table_name, columns, **kw) return operations.invoke(op) @Operations.register_operation("drop_table") class DropTableOp(MigrateOperation): """Represent a drop table operation.""" def __init__( self, table_name: str, *, schema: Optional[str] = None, table_kw: Optional[MutableMapping[Any, Any]] = None, _reverse: Optional[CreateTableOp] = None, ) -> None: self.table_name = table_name self.schema = schema self.table_kw = table_kw or {} self.comment = self.table_kw.pop("comment", None) self.info = self.table_kw.pop("info", None) self.prefixes = self.table_kw.pop("prefixes", None) self._reverse = _reverse def to_diff_tuple(self) -> Tuple[str, Table]: return ("remove_table", self.to_table()) def reverse(self) -> CreateTableOp: return CreateTableOp.from_table(self.to_table()) @classmethod def from_table( cls, table: Table, *, _namespace_metadata: Optional[MetaData] = None ) -> DropTableOp: return cls( table.name, schema=table.schema, table_kw={ "comment": table.comment, "info": dict(table.info), "prefixes": list(table._prefixes), **table.kwargs, }, _reverse=CreateTableOp.from_table( table, _namespace_metadata=_namespace_metadata ), ) def to_table( self, migration_context: Optional[MigrationContext] = None ) -> Table: if self._reverse: cols_and_constraints = self._reverse.columns else: cols_and_constraints = [] schema_obj = schemaobj.SchemaObjects(migration_context) t = schema_obj.table( self.table_name, *cols_and_constraints, comment=self.comment, info=self.info.copy() if self.info else {}, prefixes=list(self.prefixes) if self.prefixes else [], schema=self.schema, _constraints_included=self._reverse._constraints_included if self._reverse else False, **self.table_kw, ) return t @classmethod def drop_table( cls, operations: Operations, table_name: str, *, schema: Optional[str] = None, **kw: Any, ) -> None: r"""Issue a "drop table" instruction using the current migration context. e.g.:: drop_table("accounts") :param table_name: Name of the table :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. :param \**kw: Other keyword arguments are passed to the underlying :class:`sqlalchemy.schema.Table` object created for the command. """ op = cls(table_name, schema=schema, table_kw=kw) operations.invoke(op) class AlterTableOp(MigrateOperation): """Represent an alter table operation.""" def __init__( self, table_name: str, *, schema: Optional[str] = None, ) -> None: self.table_name = table_name self.schema = schema @Operations.register_operation("rename_table") class RenameTableOp(AlterTableOp): """Represent a rename table operation.""" def __init__( self, old_table_name: str, new_table_name: str, *, schema: Optional[str] = None, ) -> None: super().__init__(old_table_name, schema=schema) self.new_table_name = new_table_name @classmethod def rename_table( cls, operations: Operations, old_table_name: str, new_table_name: str, *, schema: Optional[str] = None, ) -> None: """Emit an ALTER TABLE to rename a table. :param old_table_name: old name. :param new_table_name: new name. :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. """ op = cls(old_table_name, new_table_name, schema=schema) return operations.invoke(op) @Operations.register_operation("create_table_comment") @BatchOperations.register_operation( "create_table_comment", "batch_create_table_comment" ) class CreateTableCommentOp(AlterTableOp): """Represent a COMMENT ON `table` operation.""" def __init__( self, table_name: str, comment: Optional[str], *, schema: Optional[str] = None, existing_comment: Optional[str] = None, ) -> None: self.table_name = table_name self.comment = comment self.existing_comment = existing_comment self.schema = schema @classmethod def create_table_comment( cls, operations: Operations, table_name: str, comment: Optional[str], *, existing_comment: Optional[str] = None, schema: Optional[str] = None, ) -> None: """Emit a COMMENT ON operation to set the comment for a table. :param table_name: string name of the target table. :param comment: string value of the comment being registered against the specified table. :param existing_comment: String value of a comment already registered on the specified table, used within autogenerate so that the operation is reversible, but not required for direct use. .. seealso:: :meth:`.Operations.drop_table_comment` :paramref:`.Operations.alter_column.comment` """ op = cls( table_name, comment, existing_comment=existing_comment, schema=schema, ) return operations.invoke(op) @classmethod def batch_create_table_comment( cls, operations: BatchOperations, comment: Optional[str], *, existing_comment: Optional[str] = None, ) -> None: """Emit a COMMENT ON operation to set the comment for a table using the current batch migration context. :param comment: string value of the comment being registered against the specified table. :param existing_comment: String value of a comment already registered on the specified table, used within autogenerate so that the operation is reversible, but not required for direct use. """ op = cls( operations.impl.table_name, comment, existing_comment=existing_comment, schema=operations.impl.schema, ) return operations.invoke(op) def reverse(self): """Reverses the COMMENT ON operation against a table.""" if self.existing_comment is None: return DropTableCommentOp( self.table_name, existing_comment=self.comment, schema=self.schema, ) else: return CreateTableCommentOp( self.table_name, self.existing_comment, existing_comment=self.comment, schema=self.schema, ) def to_table(self, migration_context=None): schema_obj = schemaobj.SchemaObjects(migration_context) return schema_obj.table( self.table_name, schema=self.schema, comment=self.comment ) def to_diff_tuple(self): return ("add_table_comment", self.to_table(), self.existing_comment) @Operations.register_operation("drop_table_comment") @BatchOperations.register_operation( "drop_table_comment", "batch_drop_table_comment" ) class DropTableCommentOp(AlterTableOp): """Represent an operation to remove the comment from a table.""" def __init__( self, table_name: str, *, schema: Optional[str] = None, existing_comment: Optional[str] = None, ) -> None: self.table_name = table_name self.existing_comment = existing_comment self.schema = schema @classmethod def drop_table_comment( cls, operations: Operations, table_name: str, *, existing_comment: Optional[str] = None, schema: Optional[str] = None, ) -> None: """Issue a "drop table comment" operation to remove an existing comment set on a table. :param table_name: string name of the target table. :param existing_comment: An optional string value of a comment already registered on the specified table. .. seealso:: :meth:`.Operations.create_table_comment` :paramref:`.Operations.alter_column.comment` """ op = cls(table_name, existing_comment=existing_comment, schema=schema) return operations.invoke(op) @classmethod def batch_drop_table_comment( cls, operations: BatchOperations, *, existing_comment: Optional[str] = None, ) -> None: """Issue a "drop table comment" operation to remove an existing comment set on a table using the current batch operations context. :param existing_comment: An optional string value of a comment already registered on the specified table. """ op = cls( operations.impl.table_name, existing_comment=existing_comment, schema=operations.impl.schema, ) return operations.invoke(op) def reverse(self): """Reverses the COMMENT ON operation against a table.""" return CreateTableCommentOp( self.table_name, self.existing_comment, schema=self.schema ) def to_table(self, migration_context=None): schema_obj = schemaobj.SchemaObjects(migration_context) return schema_obj.table(self.table_name, schema=self.schema) def to_diff_tuple(self): return ("remove_table_comment", self.to_table()) @Operations.register_operation("alter_column") @BatchOperations.register_operation("alter_column", "batch_alter_column") class AlterColumnOp(AlterTableOp): """Represent an alter column operation.""" def __init__( self, table_name: str, column_name: str, *, schema: Optional[str] = None, existing_type: Optional[Any] = None, existing_server_default: Any = False, existing_nullable: Optional[bool] = None, existing_comment: Optional[str] = None, modify_nullable: Optional[bool] = None, modify_comment: Optional[Union[str, Literal[False]]] = False, modify_server_default: Any = False, modify_name: Optional[str] = None, modify_type: Optional[Any] = None, **kw: Any, ) -> None: super().__init__(table_name, schema=schema) self.column_name = column_name self.existing_type = existing_type self.existing_server_default = existing_server_default self.existing_nullable = existing_nullable self.existing_comment = existing_comment self.modify_nullable = modify_nullable self.modify_comment = modify_comment self.modify_server_default = modify_server_default self.modify_name = modify_name self.modify_type = modify_type self.kw = kw def to_diff_tuple(self) -> Any: col_diff = [] schema, tname, cname = self.schema, self.table_name, self.column_name if self.modify_type is not None: col_diff.append( ( "modify_type", schema, tname, cname, { "existing_nullable": self.existing_nullable, "existing_server_default": ( self.existing_server_default ), "existing_comment": self.existing_comment, }, self.existing_type, self.modify_type, ) ) if self.modify_nullable is not None: col_diff.append( ( "modify_nullable", schema, tname, cname, { "existing_type": self.existing_type, "existing_server_default": ( self.existing_server_default ), "existing_comment": self.existing_comment, }, self.existing_nullable, self.modify_nullable, ) ) if self.modify_server_default is not False: col_diff.append( ( "modify_default", schema, tname, cname, { "existing_nullable": self.existing_nullable, "existing_type": self.existing_type, "existing_comment": self.existing_comment, }, self.existing_server_default, self.modify_server_default, ) ) if self.modify_comment is not False: col_diff.append( ( "modify_comment", schema, tname, cname, { "existing_nullable": self.existing_nullable, "existing_type": self.existing_type, "existing_server_default": ( self.existing_server_default ), }, self.existing_comment, self.modify_comment, ) ) return col_diff def has_changes(self) -> bool: hc1 = ( self.modify_nullable is not None or self.modify_server_default is not False or self.modify_type is not None or self.modify_comment is not False ) if hc1: return True for kw in self.kw: if kw.startswith("modify_"): return True else: return False def reverse(self) -> AlterColumnOp: kw = self.kw.copy() kw["existing_type"] = self.existing_type kw["existing_nullable"] = self.existing_nullable kw["existing_server_default"] = self.existing_server_default kw["existing_comment"] = self.existing_comment if self.modify_type is not None: kw["modify_type"] = self.modify_type if self.modify_nullable is not None: kw["modify_nullable"] = self.modify_nullable if self.modify_server_default is not False: kw["modify_server_default"] = self.modify_server_default if self.modify_comment is not False: kw["modify_comment"] = self.modify_comment # TODO: make this a little simpler all_keys = { m.group(1) for m in [re.match(r"^(?:existing_|modify_)(.+)$", k) for k in kw] if m } for k in all_keys: if "modify_%s" % k in kw: swap = kw["existing_%s" % k] kw["existing_%s" % k] = kw["modify_%s" % k] kw["modify_%s" % k] = swap return self.__class__( self.table_name, self.column_name, schema=self.schema, **kw ) @classmethod def alter_column( cls, operations: Operations, table_name: str, column_name: str, *, nullable: Optional[bool] = None, comment: Optional[Union[str, Literal[False]]] = False, server_default: Any = False, new_column_name: Optional[str] = None, type_: Optional[Union[TypeEngine, Type[TypeEngine]]] = None, existing_type: Optional[Union[TypeEngine, Type[TypeEngine]]] = None, existing_server_default: Optional[ Union[str, bool, Identity, Computed] ] = False, existing_nullable: Optional[bool] = None, existing_comment: Optional[str] = None, schema: Optional[str] = None, **kw: Any, ) -> None: r"""Issue an "alter column" instruction using the current migration context. Generally, only that aspect of the column which is being changed, i.e. name, type, nullability, default, needs to be specified. Multiple changes can also be specified at once and the backend should "do the right thing", emitting each change either separately or together as the backend allows. MySQL has special requirements here, since MySQL cannot ALTER a column without a full specification. When producing MySQL-compatible migration files, it is recommended that the ``existing_type``, ``existing_server_default``, and ``existing_nullable`` parameters be present, if not being altered. Type changes which are against the SQLAlchemy "schema" types :class:`~sqlalchemy.types.Boolean` and :class:`~sqlalchemy.types.Enum` may also add or drop constraints which accompany those types on backends that don't support them natively. The ``existing_type`` argument is used in this case to identify and remove a previous constraint that was bound to the type object. :param table_name: string name of the target table. :param column_name: string name of the target column, as it exists before the operation begins. :param nullable: Optional; specify ``True`` or ``False`` to alter the column's nullability. :param server_default: Optional; specify a string SQL expression, :func:`~sqlalchemy.sql.expression.text`, or :class:`~sqlalchemy.schema.DefaultClause` to indicate an alteration to the column's default value. Set to ``None`` to have the default removed. :param comment: optional string text of a new comment to add to the column. :param new_column_name: Optional; specify a string name here to indicate the new name within a column rename operation. :param type\_: Optional; a :class:`~sqlalchemy.types.TypeEngine` type object to specify a change to the column's type. For SQLAlchemy types that also indicate a constraint (i.e. :class:`~sqlalchemy.types.Boolean`, :class:`~sqlalchemy.types.Enum`), the constraint is also generated. :param autoincrement: set the ``AUTO_INCREMENT`` flag of the column; currently understood by the MySQL dialect. :param existing_type: Optional; a :class:`~sqlalchemy.types.TypeEngine` type object to specify the previous type. This is required for all MySQL column alter operations that don't otherwise specify a new type, as well as for when nullability is being changed on a SQL Server column. It is also used if the type is a so-called SQLlchemy "schema" type which may define a constraint (i.e. :class:`~sqlalchemy.types.Boolean`, :class:`~sqlalchemy.types.Enum`), so that the constraint can be dropped. :param existing_server_default: Optional; The existing default value of the column. Required on MySQL if an existing default is not being changed; else MySQL removes the default. :param existing_nullable: Optional; the existing nullability of the column. Required on MySQL if the existing nullability is not being changed; else MySQL sets this to NULL. :param existing_autoincrement: Optional; the existing autoincrement of the column. Used for MySQL's system of altering a column that specifies ``AUTO_INCREMENT``. :param existing_comment: string text of the existing comment on the column to be maintained. Required on MySQL if the existing comment on the column is not being changed. :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. :param postgresql_using: String argument which will indicate a SQL expression to render within the Postgresql-specific USING clause within ALTER COLUMN. This string is taken directly as raw SQL which must explicitly include any necessary quoting or escaping of tokens within the expression. """ alt = cls( table_name, column_name, schema=schema, existing_type=existing_type, existing_server_default=existing_server_default, existing_nullable=existing_nullable, existing_comment=existing_comment, modify_name=new_column_name, modify_type=type_, modify_server_default=server_default, modify_nullable=nullable, modify_comment=comment, **kw, ) return operations.invoke(alt) @classmethod def batch_alter_column( cls, operations: BatchOperations, column_name: str, *, nullable: Optional[bool] = None, comment: Optional[Union[str, Literal[False]]] = False, server_default: Any = False, new_column_name: Optional[str] = None, type_: Optional[Union[TypeEngine, Type[TypeEngine]]] = None, existing_type: Optional[Union[TypeEngine, Type[TypeEngine]]] = None, existing_server_default: Optional[ Union[str, bool, Identity, Computed] ] = False, existing_nullable: Optional[bool] = None, existing_comment: Optional[str] = None, insert_before: Optional[str] = None, insert_after: Optional[str] = None, **kw: Any, ) -> None: """Issue an "alter column" instruction using the current batch migration context. Parameters are the same as that of :meth:`.Operations.alter_column`, as well as the following option(s): :param insert_before: String name of an existing column which this column should be placed before, when creating the new table. :param insert_after: String name of an existing column which this column should be placed after, when creating the new table. If both :paramref:`.BatchOperations.alter_column.insert_before` and :paramref:`.BatchOperations.alter_column.insert_after` are omitted, the column is inserted after the last existing column in the table. .. seealso:: :meth:`.Operations.alter_column` """ alt = cls( operations.impl.table_name, column_name, schema=operations.impl.schema, existing_type=existing_type, existing_server_default=existing_server_default, existing_nullable=existing_nullable, existing_comment=existing_comment, modify_name=new_column_name, modify_type=type_, modify_server_default=server_default, modify_nullable=nullable, modify_comment=comment, insert_before=insert_before, insert_after=insert_after, **kw, ) return operations.invoke(alt) @Operations.register_operation("add_column") @BatchOperations.register_operation("add_column", "batch_add_column") class AddColumnOp(AlterTableOp): """Represent an add column operation.""" def __init__( self, table_name: str, column: Column[Any], *, schema: Optional[str] = None, **kw: Any, ) -> None: super().__init__(table_name, schema=schema) self.column = column self.kw = kw def reverse(self) -> DropColumnOp: return DropColumnOp.from_column_and_tablename( self.schema, self.table_name, self.column ) def to_diff_tuple( self, ) -> Tuple[str, Optional[str], str, Column[Any]]: return ("add_column", self.schema, self.table_name, self.column) def to_column(self) -> Column: return self.column @classmethod def from_column(cls, col: Column) -> AddColumnOp: return cls(col.table.name, col, schema=col.table.schema) @classmethod def from_column_and_tablename( cls, schema: Optional[str], tname: str, col: Column[Any], ) -> AddColumnOp: return cls(tname, col, schema=schema) @classmethod def add_column( cls, operations: Operations, table_name: str, column: Column[Any], *, schema: Optional[str] = None, ) -> None: """Issue an "add column" instruction using the current migration context. e.g.:: from alembic import op from sqlalchemy import Column, String op.add_column("organization", Column("name", String())) The :meth:`.Operations.add_column` method typically corresponds to the SQL command "ALTER TABLE... ADD COLUMN". Within the scope of this command, the column's name, datatype, nullability, and optional server-generated defaults may be indicated. .. note:: With the exception of NOT NULL constraints or single-column FOREIGN KEY constraints, other kinds of constraints such as PRIMARY KEY, UNIQUE or CHECK constraints **cannot** be generated using this method; for these constraints, refer to operations such as :meth:`.Operations.create_primary_key` and :meth:`.Operations.create_check_constraint`. In particular, the following :class:`~sqlalchemy.schema.Column` parameters are **ignored**: * :paramref:`~sqlalchemy.schema.Column.primary_key` - SQL databases typically do not support an ALTER operation that can add individual columns one at a time to an existing primary key constraint, therefore it's less ambiguous to use the :meth:`.Operations.create_primary_key` method, which assumes no existing primary key constraint is present. * :paramref:`~sqlalchemy.schema.Column.unique` - use the :meth:`.Operations.create_unique_constraint` method * :paramref:`~sqlalchemy.schema.Column.index` - use the :meth:`.Operations.create_index` method The provided :class:`~sqlalchemy.schema.Column` object may include a :class:`~sqlalchemy.schema.ForeignKey` constraint directive, referencing a remote table name. For this specific type of constraint, Alembic will automatically emit a second ALTER statement in order to add the single-column FOREIGN KEY constraint separately:: from alembic import op from sqlalchemy import Column, INTEGER, ForeignKey op.add_column( "organization", Column("account_id", INTEGER, ForeignKey("accounts.id")), ) The column argument passed to :meth:`.Operations.add_column` is a :class:`~sqlalchemy.schema.Column` construct, used in the same way it's used in SQLAlchemy. In particular, values or functions to be indicated as producing the column's default value on the database side are specified using the ``server_default`` parameter, and not ``default`` which only specifies Python-side defaults:: from alembic import op from sqlalchemy import Column, TIMESTAMP, func # specify "DEFAULT NOW" along with the column add op.add_column( "account", Column("timestamp", TIMESTAMP, server_default=func.now()), ) :param table_name: String name of the parent table. :param column: a :class:`sqlalchemy.schema.Column` object representing the new column. :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. """ op = cls(table_name, column, schema=schema) return operations.invoke(op) @classmethod def batch_add_column( cls, operations: BatchOperations, column: Column[Any], *, insert_before: Optional[str] = None, insert_after: Optional[str] = None, ) -> None: """Issue an "add column" instruction using the current batch migration context. .. seealso:: :meth:`.Operations.add_column` """ kw = {} if insert_before: kw["insert_before"] = insert_before if insert_after: kw["insert_after"] = insert_after op = cls( operations.impl.table_name, column, schema=operations.impl.schema, **kw, ) return operations.invoke(op) @Operations.register_operation("drop_column") @BatchOperations.register_operation("drop_column", "batch_drop_column") class DropColumnOp(AlterTableOp): """Represent a drop column operation.""" def __init__( self, table_name: str, column_name: str, *, schema: Optional[str] = None, _reverse: Optional[AddColumnOp] = None, **kw: Any, ) -> None: super().__init__(table_name, schema=schema) self.column_name = column_name self.kw = kw self._reverse = _reverse def to_diff_tuple( self, ) -> Tuple[str, Optional[str], str, Column[Any]]: return ( "remove_column", self.schema, self.table_name, self.to_column(), ) def reverse(self) -> AddColumnOp: if self._reverse is None: raise ValueError( "operation is not reversible; " "original column is not present" ) return AddColumnOp.from_column_and_tablename( self.schema, self.table_name, self._reverse.column ) @classmethod def from_column_and_tablename( cls, schema: Optional[str], tname: str, col: Column[Any], ) -> DropColumnOp: return cls( tname, col.name, schema=schema, _reverse=AddColumnOp.from_column_and_tablename(schema, tname, col), ) def to_column( self, migration_context: Optional[MigrationContext] = None ) -> Column: if self._reverse is not None: return self._reverse.column schema_obj = schemaobj.SchemaObjects(migration_context) return schema_obj.column(self.column_name, NULLTYPE) @classmethod def drop_column( cls, operations: Operations, table_name: str, column_name: str, *, schema: Optional[str] = None, **kw: Any, ) -> None: """Issue a "drop column" instruction using the current migration context. e.g.:: drop_column("organization", "account_id") :param table_name: name of table :param column_name: name of column :param schema: Optional schema name to operate within. To control quoting of the schema outside of the default behavior, use the SQLAlchemy construct :class:`~sqlalchemy.sql.elements.quoted_name`. :param mssql_drop_check: Optional boolean. When ``True``, on Microsoft SQL Server only, first drop the CHECK constraint on the column using a SQL-script-compatible block that selects into a @variable from sys.check_constraints, then exec's a separate DROP CONSTRAINT for that constraint. :param mssql_drop_default: Optional boolean. When ``True``, on Microsoft SQL Server only, first drop the DEFAULT constraint on the column using a SQL-script-compatible block that selects into a @variable from sys.default_constraints, then exec's a separate DROP CONSTRAINT for that default. :param mssql_drop_foreign_key: Optional boolean. When ``True``, on Microsoft SQL Server only, first drop a single FOREIGN KEY constraint on the column using a SQL-script-compatible block that selects into a @variable from sys.foreign_keys/sys.foreign_key_columns, then exec's a separate DROP CONSTRAINT for that default. Only works if the column has exactly one FK constraint which refers to it, at the moment. """ op = cls(table_name, column_name, schema=schema, **kw) return operations.invoke(op) @classmethod def batch_drop_column( cls, operations: BatchOperations, column_name: str, **kw: Any ) -> None: """Issue a "drop column" instruction using the current batch migration context. .. seealso:: :meth:`.Operations.drop_column` """ op = cls( operations.impl.table_name, column_name, schema=operations.impl.schema, **kw, ) return operations.invoke(op) @Operations.register_operation("bulk_insert") class BulkInsertOp(MigrateOperation): """Represent a bulk insert operation.""" def __init__( self, table: Union[Table, TableClause], rows: List[dict], *, multiinsert: bool = True, ) -> None: self.table = table self.rows = rows self.multiinsert = multiinsert @classmethod def bulk_insert( cls, operations: Operations, table: Union[Table, TableClause], rows: List[dict], *, multiinsert: bool = True, ) -> None: """Issue a "bulk insert" operation using the current migration context. This provides a means of representing an INSERT of multiple rows which works equally well in the context of executing on a live connection as well as that of generating a SQL script. In the case of a SQL script, the values are rendered inline into the statement. e.g.:: from alembic import op from datetime import date from sqlalchemy.sql import table, column from sqlalchemy import String, Integer, Date # Create an ad-hoc table to use for the insert statement. accounts_table = table( "account", column("id", Integer), column("name", String), column("create_date", Date), ) op.bulk_insert( accounts_table, [ { "id": 1, "name": "John Smith", "create_date": date(2010, 10, 5), }, { "id": 2, "name": "Ed Williams", "create_date": date(2007, 5, 27), }, { "id": 3, "name": "Wendy Jones", "create_date": date(2008, 8, 15), }, ], ) When using --sql mode, some datatypes may not render inline automatically, such as dates and other special types. When this issue is present, :meth:`.Operations.inline_literal` may be used:: op.bulk_insert( accounts_table, [ { "id": 1, "name": "John Smith", "create_date": op.inline_literal("2010-10-05"), }, { "id": 2, "name": "Ed Williams", "create_date": op.inline_literal("2007-05-27"), }, { "id": 3, "name": "Wendy Jones", "create_date": op.inline_literal("2008-08-15"), }, ], multiinsert=False, ) When using :meth:`.Operations.inline_literal` in conjunction with :meth:`.Operations.bulk_insert`, in order for the statement to work in "online" (e.g. non --sql) mode, the :paramref:`~.Operations.bulk_insert.multiinsert` flag should be set to ``False``, which will have the effect of individual INSERT statements being emitted to the database, each with a distinct VALUES clause, so that the "inline" values can still be rendered, rather than attempting to pass the values as bound parameters. :param table: a table object which represents the target of the INSERT. :param rows: a list of dictionaries indicating rows. :param multiinsert: when at its default of True and --sql mode is not enabled, the INSERT statement will be executed using "executemany()" style, where all elements in the list of dictionaries are passed as bound parameters in a single list. Setting this to False results in individual INSERT statements being emitted per parameter set, and is needed in those cases where non-literal values are present in the parameter sets. """ op = cls(table, rows, multiinsert=multiinsert) operations.invoke(op) @Operations.register_operation("execute") @BatchOperations.register_operation("execute", "batch_execute") class ExecuteSQLOp(MigrateOperation): """Represent an execute SQL operation.""" def __init__( self, sqltext: Union[Update, str, Insert, TextClause], *, execution_options: Optional[dict[str, Any]] = None, ) -> None: self.sqltext = sqltext self.execution_options = execution_options @classmethod def execute( cls, operations: Operations, sqltext: Union[str, TextClause, Update], *, execution_options: Optional[dict[str, Any]] = None, ) -> None: r"""Execute the given SQL using the current migration context. The given SQL can be a plain string, e.g.:: op.execute("INSERT INTO table (foo) VALUES ('some value')") Or it can be any kind of Core SQL Expression construct, such as below where we use an update construct:: from sqlalchemy.sql import table, column from sqlalchemy import String from alembic import op account = table("account", column("name", String)) op.execute( account.update() .where(account.c.name == op.inline_literal("account 1")) .values({"name": op.inline_literal("account 2")}) ) Above, we made use of the SQLAlchemy :func:`sqlalchemy.sql.expression.table` and :func:`sqlalchemy.sql.expression.column` constructs to make a brief, ad-hoc table construct just for our UPDATE statement. A full :class:`~sqlalchemy.schema.Table` construct of course works perfectly fine as well, though note it's a recommended practice to at least ensure the definition of a table is self-contained within the migration script, rather than imported from a module that may break compatibility with older migrations. In a SQL script context, the statement is emitted directly to the output stream. There is *no* return result, however, as this function is oriented towards generating a change script that can run in "offline" mode. Additionally, parameterized statements are discouraged here, as they *will not work* in offline mode. Above, we use :meth:`.inline_literal` where parameters are to be used. For full interaction with a connected database where parameters can also be used normally, use the "bind" available from the context:: from alembic import op connection = op.get_bind() connection.execute( account.update() .where(account.c.name == "account 1") .values({"name": "account 2"}) ) Additionally, when passing the statement as a plain string, it is first coerceed into a :func:`sqlalchemy.sql.expression.text` construct before being passed along. In the less likely case that the literal SQL string contains a colon, it must be escaped with a backslash, as:: op.execute(r"INSERT INTO table (foo) VALUES ('\:colon_value')") :param sqltext: Any legal SQLAlchemy expression, including: * a string * a :func:`sqlalchemy.sql.expression.text` construct. * a :func:`sqlalchemy.sql.expression.insert` construct. * a :func:`sqlalchemy.sql.expression.update`, :func:`sqlalchemy.sql.expression.insert`, or :func:`sqlalchemy.sql.expression.delete` construct. * Any "executable" described in SQLAlchemy Core documentation, noting that no result set is returned. .. note:: when passing a plain string, the statement is coerced into a :func:`sqlalchemy.sql.expression.text` construct. This construct considers symbols with colons, e.g. ``:foo`` to be bound parameters. To avoid this, ensure that colon symbols are escaped, e.g. ``\:foo``. :param execution_options: Optional dictionary of execution options, will be passed to :meth:`sqlalchemy.engine.Connection.execution_options`. """ op = cls(sqltext, execution_options=execution_options) return operations.invoke(op) @classmethod def batch_execute( cls, operations: Operations, sqltext: Union[str, TextClause, Update], *, execution_options: Optional[dict[str, Any]] = None, ) -> None: """Execute the given SQL using the current migration context. .. seealso:: :meth:`.Operations.execute` """ return cls.execute( operations, sqltext, execution_options=execution_options ) class OpContainer(MigrateOperation): """Represent a sequence of operations operation.""" def __init__(self, ops: Sequence[MigrateOperation] = ()) -> None: self.ops = list(ops) def is_empty(self) -> bool: return not self.ops def as_diffs(self) -> Any: return list(OpContainer._ops_as_diffs(self)) @classmethod def _ops_as_diffs( cls, migrations: OpContainer ) -> Iterator[Tuple[Any,...]]: for op in migrations.ops: if hasattr(op, "ops"): yield from cls._ops_as_diffs(cast("OpContainer", op)) else: yield op.to_diff_tuple() class ModifyTableOps(OpContainer): """Contains a sequence of operations that all apply to a single Table.""" def __init__( self, table_name: str, ops: Sequence[MigrateOperation], *, schema: Optional[str] = None, ) -> None: super().__init__(ops) self.table_name = table_name self.schema = schema def reverse(self) -> ModifyTableOps: return ModifyTableOps( self.table_name, ops=list(reversed([op.reverse() for op in self.ops])), schema=self.schema, ) class UpgradeOps(OpContainer): """contains a sequence of operations that would apply to the 'upgrade' stream of a script. .. seealso:: :ref:`customizing_revision` """ def __init__( self, ops: Sequence[MigrateOperation] = (), upgrade_token: str = "upgrades", ) -> None: super().__init__(ops=ops) self.upgrade_token = upgrade_token def reverse_into(self, downgrade_ops: DowngradeOps) -> DowngradeOps: downgrade_ops.ops[:] = list( # type:ignore[index] reversed([op.reverse() for op in self.ops]) ) return downgrade_ops def reverse(self) -> DowngradeOps: return self.reverse_into(DowngradeOps(ops=[])) class DowngradeOps(OpContainer): """contains a sequence of operations that would apply to the 'downgrade' stream of a script. .. seealso:: :ref:`customizing_revision` """ def __init__( self, ops: Sequence[MigrateOperation] = (), downgrade_token: str = "downgrades", ) -> None: super().__init__(ops=ops) self.downgrade_token = downgrade_token def reverse(self): return UpgradeOps( ops=list(reversed([op.reverse() for op in self.ops])) ) class MigrationScript(MigrateOperation): """represents a migration script. E.g. when autogenerate encounters this object, this corresponds to the production of an actual script file. A normal :class:`.MigrationScript` object would contain a single :class:`.UpgradeOps` and a single :class:`.DowngradeOps` directive. These are accessible via the ``.upgrade_ops`` and ``.downgrade_ops`` attributes. In the case of an autogenerate operation that runs multiple times, such as the multiple database example in the "multidb" template, the ``.upgrade_ops`` and ``.downgrade_ops`` attributes are disabled, and instead these objects should be accessed via the ``.upgrade_ops_list`` and ``.downgrade_ops_list`` list-based attributes. These latter attributes are always available at the very least as single-element lists. .. seealso:: :ref:`customizing_revision` """ _needs_render: Optional[bool] def __init__( self, rev_id: Optional[str], upgrade_ops: UpgradeOps, downgrade_ops: DowngradeOps, *, message: Optional[str] = None, imports: Set[str] = set(), head: Optional[str] = None, splice: Optional[bool] = None, branch_label: Optional[_RevIdType] = None, version_path: Optional[str] = None, depends_on: Optional[_RevIdType] = None, ) -> None: self.rev_id = rev_id self.message = message self.imports = imports self.head = head self.splice = splice self.branch_label = branch_label self.version_path = version_path self.depends_on = depends_on self.upgrade_ops = upgrade_ops self.downgrade_ops = downgrade_ops @property def upgrade_ops(self): """An instance of :class:`.UpgradeOps`. .. seealso:: :attr:`.MigrationScript.upgrade_ops_list` """ if len(self._upgrade_ops) > 1: raise ValueError( "This MigrationScript instance has a multiple-entry " "list for UpgradeOps; please use the " "upgrade_ops_list attribute." ) elif not self._upgrade_ops: return None else: return self._upgrade_ops[0] @upgrade_ops.setter def upgrade_ops(self, upgrade_ops): self._upgrade_ops = util.to_list(upgrade_ops) for elem in self._upgrade_ops: assert isinstance(elem, UpgradeOps) @property def downgrade_ops(self): """An instance of :class:`.DowngradeOps`. .. seealso:: :attr:`.MigrationScript.downgrade_ops_list` """ if len(self._downgrade_ops) > 1: raise ValueError( "This MigrationScript instance has a multiple-entry " "list for DowngradeOps; please use the " "downgrade_ops_list attribute." ) elif not self._downgrade_ops: return None else: return self._downgrade_ops[0] @downgrade_ops.setter def downgrade_ops(self, downgrade_ops): self._downgrade_ops = util.to_list(downgrade_ops) for elem in self._downgrade_ops: assert isinstance(elem, DowngradeOps) @property def upgrade_ops_list(self) -> List[UpgradeOps]: """A list of :class:`.UpgradeOps` instances. This is used in place of the :attr:`.MigrationScript.upgrade_ops` attribute when dealing with a revision operation that does multiple autogenerate passes. """ return self._upgrade_ops @property def downgrade_ops_list(self) -> List[DowngradeOps]: """A list of :class:`.DowngradeOps` instances. This is used in place of the :attr:`.MigrationScript.downgrade_ops` attribute when dealing with a revision operation that does multiple autogenerate passes. """ return self._downgrade_ops

scikit-learn__scikit-learn

calibration.rst

Tutorial

Generate tutorial about probability calibration

BSD 3-Clause New or Revised License

scikit-learn__scikit-learn/doc/modules/calibration.rst

scikit-learn__scikit-learn/sklearn/ensemble

Probability calibration When performing classification you often want not only to predict the class label, but also obtain a probability of the respective label. This probability gives you some kind of confidence on the prediction. Some models can give you poor estimates of the class probabilities and some even do not support probability prediction (e.g., some instances of ~sklearn.linear_model.SGDClassifier). The calibration module allows you to better calibrate the probabilities of a given model, or to add support for probability prediction. Well calibrated classifiers are probabilistic classifiers for which the output of the predict_proba method can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that among the samples to which it gave a predict_proba value close to, say, 0.8, approximately 80% actually belong to the positive class. Before we show how to re-calibrate a classifier, we first need a way to detect how good a classifier is calibrated. Note Strictly proper scoring rules for probabilistic predictions like sklearn.metrics.brier_score_loss and sklearn.metrics.log_loss assess calibration (reliability) and discriminative power (resolution) of a model, as well as the randomness of the data (uncertainty) at the same time. This follows from the well-known Brier score decomposition of Murphy. As it is not clear which term dominates, the score is of limited use for assessing calibration alone (unless one computes each term of the decomposition). A lower Brier loss, for instance, does not necessarily mean a better calibrated model, it could also mean a worse calibrated model with much more discriminatory power, e.g. using many more features. Calibration curves Calibration curves, also referred to as reliability diagrams (Wilks 1995), compare how well the probabilistic predictions of a binary classifier are calibrated. It plots the frequency of the positive label (to be more precise, an estimation of the conditional event probability P(Y=1|predict_proba)) on the y-axis against the predicted probability predict_proba of a model on the x-axis. The tricky part is to get values for the y-axis. In scikit-learn, this is accomplished by binning the predictions such that the x-axis represents the average predicted probability in each bin. The y-axis is then the fraction of positives given the predictions of that bin, i.e. the proportion of samples whose class is the positive class (in each bin). The top calibration curve plot is created with CalibrationDisplay.from_estimator, which uses calibration_curve to calculate the per bin average predicted probabilities and fraction of positives. CalibrationDisplay.from_estimator takes as input a fitted classifier, which is used to calculate the predicted probabilities. The classifier thus must have predict_proba method. For the few classifiers that do not have a predict_proba method, it is possible to use CalibratedClassifierCV to calibrate the classifier outputs to probabilities. The bottom histogram gives some insight into the behavior of each classifier by showing the number of samples in each predicted probability bin. LogisticRegression returns well calibrated predictions by default as it has a canonical link function for its loss, i.e. the logit-link for the log_loss. This leads to the so-called balance property, see and Logistic_regression. In contrast to that, the other shown models return biased probabilities; with different biases per model. GaussianNB (Naive Bayes) tends to push probabilities to 0 or 1 (note the counts in the histograms). This is mainly because it makes the assumption that features are conditionally independent given the class, which is not the case in this dataset which contains 2 redundant features. RandomForestClassifier shows the opposite behavior: the histograms show peaks at probabilities approximately 0.2 and 0.9, while probabilities close to 0 or 1 are very rare. An explanation for this is given by Niculescu-Mizil and Caruana: "Methods such as bagging and random forests that average predictions from a base set of models can have difficulty making predictions near 0 and 1 because variance in the underlying base models will bias predictions that should be near zero or one away from these values. Because predictions are restricted to the interval [0,1], errors caused by variance tend to be one-sided near zero and one. For example, if a model should predict p = 0 for a case, the only way bagging can achieve this is if all bagged trees predict zero. If we add noise to the trees that bagging is averaging over, this noise will cause some trees to predict values larger than 0 for this case, thus moving the average prediction of the bagged ensemble away from 0. We observe this effect most strongly with random forests because the base-level trees trained with random forests have relatively high variance due to feature subsetting." As a result, the calibration curve shows a characteristic sigmoid shape, indicating that the classifier could trust its "intuition" more and return probabilities closer to 0 or 1 typically. LinearSVC (SVC) shows an even more sigmoid curve than the random forest, which is typical for maximum-margin methods (compare Niculescu-Mizil and Caruana), which focus on difficult to classify samples that are close to the decision boundary (the support vectors). Calibrating a classifier Calibrating a classifier consists of fitting a regressor (called a calibrator) that maps the output of the classifier (as given by decision_function or predict_proba) to a calibrated probability in [0, 1]. Denoting the output of the classifier for a given sample by f_(i), the calibrator tries to predict the conditional event probability P(y_(i)=1|f_(i)). Ideally, the calibrator is fit on a dataset independent of the training data used to fit the classifier in the first place. This is because performance of the classifier on its training data would be better than for novel data. Using the classifier output of training data to fit the calibrator would thus result in a biased calibrator that maps to probabilities closer to 0 and 1 than it should. Usage The CalibratedClassifierCV class is used to calibrate a classifier. CalibratedClassifierCV uses a cross-validation approach to ensure unbiased data is always used to fit the calibrator. The data is split into k (train_set, test_set) couples (as determined by cv). When ensemble=True (default), the following procedure is repeated independently for each cross-validation split: a clone of base_estimator is first trained on the train subset. Then its predictions on the test subset are used to fit a calibrator (either a sigmoid or isotonic regressor). This results in an ensemble of k (classifier, calibrator) couples where each calibrator maps the output of its corresponding classifier into [0, 1]. Each couple is exposed in the calibrated_classifiers_ attribute, where each entry is a calibrated classifier with a predict_proba method that outputs calibrated probabilities. The output of predict_proba for the main CalibratedClassifierCV instance corresponds to the average of the predicted probabilities of the k estimators in the calibrated_classifiers_ list. The output of predict is the class that has the highest probability. When ensemble=False, cross-validation is used to obtain 'unbiased' predictions for all the data, via ~sklearn.model_selection.cross_val_predict. These unbiased predictions are then used to train the calibrator. The attribute calibrated_classifiers_ consists of only one (classifier, calibrator) couple where the classifier is the base_estimator trained on all the data. In this case the output of predict_proba for CalibratedClassifierCV is the predicted probabilities obtained from the single (classifier, calibrator) couple. The main advantage of ensemble=True is to benefit from the traditional ensembling effect (similar to bagging). The resulting ensemble should both be well calibrated and slightly more accurate than with ensemble=False. The main advantage of using ensemble=False is computational: it reduces the overall fit time by training only a single base classifier and calibrator pair, decreases the final model size and increases prediction speed. Alternatively an already fitted classifier can be calibrated by setting cv="prefit". In this case, the data is not split and all of it is used to fit the regressor. It is up to the user to make sure that the data used for fitting the classifier is disjoint from the data used for fitting the regressor. CalibratedClassifierCV supports the use of two regression techniques for calibration via the method parameter: "sigmoid" and "isotonic". Sigmoid The sigmoid regressor, method="sigmoid" is based on Platt's logistic model: $$p(y_i = 1 | f_i) = \frac{1}{1 + \exp(A f_i + B)} \,,$$ where y_(i) is the true label of sample i and f_(i) is the output of the un-calibrated classifier for sample i. A and B are real numbers to be determined when fitting the regressor via maximum likelihood. The sigmoid method assumes the calibration curve <calibration_curve> can be corrected by applying a sigmoid function to the raw predictions. This assumption has been empirically justified in the case of svm with common kernel functions on various benchmark datasets in section 2.1 of Platt 1999 but does not necessarily hold in general. Additionally, the logistic model works best if the calibration error is symmetrical, meaning the classifier output for each binary class is normally distributed with the same variance. This can be a problem for highly imbalanced classification problems, where outputs do not have equal variance. In general this method is most effective for small sample sizes or when the un-calibrated model is under-confident and has similar calibration errors for both high and low outputs. Isotonic The method="isotonic" fits a non-parametric isotonic regressor, which outputs a step-wise non-decreasing function, see sklearn.isotonic. It minimizes: $$\sum_{i=1}^{n} (y_i - \hat{f}_i)^2$$ subject to f̂_(i) ≥ f̂_(j) whenever f_(i) ≥ f_(j). y_(i) is the true label of sample i and f̂_(i) is the output of the calibrated classifier for sample i (i.e., the calibrated probability). This method is more general when compared to 'sigmoid' as the only restriction is that the mapping function is monotonically increasing. It is thus more powerful as it can correct any monotonic distortion of the un-calibrated model. However, it is more prone to overfitting, especially on small datasets. Overall, 'isotonic' will perform as well as or better than 'sigmoid' when there is enough data (greater than ~ 1000 samples) to avoid overfitting. Note Impact on ranking metrics like AUC It is generally expected that calibration does not affect ranking metrics such as ROC-AUC. However, these metrics might differ after calibration when using method="isotonic" since isotonic regression introduces ties in the predicted probabilities. This can be seen as within the uncertainty of the model predictions. In case, you strictly want to keep the ranking and thus AUC scores, use method="logistic" which is a strictly monotonic transformation and thus keeps the ranking. Multiclass support Both isotonic and sigmoid regressors only support 1-dimensional data (e.g., binary classification output) but are extended for multiclass classification if the base_estimator supports multiclass predictions. For multiclass predictions, CalibratedClassifierCV calibrates for each class separately in a ovr_classification fashion. When predicting probabilities, the calibrated probabilities for each class are predicted separately. As those probabilities do not necessarily sum to one, a postprocessing is performed to normalize them.

"""Calibration of predicted probabilities.""" # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr> # Balazs Kegl <balazs.kegl@gmail.com> # Jan Hendrik Metzen <jhm@informatik.uni-bremen.de> # Mathieu Blondel <mathieu@mblondel.org> # # License: BSD 3 clause import warnings from functools import partial from inspect import signature from math import log from numbers import Integral, Real import numpy as np from scipy.optimize import minimize from scipy.special import expit from sklearn.utils import Bunch from._loss import HalfBinomialLoss from.base import ( BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone, ) from.isotonic import IsotonicRegression from.model_selection import check_cv, cross_val_predict from.preprocessing import LabelEncoder, label_binarize from.svm import LinearSVC from.utils import ( _safe_indexing, column_or_1d, indexable, ) from.utils._param_validation import ( HasMethods, Hidden, Interval, StrOptions, validate_params, ) from.utils._plotting import _BinaryClassifierCurveDisplayMixin from.utils.metadata_routing import ( MetadataRouter, MethodMapping, _routing_enabled, process_routing, ) from.utils.multiclass import check_classification_targets from.utils.parallel import Parallel, delayed from.utils.validation import ( _check_method_params, _check_pos_label_consistency, _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted, ) class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator): """Probability calibration with isotonic regression or logistic regression. This class uses cross-validation to both estimate the parameters of a classifier and subsequently calibrate a classifier. With default `ensemble=True`, for each cv split it fits a copy of the base estimator to the training subset, and calibrates it using the testing subset. For prediction, predicted probabilities are averaged across these individual calibrated classifiers. When `ensemble=False`, cross-validation is used to obtain unbiased predictions, via :func:`~sklearn.model_selection.cross_val_predict`, which are then used for calibration. For prediction, the base estimator, trained using all the data, is used. This is the method implemented when `probabilities=True` for :mod:`sklearn.svm` estimators. Already fitted classifiers can be calibrated via the parameter `cv="prefit"`. In this case, no cross-validation is used and all provided data is used for calibration. The user has to take care manually that data for model fitting and calibration are disjoint. The calibration is based on the :term:`decision_function` method of the `estimator` if it exists, else on :term:`predict_proba`. Read more in the :ref:`User Guide <calibration>`. Parameters ---------- estimator : estimator instance, default=None The classifier whose output need to be calibrated to provide more accurate `predict_proba` outputs. The default classifier is a :class:`~sklearn.svm.LinearSVC`. .. versionadded:: 1.2 method : {'sigmoid', 'isotonic'}, default='sigmoid' The method to use for calibration. Can be'sigmoid' which corresponds to Platt's method (i.e. a logistic regression model) or 'isotonic' which is a non-parametric approach. It is not advised to use isotonic calibration with too few calibration samples ``(<<1000)`` since it tends to overfit. cv : int, cross-validation generator, iterable or "prefit", \ default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross-validation, - integer, to specify the number of folds. - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if ``y`` is binary or multiclass, :class:`~sklearn.model_selection.StratifiedKFold` is used. If ``y`` is neither binary nor multiclass, :class:`~sklearn.model_selection.KFold` is used. Refer to the :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here. If "prefit" is passed, it is assumed that `estimator` has been fitted already and all data is used for calibration. .. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. Base estimator clones are fitted in parallel across cross-validation iterations. Therefore parallelism happens only when `cv!= "prefit"`. See :term:`Glossary <n_jobs>` for more details. .. versionadded:: 0.24 ensemble : bool, default=True Determines how the calibrator is fitted when `cv` is not `'prefit'`. Ignored if `cv='prefit'`. If `True`, the `estimator` is fitted using training data, and calibrated using testing data, for each `cv` fold. The final estimator is an ensemble of `n_cv` fitted classifier and calibrator pairs, where `n_cv` is the number of cross-validation folds. The output is the average predicted probabilities of all pairs. If `False`, `cv` is used to compute unbiased predictions, via :func:`~sklearn.model_selection.cross_val_predict`, which are then used for calibration. At prediction time, the classifier used is the `estimator` trained on all the data. Note that this method is also internally implemented in :mod:`sklearn.svm` estimators with the `probabilities=True` parameter. .. versionadded:: 0.24 base_estimator : estimator instance This parameter is deprecated. Use `estimator` instead. .. deprecated:: 1.2 The parameter `base_estimator` is deprecated in 1.2 and will be removed in 1.4. Use `estimator` instead. Attributes ---------- classes_ : ndarray of shape (n_classes,) The class labels. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 1.0 calibrated_classifiers_ : list (len() equal to cv or 1 if `cv="prefit"` \ or `ensemble=False`) The list of classifier and calibrator pairs. - When `cv="prefit"`, the fitted `estimator` and fitted calibrator. - When `cv` is not "prefit" and `ensemble=True`, `n_cv` fitted `estimator` and calibrator pairs. `n_cv` is the number of cross-validation folds. - When `cv` is not "prefit" and `ensemble=False`, the `estimator`, fitted on all the data, and fitted calibrator. .. versionchanged:: 0.24 Single calibrated classifier case when `ensemble=False`. See Also -------- calibration_curve : Compute true and predicted probabilities for a calibration curve. References ---------- .. [1] Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, B. Zadrozny & C. Elkan, ICML 2001 .. [2] Transforming Classifier Scores into Accurate Multiclass Probability Estimates, B. Zadrozny & C. Elkan, (KDD 2002) .. [3] Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, J. Platt, (1999) .. [4] Predicting Good Probabilities with Supervised Learning, A. Niculescu-Mizil & R. Caruana, ICML 2005 Examples -------- >>> from sklearn.datasets import make_classification >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.calibration import CalibratedClassifierCV >>> X, y = make_classification(n_samples=100, n_features=2, ... n_redundant=0, random_state=42) >>> base_clf = GaussianNB() >>> calibrated_clf = CalibratedClassifierCV(base_clf, cv=3) >>> calibrated_clf.fit(X, y) CalibratedClassifierCV(...) >>> len(calibrated_clf.calibrated_classifiers_) 3 >>> calibrated_clf.predict_proba(X)[:5, :] array([[0.110..., 0.889...], [0.072..., 0.927...], [0.928..., 0.071...], [0.928..., 0.071...], [0.071..., 0.928...]]) >>> from sklearn.model_selection import train_test_split >>> X, y = make_classification(n_samples=100, n_features=2, ... n_redundant=0, random_state=42) >>> X_train, X_calib, y_train, y_calib = train_test_split( ... X, y, random_state=42 ... ) >>> base_clf = GaussianNB() >>> base_clf.fit(X_train, y_train) GaussianNB() >>> calibrated_clf = CalibratedClassifierCV(base_clf, cv="prefit") >>> calibrated_clf.fit(X_calib, y_calib) CalibratedClassifierCV(...) >>> len(calibrated_clf.calibrated_classifiers_) 1 >>> calibrated_clf.predict_proba([[-0.5, 0.5]]) array([[0.936..., 0.063...]]) """ _parameter_constraints: dict = { "estimator": [ HasMethods(["fit", "predict_proba"]), HasMethods(["fit", "decision_function"]), None, ], "method": [StrOptions({"isotonic", "sigmoid"})], "cv": ["cv_object", StrOptions({"prefit"})], "n_jobs": [Integral, None], "ensemble": ["boolean"], "base_estimator": [ HasMethods(["fit", "predict_proba"]), HasMethods(["fit", "decision_function"]), None, Hidden(StrOptions({"deprecated"})), ], } def __init__( self, estimator=None, *, method="sigmoid", cv=None, n_jobs=None, ensemble=True, base_estimator="deprecated", ): self.estimator = estimator self.method = method self.cv = cv self.n_jobs = n_jobs self.ensemble = ensemble self.base_estimator = base_estimator def _get_estimator(self): """Resolve which estimator to return (default is LinearSVC)""" # TODO(1.4): Remove when base_estimator is removed if self.base_estimator!= "deprecated": if self.estimator is not None: raise ValueError( "Both `base_estimator` and `estimator` are set. Only set " "`estimator` since `base_estimator` is deprecated." ) warnings.warn( ( "`base_estimator` was renamed to `estimator` in version 1.2 and " "will be removed in 1.4." ), FutureWarning, ) estimator = self.base_estimator else: estimator = self.estimator if estimator is None: # we want all classifiers that don't expose a random_state # to be deterministic (and we don't want to expose this one). estimator = LinearSVC(random_state=0, dual="auto") if _routing_enabled(): estimator.set_fit_request(sample_weight=True) return estimator @_fit_context( # CalibratedClassifierCV.estimator is not validated yet prefer_skip_nested_validation=False ) def fit(self, X, y, sample_weight=None, **fit_params): """Fit the calibrated model. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. **fit_params : dict Parameters to pass to the `fit` method of the underlying classifier. Returns ------- self : object Returns an instance of self. """ check_classification_targets(y) X, y = indexable(X, y) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X) estimator = self._get_estimator() self.calibrated_classifiers_ = [] if self.cv == "prefit": # `classes_` should be consistent with that of estimator check_is_fitted(self.estimator, attributes=["classes_"]) self.classes_ = self.estimator.classes_ pred_method, method_name = _get_prediction_method(estimator) n_classes = len(self.classes_) predictions = _compute_predictions(pred_method, method_name, X, n_classes) calibrated_classifier = _fit_calibrator( estimator, predictions, y, self.classes_, self.method, sample_weight, ) self.calibrated_classifiers_.append(calibrated_classifier) else: # Set `classes_` using all `y` label_encoder_ = LabelEncoder().fit(y) self.classes_ = label_encoder_.classes_ n_classes = len(self.classes_) if _routing_enabled(): routed_params = process_routing( self, "fit", sample_weight=sample_weight, **fit_params, ) else: # sample_weight checks fit_parameters = signature(estimator.fit).parameters supports_sw = "sample_weight" in fit_parameters if sample_weight is not None and not supports_sw: estimator_name = type(estimator).__name__ warnings.warn( f"Since {estimator_name} does not appear to accept" " sample_weight, sample weights will only be used for the" " calibration itself. This can be caused by a limitation of" " the current scikit-learn API. See the following issue for" " more details:" " https://github.com/scikit-learn/scikit-learn/issues/21134." " Be warned that the result of the calibration is likely to be" " incorrect." ) routed_params = Bunch() routed_params.splitter = Bunch(split={}) # no routing for splitter routed_params.estimator = Bunch(fit=fit_params) if sample_weight is not None and supports_sw: routed_params.estimator.fit["sample_weight"] = sample_weight # Check that each cross-validation fold can have at least one # example per class if isinstance(self.cv, int): n_folds = self.cv elif hasattr(self.cv, "n_splits"): n_folds = self.cv.n_splits else: n_folds = None if n_folds and np.any( [np.sum(y == class_) < n_folds for class_ in self.classes_] ): raise ValueError( f"Requesting {n_folds}-fold " "cross-validation but provided less than " f"{n_folds} examples for at least one class." ) cv = check_cv(self.cv, y, classifier=True) if self.ensemble: parallel = Parallel(n_jobs=self.n_jobs) self.calibrated_classifiers_ = parallel( delayed(_fit_classifier_calibrator_pair)( clone(estimator), X, y, train=train, test=test, method=self.method, classes=self.classes_, sample_weight=sample_weight, fit_params=routed_params.estimator.fit, ) for train, test in cv.split(X, y, **routed_params.splitter.split) ) else: this_estimator = clone(estimator) _, method_name = _get_prediction_method(this_estimator) pred_method = partial( cross_val_predict, estimator=this_estimator, X=X, y=y, cv=cv, method=method_name, n_jobs=self.n_jobs, params=routed_params.estimator.fit, ) predictions = _compute_predictions( pred_method, method_name, X, n_classes ) this_estimator.fit(X, y, **routed_params.estimator.fit) # Note: Here we don't pass on fit_params because the supported # calibrators don't support fit_params anyway calibrated_classifier = _fit_calibrator( this_estimator, predictions, y, self.classes_, self.method, sample_weight, ) self.calibrated_classifiers_.append(calibrated_classifier) first_clf = self.calibrated_classifiers_[0].estimator if hasattr(first_clf, "n_features_in_"): self.n_features_in_ = first_clf.n_features_in_ if hasattr(first_clf, "feature_names_in_"): self.feature_names_in_ = first_clf.feature_names_in_ return self def predict_proba(self, X): """Calibrated probabilities of classification. This function returns calibrated probabilities of classification according to each class on an array of test vectors X. Parameters ---------- X : array-like of shape (n_samples, n_features) The samples, as accepted by `estimator.predict_proba`. Returns ------- C : ndarray of shape (n_samples, n_classes) The predicted probas. """ check_is_fitted(self) # Compute the arithmetic mean of the predictions of the calibrated # classifiers mean_proba = np.zeros((_num_samples(X), len(self.classes_))) for calibrated_classifier in self.calibrated_classifiers_: proba = calibrated_classifier.predict_proba(X) mean_proba += proba mean_proba /= len(self.calibrated_classifiers_) return mean_proba def predict(self, X): """Predict the target of new samples. The predicted class is the class that has the highest probability, and can thus be different from the prediction of the uncalibrated classifier. Parameters ---------- X : array-like of shape (n_samples, n_features) The samples, as accepted by `estimator.predict`. Returns ------- C : ndarray of shape (n_samples,) The predicted class. """ check_is_fitted(self) return self.classes_[np.argmax(self.predict_proba(X), axis=1)] def get_metadata_routing(self): """Get metadata routing of this object. Please check :ref:`User Guide <metadata_routing>` on how the routing mechanism works. Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. """ router = ( MetadataRouter(owner=self.__class__.__name__) .add_self_request(self) .add( estimator=self._get_estimator(), method_mapping=MethodMapping().add(callee="fit", caller="fit"), ) .add( splitter=self.cv, method_mapping=MethodMapping().add(callee="split", caller="fit"), ) ) return router def _more_tags(self): return { "_xfail_checks": { "check_sample_weights_invariance": ( "Due to the cross-validation and sample ordering, removing a sample" " is not strictly equal to putting is weight to zero. Specific unit" " tests are added for CalibratedClassifierCV specifically." ), } } def _fit_classifier_calibrator_pair( estimator, X, y, train, test, method, classes, sample_weight=None, fit_params=None, ): """Fit a classifier/calibration pair on a given train/test split. Fit the classifier on the train set, compute its predictions on the test set and use the predictions as input to fit the calibrator along with the test labels. Parameters ---------- estimator : estimator instance Cloned base estimator. X : array-like, shape (n_samples, n_features) Sample data. y : array-like, shape (n_samples,) Targets. train : ndarray, shape (n_train_indices,) Indices of the training subset. test : ndarray, shape (n_test_indices,) Indices of the testing subset. method : {'sigmoid', 'isotonic'} Method to use for calibration. classes : ndarray, shape (n_classes,) The target classes. sample_weight : array-like, default=None Sample weights for `X`. fit_params : dict, default=None Parameters to pass to the `fit` method of the underlying classifier. Returns ------- calibrated_classifier : _CalibratedClassifier instance """ fit_params_train = _check_method_params(X, params=fit_params, indices=train) X_train, y_train = _safe_indexing(X, train), _safe_indexing(y, train) X_test, y_test = _safe_indexing(X, test), _safe_indexing(y, test) estimator.fit(X_train, y_train, **fit_params_train) n_classes = len(classes) pred_method, method_name = _get_prediction_method(estimator) predictions = _compute_predictions(pred_method, method_name, X_test, n_classes) sw_test = None if sample_weight is None else _safe_indexing(sample_weight, test) calibrated_classifier = _fit_calibrator( estimator, predictions, y_test, classes, method, sample_weight=sw_test ) return calibrated_classifier def _get_prediction_method(clf): """Return prediction method. `decision_function` method of `clf` returned, if it exists, otherwise `predict_proba` method returned. Parameters ---------- clf : Estimator instance Fitted classifier to obtain the prediction method from. Returns ------- prediction_method : callable The prediction method. method_name : str The name of the prediction method. """ if hasattr(clf, "decision_function"): method = getattr(clf, "decision_function") return method, "decision_function" if hasattr(clf, "predict_proba"): method = getattr(clf, "predict_proba") return method, "predict_proba" def _compute_predictions(pred_method, method_name, X, n_classes): """Return predictions for `X` and reshape binary outputs to shape (n_samples, 1). Parameters ---------- pred_method : callable Prediction method. method_name: str Name of the prediction method X : array-like or None Data used to obtain predictions. n_classes : int Number of classes present. Returns ------- predictions : array-like, shape (X.shape[0], len(clf.classes_)) The predictions. Note if there are 2 classes, array is of shape (X.shape[0], 1). """ predictions = pred_method(X=X) if method_name == "decision_function": if predictions.ndim == 1: predictions = predictions[:, np.newaxis] elif method_name == "predict_proba": if n_classes == 2: predictions = predictions[:, 1:] else: # pragma: no cover # this branch should be unreachable. raise ValueError(f"Invalid prediction method: {method_name}") return predictions def _fit_calibrator(clf, predictions, y, classes, method, sample_weight=None): """Fit calibrator(s) and return a `_CalibratedClassifier` instance. `n_classes` (i.e. `len(clf.classes_)`) calibrators are fitted. However, if `n_classes` equals 2, one calibrator is fitted. Parameters ---------- clf : estimator instance Fitted classifier. predictions : array-like, shape (n_samples, n_classes) or (n_samples, 1) \ when binary. Raw predictions returned by the un-calibrated base classifier. y : array-like, shape (n_samples,) The targets. classes : ndarray, shape (n_classes,) All the prediction classes. method : {'sigmoid', 'isotonic'} The method to use for calibration. sample_weight : ndarray, shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Returns ------- pipeline : _CalibratedClassifier instance """ Y = label_binarize(y, classes=classes) label_encoder = LabelEncoder().fit(classes) pos_class_indices = label_encoder.transform(clf.classes_) calibrators = [] for class_idx, this_pred in zip(pos_class_indices, predictions.T): if method == "isotonic": calibrator = IsotonicRegression(out_of_bounds="clip") else: # "sigmoid" calibrator = _SigmoidCalibration() calibrator.fit(this_pred, Y[:, class_idx], sample_weight) calibrators.append(calibrator) pipeline = _CalibratedClassifier(clf, calibrators, method=method, classes=classes) return pipeline class _CalibratedClassifier: """Pipeline-like chaining a fitted classifier and its fitted calibrators. Parameters ---------- estimator : estimator instance Fitted classifier. calibrators : list of fitted estimator instances List of fitted calibrators (either 'IsotonicRegression' or '_SigmoidCalibration'). The number of calibrators equals the number of classes. However, if there are 2 classes, the list contains only one fitted calibrator. classes : array-like of shape (n_classes,) All the prediction classes. method : {'sigmoid', 'isotonic'}, default='sigmoid' The method to use for calibration. Can be'sigmoid' which corresponds to Platt's method or 'isotonic' which is a non-parametric approach based on isotonic regression. """ def __init__(self, estimator, calibrators, *, classes, method="sigmoid"): self.estimator = estimator self.calibrators = calibrators self.classes = classes self.method = method def predict_proba(self, X): """Calculate calibrated probabilities. Calculates classification calibrated probabilities for each class, in a one-vs-all manner, for `X`. Parameters ---------- X : ndarray of shape (n_samples, n_features) The sample data. Returns ------- proba : array, shape (n_samples, n_classes) The predicted probabilities. Can be exact zeros. """ n_classes = len(self.classes) pred_method, method_name = _get_prediction_method(self.estimator) predictions = _compute_predictions(pred_method, method_name, X, n_classes) label_encoder = LabelEncoder().fit(self.classes) pos_class_indices = label_encoder.transform(self.estimator.classes_) proba = np.zeros((_num_samples(X), n_classes)) for class_idx, this_pred, calibrator in zip( pos_class_indices, predictions.T, self.calibrators ): if n_classes == 2: # When binary, `predictions` consists only of predictions for # clf.classes_[1] but `pos_class_indices` = 0 class_idx += 1 proba[:, class_idx] = calibrator.predict(this_pred) # Normalize the probabilities if n_classes == 2: proba[:, 0] = 1.0 - proba[:, 1] else: denominator = np.sum(proba, axis=1)[:, np.newaxis] # In the edge case where for each class calibrator returns a null # probability for a given sample, use the uniform distribution # instead. uniform_proba = np.full_like(proba, 1 / n_classes) proba = np.divide( proba, denominator, out=uniform_proba, where=denominator!= 0 ) # Deal with cases where the predicted probability minimally exceeds 1.0 proba[(1.0 < proba) & (proba <= 1.0 + 1e-5)] = 1.0 return proba # The max_abs_prediction_threshold was approximated using # logit(np.finfo(np.float64).eps) which is about -36 def _sigmoid_calibration( predictions, y, sample_weight=None, max_abs_prediction_threshold=30 ): """Probability Calibration with sigmoid method (Platt 2000) Parameters ---------- predictions : ndarray of shape (n_samples,) The decision function or predict proba for the samples. y : ndarray of shape (n_samples,) The targets. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Returns ------- a : float The slope. b : float The intercept. References ---------- Platt, "Probabilistic Outputs for Support Vector Machines" """ predictions = column_or_1d(predictions) y = column_or_1d(y) F = predictions # F follows Platt's notations scale_constant = 1.0 max_prediction = np.max(np.abs(F)) # If the predictions have large values we scale them in order to bring # them within a suitable range. This has no effect on the final # (prediction) result because linear models like Logisitic Regression # without a penalty are invariant to multiplying the features by a # constant. if max_prediction >= max_abs_prediction_threshold: scale_constant = max_prediction # We rescale the features in a copy: inplace rescaling could confuse # the caller and make the code harder to reason about. F = F / scale_constant # Bayesian priors (see Platt end of section 2.2): # It corresponds to the number of samples, taking into account the # `sample_weight`. mask_negative_samples = y <= 0 if sample_weight is not None: prior0 = (sample_weight[mask_negative_samples]).sum() prior1 = (sample_weight[~mask_negative_samples]).sum() else: prior0 = float(np.sum(mask_negative_samples)) prior1 = y.shape[0] - prior0 T = np.zeros_like(y, dtype=np.float64) T[y > 0] = (prior1 + 1.0) / (prior1 + 2.0) T[y <= 0] = 1.0 / (prior0 + 2.0) bin_loss = HalfBinomialLoss() def loss_grad(AB): l, g = bin_loss.loss_gradient( y_true=T, raw_prediction=-(AB[0] * F + AB[1]), sample_weight=sample_weight, ) loss = l.sum() grad = np.array([-g @ F, -g.sum()]) return loss, grad AB0 = np.array([0.0, log((prior0 + 1.0) / (prior1 + 1.0))]) opt_result = minimize( loss_grad, AB0, method="L-BFGS-B", jac=True, options={ "gtol": 1e-6, "ftol": 64 * np.finfo(float).eps, }, ) AB_ = opt_result.x # The tuned multiplicative parameter is converted back to the original # input feature scale. The offset parameter does not need rescaling since # we did not rescale the outcome variable. return AB_[0] / scale_constant, AB_[1] class _SigmoidCalibration(RegressorMixin, BaseEstimator): """Sigmoid regression model. Attributes ---------- a_ : float The slope. b_ : float The intercept. """ def fit(self, X, y, sample_weight=None): """Fit the model using X, y as training data. Parameters ---------- X : array-like of shape (n_samples,) Training data. y : array-like of shape (n_samples,) Training target. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Returns ------- self : object Returns an instance of self. """ X = column_or_1d(X) y = column_or_1d(y) X, y = indexable(X, y) self.a_, self.b_ = _sigmoid_calibration(X, y, sample_weight) return self def predict(self, T): """Predict new data by linear interpolation. Parameters ---------- T : array-like of shape (n_samples,) Data to predict from. Returns ------- T_ : ndarray of shape (n_samples,) The predicted data. """ T = column_or_1d(T) return expit(-(self.a_ * T + self.b_)) @validate_params( { "y_true": ["array-like"], "y_prob": ["array-like"], "pos_label": [Real, str, "boolean", None], "n_bins": [Interval(Integral, 1, None, closed="left")], "strategy": [StrOptions({"uniform", "quantile"})], }, prefer_skip_nested_validation=True, ) def calibration_curve( y_true, y_prob, *, pos_label=None, n_bins=5, strategy="uniform", ): """Compute true and predicted probabilities for a calibration curve. The method assumes the inputs come from a binary classifier, and discretize the [0, 1] interval into bins. Calibration curves may also be referred to as reliability diagrams. Read more in the :ref:`User Guide <calibration>`. Parameters ---------- y_true : array-like of shape (n_samples,) True targets. y_prob : array-like of shape (n_samples,) Probabilities of the positive class. pos_label : int, float, bool or str, default=None The label of the positive class. .. versionadded:: 1.1 n_bins : int, default=5 Number of bins to discretize the [0, 1] interval. A bigger number requires more data. Bins with no samples (i.e. without corresponding values in `y_prob`) will not be returned, thus the returned arrays may have less than `n_bins` values. strategy : {'uniform', 'quantile'}, default='uniform' Strategy used to define the widths of the bins. uniform The bins have identical widths. quantile The bins have the same number of samples and depend on `y_prob`. Returns ------- prob_true : ndarray of shape (n_bins,) or smaller The proportion of samples whose class is the positive class, in each bin (fraction of positives). prob_pred : ndarray of shape (n_bins,) or smaller The mean predicted probability in each bin. References ---------- Alexandru Niculescu-Mizil and Rich Caruana (2005) Predicting Good Probabilities With Supervised Learning, in Proceedings of the 22nd International Conference on Machine Learning (ICML). See section 4 (Qualitative Analysis of Predictions). Examples -------- >>> import numpy as np >>> from sklearn.calibration import calibration_curve >>> y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1]) >>> y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.]) >>> prob_true, prob_pred = calibration_curve(y_true, y_pred, n_bins=3) >>> prob_true array([0., 0.5, 1. ]) >>> prob_pred array([0.2 , 0.525, 0.85 ]) """ y_true = column_or_1d(y_true) y_prob = column_or_1d(y_prob) check_consistent_length(y_true, y_prob) pos_label = _check_pos_label_consistency(pos_label, y_true) if y_prob.min() < 0 or y_prob.max() > 1: raise ValueError("y_prob has values outside [0, 1].") labels = np.unique(y_true) if len(labels) > 2: raise ValueError( f"Only binary classification is supported. Provided labels {labels}." ) y_true = y_true == pos_label if strategy == "quantile": # Determine bin edges by distribution of data quantiles = np.linspace(0, 1, n_bins + 1) bins = np.percentile(y_prob, quantiles * 100) elif strategy == "uniform": bins = np.linspace(0.0, 1.0, n_bins + 1) else: raise ValueError( "Invalid entry to'strategy' input. Strategy " "must be either 'quantile' or 'uniform'." ) binids = np.searchsorted(bins[1:-1], y_prob) bin_sums = np.bincount(binids, weights=y_prob, minlength=len(bins)) bin_true = np.bincount(binids, weights=y_true, minlength=len(bins)) bin_total = np.bincount(binids, minlength=len(bins)) nonzero = bin_total!= 0 prob_true = bin_true[nonzero] / bin_total[nonzero] prob_pred = bin_sums[nonzero] / bin_total[nonzero] return prob_true, prob_pred class CalibrationDisplay(_BinaryClassifierCurveDisplayMixin): """Calibration curve (also known as reliability diagram) visualization. It is recommended to use :func:`~sklearn.calibration.CalibrationDisplay.from_estimator` or :func:`~sklearn.calibration.CalibrationDisplay.from_predictions` to create a `CalibrationDisplay`. All parameters are stored as attributes. Read more about calibration in the :ref:`User Guide <calibration>` and more about the scikit-learn visualization API in :ref:`visualizations`. .. versionadded:: 1.0 Parameters ---------- prob_true : ndarray of shape (n_bins,) The proportion of samples whose class is the positive class (fraction of positives), in each bin. prob_pred : ndarray of shape (n_bins,) The mean predicted probability in each bin. y_prob : ndarray of shape (n_samples,) Probability estimates for the positive class, for each sample. estimator_name : str, default=None Name of estimator. If None, the estimator name is not shown. pos_label : int, float, bool or str, default=None The positive class when computing the calibration curve. By default, `estimators.classes_[1]` is considered as the positive class. .. versionadded:: 1.1 Attributes ---------- line_ : matplotlib Artist Calibration curve. ax_ : matplotlib Axes Axes with calibration curve. figure_ : matplotlib Figure Figure containing the curve. See Also -------- calibration_curve : Compute true and predicted probabilities for a calibration curve. CalibrationDisplay.from_predictions : Plot calibration curve using true and predicted labels. CalibrationDisplay.from_estimator : Plot calibration curve using an estimator and data. Examples -------- >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.calibration import calibration_curve, CalibrationDisplay >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> clf = LogisticRegression(random_state=0) >>> clf.fit(X_train, y_train) LogisticRegression(random_state=0) >>> y_prob = clf.predict_proba(X_test)[:, 1] >>> prob_true, prob_pred = calibration_curve(y_test, y_prob, n_bins=10) >>> disp = CalibrationDisplay(prob_true, prob_pred, y_prob) >>> disp.plot() <...> """ def __init__( self, prob_true, prob_pred, y_prob, *, estimator_name=None, pos_label=None ): self.prob_true = prob_true self.prob_pred = prob_pred self.y_prob = y_prob self.estimator_name = estimator_name self.pos_label = pos_label def plot(self, *, ax=None, name=None, ref_line=True, **kwargs): """Plot visualization. Extra keyword arguments will be passed to :func:`matplotlib.pyplot.plot`. Parameters ---------- ax : Matplotlib Axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. name : str, default=None Name for labeling curve. If `None`, use `estimator_name` if not `None`, otherwise no labeling is shown. ref_line : bool, default=True If `True`, plots a reference line representing a perfectly calibrated classifier. **kwargs : dict Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`. Returns ------- display : :class:`~sklearn.calibration.CalibrationDisplay` Object that stores computed values. """ self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=name) info_pos_label = ( f"(Positive class: {self.pos_label})" if self.pos_label is not None else "" ) line_kwargs = {"marker": "s", "linestyle": "-"} if name is not None: line_kwargs["label"] = name line_kwargs.update(**kwargs) ref_line_label = "Perfectly calibrated" existing_ref_line = ref_line_label in self.ax_.get_legend_handles_labels()[1] if ref_line and not existing_ref_line: self.ax_.plot([0, 1], [0, 1], "k:", label=ref_line_label) self.line_ = self.ax_.plot(self.prob_pred, self.prob_true, **line_kwargs)[0] # We always have to show the legend for at least the reference line self.ax_.legend(loc="lower right") xlabel = f"Mean predicted probability {info_pos_label}" ylabel = f"Fraction of positives {info_pos_label}" self.ax_.set(xlabel=xlabel, ylabel=ylabel) return self @classmethod def from_estimator( cls, estimator, X, y, *, n_bins=5, strategy="uniform", pos_label=None, name=None, ref_line=True, ax=None, **kwargs, ): """Plot calibration curve using a binary classifier and data. A calibration curve, also known as a reliability diagram, uses inputs from a binary classifier and plots the average predicted probability for each bin against the fraction of positive classes, on the y-axis. Extra keyword arguments will be passed to :func:`matplotlib.pyplot.plot`. Read more about calibration in the :ref:`User Guide <calibration>` and more about the scikit-learn visualization API in :ref:`visualizations`. .. versionadded:: 1.0 Parameters ---------- estimator : estimator instance Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline` in which the last estimator is a classifier. The classifier must have a :term:`predict_proba` method. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input values. y : array-like of shape (n_samples,) Binary target values. n_bins : int, default=5 Number of bins to discretize the [0, 1] interval into when calculating the calibration curve. A bigger number requires more data. strategy : {'uniform', 'quantile'}, default='uniform' Strategy used to define the widths of the bins. - `'uniform'`: The bins have identical widths. - `'quantile'`: The bins have the same number of samples and depend on predicted probabilities. pos_label : int, float, bool or str, default=None The positive class when computing the calibration curve. By default, `estimators.classes_[1]` is considered as the positive class. .. versionadded:: 1.1 name : str, default=None Name for labeling curve. If `None`, the name of the estimator is used. ref_line : bool, default=True If `True`, plots a reference line representing a perfectly calibrated classifier. ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. **kwargs : dict Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`. Returns ------- display : :class:`~sklearn.calibration.CalibrationDisplay`. Object that stores computed values. See Also -------- CalibrationDisplay.from_predictions : Plot calibration curve using true and predicted labels. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.calibration import CalibrationDisplay >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> clf = LogisticRegression(random_state=0) >>> clf.fit(X_train, y_train) LogisticRegression(random_state=0) >>> disp = CalibrationDisplay.from_estimator(clf, X_test, y_test) >>> plt.show() """ y_prob, pos_label, name = cls._validate_and_get_response_values( estimator, X, y, response_method="predict_proba", pos_label=pos_label, name=name, ) return cls.from_predictions( y, y_prob, n_bins=n_bins, strategy=strategy, pos_label=pos_label, name=name, ref_line=ref_line, ax=ax, **kwargs, ) @classmethod def from_predictions( cls, y_true, y_prob, *, n_bins=5, strategy="uniform", pos_label=None, name=None, ref_line=True, ax=None, **kwargs, ): """Plot calibration curve using true labels and predicted probabilities. Calibration curve, also known as reliability diagram, uses inputs from a binary classifier and plots the average predicted probability for each bin against the fraction of positive classes, on the y-axis. Extra keyword arguments will be passed to :func:`matplotlib.pyplot.plot`. Read more about calibration in the :ref:`User Guide <calibration>` and more about the scikit-learn visualization API in :ref:`visualizations`. .. versionadded:: 1.0 Parameters ---------- y_true : array-like of shape (n_samples,) True labels. y_prob : array-like of shape (n_samples,) The predicted probabilities of the positive class. n_bins : int, default=5 Number of bins to discretize the [0, 1] interval into when calculating the calibration curve. A bigger number requires more data. strategy : {'uniform', 'quantile'}, default='uniform' Strategy used to define the widths of the bins. - `'uniform'`: The bins have identical widths. - `'quantile'`: The bins have the same number of samples and depend on predicted probabilities. pos_label : int, float, bool or str, default=None The positive class when computing the calibration curve. By default, `estimators.classes_[1]` is considered as the positive class. .. versionadded:: 1.1 name : str, default=None Name for labeling curve. ref_line : bool, default=True If `True`, plots a reference line representing a perfectly calibrated classifier. ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. **kwargs : dict Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`. Returns ------- display : :class:`~sklearn.calibration.CalibrationDisplay`. Object that stores computed values. See Also -------- CalibrationDisplay.from_estimator : Plot calibration curve using an estimator and data. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.calibration import CalibrationDisplay >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> clf = LogisticRegression(random_state=0) >>> clf.fit(X_train, y_train) LogisticRegression(random_state=0) >>> y_prob = clf.predict_proba(X_test)[:, 1] >>> disp = CalibrationDisplay.from_predictions(y_test, y_prob) >>> plt.show() """ pos_label_validated, name = cls._validate_from_predictions_params( y_true, y_prob, sample_weight=None, pos_label=pos_label, name=name ) prob_true, prob_pred = calibration_curve( y_true, y_prob, n_bins=n_bins, strategy=strategy, pos_label=pos_label ) disp = cls( prob_true=prob_true, prob_pred=prob_pred, y_prob=y_prob, estimator_name=name, pos_label=pos_label_validated, ) return disp.plot(ax=ax, ref_line=ref_line, **kwargs) """ The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These are supervised learning methods based on applying Bayes' theorem with strong (naive) feature independence assumptions. """ # Author: Vincent Michel <vincent.michel@inria.fr> # Minor fixes by Fabian Pedregosa # Amit Aides <amitibo@tx.technion.ac.il> # Yehuda Finkelstein <yehudaf@tx.technion.ac.il> # Lars Buitinck # Jan Hendrik Metzen <jhm@informatik.uni-bremen.de> # (parts based on earlier work by Mathieu Blondel) # # License: BSD 3 clause import warnings from abc import ABCMeta, abstractmethod from numbers import Integral, Real import numpy as np from scipy.special import logsumexp from.base import BaseEstimator, ClassifierMixin, _fit_context from.preprocessing import LabelBinarizer, binarize, label_binarize from.utils._param_validation import Hidden, Interval, StrOptions from.utils.extmath import safe_sparse_dot from.utils.multiclass import _check_partial_fit_first_call from.utils.validation import _check_sample_weight, check_is_fitted, check_non_negative __all__ = [ "BernoulliNB", "GaussianNB", "MultinomialNB", "ComplementNB", "CategoricalNB", ] class _BaseNB(ClassifierMixin, BaseEstimator, metaclass=ABCMeta): """Abstract base class for naive Bayes estimators""" @abstractmethod def _joint_log_likelihood(self, X): """Compute the unnormalized posterior log probability of X I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of shape (n_samples, n_classes). Public methods predict, predict_proba, predict_log_proba, and predict_joint_log_proba pass the input through _check_X before handing it over to _joint_log_likelihood. The term "joint log likelihood" is used interchangibly with "joint log probability". """ @abstractmethod def _check_X(self, X): """To be overridden in subclasses with the actual checks. Only used in predict* methods. """ def predict_joint_log_proba(self, X): """Return joint log probability estimates for the test vector X. For each row x of X and class y, the joint log probability is given by ``log P(x, y) = log P(y) + log P(x|y),`` where ``log P(y)`` is the class prior probability and ``log P(x|y)`` is the class-conditional probability. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- C : ndarray of shape (n_samples, n_classes) Returns the joint log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`. """ check_is_fitted(self) X = self._check_X(X) return self._joint_log_likelihood(X) def predict(self, X): """ Perform classification on an array of test vectors X. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- C : ndarray of shape (n_samples,) Predicted target values for X. """ check_is_fitted(self) X = self._check_X(X) jll = self._joint_log_likelihood(X) return self.classes_[np.argmax(jll, axis=1)] def predict_log_proba(self, X): """ Return log-probability estimates for the test vector X. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- C : array-like of shape (n_samples, n_classes) Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`. """ check_is_fitted(self) X = self._check_X(X) jll = self._joint_log_likelihood(X) # normalize by P(x) = P(f_1,..., f_n) log_prob_x = logsumexp(jll, axis=1) return jll - np.atleast_2d(log_prob_x).T def predict_proba(self, X): """ Return probability estimates for the test vector X. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- C : array-like of shape (n_samples, n_classes) Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`. """ return np.exp(self.predict_log_proba(X)) class GaussianNB(_BaseNB): """ Gaussian Naive Bayes (GaussianNB). Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque: http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf Read more in the :ref:`User Guide <gaussian_naive_bayes>`. Parameters ---------- priors : array-like of shape (n_classes,), default=None Prior probabilities of the classes. If specified, the priors are not adjusted according to the data. var_smoothing : float, default=1e-9 Portion of the largest variance of all features that is added to variances for calculation stability. .. versionadded:: 0.20 Attributes ---------- class_count_ : ndarray of shape (n_classes,) number of training samples observed in each class. class_prior_ : ndarray of shape (n_classes,) probability of each class. classes_ : ndarray of shape (n_classes,) class labels known to the classifier. epsilon_ : float absolute additive value to variances. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 var_ : ndarray of shape (n_classes, n_features) Variance of each feature per class. .. versionadded:: 1.0 theta_ : ndarray of shape (n_classes, n_features) mean of each feature per class. See Also -------- BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models. CategoricalNB : Naive Bayes classifier for categorical features. ComplementNB : Complement Naive Bayes classifier. MultinomialNB : Naive Bayes classifier for multinomial models. Examples -------- >>> import numpy as np >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> Y = np.array([1, 1, 1, 2, 2, 2]) >>> from sklearn.naive_bayes import GaussianNB >>> clf = GaussianNB() >>> clf.fit(X, Y) GaussianNB() >>> print(clf.predict([[-0.8, -1]])) [1] >>> clf_pf = GaussianNB() >>> clf_pf.partial_fit(X, Y, np.unique(Y)) GaussianNB() >>> print(clf_pf.predict([[-0.8, -1]])) [1] """ _parameter_constraints: dict = { "priors": ["array-like", None], "var_smoothing": [Interval(Real, 0, None, closed="left")], } def __init__(self, *, priors=None, var_smoothing=1e-9): self.priors = priors self.var_smoothing = var_smoothing @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y, sample_weight=None): """Fit Gaussian Naive Bayes according to X, y. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). .. versionadded:: 0.17 Gaussian Naive Bayes supports fitting with *sample_weight*. Returns ------- self : object Returns the instance itself. """ y = self._validate_data(y=y) return self._partial_fit( X, y, np.unique(y), _refit=True, sample_weight=sample_weight ) def _check_X(self, X): """Validate X, used only in predict* methods.""" return self._validate_data(X, reset=False) @staticmethod def _update_mean_variance(n_past, mu, var, X, sample_weight=None): """Compute online update of Gaussian mean and variance. Given starting sample count, mean, and variance, a new set of points X, and optionally sample weights, return the updated mean and variance. (NB - each dimension (column) in X is treated as independent -- you get variance, not covariance). Can take scalar mean and variance, or vector mean and variance to simultaneously update a number of independent Gaussians. See Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque: http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf Parameters ---------- n_past : int Number of samples represented in old mean and variance. If sample weights were given, this should contain the sum of sample weights represented in old mean and variance. mu : array-like of shape (number of Gaussians,) Means for Gaussians in original set. var : array-like of shape (number of Gaussians,) Variances for Gaussians in original set. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- total_mu : array-like of shape (number of Gaussians,) Updated mean for each Gaussian over the combined set. total_var : array-like of shape (number of Gaussians,) Updated variance for each Gaussian over the combined set. """ if X.shape[0] == 0: return mu, var # Compute (potentially weighted) mean and variance of new datapoints if sample_weight is not None: n_new = float(sample_weight.sum()) if np.isclose(n_new, 0.0): return mu, var new_mu = np.average(X, axis=0, weights=sample_weight) new_var = np.average((X - new_mu) ** 2, axis=0, weights=sample_weight) else: n_new = X.shape[0] new_var = np.var(X, axis=0) new_mu = np.mean(X, axis=0) if n_past == 0: return new_mu, new_var n_total = float(n_past + n_new) # Combine mean of old and new data, taking into consideration # (weighted) number of observations total_mu = (n_new * new_mu + n_past * mu) / n_total # Combine variance of old and new data, taking into consideration # (weighted) number of observations. This is achieved by combining # the sum-of-squared-differences (ssd) old_ssd = n_past * var new_ssd = n_new * new_var total_ssd = old_ssd + new_ssd + (n_new * n_past / n_total) * (mu - new_mu) ** 2 total_var = total_ssd / n_total return total_mu, total_var @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, X, y, classes=None, sample_weight=None): """Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning. This is especially useful when the whole dataset is too big to fit in memory at once. This method has some performance and numerical stability overhead, hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). .. versionadded:: 0.17 Returns ------- self : object Returns the instance itself. """ return self._partial_fit( X, y, classes, _refit=False, sample_weight=sample_weight ) def _partial_fit(self, X, y, classes=None, _refit=False, sample_weight=None): """Actual implementation of Gaussian NB fitting. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls. _refit : bool, default=False If true, act as though this were the first time we called _partial_fit (ie, throw away any past fitting and start over). sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- self : object """ if _refit: self.classes_ = None first_call = _check_partial_fit_first_call(self, classes) X, y = self._validate_data(X, y, reset=first_call) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X) # If the ratio of data variance between dimensions is too small, it # will cause numerical errors. To address this, we artificially # boost the variance by epsilon, a small fraction of the standard # deviation of the largest dimension. self.epsilon_ = self.var_smoothing * np.var(X, axis=0).max() if first_call: # This is the first call to partial_fit: # initialize various cumulative counters n_features = X.shape[1] n_classes = len(self.classes_) self.theta_ = np.zeros((n_classes, n_features)) self.var_ = np.zeros((n_classes, n_features)) self.class_count_ = np.zeros(n_classes, dtype=np.float64) # Initialise the class prior # Take into account the priors if self.priors is not None: priors = np.asarray(self.priors) # Check that the provided prior matches the number of classes if len(priors)!= n_classes: raise ValueError("Number of priors must match number of classes.") # Check that the sum is 1 if not np.isclose(priors.sum(), 1.0): raise ValueError("The sum of the priors should be 1.") # Check that the priors are non-negative if (priors < 0).any(): raise ValueError("Priors must be non-negative.") self.class_prior_ = priors else: # Initialize the priors to zeros for each class self.class_prior_ = np.zeros(len(self.classes_), dtype=np.float64) else: if X.shape[1]!= self.theta_.shape[1]: msg = "Number of features %d does not match previous data %d." raise ValueError(msg % (X.shape[1], self.theta_.shape[1])) # Put epsilon back in each time self.var_[:, :] -= self.epsilon_ classes = self.classes_ unique_y = np.unique(y) unique_y_in_classes = np.isin(unique_y, classes) if not np.all(unique_y_in_classes): raise ValueError( "The target label(s) %s in y do not exist in the initial classes %s" % (unique_y[~unique_y_in_classes], classes) ) for y_i in unique_y: i = classes.searchsorted(y_i) X_i = X[y == y_i, :] if sample_weight is not None: sw_i = sample_weight[y == y_i] N_i = sw_i.sum() else: sw_i = None N_i = X_i.shape[0] new_theta, new_sigma = self._update_mean_variance( self.class_count_[i], self.theta_[i, :], self.var_[i, :], X_i, sw_i ) self.theta_[i, :] = new_theta self.var_[i, :] = new_sigma self.class_count_[i] += N_i self.var_[:, :] += self.epsilon_ # Update if only no priors is provided if self.priors is None: # Empirical prior, with sample_weight taken into account self.class_prior_ = self.class_count_ / self.class_count_.sum() return self def _joint_log_likelihood(self, X): joint_log_likelihood = [] for i in range(np.size(self.classes_)): jointi = np.log(self.class_prior_[i]) n_ij = -0.5 * np.sum(np.log(2.0 * np.pi * self.var_[i, :])) n_ij -= 0.5 * np.sum(((X - self.theta_[i, :]) ** 2) / (self.var_[i, :]), 1) joint_log_likelihood.append(jointi + n_ij) joint_log_likelihood = np.array(joint_log_likelihood).T return joint_log_likelihood class _BaseDiscreteNB(_BaseNB): """Abstract base class for naive Bayes on discrete/categorical data Any estimator based on this class should provide: __init__ _joint_log_likelihood(X) as per _BaseNB _update_feature_log_prob(alpha) _count(X, Y) """ _parameter_constraints: dict = { "alpha": [Interval(Real, 0, None, closed="left"), "array-like"], "fit_prior": ["boolean"], "class_prior": ["array-like", None], "force_alpha": ["boolean", Hidden(StrOptions({"warn"}))], } def __init__(self, alpha=1.0, fit_prior=True, class_prior=None, force_alpha="warn"): self.alpha = alpha self.fit_prior = fit_prior self.class_prior = class_prior self.force_alpha = force_alpha @abstractmethod def _count(self, X, Y): """Update counts that are used to calculate probabilities. The counts make up a sufficient statistic extracted from the data. Accordingly, this method is called each time `fit` or `partial_fit` update the model. `class_count_` and `feature_count_` must be updated here along with any model specific counts. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The input samples. Y : ndarray of shape (n_samples, n_classes) Binarized class labels. """ @abstractmethod def _update_feature_log_prob(self, alpha): """Update feature log probabilities based on counts. This method is called each time `fit` or `partial_fit` update the model. Parameters ---------- alpha : float smoothing parameter. See :meth:`_check_alpha`. """ def _check_X(self, X): """Validate X, used only in predict* methods.""" return self._validate_data(X, accept_sparse="csr", reset=False) def _check_X_y(self, X, y, reset=True): """Validate X and y in fit methods.""" return self._validate_data(X, y, accept_sparse="csr", reset=reset) def _update_class_log_prior(self, class_prior=None): """Update class log priors. The class log priors are based on `class_prior`, class count or the number of classes. This method is called each time `fit` or `partial_fit` update the model. """ n_classes = len(self.classes_) if class_prior is not None: if len(class_prior)!= n_classes: raise ValueError("Number of priors must match number of classes.") self.class_log_prior_ = np.log(class_prior) elif self.fit_prior: with warnings.catch_warnings(): # silence the warning when count is 0 because class was not yet # observed warnings.simplefilter("ignore", RuntimeWarning) log_class_count = np.log(self.class_count_) # empirical prior, with sample_weight taken into account self.class_log_prior_ = log_class_count - np.log(self.class_count_.sum()) else: self.class_log_prior_ = np.full(n_classes, -np.log(n_classes)) def _check_alpha(self): alpha = ( np.asarray(self.alpha) if not isinstance(self.alpha, Real) else self.alpha ) alpha_min = np.min(alpha) if isinstance(alpha, np.ndarray): if not alpha.shape[0] == self.n_features_in_: raise ValueError( "When alpha is an array, it should contains `n_features`. " f"Got {alpha.shape[0]} elements instead of {self.n_features_in_}." ) # check that all alpha are positive if alpha_min < 0: raise ValueError("All values in alpha must be greater than 0.") alpha_lower_bound = 1e-10 # TODO(1.4): Replace w/ deprecation of self.force_alpha # See gh #22269 _force_alpha = self.force_alpha if _force_alpha == "warn" and alpha_min < alpha_lower_bound: _force_alpha = False warnings.warn( ( "The default value for `force_alpha` will change to `True` in 1.4." " To suppress this warning, manually set the value of" " `force_alpha`." ), FutureWarning, ) if alpha_min < alpha_lower_bound and not _force_alpha: warnings.warn( "alpha too small will result in numeric errors, setting alpha =" f" {alpha_lower_bound:.1e}. Use `force_alpha=True` to keep alpha" " unchanged." ) return np.maximum(alpha, alpha_lower_bound) return alpha @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, X, y, classes=None, sample_weight=None): """Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning. This is especially useful when the whole dataset is too big to fit in memory at once. This method has some performance overhead hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- self : object Returns the instance itself. """ first_call = not hasattr(self, "classes_") X, y = self._check_X_y(X, y, reset=first_call) _, n_features = X.shape if _check_partial_fit_first_call(self, classes): # This is the first call to partial_fit: # initialize various cumulative counters n_classes = len(classes) self._init_counters(n_classes, n_features) Y = label_binarize(y, classes=self.classes_) if Y.shape[1] == 1: if len(self.classes_) == 2: Y = np.concatenate((1 - Y, Y), axis=1) else: # degenerate case: just one class Y = np.ones_like(Y) if X.shape[0]!= Y.shape[0]: msg = "X.shape[0]=%d and y.shape[0]=%d are incompatible." raise ValueError(msg % (X.shape[0], y.shape[0])) # label_binarize() returns arrays with dtype=np.int64. # We convert it to np.float64 to support sample_weight consistently Y = Y.astype(np.float64, copy=False) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X) sample_weight = np.atleast_2d(sample_weight) Y *= sample_weight.T class_prior = self.class_prior # Count raw events from data before updating the class log prior # and feature log probas self._count(X, Y) # XXX: OPTIM: we could introduce a public finalization method to # be called by the user explicitly just once after several consecutive # calls to partial_fit and prior any call to predict[_[log_]proba] # to avoid computing the smooth log probas at each call to partial fit alpha = self._check_alpha() self._update_feature_log_prob(alpha) self._update_class_log_prior(class_prior=class_prior) return self @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y, sample_weight=None): """Fit Naive Bayes classifier according to X, y. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- self : object Returns the instance itself. """ X, y = self._check_X_y(X, y) _, n_features = X.shape labelbin = LabelBinarizer() Y = labelbin.fit_transform(y) self.classes_ = labelbin.classes_ if Y.shape[1] == 1: if len(self.classes_) == 2: Y = np.concatenate((1 - Y, Y), axis=1) else: # degenerate case: just one class Y = np.ones_like(Y) # LabelBinarizer().fit_transform() returns arrays with dtype=np.int64. # We convert it to np.float64 to support sample_weight consistently; # this means we also don't have to cast X to floating point if sample_weight is not None: Y = Y.astype(np.float64, copy=False) sample_weight = _check_sample_weight(sample_weight, X) sample_weight = np.atleast_2d(sample_weight) Y *= sample_weight.T class_prior = self.class_prior # Count raw events from data before updating the class log prior # and feature log probas n_classes = Y.shape[1] self._init_counters(n_classes, n_features) self._count(X, Y) alpha = self._check_alpha() self._update_feature_log_prob(alpha) self._update_class_log_prior(class_prior=class_prior) return self def _init_counters(self, n_classes, n_features): self.class_count_ = np.zeros(n_classes, dtype=np.float64) self.feature_count_ = np.zeros((n_classes, n_features), dtype=np.float64) def _more_tags(self): return {"poor_score": True} class MultinomialNB(_BaseDiscreteNB): """ Naive Bayes classifier for multinomial models. The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work. Read more in the :ref:`User Guide <multinomial_naive_bayes>`. Parameters ---------- alpha : float or array-like of shape (n_features,), default=1.0 Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing). force_alpha : bool, default=False If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0. .. versionadded:: 1.2 .. deprecated:: 1.2 The default value of `force_alpha` will change to `True` in v1.4. fit_prior : bool, default=True Whether to learn class prior probabilities or not. If false, a uniform prior will be used. class_prior : array-like of shape (n_classes,), default=None Prior probabilities of the classes. If specified, the priors are not adjusted according to the data. Attributes ---------- class_count_ : ndarray of shape (n_classes,) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided. class_log_prior_ : ndarray of shape (n_classes,) Smoothed empirical log probability for each class. classes_ : ndarray of shape (n_classes,) Class labels known to the classifier feature_count_ : ndarray of shape (n_classes, n_features) Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided. feature_log_prob_ : ndarray of shape (n_classes, n_features) Empirical log probability of features given a class, ``P(x_i|y)``. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models. CategoricalNB : Naive Bayes classifier for categorical features. ComplementNB : Complement Naive Bayes classifier. GaussianNB : Gaussian Naive Bayes. References ---------- C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 234-265. https://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html Examples -------- >>> import numpy as np >>> rng = np.random.RandomState(1) >>> X = rng.randint(5, size=(6, 100)) >>> y = np.array([1, 2, 3, 4, 5, 6]) >>> from sklearn.naive_bayes import MultinomialNB >>> clf = MultinomialNB(force_alpha=True) >>> clf.fit(X, y) MultinomialNB(force_alpha=True) >>> print(clf.predict(X[2:3])) [3] """ def __init__( self, *, alpha=1.0, force_alpha="warn", fit_prior=True, class_prior=None ): super().__init__( alpha=alpha, fit_prior=fit_prior, class_prior=class_prior, force_alpha=force_alpha, ) def _more_tags(self): return {"requires_positive_X": True} def _count(self, X, Y): """Count and smooth feature occurrences.""" check_non_negative(X, "MultinomialNB (input X)") self.feature_count_ += safe_sparse_dot(Y.T, X) self.class_count_ += Y.sum(axis=0) def _update_feature_log_prob(self, alpha): """Apply smoothing to raw counts and recompute log probabilities""" smoothed_fc = self.feature_count_ + alpha smoothed_cc = smoothed_fc.sum(axis=1) self.feature_log_prob_ = np.log(smoothed_fc) - np.log( smoothed_cc.reshape(-1, 1) ) def _joint_log_likelihood(self, X): """Calculate the posterior log probability of the samples X""" return safe_sparse_dot(X, self.feature_log_prob_.T) + self.class_log_prior_ class ComplementNB(_BaseDiscreteNB): """The Complement Naive Bayes classifier described in Rennie et al. (2003). The Complement Naive Bayes classifier was designed to correct the "severe assumptions" made by the standard Multinomial Naive Bayes classifier. It is particularly suited for imbalanced data sets. Read more in the :ref:`User Guide <complement_naive_bayes>`. .. versionadded:: 0.20 Parameters ---------- alpha : float or array-like of shape (n_features,), default=1.0 Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing). force_alpha : bool, default=False If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0. .. versionadded:: 1.2 .. deprecated:: 1.2 The default value of `force_alpha` will change to `True` in v1.4. fit_prior : bool, default=True Only used in edge case with a single class in the training set. class_prior : array-like of shape (n_classes,), default=None Prior probabilities of the classes. Not used. norm : bool, default=False Whether or not a second normalization of the weights is performed. The default behavior mirrors the implementations found in Mahout and Weka, which do not follow the full algorithm described in Table 9 of the paper. Attributes ---------- class_count_ : ndarray of shape (n_classes,) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided. class_log_prior_ : ndarray of shape (n_classes,) Smoothed empirical log probability for each class. Only used in edge case with a single class in the training set. classes_ : ndarray of shape (n_classes,) Class labels known to the classifier feature_all_ : ndarray of shape (n_features,) Number of samples encountered for each feature during fitting. This value is weighted by the sample weight when provided. feature_count_ : ndarray of shape (n_classes, n_features) Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided. feature_log_prob_ : ndarray of shape (n_classes, n_features) Empirical weights for class complements. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models. CategoricalNB : Naive Bayes classifier for categorical features. GaussianNB : Gaussian Naive Bayes. MultinomialNB : Naive Bayes classifier for multinomial models. References ---------- Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of naive bayes text classifiers. In ICML (Vol. 3, pp. 616-623). https://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf Examples -------- >>> import numpy as np >>> rng = np.random.RandomState(1) >>> X = rng.randint(5, size=(6, 100)) >>> y = np.array([1, 2, 3, 4, 5, 6]) >>> from sklearn.naive_bayes import ComplementNB >>> clf = ComplementNB(force_alpha=True) >>> clf.fit(X, y) ComplementNB(force_alpha=True) >>> print(clf.predict(X[2:3])) [3] """ _parameter_constraints: dict = { **_BaseDiscreteNB._parameter_constraints, "norm": ["boolean"], } def __init__( self, *, alpha=1.0, force_alpha="warn", fit_prior=True, class_prior=None, norm=False, ): super().__init__( alpha=alpha, force_alpha=force_alpha, fit_prior=fit_prior, class_prior=class_prior, ) self.norm = norm def _more_tags(self): return {"requires_positive_X": True} def _count(self, X, Y): """Count feature occurrences.""" check_non_negative(X, "ComplementNB (input X)") self.feature_count_ += safe_sparse_dot(Y.T, X) self.class_count_ += Y.sum(axis=0) self.feature_all_ = self.feature_count_.sum(axis=0) def _update_feature_log_prob(self, alpha): """Apply smoothing to raw counts and compute the weights.""" comp_count = self.feature_all_ + alpha - self.feature_count_ logged = np.log(comp_count / comp_count.sum(axis=1, keepdims=True)) # _BaseNB.predict uses argmax, but ComplementNB operates with argmin. if self.norm: summed = logged.sum(axis=1, keepdims=True) feature_log_prob = logged / summed else: feature_log_prob = -logged self.feature_log_prob_ = feature_log_prob def _joint_log_likelihood(self, X): """Calculate the class scores for the samples in X.""" jll = safe_sparse_dot(X, self.feature_log_prob_.T) if len(self.classes_) == 1: jll += self.class_log_prior_ return jll class BernoulliNB(_BaseDiscreteNB): """Naive Bayes classifier for multivariate Bernoulli models. Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features. Read more in the :ref:`User Guide <bernoulli_naive_bayes>`. Parameters ---------- alpha : float or array-like of shape (n_features,), default=1.0 Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing). force_alpha : bool, default=False If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0. .. versionadded:: 1.2 .. deprecated:: 1.2 The default value of `force_alpha` will change to `True` in v1.4. binarize : float or None, default=0.0 Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors. fit_prior : bool, default=True Whether to learn class prior probabilities or not. If false, a uniform prior will be used. class_prior : array-like of shape (n_classes,), default=None Prior probabilities of the classes. If specified, the priors are not adjusted according to the data. Attributes ---------- class_count_ : ndarray of shape (n_classes,) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided. class_log_prior_ : ndarray of shape (n_classes,) Log probability of each class (smoothed). classes_ : ndarray of shape (n_classes,) Class labels known to the classifier feature_count_ : ndarray of shape (n_classes, n_features) Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided. feature_log_prob_ : ndarray of shape (n_classes, n_features) Empirical log probability of features given a class, P(x_i|y). n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- CategoricalNB : Naive Bayes classifier for categorical features. ComplementNB : The Complement Naive Bayes classifier described in Rennie et al. (2003). GaussianNB : Gaussian Naive Bayes (GaussianNB). MultinomialNB : Naive Bayes classifier for multinomial models. References ---------- C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 234-265. https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html A. McCallum and K. Nigam (1998). A comparison of event models for naive Bayes text classification. Proc. AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48. V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS). Examples -------- >>> import numpy as np >>> rng = np.random.RandomState(1) >>> X = rng.randint(5, size=(6, 100)) >>> Y = np.array([1, 2, 3, 4, 4, 5]) >>> from sklearn.naive_bayes import BernoulliNB >>> clf = BernoulliNB(force_alpha=True) >>> clf.fit(X, Y) BernoulliNB(force_alpha=True) >>> print(clf.predict(X[2:3])) [3] """ _parameter_constraints: dict = { **_BaseDiscreteNB._parameter_constraints, "binarize": [None, Interval(Real, 0, None, closed="left")], } def __init__( self, *, alpha=1.0, force_alpha="warn", binarize=0.0, fit_prior=True, class_prior=None, ): super().__init__( alpha=alpha, fit_prior=fit_prior, class_prior=class_prior, force_alpha=force_alpha, ) self.binarize = binarize def _check_X(self, X): """Validate X, used only in predict* methods.""" X = super()._check_X(X) if self.binarize is not None: X = binarize(X, threshold=self.binarize) return X def _check_X_y(self, X, y, reset=True): X, y = super()._check_X_y(X, y, reset=reset) if self.binarize is not None: X = binarize(X, threshold=self.binarize) return X, y def _count(self, X, Y): """Count and smooth feature occurrences.""" self.feature_count_ += safe_sparse_dot(Y.T, X) self.class_count_ += Y.sum(axis=0) def _update_feature_log_prob(self, alpha): """Apply smoothing to raw counts and recompute log probabilities""" smoothed_fc = self.feature_count_ + alpha smoothed_cc = self.class_count_ + alpha * 2 self.feature_log_prob_ = np.log(smoothed_fc) - np.log( smoothed_cc.reshape(-1, 1) ) def _joint_log_likelihood(self, X): """Calculate the posterior log probability of the samples X""" n_features = self.feature_log_prob_.shape[1] n_features_X = X.shape[1] if n_features_X!= n_features: raise ValueError( "Expected input with %d features, got %d instead" % (n_features, n_features_X) ) neg_prob = np.log(1 - np.exp(self.feature_log_prob_)) # Compute neg_prob · (1 - X).T as ∑neg_prob - X · neg_prob jll = safe_sparse_dot(X, (self.feature_log_prob_ - neg_prob).T) jll += self.class_log_prior_ + neg_prob.sum(axis=1) return jll class CategoricalNB(_BaseDiscreteNB): """Naive Bayes classifier for categorical features. The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution. Read more in the :ref:`User Guide <categorical_naive_bayes>`. Parameters ---------- alpha : float, default=1.0 Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing). force_alpha : bool, default=False If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0. .. versionadded:: 1.2 .. deprecated:: 1.2 The default value of `force_alpha` will change to `True` in v1.4. fit_prior : bool, default=True Whether to learn class prior probabilities or not. If false, a uniform prior will be used. class_prior : array-like of shape (n_classes,), default=None Prior probabilities of the classes. If specified, the priors are not adjusted according to the data. min_categories : int or array-like of shape (n_features,), default=None Minimum number of categories per feature. - integer: Sets the minimum number of categories per feature to `n_categories` for each features. - array-like: shape (n_features,) where `n_categories[i]` holds the minimum number of categories for the ith column of the input. - None (default): Determines the number of categories automatically from the training data. .. versionadded:: 0.24 Attributes ---------- category_count_ : list of arrays of shape (n_features,) Holds arrays of shape (n_classes, n_categories of respective feature) for each feature. Each array provides the number of samples encountered for each class and category of the specific feature. class_count_ : ndarray of shape (n_classes,) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided. class_log_prior_ : ndarray of shape (n_classes,) Smoothed empirical log probability for each class. classes_ : ndarray of shape (n_classes,) Class labels known to the classifier feature_log_prob_ : list of arrays of shape (n_features,) Holds arrays of shape (n_classes, n_categories of respective feature) for each feature. Each array provides the empirical log probability of categories given the respective feature and class, ``P(x_i|y)``. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_categories_ : ndarray of shape (n_features,), dtype=np.int64 Number of categories for each feature. This value is inferred from the data or set by the minimum number of categories. .. versionadded:: 0.24 See Also -------- BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models. ComplementNB : Complement Naive Bayes classifier. GaussianNB : Gaussian Naive Bayes. MultinomialNB : Naive Bayes classifier for multinomial models. Examples -------- >>> import numpy as np >>> rng = np.random.RandomState(1) >>> X = rng.randint(5, size=(6, 100)) >>> y = np.array([1, 2, 3, 4, 5, 6]) >>> from sklearn.naive_bayes import CategoricalNB >>> clf = CategoricalNB(force_alpha=True) >>> clf.fit(X, y) CategoricalNB(force_alpha=True) >>> print(clf.predict(X[2:3])) [3] """ _parameter_constraints: dict = { **_BaseDiscreteNB._parameter_constraints, "min_categories": [ None, "array-like", Interval(Integral, 1, None, closed="left"), ], "alpha": [Interval(Real, 0, None, closed="left")], } def __init__( self, *, alpha=1.0, force_alpha="warn", fit_prior=True, class_prior=None, min_categories=None, ): super().__init__( alpha=alpha, force_alpha=force_alpha, fit_prior=fit_prior, class_prior=class_prior, ) self.min_categories = min_categories def fit(self, X, y, sample_weight=None): """Fit Naive Bayes classifier according to X, y. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0,..., n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- self : object Returns the instance itself. """ return super().fit(X, y, sample_weight=sample_weight) def partial_fit(self, X, y, classes=None, sample_weight=None): """Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning. This is especially useful when the whole dataset is too big to fit in memory at once. This method has some performance overhead hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0,..., n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder. y : array-like of shape (n_samples,) Target values. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- self : object Returns the instance itself. """ return super().partial_fit(X, y, classes, sample_weight=sample_weight) def _more_tags(self): return {"requires_positive_X": True} def _check_X(self, X): """Validate X, used only in predict* methods.""" X = self._validate_data( X, dtype="int", accept_sparse=False, force_all_finite=True, reset=False ) check_non_negative(X, "CategoricalNB (input X)") return X def _check_X_y(self, X, y, reset=True): X, y = self._validate_data( X, y, dtype="int", accept_sparse=False, force_all_finite=True, reset=reset ) check_non_negative(X, "CategoricalNB (input X)") return X, y def _init_counters(self, n_classes, n_features): self.class_count_ = np.zeros(n_classes, dtype=np.float64) self.category_count_ = [np.zeros((n_classes, 0)) for _ in range(n_features)] @staticmethod def _validate_n_categories(X, min_categories): # rely on max for n_categories categories are encoded between 0...n-1 n_categories_X = X.max(axis=0) + 1 min_categories_ = np.array(min_categories) if min_categories is not None: if not np.issubdtype(min_categories_.dtype, np.signedinteger): raise ValueError( "'min_categories' should have integral type. Got " f"{min_categories_.dtype} instead." ) n_categories_ = np.maximum(n_categories_X, min_categories_, dtype=np.int64) if n_categories_.shape!= n_categories_X.shape: raise ValueError( f"'min_categories' should have shape ({X.shape[1]}," ") when an array-like is provided. Got" f" {min_categories_.shape} instead." ) return n_categories_ else: return n_categories_X def _count(self, X, Y): def _update_cat_count_dims(cat_count, highest_feature): diff = highest_feature + 1 - cat_count.shape[1] if diff > 0: # we append a column full of zeros for each new category return np.pad(cat_count, [(0, 0), (0, diff)], "constant") return cat_count def _update_cat_count(X_feature, Y, cat_count, n_classes): for j in range(n_classes): mask = Y[:, j].astype(bool) if Y.dtype.type == np.int64: weights = None else: weights = Y[mask, j] counts = np.bincount(X_feature[mask], weights=weights) indices = np.nonzero(counts)[0] cat_count[j, indices] += counts[indices] self.class_count_ += Y.sum(axis=0) self.n_categories_ = self._validate_n_categories(X, self.min_categories) for i in range(self.n_features_in_): X_feature = X[:, i] self.category_count_[i] = _update_cat_count_dims( self.category_count_[i], self.n_categories_[i] - 1 ) _update_cat_count( X_feature, Y, self.category_count_[i], self.class_count_.shape[0] ) def _update_feature_log_prob(self, alpha): feature_log_prob = [] for i in range(self.n_features_in_): smoothed_cat_count = self.category_count_[i] + alpha smoothed_class_count = smoothed_cat_count.sum(axis=1) feature_log_prob.append( np.log(smoothed_cat_count) - np.log(smoothed_class_count.reshape(-1, 1)) ) self.feature_log_prob_ = feature_log_prob def _joint_log_likelihood(self, X): self._check_n_features(X, reset=False) jll = np.zeros((X.shape[0], self.class_count_.shape[0])) for i in range(self.n_features_in_): indices = X[:, i] jll += self.feature_log_prob_[i][:, indices].T total_ll = jll + self.class_log_prior_ return total_ll

scikit-learn__scikit-learn

compose.rst

Tutorial

Generate tutorial about pipelines

BSD 3-Clause New or Revised License

scikit-learn__scikit-learn/doc/modules/compose.rst

Pipelines and composite estimators Transformers are usually combined with classifiers, regressors or other estimators to build a composite estimator. The most common tool is a Pipeline <pipeline>. Pipeline is often used in combination with FeatureUnion <feature_union> which concatenates the output of transformers into a composite feature space. TransformedTargetRegressor <transformed_target_regressor> deals with transforming the target (i.e. log-transform y). In contrast, Pipelines only transform the observed data (X). Pipeline: chaining estimators Pipeline can be used to chain multiple estimators into one. This is useful as there is often a fixed sequence of steps in processing the data, for example feature selection, normalization and classification. Pipeline serves multiple purposes here: Convenience and encapsulation You only have to call fit and predict once on your data to fit a whole sequence of estimators. Joint parameter selection You can grid search <grid_search> over parameters of all estimators in the pipeline at once. Safety Pipelines help avoid leaking statistics from your test data into the trained model in cross-validation, by ensuring that the same samples are used to train the transformers and predictors. All estimators in a pipeline, except the last one, must be transformers (i.e. must have a transform method). The last estimator may be any type (transformer, classifier, etc.). Note Calling fit on the pipeline is the same as calling fit on each estimator in turn, transform the input and pass it on to the next step. The pipeline has all the methods that the last estimator in the pipeline has, i.e. if the last estimator is a classifier, the Pipeline can be used as a classifier. If the last estimator is a transformer, again, so is the pipeline. Usage Construction The Pipeline is built using a list of (key, value) pairs, where the key is a string containing the name you want to give this step and value is an estimator object: >>> from sklearn.pipeline import Pipeline >>> from sklearn.svm import SVC >>> from sklearn.decomposition import PCA >>> estimators = [('reduce_dim', PCA()), ('clf', SVC())] >>> pipe = Pipeline(estimators) >>> pipe Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())]) The utility function make_pipeline is a shorthand for constructing pipelines; it takes a variable number of estimators and returns a pipeline, filling in the names automatically: >>> from sklearn.pipeline import make_pipeline >>> make_pipeline(PCA(), SVC()) Pipeline(steps=[('pca', PCA()), ('svc', SVC())]) Accessing steps The estimators of a pipeline are stored as a list in the steps attribute, but can be accessed by index or name by indexing (with [idx]) the Pipeline: >>> pipe.steps[0] ('reduce_dim', PCA()) >>> pipe[0] PCA() >>> pipe['reduce_dim'] PCA() Pipeline's named_steps attribute allows accessing steps by name with tab completion in interactive environments: >>> pipe.named_steps.reduce_dim is pipe['reduce_dim'] True A sub-pipeline can also be extracted using the slicing notation commonly used for Python Sequences such as lists or strings (although only a step of 1 is permitted). This is convenient for performing only some of the transformations (or their inverse): >>> pipe[:1] Pipeline(steps=[('reduce_dim', PCA())]) >>> pipe[-1:] Pipeline(steps=[('clf', SVC())]) Nested parameters Parameters of the estimators in the pipeline can be accessed using the <estimator>__<parameter> syntax: >>> pipe.set_params(clf__C=10) Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC(C=10))]) This is particularly important for doing grid searches: >>> from sklearn.model_selection import GridSearchCV >>> param_grid = dict(reduce_dim__n_components=[2, 5, 10], ... clf__C=[0.1, 10, 100]) >>> grid_search = GridSearchCV(pipe, param_grid=param_grid) Individual steps may also be replaced as parameters, and non-final steps may be ignored by setting them to 'passthrough': >>> from sklearn.linear_model import LogisticRegression >>> param_grid = dict(reduce_dim=['passthrough', PCA(5), PCA(10)], ... clf=[SVC(), LogisticRegression()], ... clf__C=[0.1, 10, 100]) >>> grid_search = GridSearchCV(pipe, param_grid=param_grid) The estimators of the pipeline can be retrieved by index: >>> pipe[0] PCA() or by name: >>> pipe['reduce_dim'] PCA() To enable model inspection, ~sklearn.pipeline.Pipeline has a get_feature_names_out() method, just like all transformers. You can use pipeline slicing to get the feature names going into each step: >>> from sklearn.datasets import load_iris >>> from sklearn.feature_selection import SelectKBest >>> iris = load_iris() >>> pipe = Pipeline(steps=[ ... ('select', SelectKBest(k=2)), ... ('clf', LogisticRegression())]) >>> pipe.fit(iris.data, iris.target) Pipeline(steps=[('select', SelectKBest(...)), ('clf', LogisticRegression(...))]) >>> pipe[:-1].get_feature_names_out() array(['x2', 'x3'], ...) You can also provide custom feature names for the input data using get_feature_names_out: >>> pipe[:-1].get_feature_names_out(iris.feature_names) array(['petal length (cm)', 'petal width (cm)'], ...) Examples: - sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py - sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py - sphx_glr_auto_examples_compose_plot_digits_pipe.py - sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py - sphx_glr_auto_examples_svm_plot_svm_anova.py - sphx_glr_auto_examples_compose_plot_compare_reduction.py - sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py See Also: - composite_grid_search Caching transformers: avoid repeated computation sklearn.pipeline Fitting transformers may be computationally expensive. With its memory parameter set, Pipeline will cache each transformer after calling fit. This feature is used to avoid computing the fit transformers within a pipeline if the parameters and input data are identical. A typical example is the case of a grid search in which the transformers can be fitted only once and reused for each configuration. The last step will never be cached, even if it is a transformer. The parameter memory is needed in order to cache the transformers. memory can be either a string containing the directory where to cache the transformers or a joblib.Memory object: >>> from tempfile import mkdtemp >>> from shutil import rmtree >>> from sklearn.decomposition import PCA >>> from sklearn.svm import SVC >>> from sklearn.pipeline import Pipeline >>> estimators = [('reduce_dim', PCA()), ('clf', SVC())] >>> cachedir = mkdtemp() >>> pipe = Pipeline(estimators, memory=cachedir) >>> pipe Pipeline(memory=..., steps=[('reduce_dim', PCA()), ('clf', SVC())]) >>> # Clear the cache directory when you don't need it anymore >>> rmtree(cachedir) Warning: Side effect of caching transformers Using a Pipeline without cache enabled, it is possible to inspect the original instance such as: >>> from sklearn.datasets import load_digits >>> X_digits, y_digits = load_digits(return_X_y=True) >>> pca1 = PCA() >>> svm1 = SVC() >>> pipe = Pipeline([('reduce_dim', pca1), ('clf', svm1)]) >>> pipe.fit(X_digits, y_digits) Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())]) >>> # The pca instance can be inspected directly >>> print(pca1.components_) [[-1.77484909e-19 ... 4.07058917e-18]] Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. In following example, accessing the ~sklearn.decomposition.PCA instance pca2 will raise an AttributeError since pca2 will be an unfitted transformer. Instead, use the attribute named_steps to inspect estimators within the pipeline: >>> cachedir = mkdtemp() >>> pca2 = PCA() >>> svm2 = SVC() >>> cached_pipe = Pipeline([('reduce_dim', pca2), ('clf', svm2)], ... memory=cachedir) >>> cached_pipe.fit(X_digits, y_digits) Pipeline(memory=..., steps=[('reduce_dim', PCA()), ('clf', SVC())]) >>> print(cached_pipe.named_steps['reduce_dim'].components_) [[-1.77484909e-19 ... 4.07058917e-18]] >>> # Remove the cache directory >>> rmtree(cachedir) Transforming target in regression ~sklearn.compose.TransformedTargetRegressor transforms the targets y before fitting a regression model. The predictions are mapped back to the original space via an inverse transform. It takes as an argument the regressor that will be used for prediction, and the transformer that will be applied to the target variable: >>> import numpy as np >>> from sklearn.datasets import fetch_california_housing >>> from sklearn.compose import TransformedTargetRegressor >>> from sklearn.preprocessing import QuantileTransformer >>> from sklearn.linear_model import LinearRegression >>> from sklearn.model_selection import train_test_split >>> X, y = fetch_california_housing(return_X_y=True) >>> X, y = X[:2000, :], y[:2000] # select a subset of data >>> transformer = QuantileTransformer(output_distribution='normal') >>> regressor = LinearRegression() >>> regr = TransformedTargetRegressor(regressor=regressor, ... transformer=transformer) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) >>> regr.fit(X_train, y_train) TransformedTargetRegressor(...) >>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test))) R2 score: 0.61 >>> raw_target_regr = LinearRegression().fit(X_train, y_train) >>> print('R2 score: {0:.2f}'.format(raw_target_regr.score(X_test, y_test))) R2 score: 0.59 For simple transformations, instead of a Transformer object, a pair of functions can be passed, defining the transformation and its inverse mapping: >>> def func(x): ... return np.log(x) >>> def inverse_func(x): ... return np.exp(x) Subsequently, the object is created as: >>> regr = TransformedTargetRegressor(regressor=regressor, ... func=func, ... inverse_func=inverse_func) >>> regr.fit(X_train, y_train) TransformedTargetRegressor(...) >>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test))) R2 score: 0.51 By default, the provided functions are checked at each fit to be the inverse of each other. However, it is possible to bypass this checking by setting check_inverse to False: >>> def inverse_func(x): ... return x >>> regr = TransformedTargetRegressor(regressor=regressor, ... func=func, ... inverse_func=inverse_func, ... check_inverse=False) >>> regr.fit(X_train, y_train) TransformedTargetRegressor(...) >>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test))) R2 score: -1.57 Note The transformation can be triggered by setting either transformer or the pair of functions func and inverse_func. However, setting both options will raise an error. FeatureUnion: composite feature spaces FeatureUnion combines several transformer objects into a new transformer that combines their output. A FeatureUnion takes a list of transformer objects. During fitting, each of these is fit to the data independently. The transformers are applied in parallel, and the feature matrices they output are concatenated side-by-side into a larger matrix. When you want to apply different transformations to each field of the data, see the related class ~sklearn.compose.ColumnTransformer (see user guide <column_transformer>). FeatureUnion serves the same purposes as Pipeline -convenience and joint parameter estimation and validation. FeatureUnion and Pipeline can be combined to create complex models. (A FeatureUnion has no way of checking whether two transformers might produce identical features. It only produces a union when the feature sets are disjoint, and making sure they are is the caller's responsibility.) Usage A FeatureUnion is built using a list of (key, value) pairs, where the key is the name you want to give to a given transformation (an arbitrary string; it only serves as an identifier) and value is an estimator object: >>> from sklearn.pipeline import FeatureUnion >>> from sklearn.decomposition import PCA >>> from sklearn.decomposition import KernelPCA >>> estimators = [('linear_pca', PCA()), ('kernel_pca', KernelPCA())] >>> combined = FeatureUnion(estimators) >>> combined FeatureUnion(transformer_list=[('linear_pca', PCA()), ('kernel_pca', KernelPCA())]) Like pipelines, feature unions have a shorthand constructor called make_union that does not require explicit naming of the components. Like Pipeline, individual steps may be replaced using set_params, and ignored by setting to 'drop': >>> combined.set_params(kernel_pca='drop') FeatureUnion(transformer_list=[('linear_pca', PCA()), ('kernel_pca', 'drop')]) ColumnTransformer for heterogeneous data Many datasets contain features of different types, say text, floats, and dates, where each type of feature requires separate preprocessing or feature extraction steps. Often it is easiest to preprocess data before applying scikit-learn methods, for example using pandas. Processing your data before passing it to scikit-learn might be problematic for one of the following reasons: 1. Incorporating statistics from test data into the preprocessors makes cross-validation scores unreliable (known as data leakage), for example in the case of scalers or imputing missing values. 2. You may want to include the parameters of the preprocessors in a parameter search <grid_search>. The ~sklearn.compose.ColumnTransformer helps performing different transformations for different columns of the data, within a ~sklearn.pipeline.Pipeline that is safe from data leakage and that can be parametrized. ~sklearn.compose.ColumnTransformer works on arrays, sparse matrices, and pandas DataFrames. To each column, a different transformation can be applied, such as preprocessing or a specific feature extraction method: >>> import pandas as pd >>> X = pd.DataFrame( ... {'city': ['London', 'London', 'Paris', 'Sallisaw'], ... 'title': ["His Last Bow", "How Watson Learned the Trick", ... "A Moveable Feast", "The Grapes of Wrath"], ... 'expert_rating': [5, 3, 4, 5], ... 'user_rating': [4, 5, 4, 3]}) For this data, we might want to encode the 'city' column as a categorical variable using ~sklearn.preprocessing.OneHotEncoder but apply a ~sklearn.feature_extraction.text.CountVectorizer to the 'title' column. As we might use multiple feature extraction methods on the same column, we give each transformer a unique name, say 'city_category' and 'title_bow'. By default, the remaining rating columns are ignored (remainder='drop'): >>> from sklearn.compose import ColumnTransformer >>> from sklearn.feature_extraction.text import CountVectorizer >>> from sklearn.preprocessing import OneHotEncoder >>> column_trans = ColumnTransformer( ... [('categories', OneHotEncoder(dtype='int'), ['city']), ... ('title_bow', CountVectorizer(), 'title')], ... remainder='drop', verbose_feature_names_out=False) >>> column_trans.fit(X) ColumnTransformer(transformers=[('categories', OneHotEncoder(dtype='int'), ['city']), ('title_bow', CountVectorizer(), 'title')], verbose_feature_names_out=False) >>> column_trans.get_feature_names_out() array(['city_London', 'city_Paris', 'city_Sallisaw', 'bow', 'feast', 'grapes', 'his', 'how', 'last', 'learned', 'moveable', 'of', 'the', 'trick', 'watson', 'wrath'], ...) >>> column_trans.transform(X).toarray() array([[1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0], [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1]]...) In the above example, the ~sklearn.feature_extraction.text.CountVectorizer expects a 1D array as input and therefore the columns were specified as a string ('title'). However, ~sklearn.preprocessing.OneHotEncoder as most of other transformers expects 2D data, therefore in that case you need to specify the column as a list of strings (['city']). Apart from a scalar or a single item list, the column selection can be specified as a list of multiple items, an integer array, a slice, a boolean mask, or with a ~sklearn.compose.make_column_selector. The ~sklearn.compose.make_column_selector is used to select columns based on data type or column name: >>> from sklearn.preprocessing import StandardScaler >>> from sklearn.compose import make_column_selector >>> ct = ColumnTransformer([ ... ('scale', StandardScaler(), ... make_column_selector(dtype_include=np.number)), ... ('onehot', ... OneHotEncoder(), ... make_column_selector(pattern='city', dtype_include=object))]) >>> ct.fit_transform(X) array([[ 0.904..., 0. , 1. , 0. , 0. ], [-1.507..., 1.414..., 1. , 0. , 0. ], [-0.301..., 0. , 0. , 1. , 0. ], [ 0.904..., -1.414..., 0. , 0. , 1. ]]) Strings can reference columns if the input is a DataFrame, integers are always interpreted as the positional columns. We can keep the remaining rating columns by setting remainder='passthrough'. The values are appended to the end of the transformation: >>> column_trans = ColumnTransformer( ... [('city_category', OneHotEncoder(dtype='int'),['city']), ... ('title_bow', CountVectorizer(), 'title')], ... remainder='passthrough') >>> column_trans.fit_transform(X) array([[1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 4], [1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 3, 5], [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 4], [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 5, 3]]...) The remainder parameter can be set to an estimator to transform the remaining rating columns. The transformed values are appended to the end of the transformation: >>> from sklearn.preprocessing import MinMaxScaler >>> column_trans = ColumnTransformer( ... [('city_category', OneHotEncoder(), ['city']), ... ('title_bow', CountVectorizer(), 'title')], ... remainder=MinMaxScaler()) >>> column_trans.fit_transform(X)[:, -2:] array([[1. , 0.5], [0. , 1. ], [0.5, 0.5], [1. , 0. ]]) The ~sklearn.compose.make_column_transformer function is available to more easily create a ~sklearn.compose.ColumnTransformer object. Specifically, the names will be given automatically. The equivalent for the above example would be: >>> from sklearn.compose import make_column_transformer >>> column_trans = make_column_transformer( ... (OneHotEncoder(), ['city']), ... (CountVectorizer(), 'title'), ... remainder=MinMaxScaler()) >>> column_trans ColumnTransformer(remainder=MinMaxScaler(), transformers=[('onehotencoder', OneHotEncoder(), ['city']), ('countvectorizer', CountVectorizer(), 'title')]) If ~sklearn.compose.ColumnTransformer is fitted with a dataframe and the dataframe only has string column names, then transforming a dataframe will use the column names to select the columns: >>> ct = ColumnTransformer( ... [("scale", StandardScaler(), ["expert_rating"])]).fit(X) >>> X_new = pd.DataFrame({"expert_rating": [5, 6, 1], ... "ignored_new_col": [1.2, 0.3, -0.1]}) >>> ct.transform(X_new) array([[ 0.9...], [ 2.1...], [-3.9...]]) Visualizing Composite Estimators Estimators are displayed with an HTML representation when shown in a jupyter notebook. This is useful to diagnose or visualize a Pipeline with many estimators. This visualization is activated by default: >>> column_trans # doctest: +SKIP It can be deactivated by setting the display option in ~sklearn.set_config to 'text': >>> from sklearn import set_config >>> set_config(display='text') # doctest: +SKIP >>> # displays text representation in a jupyter context >>> column_trans # doctest: +SKIP An example of the HTML output can be seen in the HTML representation of Pipeline section of sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py. As an alternative, the HTML can be written to a file using ~sklearn.utils.estimator_html_repr: >>> from sklearn.utils import estimator_html_repr >>> with open('my_estimator.html', 'w') as f: # doctest: +SKIP ... f.write(estimator_html_repr(clf))

""" The :mod:`sklearn.pipeline` module implements utilities to build a composite estimator, as a chain of transforms and estimators. """ # Author: Edouard Duchesnay # Gael Varoquaux # Virgile Fritsch # Alexandre Gramfort # Lars Buitinck # License: BSD from collections import defaultdict from itertools import islice import numpy as np from scipy import sparse from.base import TransformerMixin, _fit_context, clone from.exceptions import NotFittedError from.preprocessing import FunctionTransformer from.utils import Bunch, _print_elapsed_time, check_pandas_support from.utils._estimator_html_repr import _VisualBlock from.utils._metadata_requests import METHODS from.utils._param_validation import HasMethods, Hidden from.utils._set_output import _get_output_config, _safe_set_output from.utils._tags import _safe_tags from.utils.metadata_routing import ( MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing, ) from.utils.metaestimators import _BaseComposition, available_if from.utils.parallel import Parallel, delayed from.utils.validation import check_is_fitted, check_memory __all__ = ["Pipeline", "FeatureUnion", "make_pipeline", "make_union"] def _final_estimator_has(attr): """Check that final_estimator has `attr`. Used together with `available_if` in `Pipeline`.""" def check(self): # raise original `AttributeError` if `attr` does not exist getattr(self._final_estimator, attr) return True return check class Pipeline(_BaseComposition): """ Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final estimator only needs to implement `fit`. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a `'__'`, as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to `'passthrough'` or `None`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py` shows how to grid search on a pipeline using `'__'` as a separator in the parameter names. Read more in the :ref:`User Guide <pipeline>`. .. versionadded:: 0.5 Parameters ---------- steps : list of tuple List of (name, transform) tuples (implementing `fit`/`transform`) that are chained in sequential order. The last transform must be an estimator. memory : str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed. Attributes ---------- named_steps : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters. classes_ : ndarray of shape (n_classes,) The classes labels. Only exist if the last step of the pipeline is a classifier. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying first estimator in `steps` exposes such an attribute when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 1.0 See Also -------- make_pipeline : Convenience function for simplified pipeline construction. Examples -------- >>> from sklearn.svm import SVC >>> from sklearn.preprocessing import StandardScaler >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split >>> from sklearn.pipeline import Pipeline >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, ... random_state=0) >>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())]) >>> # The pipeline can be used as any other estimator >>> # and avoids leaking the test set into the train set >>> pipe.fit(X_train, y_train).score(X_test, y_test) 0.88 >>> # An estimator's parameter can be set using '__' syntax >>> pipe.set_params(svc__C=10).fit(X_train, y_train).score(X_test, y_test) 0.76 """ # BaseEstimator interface _required_parameters = ["steps"] _parameter_constraints: dict = { "steps": [list, Hidden(tuple)], "memory": [None, str, HasMethods(["cache"])], "verbose": ["boolean"], } def __init__(self, steps, *, memory=None, verbose=False): self.steps = steps self.memory = memory self.verbose = verbose def set_output(self, *, transform=None): """Set the output container when `"transform"` and `"fit_transform"` are called. Calling `set_output` will set the output of all estimators in `steps`. Parameters ---------- transform : {"default", "pandas"}, default=None Configure output of `transform` and `fit_transform`. - `"default"`: Default output format of a transformer - `"pandas"`: DataFrame output - `None`: Transform configuration is unchanged Returns ------- self : estimator instance Estimator instance. """ for _, _, step in self._iter(): _safe_set_output(step, transform=transform) return self def get_params(self, deep=True): """Get parameters for this estimator. Returns the parameters given in the constructor as well as the estimators contained within the `steps` of the `Pipeline`. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ return self._get_params("steps", deep=deep) def set_params(self, **kwargs): """Set the parameters of this estimator. Valid parameter keys can be listed with ``get_params()``. Note that you can directly set the parameters of the estimators contained in `steps`. Parameters ---------- **kwargs : dict Parameters of this estimator or parameters of estimators contained in `steps`. Parameters of the steps may be set using its name and the parameter name separated by a '__'. Returns ------- self : object Pipeline class instance. """ self._set_params("steps", **kwargs) return self def _validate_steps(self): names, estimators = zip(*self.steps) # validate names self._validate_names(names) # validate estimators transformers = estimators[:-1] estimator = estimators[-1] for t in transformers: if t is None or t == "passthrough": continue if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr( t, "transform" ): raise TypeError( "All intermediate steps should be " "transformers and implement fit and transform " "or be the string 'passthrough' " "'%s' (type %s) doesn't" % (t, type(t)) ) # We allow last estimator to be None as an identity transformation if ( estimator is not None and estimator!= "passthrough" and not hasattr(estimator, "fit") ): raise TypeError( "Last step of Pipeline should implement fit " "or be the string 'passthrough'. " "'%s' (type %s) doesn't" % (estimator, type(estimator)) ) def _iter(self, with_final=True, filter_passthrough=True): """ Generate (idx, (name, trans)) tuples from self.steps When filter_passthrough is True, 'passthrough' and None transformers are filtered out. """ stop = len(self.steps) if not with_final: stop -= 1 for idx, (name, trans) in enumerate(islice(self.steps, 0, stop)): if not filter_passthrough: yield idx, name, trans elif trans is not None and trans!= "passthrough": yield idx, name, trans def __len__(self): """ Returns the length of the Pipeline """ return len(self.steps) def __getitem__(self, ind): """Returns a sub-pipeline or a single estimator in the pipeline Indexing with an integer will return an estimator; using a slice returns another Pipeline instance which copies a slice of this Pipeline. This copy is shallow: modifying (or fitting) estimators in the sub-pipeline will affect the larger pipeline and vice-versa. However, replacing a value in `step` will not affect a copy. """ if isinstance(ind, slice): if ind.step not in (1, None): raise ValueError("Pipeline slicing only supports a step of 1") return self.__class__( self.steps[ind], memory=self.memory, verbose=self.verbose ) try: name, est = self.steps[ind] except TypeError: # Not an int, try get step by name return self.named_steps[ind] return est @property def _estimator_type(self): return self.steps[-1][1]._estimator_type @property def named_steps(self): """Access the steps by name. Read-only attribute to access any step by given name. Keys are steps names and values are the steps objects.""" # Use Bunch object to improve autocomplete return Bunch(**dict(self.steps)) @property def _final_estimator(self): try: estimator = self.steps[-1][1] return "passthrough" if estimator is None else estimator except (ValueError, AttributeError, TypeError): # This condition happens when a call to a method is first calling # `_available_if` and `fit` did not validate `steps` yet. We # return `None` and an `InvalidParameterError` will be raised # right after. return None def _log_message(self, step_idx): if not self.verbose: return None name, _ = self.steps[step_idx] return "(step %d of %d) Processing %s" % (step_idx + 1, len(self.steps), name) def _check_method_params(self, method, props, **kwargs): if _routing_enabled(): routed_params = process_routing(self, method, **props, **kwargs) return routed_params else: fit_params_steps = Bunch( **{ name: Bunch(**{method: {} for method in METHODS}) for name, step in self.steps if step is not None } ) for pname, pval in props.items(): if "__" not in pname: raise ValueError( "Pipeline.fit does not accept the {} parameter. " "You can pass parameters to specific steps of your " "pipeline using the stepname__parameter format, e.g. " "`Pipeline.fit(X, y, logisticregression__sample_weight" "=sample_weight)`.".format(pname) ) step, param = pname.split("__", 1) fit_params_steps[step]["fit"][param] = pval # without metadata routing, fit_transform and fit_predict # get all the same params and pass it to the last fit. fit_params_steps[step]["fit_transform"][param] = pval fit_params_steps[step]["fit_predict"][param] = pval return fit_params_steps # Estimator interface def _fit(self, X, y=None, routed_params=None): # shallow copy of steps - this should really be steps_ self.steps = list(self.steps) self._validate_steps() # Setup the memory memory = check_memory(self.memory) fit_transform_one_cached = memory.cache(_fit_transform_one) for step_idx, name, transformer in self._iter( with_final=False, filter_passthrough=False ): if transformer is None or transformer == "passthrough": with _print_elapsed_time("Pipeline", self._log_message(step_idx)): continue if hasattr(memory, "location") and memory.location is None: # we do not clone when caching is disabled to # preserve backward compatibility cloned_transformer = transformer else: cloned_transformer = clone(transformer) # Fit or load from cache the current transformer X, fitted_transformer = fit_transform_one_cached( cloned_transformer, X, y, None, message_clsname="Pipeline", message=self._log_message(step_idx), params=routed_params[name], ) # Replace the transformer of the step with the fitted # transformer. This is necessary when loading the transformer # from the cache. self.steps[step_idx] = (name, fitted_transformer) return X @_fit_context( # estimators in Pipeline.steps are not validated yet prefer_skip_nested_validation=False ) def fit(self, X, y=None, **params): """Fit the model. Fit all the transformers one after the other and transform the data. Finally, fit the transformed data using the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True` is set via :func:`~sklearn.set_config`. See :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Returns ------- self : object Pipeline with fitted steps. """ routed_params = self._check_method_params(method="fit", props=params) Xt = self._fit(X, y, routed_params) with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): if self._final_estimator!= "passthrough": last_step_params = routed_params[self.steps[-1][0]] self._final_estimator.fit(Xt, y, **last_step_params["fit"]) return self def _can_fit_transform(self): return ( self._final_estimator == "passthrough" or hasattr(self._final_estimator, "transform") or hasattr(self._final_estimator, "fit_transform") ) @available_if(_can_fit_transform) @_fit_context( # estimators in Pipeline.steps are not validated yet prefer_skip_nested_validation=False ) def fit_transform(self, X, y=None, **params): """Fit the model and transform with the final estimator. Fits all the transformers one after the other and transform the data. Then uses `fit_transform` on transformed data with the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Returns ------- Xt : ndarray of shape (n_samples, n_transformed_features) Transformed samples. """ routed_params = self._check_method_params(method="fit_transform", props=params) Xt = self._fit(X, y, routed_params) last_step = self._final_estimator with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): if last_step == "passthrough": return Xt last_step_params = routed_params[self.steps[-1][0]] if hasattr(last_step, "fit_transform"): return last_step.fit_transform( Xt, y, **last_step_params["fit_transform"] ) else: return last_step.fit(Xt, y, **last_step_params["fit"]).transform( Xt, **last_step_params["transform"] ) @available_if(_final_estimator_has("predict")) def predict(self, X, **params): """Transform the data, and apply `predict` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict` method. Only valid if the final estimator implements `predict`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the ``predict`` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True` is set via :func:`~sklearn.set_config`. See :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Note that while this may be used to return uncertainties from some models with ``return_std`` or ``return_cov``, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator. Returns ------- y_pred : ndarray Result of calling `predict` on the final estimator. """ Xt = X if not _routing_enabled(): for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][1].predict(Xt, **params) # metadata routing enabled routed_params = process_routing(self, "predict", **params) for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt, **routed_params[name].transform) return self.steps[-1][1].predict(Xt, **routed_params[self.steps[-1][0]].predict) @available_if(_final_estimator_has("fit_predict")) @_fit_context( # estimators in Pipeline.steps are not validated yet prefer_skip_nested_validation=False ) def fit_predict(self, X, y=None, **params): """Transform the data, and apply `fit_predict` with the final estimator. Call `fit_transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `fit_predict` method. Only valid if the final estimator implements `fit_predict`. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the ``predict`` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Note that while this may be used to return uncertainties from some models with ``return_std`` or ``return_cov``, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator. Returns ------- y_pred : ndarray Result of calling `fit_predict` on the final estimator. """ routed_params = self._check_method_params(method="fit_predict", props=params) Xt = self._fit(X, y, routed_params) params_last_step = routed_params[self.steps[-1][0]] with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): y_pred = self.steps[-1][1].fit_predict( Xt, y, **params_last_step.get("fit_predict", {}) ) return y_pred @available_if(_final_estimator_has("predict_proba")) def predict_proba(self, X, **params): """Transform the data, and apply `predict_proba` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict_proba` method. Only valid if the final estimator implements `predict_proba`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the `predict_proba` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Returns ------- y_proba : ndarray of shape (n_samples, n_classes) Result of calling `predict_proba` on the final estimator. """ Xt = X if not _routing_enabled(): for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][1].predict_proba(Xt, **params) # metadata routing enabled routed_params = process_routing(self, "predict_proba", **params) for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt, **routed_params[name].transform) return self.steps[-1][1].predict_proba( Xt, **routed_params[self.steps[-1][0]].predict_proba ) @available_if(_final_estimator_has("decision_function")) def decision_function(self, X, **params): """Transform the data, and apply `decision_function` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `decision_function` method. Only valid if the final estimator implements `decision_function`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **params : dict of string -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Returns ------- y_score : ndarray of shape (n_samples, n_classes) Result of calling `decision_function` on the final estimator. """ _raise_for_params(params, self, "decision_function") # not branching here since params is only available if # enable_metadata_routing=True routed_params = process_routing(self, "decision_function", **params) Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform( Xt, **routed_params.get(name, {}).get("transform", {}) ) return self.steps[-1][1].decision_function( Xt, **routed_params.get(self.steps[-1][0], {}).get("decision_function", {}) ) @available_if(_final_estimator_has("score_samples")) def score_samples(self, X): """Transform the data, and apply `score_samples` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `score_samples` method. Only valid if the final estimator implements `score_samples`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : ndarray of shape (n_samples,) Result of calling `score_samples` on the final estimator. """ Xt = X for _, _, transformer in self._iter(with_final=False): Xt = transformer.transform(Xt) return self.steps[-1][1].score_samples(Xt) @available_if(_final_estimator_has("predict_log_proba")) def predict_log_proba(self, X, **params): """Transform the data, and apply `predict_log_proba` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict_log_proba` method. Only valid if the final estimator implements `predict_log_proba`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the `predict_log_proba` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Returns ------- y_log_proba : ndarray of shape (n_samples, n_classes) Result of calling `predict_log_proba` on the final estimator. """ Xt = X if not _routing_enabled(): for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][1].predict_log_proba(Xt, **params) # metadata routing enabled routed_params = process_routing(self, "predict_log_proba", **params) for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt, **routed_params[name].transform) return self.steps[-1][1].predict_log_proba( Xt, **routed_params[self.steps[-1][0]].predict_log_proba ) def _can_transform(self): return self._final_estimator == "passthrough" or hasattr( self._final_estimator, "transform" ) @available_if(_can_transform) def transform(self, X, **params): """Transform the data, and apply `transform` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `transform` method. Only valid if the final estimator implements `transform`. This also works where final estimator is `None` in which case all prior transformations are applied. Parameters ---------- X : iterable Data to transform. Must fulfill input requirements of first step of the pipeline. **params : dict of str -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Returns ------- Xt : ndarray of shape (n_samples, n_transformed_features) Transformed data. """ _raise_for_params(params, self, "transform") # not branching here since params is only available if # enable_metadata_routing=True routed_params = process_routing(self, "transform", **params) Xt = X for _, name, transform in self._iter(): Xt = transform.transform(Xt, **routed_params[name].transform) return Xt def _can_inverse_transform(self): return all(hasattr(t, "inverse_transform") for _, _, t in self._iter()) @available_if(_can_inverse_transform) def inverse_transform(self, Xt, **params): """Apply `inverse_transform` for each step in a reverse order. All estimators in the pipeline must support `inverse_transform`. Parameters ---------- Xt : array-like of shape (n_samples, n_transformed_features) Data samples, where ``n_samples`` is the number of samples and ``n_features`` is the number of features. Must fulfill input requirements of last step of pipeline's ``inverse_transform`` method. **params : dict of str -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Returns ------- Xt : ndarray of shape (n_samples, n_features) Inverse transformed data, that is, data in the original feature space. """ _raise_for_params(params, self, "inverse_transform") # we don't have to branch here, since params is only non-empty if # enable_metadata_routing=True. routed_params = process_routing(self, "inverse_transform", **params) reverse_iter = reversed(list(self._iter())) for _, name, transform in reverse_iter: Xt = transform.inverse_transform( Xt, **routed_params[name].inverse_transform ) return Xt @available_if(_final_estimator_has("score")) def score(self, X, y=None, sample_weight=None, **params): """Transform the data, and apply `score` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `score` method. Only valid if the final estimator implements `score`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Targets used for scoring. Must fulfill label requirements for all steps of the pipeline. sample_weight : array-like, default=None If not None, this argument is passed as ``sample_weight`` keyword argument to the ``score`` method of the final estimator. **params : dict of str -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Returns ------- score : float Result of calling `score` on the final estimator. """ Xt = X if not _routing_enabled(): for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) score_params = {} if sample_weight is not None: score_params["sample_weight"] = sample_weight return self.steps[-1][1].score(Xt, y, **score_params) # metadata routing is enabled. routed_params = process_routing( self, "score", sample_weight=sample_weight, **params ) Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt, **routed_params[name].transform) return self.steps[-1][1].score(Xt, y, **routed_params[self.steps[-1][0]].score) @property def classes_(self): """The classes labels. Only exist if the last step is a classifier.""" return self.steps[-1][1].classes_ def _more_tags(self): tags = { "_xfail_checks": { "check_dont_overwrite_parameters": ( "Pipeline changes the `steps` parameter, which it shouldn't." "Therefore this test is x-fail until we fix this." ), "check_estimators_overwrite_params": ( "Pipeline changes the `steps` parameter, which it shouldn't." "Therefore this test is x-fail until we fix this." ), } } try: tags["pairwise"] = _safe_tags(self.steps[0][1], "pairwise") except (ValueError, AttributeError, TypeError): # This happens when the `steps` is not a list of (name, estimator) # tuples and `fit` is not called yet to validate the steps. pass try: tags["multioutput"] = _safe_tags(self.steps[-1][1], "multioutput") except (ValueError, AttributeError, TypeError): # This happens when the `steps` is not a list of (name, estimator) # tuples and `fit` is not called yet to validate the steps. pass return tags def get_feature_names_out(self, input_features=None): """Get output feature names for transformation. Transform input features using the pipeline. Parameters ---------- input_features : array-like of str or None, default=None Input features. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. """ feature_names_out = input_features for _, name, transform in self._iter(): if not hasattr(transform, "get_feature_names_out"): raise AttributeError( "Estimator {} does not provide get_feature_names_out. " "Did you mean to call pipeline[:-1].get_feature_names_out" "()?".format(name) ) feature_names_out = transform.get_feature_names_out(feature_names_out) return feature_names_out @property def n_features_in_(self): """Number of features seen during first step `fit` method.""" # delegate to first step (which will call _check_is_fitted) return self.steps[0][1].n_features_in_ @property def feature_names_in_(self): """Names of features seen during first step `fit` method.""" # delegate to first step (which will call _check_is_fitted) return self.steps[0][1].feature_names_in_ def __sklearn_is_fitted__(self): """Indicate whether pipeline has been fit.""" try: # check if the last step of the pipeline is fitted # we only check the last step since if the last step is fit, it # means the previous steps should also be fit. This is faster than # checking if every step of the pipeline is fit. check_is_fitted(self.steps[-1][1]) return True except NotFittedError: return False def _sk_visual_block_(self): _, estimators = zip(*self.steps) def _get_name(name, est): if est is None or est == "passthrough": return f"{name}: passthrough" # Is an estimator return f"{name}: {est.__class__.__name__}" names = [_get_name(name, est) for name, est in self.steps] name_details = [str(est) for est in estimators] return _VisualBlock( "serial", estimators, names=names, name_details=name_details, dash_wrapped=False, ) def get_metadata_routing(self): """Get metadata routing of this object. Please check :ref:`User Guide <metadata_routing>` on how the routing mechanism works. Returns ------- routing : MetadataRouter A :class:`~utils.metadata_routing.MetadataRouter` encapsulating routing information. """ router = MetadataRouter(owner=self.__class__.__name__) # first we add all steps except the last one for _, name, trans in self._iter(with_final=False, filter_passthrough=True): method_mapping = MethodMapping() # fit, fit_predict, and fit_transform call fit_transform if it # exists, or else fit and transform if hasattr(trans, "fit_transform"): ( method_mapping.add(caller="fit", callee="fit_transform") .add(caller="fit_transform", callee="fit_transform") .add(caller="fit_predict", callee="fit_transform") ) else: ( method_mapping.add(caller="fit", callee="fit") .add(caller="fit", callee="transform") .add(caller="fit_transform", callee="fit") .add(caller="fit_transform", callee="transform") .add(caller="fit_predict", callee="fit") .add(caller="fit_predict", callee="transform") ) ( method_mapping.add(caller="predict", callee="transform") .add(caller="predict", callee="transform") .add(caller="predict_proba", callee="transform") .add(caller="decision_function", callee="transform") .add(caller="predict_log_proba", callee="transform") .add(caller="transform", callee="transform") .add(caller="inverse_transform", callee="inverse_transform") .add(caller="score", callee="transform") ) router.add(method_mapping=method_mapping, **{name: trans}) final_name, final_est = self.steps[-1] if final_est is None or final_est == "passthrough": return router # then we add the last step method_mapping = MethodMapping() if hasattr(final_est, "fit_transform"): method_mapping.add(caller="fit_transform", callee="fit_transform") else: method_mapping.add(caller="fit", callee="fit").add( caller="fit", callee="transform" ) ( method_mapping.add(caller="fit", callee="fit") .add(caller="predict", callee="predict") .add(caller="fit_predict", callee="fit_predict") .add(caller="predict_proba", callee="predict_proba") .add(caller="decision_function", callee="decision_function") .add(caller="predict_log_proba", callee="predict_log_proba") .add(caller="transform", callee="transform") .add(caller="inverse_transform", callee="inverse_transform") .add(caller="score", callee="score") ) router.add(method_mapping=method_mapping, **{final_name: final_est}) return router def _name_estimators(estimators): """Generate names for estimators.""" names = [ estimator if isinstance(estimator, str) else type(estimator).__name__.lower() for estimator in estimators ] namecount = defaultdict(int) for est, name in zip(estimators, names): namecount[name] += 1 for k, v in list(namecount.items()): if v == 1: del namecount[k] for i in reversed(range(len(estimators))): name = names[i] if name in namecount: names[i] += "-%d" % namecount[name] namecount[name] -= 1 return list(zip(names, estimators)) def make_pipeline(*steps, memory=None, verbose=False): """Construct a :class:`Pipeline` from the given estimators. This is a shorthand for the :class:`Pipeline` constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically. Parameters ---------- *steps : list of Estimator objects List of the scikit-learn estimators that are chained together. memory : str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed. Returns ------- p : Pipeline Returns a scikit-learn :class:`Pipeline` object. See Also -------- Pipeline : Class for creating a pipeline of transforms with a final estimator. Examples -------- >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> from sklearn.pipeline import make_pipeline >>> make_pipeline(StandardScaler(), GaussianNB(priors=None)) Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb', GaussianNB())]) """ return Pipeline(_name_estimators(steps), memory=memory, verbose=verbose) def _transform_one(transformer, X, y, weight, params): """Call transform and apply weight to output. Parameters ---------- transformer : estimator Estimator to be used for transformation. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input data to be transformed. y : ndarray of shape (n_samples,) Ignored. weight : float Weight to be applied to the output of the transformation. params : dict Parameters to be passed to the transformer's ``transform`` method. This should be of the form ``process_routing()["step_name"]``. """ res = transformer.transform(X, **params.transform) # if we have a weight for this transformer, multiply output if weight is None: return res return res * weight def _fit_transform_one( transformer, X, y, weight, message_clsname="", message=None, params=None ): """ Fits ``transformer`` to ``X`` and ``y``. The transformed result is returned with the fitted transformer. If ``weight`` is not ``None``, the result will be multiplied by ``weight``. ``params`` needs to be of the form ``process_routing()["step_name"]``. """ params = params or {} with _print_elapsed_time(message_clsname, message): if hasattr(transformer, "fit_transform"): res = transformer.fit_transform(X, y, **params.get("fit_transform", {})) else: res = transformer.fit(X, y, **params.get("fit", {})).transform( X, **params.get("transform", {}) ) if weight is None: return res, transformer return res * weight, transformer def _fit_one(transformer, X, y, weight, message_clsname="", message=None, params=None): """ Fits ``transformer`` to ``X`` and ``y``. """ with _print_elapsed_time(message_clsname, message): return transformer.fit(X, y, **params["fit"]) class FeatureUnion(TransformerMixin, _BaseComposition): """Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer. Parameters of the transformers may be set using its name and the parameter name separated by a '__'. A transformer may be replaced entirely by setting the parameter with its name to another transformer, removed by setting to 'drop' or disabled by setting to 'passthrough' (features are passed without transformation). Read more in the :ref:`User Guide <feature_union>`. .. versionadded:: 0.13 Parameters ---------- transformer_list : list of (str, transformer) tuples List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer. The transformer can be 'drop' for it to be ignored or can be 'passthrough' for features to be passed unchanged. .. versionadded:: 1.1 Added the option `"passthrough"`. .. versionchanged:: 0.22 Deprecated `None` as a transformer in favor of 'drop'. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None transformer_weights : dict, default=None Multiplicative weights for features per transformer. Keys are transformer names, values the weights. Raises ValueError if key not present in ``transformer_list``. verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed. Attributes ---------- named_transformers : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. Read-only attribute to access any transformer parameter by user given name. Keys are transformer names and values are transformer parameters. .. versionadded:: 1.2 n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying first transformer in `transformer_list` exposes such an attribute when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.3 See Also -------- make_union : Convenience function for simplified feature union construction. Examples -------- >>> from sklearn.pipeline import FeatureUnion >>> from sklearn.decomposition import PCA, TruncatedSVD >>> union = FeatureUnion([("pca", PCA(n_components=1)), ... ("svd", TruncatedSVD(n_components=2))]) >>> X = [[0., 1., 3], [2., 2., 5]] >>> union.fit_transform(X) array([[ 1.5 , 3.0..., 0.8...], [-1.5 , 5.7..., -0.4...]]) >>> # An estimator's parameter can be set using '__' syntax >>> union.set_params(pca__n_components=1).fit_transform(X) array([[ 1.5 , 3.0...], [-1.5 , 5.7...]]) For a more detailed example of usage, see :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py`. """ _required_parameters = ["transformer_list"] def __init__( self, transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False ): self.transformer_list = transformer_list self.n_jobs = n_jobs self.transformer_weights = transformer_weights self.verbose = verbose def set_output(self, *, transform=None): """Set the output container when `"transform"` and `"fit_transform"` are called. `set_output` will set the output of all estimators in `transformer_list`. Parameters ---------- transform : {"default", "pandas"}, default=None Configure output of `transform` and `fit_transform`. - `"default"`: Default output format of a transformer - `"pandas"`: DataFrame output - `None`: Transform configuration is unchanged Returns ------- self : estimator instance Estimator instance. """ super().set_output(transform=transform) for _, step, _ in self._iter(): _safe_set_output(step, transform=transform) return self @property def named_transformers(self): # Use Bunch object to improve autocomplete return Bunch(**dict(self.transformer_list)) def get_params(self, deep=True): """Get parameters for this estimator. Returns the parameters given in the constructor as well as the estimators contained within the `transformer_list` of the `FeatureUnion`. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ return self._get_params("transformer_list", deep=deep) def set_params(self, **kwargs): """Set the parameters of this estimator. Valid parameter keys can be listed with ``get_params()``. Note that you can directly set the parameters of the estimators contained in `transformer_list`. Parameters ---------- **kwargs : dict Parameters of this estimator or parameters of estimators contained in `transform_list`. Parameters of the transformers may be set using its name and the parameter name separated by a '__'. Returns ------- self : object FeatureUnion class instance. """ self._set_params("transformer_list", **kwargs) return self def _validate_transformers(self): names, transformers = zip(*self.transformer_list) # validate names self._validate_names(names) # validate estimators for t in transformers: if t in ("drop", "passthrough"): continue if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr( t, "transform" ): raise TypeError( "All estimators should implement fit and " "transform. '%s' (type %s) doesn't" % (t, type(t)) ) def _validate_transformer_weights(self): if not self.transformer_weights: return transformer_names = set(name for name, _ in self.transformer_list) for name in self.transformer_weights: if name not in transformer_names: raise ValueError( f'Attempting to weight transformer "{name}", ' "but it is not present in transformer_list." ) def _iter(self): """ Generate (name, trans, weight) tuples excluding None and 'drop' transformers. """ get_weight = (self.transformer_weights or {}).get for name, trans in self.transformer_list: if trans == "drop": continue if trans == "passthrough": trans = FunctionTransformer(feature_names_out="one-to-one") yield (name, trans, get_weight(name)) def get_feature_names_out(self, input_features=None): """Get output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Input features. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. """ feature_names = [] for name, trans, _ in self._iter(): if not hasattr(trans, "get_feature_names_out"): raise AttributeError( "Transformer %s (type %s) does not provide get_feature_names_out." % (str(name), type(trans).__name__) ) feature_names.extend( [f"{name}__{f}" for f in trans.get_feature_names_out(input_features)] ) return np.asarray(feature_names, dtype=object) def fit(self, X, y=None, **fit_params): """Fit all transformers using X. Parameters ---------- X : iterable or array-like, depending on transformers Input data, used to fit transformers. y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning. **fit_params : dict, default=None Parameters to pass to the fit method of the estimator. Returns ------- self : object FeatureUnion class instance. """ transformers = self._parallel_func(X, y, fit_params, _fit_one) if not transformers: # All transformers are None return self self._update_transformer_list(transformers) return self def fit_transform(self, X, y=None, **fit_params): """Fit all transformers, transform the data and concatenate results. Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed. y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning. **fit_params : dict, default=None Parameters to pass to the fit method of the estimator. Returns ------- X_t : array-like or sparse matrix of \ shape (n_samples, sum_n_components) The `hstack` of results of transformers. `sum_n_components` is the sum of `n_components` (output dimension) over transformers. """ results = self._parallel_func(X, y, fit_params, _fit_transform_one) if not results: # All transformers are None return np.zeros((X.shape[0], 0)) Xs, transformers = zip(*results) self._update_transformer_list(transformers) return self._hstack(Xs) def _log_message(self, name, idx, total): if not self.verbose: return None return "(step %d of %d) Processing %s" % (idx, total, name) def _parallel_func(self, X, y, fit_params, func): """Runs func in parallel on X and y""" self.transformer_list = list(self.transformer_list) self._validate_transformers() self._validate_transformer_weights() transformers = list(self._iter()) params = Bunch(fit=fit_params, fit_transform=fit_params) return Parallel(n_jobs=self.n_jobs)( delayed(func)( transformer, X, y, weight, message_clsname="FeatureUnion", message=self._log_message(name, idx, len(transformers)), params=params, ) for idx, (name, transformer, weight) in enumerate(transformers, 1) ) def transform(self, X): """Transform X separately by each transformer, concatenate results. Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed. Returns ------- X_t : array-like or sparse matrix of \ shape (n_samples, sum_n_components) The `hstack` of results of transformers. `sum_n_components` is the sum of `n_components` (output dimension) over transformers. """ # TODO(SLEP6): accept **params here in `transform` and route it to the # underlying estimators. params = Bunch(transform={}) Xs = Parallel(n_jobs=self.n_jobs)( delayed(_transform_one)(trans, X, None, weight, params) for name, trans, weight in self._iter() ) if not Xs: # All transformers are None return np.zeros((X.shape[0], 0)) return self._hstack(Xs) def _hstack(self, Xs): config = _get_output_config("transform", self) if config["dense"] == "pandas" and all(hasattr(X, "iloc") for X in Xs): pd = check_pandas_support("transform") return pd.concat(Xs, axis=1) if any(sparse.issparse(f) for f in Xs): Xs = sparse.hstack(Xs).tocsr() else: Xs = np.hstack(Xs) return Xs def _update_transformer_list(self, transformers): transformers = iter(transformers) self.transformer_list[:] = [ (name, old if old == "drop" else next(transformers)) for name, old in self.transformer_list ] @property def n_features_in_(self): """Number of features seen during :term:`fit`.""" # X is passed to all transformers so we just delegate to the first one return self.transformer_list[0][1].n_features_in_ @property def feature_names_in_(self): """Names of features seen during :term:`fit`.""" # X is passed to all transformers -- delegate to the first one return self.transformer_list[0][1].feature_names_in_ def __sklearn_is_fitted__(self): # Delegate whether feature union was fitted for _, transformer, _ in self._iter(): check_is_fitted(transformer) return True def _sk_visual_block_(self): names, transformers = zip(*self.transformer_list) return _VisualBlock("parallel", transformers, names=names) def __getitem__(self, name): """Return transformer with name.""" if not isinstance(name, str): raise KeyError("Only string keys are supported") return self.named_transformers[name] def make_union(*transformers, n_jobs=None, verbose=False): """Construct a :class:`FeatureUnion` from the given transformers. This is a shorthand for the :class:`FeatureUnion` constructor; it does not require, and does not permit, naming the transformers. Instead, they will be given names automatically based on their types. It also does not allow weighting. Parameters ---------- *transformers : list of estimators One or more estimators. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None. verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed. Returns ------- f : FeatureUnion A :class:`FeatureUnion` object for concatenating the results of multiple transformer objects. See Also -------- FeatureUnion : Class for concatenating the results of multiple transformer objects. Examples -------- >>> from sklearn.decomposition import PCA, TruncatedSVD >>> from sklearn.pipeline import make_union >>> make_union(PCA(), TruncatedSVD()) FeatureUnion(transformer_list=[('pca', PCA()), ('truncatedsvd', TruncatedSVD())]) """ return FeatureUnion(_name_estimators(transformers), n_jobs=n_jobs, verbose=verbose)

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scikit-learn__scikit-learn/sklearn/feature_extraction

Feature extraction The sklearn.feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image. Note Feature extraction is very different from feature_selection: the former consists in transforming arbitrary data, such as text or images, into numerical features usable for machine learning. The latter is a machine learning technique applied on these features. Loading features from dicts The class DictVectorizer can be used to convert feature arrays represented as lists of standard Python dict objects to the NumPy/SciPy representation used by scikit-learn estimators. While not particularly fast to process, Python's dict has the advantages of being convenient to use, being sparse (absent features need not be stored) and storing feature names in addition to values. DictVectorizer implements what is called one-of-K or "one-hot" coding for categorical (aka nominal, discrete) features. Categorical features are "attribute-value" pairs where the value is restricted to a list of discrete possibilities without ordering (e.g. topic identifiers, types of objects, tags, names...). In the following, "city" is a categorical attribute while "temperature" is a traditional numerical feature: >>> measurements = [ ... {'city': 'Dubai', 'temperature': 33.}, ... {'city': 'London', 'temperature': 12.}, ... {'city': 'San Francisco', 'temperature': 18.}, ... ] >>> from sklearn.feature_extraction import DictVectorizer >>> vec = DictVectorizer() >>> vec.fit_transform(measurements).toarray() array([[ 1., 0., 0., 33.], [ 0., 1., 0., 12.], [ 0., 0., 1., 18.]]) >>> vec.get_feature_names_out() array(['city=Dubai', 'city=London', 'city=San Francisco', 'temperature'], ...) DictVectorizer accepts multiple string values for one feature, like, e.g., multiple categories for a movie. Assume a database classifies each movie using some categories (not mandatories) and its year of release. >>> movie_entry = [{'category': ['thriller', 'drama'], 'year': 2003}, ... {'category': ['animation', 'family'], 'year': 2011}, ... {'year': 1974}] >>> vec.fit_transform(movie_entry).toarray() array([[0.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 2.003e+03], [1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 2.011e+03], [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.974e+03]]) >>> vec.get_feature_names_out() array(['category=animation', 'category=drama', 'category=family', 'category=thriller', 'year'], ...) >>> vec.transform({'category': ['thriller'], ... 'unseen_feature': '3'}).toarray() array([[0., 0., 0., 1., 0.]]) DictVectorizer is also a useful representation transformation for training sequence classifiers in Natural Language Processing models that typically work by extracting feature windows around a particular word of interest. For example, suppose that we have a first algorithm that extracts Part of Speech (PoS) tags that we want to use as complementary tags for training a sequence classifier (e.g. a chunker). The following dict could be such a window of features extracted around the word 'sat' in the sentence 'The cat sat on the mat.': >>> pos_window = [ ... { ... 'word-2': 'the', ... 'pos-2': 'DT', ... 'word-1': 'cat', ... 'pos-1': 'NN', ... 'word+1': 'on', ... 'pos+1': 'PP', ... }, ... # in a real application one would extract many such dictionaries ... ] This description can be vectorized into a sparse two-dimensional matrix suitable for feeding into a classifier (maybe after being piped into a ~text.TfidfTransformer for normalization): >>> vec = DictVectorizer() >>> pos_vectorized = vec.fit_transform(pos_window) >>> pos_vectorized <1x6 sparse matrix of type '<... 'numpy.float64'>' with 6 stored elements in Compressed Sparse ... format> >>> pos_vectorized.toarray() array([[1., 1., 1., 1., 1., 1.]]) >>> vec.get_feature_names_out() array(['pos+1=PP', 'pos-1=NN', 'pos-2=DT', 'word+1=on', 'word-1=cat', 'word-2=the'], ...) As you can imagine, if one extracts such a context around each individual word of a corpus of documents the resulting matrix will be very wide (many one-hot-features) with most of them being valued to zero most of the time. So as to make the resulting data structure able to fit in memory the DictVectorizer class uses a scipy.sparse matrix by default instead of a numpy.ndarray. Feature hashing The class FeatureHasher is a high-speed, low-memory vectorizer that uses a technique known as feature hashing, or the "hashing trick". Instead of building a hash table of the features encountered in training, as the vectorizers do, instances of FeatureHasher apply a hash function to the features to determine their column index in sample matrices directly. The result is increased speed and reduced memory usage, at the expense of inspectability; the hasher does not remember what the input features looked like and has no inverse_transform method. Since the hash function might cause collisions between (unrelated) features, a signed hash function is used and the sign of the hash value determines the sign of the value stored in the output matrix for a feature. This way, collisions are likely to cancel out rather than accumulate error, and the expected mean of any output feature's value is zero. This mechanism is enabled by default with alternate_sign=True and is particularly useful for small hash table sizes (n_features < 10000). For large hash table sizes, it can be disabled, to allow the output to be passed to estimators like ~sklearn.naive_bayes.MultinomialNB or ~sklearn.feature_selection.chi2 feature selectors that expect non-negative inputs. FeatureHasher accepts either mappings (like Python's dict and its variants in the collections module), (feature, value) pairs, or strings, depending on the constructor parameter input_type. Mapping are treated as lists of (feature, value) pairs, while single strings have an implicit value of 1, so ['feat1', 'feat2', 'feat3'] is interpreted as [('feat1', 1), ('feat2', 1), ('feat3', 1)]. If a single feature occurs multiple times in a sample, the associated values will be summed (so ('feat', 2) and ('feat', 3.5) become ('feat', 5.5)). The output from FeatureHasher is always a scipy.sparse matrix in the CSR format. Feature hashing can be employed in document classification, but unlike ~text.CountVectorizer, FeatureHasher does not do word splitting or any other preprocessing except Unicode-to-UTF-8 encoding; see hashing_vectorizer, below, for a combined tokenizer/hasher. As an example, consider a word-level natural language processing task that needs features extracted from (token, part_of_speech) pairs. One could use a Python generator function to extract features: def token_features(token, part_of_speech): if token.isdigit(): yield "numeric" else: yield "token={}".format(token.lower()) yield "token,pos={},{}".format(token, part_of_speech) if token[0].isupper(): yield "uppercase_initial" if token.isupper(): yield "all_uppercase" yield "pos={}".format(part_of_speech) Then, the raw_X to be fed to FeatureHasher.transform can be constructed using: raw_X = (token_features(tok, pos_tagger(tok)) for tok in corpus) and fed to a hasher with: hasher = FeatureHasher(input_type='string') X = hasher.transform(raw_X) to get a scipy.sparse matrix X. Note the use of a generator comprehension, which introduces laziness into the feature extraction: tokens are only processed on demand from the hasher. Implementation details FeatureHasher uses the signed 32-bit variant of MurmurHash3. As a result (and because of limitations in scipy.sparse), the maximum number of features supported is currently 2³¹ − 1. The original formulation of the hashing trick by Weinberger et al. used two separate hash functions h and ξ to determine the column index and sign of a feature, respectively. The present implementation works under the assumption that the sign bit of MurmurHash3 is independent of its other bits. Since a simple modulo is used to transform the hash function to a column index, it is advisable to use a power of two as the n_features parameter; otherwise the features will not be mapped evenly to the columns. References: - Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola and Josh Attenberg (2009). Feature hashing for large scale multitask learning. Proc. ICML. - MurmurHash3. Text feature extraction The Bag of Words representation Text Analysis is a major application field for machine learning algorithms. However the raw data, a sequence of symbols cannot be fed directly to the algorithms themselves as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length. In order to address this, scikit-learn provides utilities for the most common ways to extract numerical features from text content, namely: - tokenizing strings and giving an integer id for each possible token, for instance by using white-spaces and punctuation as token separators. - counting the occurrences of tokens in each document. - normalizing and weighting with diminishing importance tokens that occur in the majority of samples / documents. In this scheme, features and samples are defined as follows: - each individual token occurrence frequency (normalized or not) is treated as a feature. - the vector of all the token frequencies for a given document is considered a multivariate sample. A corpus of documents can thus be represented by a matrix with one row per document and one column per token (e.g. word) occurring in the corpus. We call vectorization the general process of turning a collection of text documents into numerical feature vectors. This specific strategy (tokenization, counting and normalization) is called the Bag of Words or "Bag of n-grams" representation. Documents are described by word occurrences while completely ignoring the relative position information of the words in the document. Sparsity As most documents will typically use a very small subset of the words used in the corpus, the resulting matrix will have many feature values that are zeros (typically more than 99% of them). For instance a collection of 10,000 short text documents (such as emails) will use a vocabulary with a size in the order of 100,000 unique words in total while each document will use 100 to 1000 unique words individually. In order to be able to store such a matrix in memory but also to speed up algebraic operations matrix / vector, implementations will typically use a sparse representation such as the implementations available in the scipy.sparse package. Common Vectorizer usage CountVectorizer implements both tokenization and occurrence counting in a single class: >>> from sklearn.feature_extraction.text import CountVectorizer This model has many parameters, however the default values are quite reasonable (please see the reference documentation <text_feature_extraction_ref> for the details): >>> vectorizer = CountVectorizer() >>> vectorizer CountVectorizer() Let's use it to tokenize and count the word occurrences of a minimalistic corpus of text documents: >>> corpus = [ ... 'This is the first document.', ... 'This is the second second document.', ... 'And the third one.', ... 'Is this the first document?', ... ] >>> X = vectorizer.fit_transform(corpus) >>> X <4x9 sparse matrix of type '<... 'numpy.int64'>' with 19 stored elements in Compressed Sparse ... format> The default configuration tokenizes the string by extracting words of at least 2 letters. The specific function that does this step can be requested explicitly: >>> analyze = vectorizer.build_analyzer() >>> analyze("This is a text document to analyze.") == ( ... ['this', 'is', 'text', 'document', 'to', 'analyze']) True Each term found by the analyzer during the fit is assigned a unique integer index corresponding to a column in the resulting matrix. This interpretation of the columns can be retrieved as follows: >>> vectorizer.get_feature_names_out() array(['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this'], ...) >>> X.toarray() array([[0, 1, 1, 1, 0, 0, 1, 0, 1], [0, 1, 0, 1, 0, 2, 1, 0, 1], [1, 0, 0, 0, 1, 0, 1, 1, 0], [0, 1, 1, 1, 0, 0, 1, 0, 1]]...) The converse mapping from feature name to column index is stored in the vocabulary_ attribute of the vectorizer: >>> vectorizer.vocabulary_.get('document') 1 Hence words that were not seen in the training corpus will be completely ignored in future calls to the transform method: >>> vectorizer.transform(['Something completely new.']).toarray() array([[0, 0, 0, 0, 0, 0, 0, 0, 0]]...) Note that in the previous corpus, the first and the last documents have exactly the same words hence are encoded in equal vectors. In particular we lose the information that the last document is an interrogative form. To preserve some of the local ordering information we can extract 2-grams of words in addition to the 1-grams (individual words): >>> bigram_vectorizer = CountVectorizer(ngram_range=(1, 2), ... token_pattern=r'\b\w+\b', min_df=1) >>> analyze = bigram_vectorizer.build_analyzer() >>> analyze('Bi-grams are cool!') == ( ... ['bi', 'grams', 'are', 'cool', 'bi grams', 'grams are', 'are cool']) True The vocabulary extracted by this vectorizer is hence much bigger and can now resolve ambiguities encoded in local positioning patterns: >>> X_2 = bigram_vectorizer.fit_transform(corpus).toarray() >>> X_2 array([[0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0], [0, 0, 1, 0, 0, 1, 1, 0, 0, 2, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0], [1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1]]...) In particular the interrogative form "Is this" is only present in the last document: >>> feature_index = bigram_vectorizer.vocabulary_.get('is this') >>> X_2[:, feature_index] array([0, 0, 0, 1]...) Using stop words Stop words are words like "and", "the", "him", which are presumed to be uninformative in representing the content of a text, and which may be removed to avoid them being construed as signal for prediction. Sometimes, however, similar words are useful for prediction, such as in classifying writing style or personality. There are several known issues in our provided 'english' stop word list. It does not aim to be a general, 'one-size-fits-all' solution as some tasks may require a more custom solution. See [NQY18] for more details. Please take care in choosing a stop word list. Popular stop word lists may include words that are highly informative to some tasks, such as computer. You should also make sure that the stop word list has had the same preprocessing and tokenization applied as the one used in the vectorizer. The word we've is split into we and ve by CountVectorizer's default tokenizer, so if we've is in stop_words, but ve is not, ve will be retained from we've in transformed text. Our vectorizers will try to identify and warn about some kinds of inconsistencies. References Tf–idf term weighting In a large text corpus, some words will be very present (e.g. "the", "a", "is" in English) hence carrying very little meaningful information about the actual contents of the document. If we were to feed the direct count data directly to a classifier those very frequent terms would shadow the frequencies of rarer yet more interesting terms. In order to re-weight the count features into floating point values suitable for usage by a classifier it is very common to use the tf–idf transform. Tf means term-frequency while tf–idf means term-frequency times inverse document-frequency: tf-idf(t,d) = tf(t,d) × idf(t). Using the TfidfTransformer's default settings, TfidfTransformer(norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False) the term frequency, the number of times a term occurs in a given document, is multiplied with idf component, which is computed as $\text{idf}(t) = \log{\frac{1 + n}{1+\text{df}(t)}} + 1$, where n is the total number of documents in the document set, and df(t) is the number of documents in the document set that contain term t. The resulting tf-idf vectors are then normalized by the Euclidean norm: $v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 + v{_2}^2 + \dots + v{_n}^2}}$. This was originally a term weighting scheme developed for information retrieval (as a ranking function for search engines results) that has also found good use in document classification and clustering. The following sections contain further explanations and examples that illustrate how the tf-idfs are computed exactly and how the tf-idfs computed in scikit-learn's TfidfTransformer and TfidfVectorizer differ slightly from the standard textbook notation that defines the idf as $\text{idf}(t) = \log{\frac{n}{1+\text{df}(t)}}.$ In the TfidfTransformer and TfidfVectorizer with smooth_idf=False, the "1" count is added to the idf instead of the idf's denominator: $\text{idf}(t) = \log{\frac{n}{\text{df}(t)}} + 1$ This normalization is implemented by the TfidfTransformer class: >>> from sklearn.feature_extraction.text import TfidfTransformer >>> transformer = TfidfTransformer(smooth_idf=False) >>> transformer TfidfTransformer(smooth_idf=False) Again please see the reference documentation <text_feature_extraction_ref> for the details on all the parameters. Let's take an example with the following counts. The first term is present 100% of the time hence not very interesting. The two other features only in less than 50% of the time hence probably more representative of the content of the documents: >>> counts = [[3, 0, 1], ... [2, 0, 0], ... [3, 0, 0], ... [4, 0, 0], ... [3, 2, 0], ... [3, 0, 2]] ... >>> tfidf = transformer.fit_transform(counts) >>> tfidf <6x3 sparse matrix of type '<... 'numpy.float64'>' with 9 stored elements in Compressed Sparse ... format> >>> tfidf.toarray() array([[0.81940995, 0. , 0.57320793], [1. , 0. , 0. ], [1. , 0. , 0. ], [1. , 0. , 0. ], [0.47330339, 0.88089948, 0. ], [0.58149261, 0. , 0.81355169]]) Each row is normalized to have unit Euclidean norm: $v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 + v{_2}^2 + \dots + v{_n}^2}}$ For example, we can compute the tf-idf of the first term in the first document in the counts array as follows: n = 6 df(t)_(term1) = 6 $\text{idf}(t)_{\text{term1}} = \log \frac{n}{\text{df}(t)} + 1 = \log(1)+1 = 1$ tf-idf_(term1) = tf × idf = 3 × 1 = 3 Now, if we repeat this computation for the remaining 2 terms in the document, we get tf-idf_(term2) = 0 × (log(6/1)+1) = 0 tf-idf_(term3) = 1 × (log(6/2)+1) ≈ 2.0986 and the vector of raw tf-idfs: tf-idf_(raw) = [3,0,2.0986]. Then, applying the Euclidean (L2) norm, we obtain the following tf-idfs for document 1: $\frac{[3, 0, 2.0986]}{\sqrt{\big(3^2 + 0^2 + 2.0986^2\big)}} = [ 0.819, 0, 0.573].$ Furthermore, the default parameter smooth_idf=True adds "1" to the numerator and denominator as if an extra document was seen containing every term in the collection exactly once, which prevents zero divisions: $\text{idf}(t) = \log{\frac{1 + n}{1+\text{df}(t)}} + 1$ Using this modification, the tf-idf of the third term in document 1 changes to 1.8473: tf-idf_(term3) = 1 × log (7/3) + 1 ≈ 1.8473 And the L2-normalized tf-idf changes to $\frac{[3, 0, 1.8473]}{\sqrt{\big(3^2 + 0^2 + 1.8473^2\big)}} = [0.8515, 0, 0.5243]$: >>> transformer = TfidfTransformer() >>> transformer.fit_transform(counts).toarray() array([[0.85151335, 0. , 0.52433293], [1. , 0. , 0. ], [1. , 0. , 0. ], [1. , 0. , 0. ], [0.55422893, 0.83236428, 0. ], [0.63035731, 0. , 0.77630514]]) The weights of each feature computed by the fit method call are stored in a model attribute: >>> transformer.idf_ array([1. ..., 2.25..., 1.84...]) As tf–idf is very often used for text features, there is also another class called TfidfVectorizer that combines all the options of CountVectorizer and TfidfTransformer in a single model: >>> from sklearn.feature_extraction.text import TfidfVectorizer >>> vectorizer = TfidfVectorizer() >>> vectorizer.fit_transform(corpus) <4x9 sparse matrix of type '<... 'numpy.float64'>' with 19 stored elements in Compressed Sparse ... format> While the tf–idf normalization is often very useful, there might be cases where the binary occurrence markers might offer better features. This can be achieved by using the binary parameter of CountVectorizer. In particular, some estimators such as bernoulli_naive_bayes explicitly model discrete boolean random variables. Also, very short texts are likely to have noisy tf–idf values while the binary occurrence info is more stable. As usual the best way to adjust the feature extraction parameters is to use a cross-validated grid search, for instance by pipelining the feature extractor with a classifier: - sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py Decoding text files Text is made of characters, but files are made of bytes. These bytes represent characters according to some encoding. To work with text files in Python, their bytes must be decoded to a character set called Unicode. Common encodings are ASCII, Latin-1 (Western Europe), KOI8-R (Russian) and the universal encodings UTF-8 and UTF-16. Many others exist. Note An encoding can also be called a 'character set', but this term is less accurate: several encodings can exist for a single character set. The text feature extractors in scikit-learn know how to decode text files, but only if you tell them what encoding the files are in. The CountVectorizer takes an encoding parameter for this purpose. For modern text files, the correct encoding is probably UTF-8, which is therefore the default (encoding="utf-8"). If the text you are loading is not actually encoded with UTF-8, however, you will get a UnicodeDecodeError. The vectorizers can be told to be silent about decoding errors by setting the decode_error parameter to either "ignore" or "replace". See the documentation for the Python function bytes.decode for more details (type help(bytes.decode) at the Python prompt). If you are having trouble decoding text, here are some things to try: - Find out what the actual encoding of the text is. The file might come with a header or README that tells you the encoding, or there might be some standard encoding you can assume based on where the text comes from. - You may be able to find out what kind of encoding it is in general using the UNIX command file. The Python chardet module comes with a script called chardetect.py that will guess the specific encoding, though you cannot rely on its guess being correct. - You could try UTF-8 and disregard the errors. You can decode byte strings with bytes.decode(errors='replace') to replace all decoding errors with a meaningless character, or set decode_error='replace' in the vectorizer. This may damage the usefulness of your features. - Real text may come from a variety of sources that may have used different encodings, or even be sloppily decoded in a different encoding than the one it was encoded with. This is common in text retrieved from the Web. The Python package ftfy can automatically sort out some classes of decoding errors, so you could try decoding the unknown text as latin-1 and then using ftfy to fix errors. - If the text is in a mish-mash of encodings that is simply too hard to sort out (which is the case for the 20 Newsgroups dataset), you can fall back on a simple single-byte encoding such as latin-1. Some text may display incorrectly, but at least the same sequence of bytes will always represent the same feature. For example, the following snippet uses chardet (not shipped with scikit-learn, must be installed separately) to figure out the encoding of three texts. It then vectorizes the texts and prints the learned vocabulary. The output is not shown here. >>> import chardet # doctest: +SKIP >>> text1 = b"Sei mir gegrxc3xbcxc3x9ft mein Sauerkraut" >>> text2 = b"holdselig sind deine Gerxfcche" >>> text3 = b"xffxfeAx00ux00fx00 x00Fx00lx00xfcx00gx00ex00lx00nx00 x00dx00ex00sx00 x00Gx00ex00sx00ax00nx00gx00ex00sx00,x00 x00Hx00ex00rx00zx00lx00ix00ex00bx00cx00hx00ex00nx00,x00 x00tx00rx00ax00gx00 x00ix00cx00hx00 x00dx00ix00cx00hx00 x00fx00ox00rx00tx00" >>> decoded = [x.decode(chardet.detect(x)['encoding']) ... for x in (text1, text2, text3)] # doctest: +SKIP >>> v = CountVectorizer().fit(decoded).vocabulary # doctest: +SKIP >>> for term in v: print(v) # doctest: +SKIP (Depending on the version of chardet, it might get the first one wrong.) For an introduction to Unicode and character encodings in general, see Joel Spolsky's Absolute Minimum Every Software Developer Must Know About Unicode. Applications and examples The bag of words representation is quite simplistic but surprisingly useful in practice. In particular in a supervised setting it can be successfully combined with fast and scalable linear models to train document classifiers, for instance: - sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py In an unsupervised setting it can be used to group similar documents together by applying clustering algorithms such as k_means: - sphx_glr_auto_examples_text_plot_document_clustering.py Finally it is possible to discover the main topics of a corpus by relaxing the hard assignment constraint of clustering, for instance by using NMF: - sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py Limitations of the Bag of Words representation A collection of unigrams (what bag of words is) cannot capture phrases and multi-word expressions, effectively disregarding any word order dependence. Additionally, the bag of words model doesn't account for potential misspellings or word derivations. N-grams to the rescue! Instead of building a simple collection of unigrams (n=1), one might prefer a collection of bigrams (n=2), where occurrences of pairs of consecutive words are counted. One might alternatively consider a collection of character n-grams, a representation resilient against misspellings and derivations. For example, let's say we're dealing with a corpus of two documents: ['words', 'wprds']. The second document contains a misspelling of the word 'words'. A simple bag of words representation would consider these two as very distinct documents, differing in both of the two possible features. A character 2-gram representation, however, would find the documents matching in 4 out of 8 features, which may help the preferred classifier decide better: >>> ngram_vectorizer = CountVectorizer(analyzer='char_wb', ngram_range=(2, 2)) >>> counts = ngram_vectorizer.fit_transform(['words', 'wprds']) >>> ngram_vectorizer.get_feature_names_out() array([' w', 'ds', 'or', 'pr', 'rd', 's ', 'wo', 'wp'], ...) >>> counts.toarray().astype(int) array([[1, 1, 1, 0, 1, 1, 1, 0], [1, 1, 0, 1, 1, 1, 0, 1]]) In the above example, char_wb analyzer is used, which creates n-grams only from characters inside word boundaries (padded with space on each side). The char analyzer, alternatively, creates n-grams that span across words: >>> ngram_vectorizer = CountVectorizer(analyzer='char_wb', ngram_range=(5, 5)) >>> ngram_vectorizer.fit_transform(['jumpy fox']) <1x4 sparse matrix of type '<... 'numpy.int64'>' with 4 stored elements in Compressed Sparse ... format> >>> ngram_vectorizer.get_feature_names_out() array([' fox ', ' jump', 'jumpy', 'umpy '], ...) >>> ngram_vectorizer = CountVectorizer(analyzer='char', ngram_range=(5, 5)) >>> ngram_vectorizer.fit_transform(['jumpy fox']) <1x5 sparse matrix of type '<... 'numpy.int64'>' with 5 stored elements in Compressed Sparse ... format> >>> ngram_vectorizer.get_feature_names_out() array(['jumpy', 'mpy f', 'py fo', 'umpy ', 'y fox'], ...) The word boundaries-aware variant char_wb is especially interesting for languages that use white-spaces for word separation as it generates significantly less noisy features than the raw char variant in that case. For such languages it can increase both the predictive accuracy and convergence speed of classifiers trained using such features while retaining the robustness with regards to misspellings and word derivations. While some local positioning information can be preserved by extracting n-grams instead of individual words, bag of words and bag of n-grams destroy most of the inner structure of the document and hence most of the meaning carried by that internal structure. In order to address the wider task of Natural Language Understanding, the local structure of sentences and paragraphs should thus be taken into account. Many such models will thus be casted as "Structured output" problems which are currently outside of the scope of scikit-learn. Vectorizing a large text corpus with the hashing trick The above vectorization scheme is simple but the fact that it holds an in-memory mapping from the string tokens to the integer feature indices (the vocabulary_ attribute) causes several problems when dealing with large datasets: - the larger the corpus, the larger the vocabulary will grow and hence the memory use too, - fitting requires the allocation of intermediate data structures of size proportional to that of the original dataset. - building the word-mapping requires a full pass over the dataset hence it is not possible to fit text classifiers in a strictly online manner. - pickling and un-pickling vectorizers with a large vocabulary_ can be very slow (typically much slower than pickling / un-pickling flat data structures such as a NumPy array of the same size), - it is not easily possible to split the vectorization work into concurrent sub tasks as the vocabulary_ attribute would have to be a shared state with a fine grained synchronization barrier: the mapping from token string to feature index is dependent on ordering of the first occurrence of each token hence would have to be shared, potentially harming the concurrent workers' performance to the point of making them slower than the sequential variant. It is possible to overcome those limitations by combining the "hashing trick" (Feature_hashing) implemented by the ~sklearn.feature_extraction.FeatureHasher class and the text preprocessing and tokenization features of the CountVectorizer. This combination is implementing in HashingVectorizer, a transformer class that is mostly API compatible with CountVectorizer. HashingVectorizer is stateless, meaning that you don't have to call fit on it: >>> from sklearn.feature_extraction.text import HashingVectorizer >>> hv = HashingVectorizer(n_features=10) >>> hv.transform(corpus) <4x10 sparse matrix of type '<... 'numpy.float64'>' with 16 stored elements in Compressed Sparse ... format> You can see that 16 non-zero feature tokens were extracted in the vector output: this is less than the 19 non-zeros extracted previously by the CountVectorizer on the same toy corpus. The discrepancy comes from hash function collisions because of the low value of the n_features parameter. In a real world setting, the n_features parameter can be left to its default value of 2 ** 20 (roughly one million possible features). If memory or downstream models size is an issue selecting a lower value such as 2 ** 18 might help without introducing too many additional collisions on typical text classification tasks. Note that the dimensionality does not affect the CPU training time of algorithms which operate on CSR matrices (LinearSVC(dual=True), Perceptron, SGDClassifier, PassiveAggressive) but it does for algorithms that work with CSC matrices (LinearSVC(dual=False), Lasso(), etc.). Let's try again with the default setting: >>> hv = HashingVectorizer() >>> hv.transform(corpus) <4x1048576 sparse matrix of type '<... 'numpy.float64'>' with 19 stored elements in Compressed Sparse ... format> We no longer get the collisions, but this comes at the expense of a much larger dimensionality of the output space. Of course, other terms than the 19 used here might still collide with each other. The HashingVectorizer also comes with the following limitations: - it is not possible to invert the model (no inverse_transform method), nor to access the original string representation of the features, because of the one-way nature of the hash function that performs the mapping. - it does not provide IDF weighting as that would introduce statefulness in the model. A TfidfTransformer can be appended to it in a pipeline if required. Performing out-of-core scaling with HashingVectorizer An interesting development of using a HashingVectorizer is the ability to perform out-of-core scaling. This means that we can learn from data that does not fit into the computer's main memory. A strategy to implement out-of-core scaling is to stream data to the estimator in mini-batches. Each mini-batch is vectorized using HashingVectorizer so as to guarantee that the input space of the estimator has always the same dimensionality. The amount of memory used at any time is thus bounded by the size of a mini-batch. Although there is no limit to the amount of data that can be ingested using such an approach, from a practical point of view the learning time is often limited by the CPU time one wants to spend on the task. For a full-fledged example of out-of-core scaling in a text classification task see sphx_glr_auto_examples_applications_plot_out_of_core_classification.py. Customizing the vectorizer classes It is possible to customize the behavior by passing a callable to the vectorizer constructor: >>> def my_tokenizer(s): ... return s.split() ... >>> vectorizer = CountVectorizer(tokenizer=my_tokenizer) >>> vectorizer.build_analyzer()(u"Some... punctuation!") == ( ... ['some...', 'punctuation!']) True In particular we name: - preprocessor: a callable that takes an entire document as input (as a single string), and returns a possibly transformed version of the document, still as an entire string. This can be used to remove HTML tags, lowercase the entire document, etc. - tokenizer: a callable that takes the output from the preprocessor and splits it into tokens, then returns a list of these. - analyzer: a callable that replaces the preprocessor and tokenizer. The default analyzers all call the preprocessor and tokenizer, but custom analyzers will skip this. N-gram extraction and stop word filtering take place at the analyzer level, so a custom analyzer may have to reproduce these steps. (Lucene users might recognize these names, but be aware that scikit-learn concepts may not map one-to-one onto Lucene concepts.) To make the preprocessor, tokenizer and analyzers aware of the model parameters it is possible to derive from the class and override the build_preprocessor, build_tokenizer and build_analyzer factory methods instead of passing custom functions. Some tips and tricks: - If documents are pre-tokenized by an external package, then store them in files (or strings) with the tokens separated by whitespace and pass analyzer=str.split - Fancy token-level analysis such as stemming, lemmatizing, compound splitting, filtering based on part-of-speech, etc. are not included in the scikit-learn codebase, but can be added by customizing either the tokenizer or the analyzer. Here's a CountVectorizer with a tokenizer and lemmatizer using NLTK: >>> from nltk import word_tokenize # doctest: +SKIP >>> from nltk.stem import WordNetLemmatizer # doctest: +SKIP >>> class LemmaTokenizer: ... def __init__(self): ... self.wnl = WordNetLemmatizer() ... def __call__(self, doc): ... return [self.wnl.lemmatize(t) for t in word_tokenize(doc)] ... >>> vect = CountVectorizer(tokenizer=LemmaTokenizer()) # doctest: +SKIP (Note that this will not filter out punctuation.) The following example will, for instance, transform some British spelling to American spelling: >>> import re >>> def to_british(tokens): ... for t in tokens: ... t = re.sub(r"(...)our$", r"\1or", t) ... t = re.sub(r"([bt])re$", r"\1er", t) ... t = re.sub(r"([iy])s(e$|ing|ation)", r"\1z\2", t) ... t = re.sub(r"ogue$", "og", t) ... yield t ... >>> class CustomVectorizer(CountVectorizer): ... def build_tokenizer(self): ... tokenize = super().build_tokenizer() ... return lambda doc: list(to_british(tokenize(doc))) ... >>> print(CustomVectorizer().build_analyzer()(u"color colour")) [...'color', ...'color'] for other styles of preprocessing; examples include stemming, lemmatization, or normalizing numerical tokens, with the latter illustrated in: - sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py Customizing the vectorizer can also be useful when handling Asian languages that do not use an explicit word separator such as whitespace. Image feature extraction Patch extraction The extract_patches_2d function extracts patches from an image stored as a two-dimensional array, or three-dimensional with color information along the third axis. For rebuilding an image from all its patches, use reconstruct_from_patches_2d. For example let us generate a 4x4 pixel picture with 3 color channels (e.g. in RGB format): >>> import numpy as np >>> from sklearn.feature_extraction import image >>> one_image = np.arange(4 * 4 * 3).reshape((4, 4, 3)) >>> one_image[:, :, 0] # R channel of a fake RGB picture array([[ 0, 3, 6, 9], [12, 15, 18, 21], [24, 27, 30, 33], [36, 39, 42, 45]]) >>> patches = image.extract_patches_2d(one_image, (2, 2), max_patches=2, ... random_state=0) >>> patches.shape (2, 2, 2, 3) >>> patches[:, :, :, 0] array([[[ 0, 3], [12, 15]], <BLANKLINE> [[15, 18], [27, 30]]]) >>> patches = image.extract_patches_2d(one_image, (2, 2)) >>> patches.shape (9, 2, 2, 3) >>> patches[4, :, :, 0] array([[15, 18], [27, 30]]) Let us now try to reconstruct the original image from the patches by averaging on overlapping areas: >>> reconstructed = image.reconstruct_from_patches_2d(patches, (4, 4, 3)) >>> np.testing.assert_array_equal(one_image, reconstructed) The PatchExtractor class works in the same way as extract_patches_2d, only it supports multiple images as input. It is implemented as a scikit-learn transformer, so it can be used in pipelines. See: >>> five_images = np.arange(5 * 4 * 4 * 3).reshape(5, 4, 4, 3) >>> patches = image.PatchExtractor(patch_size=(2, 2)).transform(five_images) >>> patches.shape (45, 2, 2, 3) Connectivity graph of an image Several estimators in the scikit-learn can use connectivity information between features or samples. For instance Ward clustering (hierarchical_clustering) can cluster together only neighboring pixels of an image, thus forming contiguous patches: For this purpose, the estimators use a 'connectivity' matrix, giving which samples are connected. The function img_to_graph returns such a matrix from a 2D or 3D image. Similarly, grid_to_graph build a connectivity matrix for images given the shape of these image. These matrices can be used to impose connectivity in estimators that use connectivity information, such as Ward clustering (hierarchical_clustering), but also to build precomputed kernels, or similarity matrices.

# Authors: Olivier Grisel <olivier.grisel@ensta.org> # Mathieu Blondel <mathieu@mblondel.org> # Lars Buitinck # Robert Layton <robertlayton@gmail.com> # Jochen Wersdörfer <jochen@wersdoerfer.de> # Roman Sinayev <roman.sinayev@gmail.com> # # License: BSD 3 clause """ The :mod:`sklearn.feature_extraction.text` submodule gathers utilities to build feature vectors from text documents. """ import array import re import unicodedata import warnings from collections import defaultdict from collections.abc import Mapping from functools import partial from numbers import Integral from operator import itemgetter import numpy as np import scipy.sparse as sp from..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context from..exceptions import NotFittedError from..preprocessing import normalize from..utils import _IS_32BIT from..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions from..utils.validation import FLOAT_DTYPES, check_array, check_is_fitted from._hash import FeatureHasher from._stop_words import ENGLISH_STOP_WORDS __all__ = [ "HashingVectorizer", "CountVectorizer", "ENGLISH_STOP_WORDS", "TfidfTransformer", "TfidfVectorizer", "strip_accents_ascii", "strip_accents_unicode", "strip_tags", ] def _preprocess(doc, accent_function=None, lower=False): """Chain together an optional series of text preprocessing steps to apply to a document. Parameters ---------- doc: str The string to preprocess accent_function: callable, default=None Function for handling accented characters. Common strategies include normalizing and removing. lower: bool, default=False Whether to use str.lower to lowercase all of the text Returns ------- doc: str preprocessed string """ if lower: doc = doc.lower() if accent_function is not None: doc = accent_function(doc) return doc def _analyze( doc, analyzer=None, tokenizer=None, ngrams=None, preprocessor=None, decoder=None, stop_words=None, ): """Chain together an optional series of text processing steps to go from a single document to ngrams, with or without tokenizing or preprocessing. If analyzer is used, only the decoder argument is used, as the analyzer is intended to replace the preprocessor, tokenizer, and ngrams steps. Parameters ---------- analyzer: callable, default=None tokenizer: callable, default=None ngrams: callable, default=None preprocessor: callable, default=None decoder: callable, default=None stop_words: list, default=None Returns ------- ngrams: list A sequence of tokens, possibly with pairs, triples, etc. """ if decoder is not None: doc = decoder(doc) if analyzer is not None: doc = analyzer(doc) else: if preprocessor is not None: doc = preprocessor(doc) if tokenizer is not None: doc = tokenizer(doc) if ngrams is not None: if stop_words is not None: doc = ngrams(doc, stop_words) else: doc = ngrams(doc) return doc def strip_accents_unicode(s): """Transform accentuated unicode symbols into their simple counterpart. Warning: the python-level loop and join operations make this implementation 20 times slower than the strip_accents_ascii basic normalization. Parameters ---------- s : str The string to strip. Returns ------- s : str The stripped string. See Also -------- strip_accents_ascii : Remove accentuated char for any unicode symbol that has a direct ASCII equivalent. """ try: # If `s` is ASCII-compatible, then it does not contain any accented # characters and we can avoid an expensive list comprehension s.encode("ASCII", errors="strict") return s except UnicodeEncodeError: normalized = unicodedata.normalize("NFKD", s) return "".join([c for c in normalized if not unicodedata.combining(c)]) def strip_accents_ascii(s): """Transform accentuated unicode symbols into ascii or nothing. Warning: this solution is only suited for languages that have a direct transliteration to ASCII symbols. Parameters ---------- s : str The string to strip. Returns ------- s : str The stripped string. See Also -------- strip_accents_unicode : Remove accentuated char for any unicode symbol. """ nkfd_form = unicodedata.normalize("NFKD", s) return nkfd_form.encode("ASCII", "ignore").decode("ASCII") def strip_tags(s): """Basic regexp based HTML / XML tag stripper function. For serious HTML/XML preprocessing you should rather use an external library such as lxml or BeautifulSoup. Parameters ---------- s : str The string to strip. Returns ------- s : str The stripped string. """ return re.compile(r"<([^>]+)>", flags=re.UNICODE).sub(" ", s) def _check_stop_list(stop): if stop == "english": return ENGLISH_STOP_WORDS elif isinstance(stop, str): raise ValueError("not a built-in stop list: %s" % stop) elif stop is None: return None else: # assume it's a collection return frozenset(stop) class _VectorizerMixin: """Provides common code for text vectorizers (tokenization logic).""" _white_spaces = re.compile(r"\s\s+") def decode(self, doc): """Decode the input into a string of unicode symbols. The decoding strategy depends on the vectorizer parameters. Parameters ---------- doc : bytes or str The string to decode. Returns ------- doc: str A string of unicode symbols. """ if self.input == "filename": with open(doc, "rb") as fh: doc = fh.read() elif self.input == "file": doc = doc.read() if isinstance(doc, bytes): doc = doc.decode(self.encoding, self.decode_error) if doc is np.nan: raise ValueError( "np.nan is an invalid document, expected byte or unicode string." ) return doc def _word_ngrams(self, tokens, stop_words=None): """Turn tokens into a sequence of n-grams after stop words filtering""" # handle stop words if stop_words is not None: tokens = [w for w in tokens if w not in stop_words] # handle token n-grams min_n, max_n = self.ngram_range if max_n!= 1: original_tokens = tokens if min_n == 1: # no need to do any slicing for unigrams # just iterate through the original tokens tokens = list(original_tokens) min_n += 1 else: tokens = [] n_original_tokens = len(original_tokens) # bind method outside of loop to reduce overhead tokens_append = tokens.append space_join = " ".join for n in range(min_n, min(max_n + 1, n_original_tokens + 1)): for i in range(n_original_tokens - n + 1): tokens_append(space_join(original_tokens[i : i + n])) return tokens def _char_ngrams(self, text_document): """Tokenize text_document into a sequence of character n-grams""" # normalize white spaces text_document = self._white_spaces.sub(" ", text_document) text_len = len(text_document) min_n, max_n = self.ngram_range if min_n == 1: # no need to do any slicing for unigrams # iterate through the string ngrams = list(text_document) min_n += 1 else: ngrams = [] # bind method outside of loop to reduce overhead ngrams_append = ngrams.append for n in range(min_n, min(max_n + 1, text_len + 1)): for i in range(text_len - n + 1): ngrams_append(text_document[i : i + n]) return ngrams def _char_wb_ngrams(self, text_document): """Whitespace sensitive char-n-gram tokenization. Tokenize text_document into a sequence of character n-grams operating only inside word boundaries. n-grams at the edges of words are padded with space.""" # normalize white spaces text_document = self._white_spaces.sub(" ", text_document) min_n, max_n = self.ngram_range ngrams = [] # bind method outside of loop to reduce overhead ngrams_append = ngrams.append for w in text_document.split(): w = " " + w + " " w_len = len(w) for n in range(min_n, max_n + 1): offset = 0 ngrams_append(w[offset : offset + n]) while offset + n < w_len: offset += 1 ngrams_append(w[offset : offset + n]) if offset == 0: # count a short word (w_len < n) only once break return ngrams def build_preprocessor(self): """Return a function to preprocess the text before tokenization. Returns ------- preprocessor: callable A function to preprocess the text before tokenization. """ if self.preprocessor is not None: return self.preprocessor # accent stripping if not self.strip_accents: strip_accents = None elif callable(self.strip_accents): strip_accents = self.strip_accents elif self.strip_accents == "ascii": strip_accents = strip_accents_ascii elif self.strip_accents == "unicode": strip_accents = strip_accents_unicode else: raise ValueError( 'Invalid value for "strip_accents": %s' % self.strip_accents ) return partial(_preprocess, accent_function=strip_accents, lower=self.lowercase) def build_tokenizer(self): """Return a function that splits a string into a sequence of tokens. Returns ------- tokenizer: callable A function to split a string into a sequence of tokens. """ if self.tokenizer is not None: return self.tokenizer token_pattern = re.compile(self.token_pattern) if token_pattern.groups > 1: raise ValueError( "More than 1 capturing group in token pattern. Only a single " "group should be captured." ) return token_pattern.findall def get_stop_words(self): """Build or fetch the effective stop words list. Returns ------- stop_words: list or None A list of stop words. """ return _check_stop_list(self.stop_words) def _check_stop_words_consistency(self, stop_words, preprocess, tokenize): """Check if stop words are consistent Returns ------- is_consistent : True if stop words are consistent with the preprocessor and tokenizer, False if they are not, None if the check was previously performed, "error" if it could not be performed (e.g. because of the use of a custom preprocessor / tokenizer) """ if id(self.stop_words) == getattr(self, "_stop_words_id", None): # Stop words are were previously validated return None # NB: stop_words is validated, unlike self.stop_words try: inconsistent = set() for w in stop_words or (): tokens = list(tokenize(preprocess(w))) for token in tokens: if token not in stop_words: inconsistent.add(token) self._stop_words_id = id(self.stop_words) if inconsistent: warnings.warn( "Your stop_words may be inconsistent with " "your preprocessing. Tokenizing the stop " "words generated tokens %r not in " "stop_words." % sorted(inconsistent) ) return not inconsistent except Exception: # Failed to check stop words consistency (e.g. because a custom # preprocessor or tokenizer was used) self._stop_words_id = id(self.stop_words) return "error" def build_analyzer(self): """Return a callable to process input data. The callable handles preprocessing, tokenization, and n-grams generation. Returns ------- analyzer: callable A function to handle preprocessing, tokenization and n-grams generation. """ if callable(self.analyzer): return partial(_analyze, analyzer=self.analyzer, decoder=self.decode) preprocess = self.build_preprocessor() if self.analyzer == "char": return partial( _analyze, ngrams=self._char_ngrams, preprocessor=preprocess, decoder=self.decode, ) elif self.analyzer == "char_wb": return partial( _analyze, ngrams=self._char_wb_ngrams, preprocessor=preprocess, decoder=self.decode, ) elif self.analyzer == "word": stop_words = self.get_stop_words() tokenize = self.build_tokenizer() self._check_stop_words_consistency(stop_words, preprocess, tokenize) return partial( _analyze, ngrams=self._word_ngrams, tokenizer=tokenize, preprocessor=preprocess, decoder=self.decode, stop_words=stop_words, ) else: raise ValueError( "%s is not a valid tokenization scheme/analyzer" % self.analyzer ) def _validate_vocabulary(self): vocabulary = self.vocabulary if vocabulary is not None: if isinstance(vocabulary, set): vocabulary = sorted(vocabulary) if not isinstance(vocabulary, Mapping): vocab = {} for i, t in enumerate(vocabulary): if vocab.setdefault(t, i)!= i: msg = "Duplicate term in vocabulary: %r" % t raise ValueError(msg) vocabulary = vocab else: indices = set(vocabulary.values()) if len(indices)!= len(vocabulary): raise ValueError("Vocabulary contains repeated indices.") for i in range(len(vocabulary)): if i not in indices: msg = "Vocabulary of size %d doesn't contain index %d." % ( len(vocabulary), i, ) raise ValueError(msg) if not vocabulary: raise ValueError("empty vocabulary passed to fit") self.fixed_vocabulary_ = True self.vocabulary_ = dict(vocabulary) else: self.fixed_vocabulary_ = False def _check_vocabulary(self): """Check if vocabulary is empty or missing (not fitted)""" if not hasattr(self, "vocabulary_"): self._validate_vocabulary() if not self.fixed_vocabulary_: raise NotFittedError("Vocabulary not fitted or provided") if len(self.vocabulary_) == 0: raise ValueError("Vocabulary is empty") def _validate_ngram_range(self): """Check validity of ngram_range parameter""" min_n, max_m = self.ngram_range if min_n > max_m: raise ValueError( "Invalid value for ngram_range=%s " "lower boundary larger than the upper boundary." % str(self.ngram_range) ) def _warn_for_unused_params(self): if self.tokenizer is not None and self.token_pattern is not None: warnings.warn( "The parameter 'token_pattern' will not be used" " since 'tokenizer' is not None'" ) if self.preprocessor is not None and callable(self.analyzer): warnings.warn( "The parameter 'preprocessor' will not be used" " since 'analyzer' is callable'" ) if ( self.ngram_range!= (1, 1) and self.ngram_range is not None and callable(self.analyzer) ): warnings.warn( "The parameter 'ngram_range' will not be used" " since 'analyzer' is callable'" ) if self.analyzer!= "word" or callable(self.analyzer): if self.stop_words is not None: warnings.warn( "The parameter'stop_words' will not be used" " since 'analyzer'!= 'word'" ) if ( self.token_pattern is not None and self.token_pattern!= r"(?u)\b\w\w+\b" ): warnings.warn( "The parameter 'token_pattern' will not be used" " since 'analyzer'!= 'word'" ) if self.tokenizer is not None: warnings.warn( "The parameter 'tokenizer' will not be used" " since 'analyzer'!= 'word'" ) class HashingVectorizer( TransformerMixin, _VectorizerMixin, BaseEstimator, auto_wrap_output_keys=None ): r"""Convert a collection of text documents to a matrix of token occurrences. It turns a collection of text documents into a scipy.sparse matrix holding token occurrence counts (or binary occurrence information), possibly normalized as token frequencies if norm='l1' or projected on the euclidean unit sphere if norm='l2'. This text vectorizer implementation uses the hashing trick to find the token string name to feature integer index mapping. This strategy has several advantages: - it is very low memory scalable to large datasets as there is no need to store a vocabulary dictionary in memory. - it is fast to pickle and un-pickle as it holds no state besides the constructor parameters. - it can be used in a streaming (partial fit) or parallel pipeline as there is no state computed during fit. There are also a couple of cons (vs using a CountVectorizer with an in-memory vocabulary): - there is no way to compute the inverse transform (from feature indices to string feature names) which can be a problem when trying to introspect which features are most important to a model. - there can be collisions: distinct tokens can be mapped to the same feature index. However in practice this is rarely an issue if n_features is large enough (e.g. 2 ** 18 for text classification problems). - no IDF weighting as this would render the transformer stateful. The hash function employed is the signed 32-bit version of Murmurhash3. For an efficiency comparision of the different feature extractors, see :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. Read more in the :ref:`User Guide <text_feature_extraction>`. Parameters ---------- input : {'filename', 'file', 'content'}, default='content' - If `'filename'`, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. - If `'file'`, the sequence items must have a'read' method (file-like object) that is called to fetch the bytes in memory. - If `'content'`, the input is expected to be a sequence of items that can be of type string or byte. encoding : str, default='utf-8' If bytes or files are given to analyze, this encoding is used to decode. decode_error : {'strict', 'ignore','replace'}, default='strict' Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. By default, it is 'strict', meaning that a UnicodeDecodeError will be raised. Other values are 'ignore' and'replace'. strip_accents : {'ascii', 'unicode'} or callable, default=None Remove accents and perform other character normalization during the preprocessing step. 'ascii' is a fast method that only works on characters that have a direct ASCII mapping. 'unicode' is a slightly slower method that works on any character. None (default) means no character normalization is performed. Both 'ascii' and 'unicode' use NFKD normalization from :func:`unicodedata.normalize`. lowercase : bool, default=True Convert all characters to lowercase before tokenizing. preprocessor : callable, default=None Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. Only applies if ``analyzer`` is not callable. tokenizer : callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``. stop_words : {'english'}, list, default=None If 'english', a built-in stop word list for English is used. There are several known issues with 'english' and you should consider an alternative (see :ref:`stop_words`). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. token_pattern : str or None, default=r"(?u)\\b\\w\\w+\\b" Regular expression denoting what constitutes a "token", only used if ``analyzer == 'word'``. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted. ngram_range : tuple (min_n, max_n), default=(1, 1) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. Only applies if ``analyzer`` is not callable. analyzer : {'word', 'char', 'char_wb'} or callable, default='word' Whether the feature should be made of word or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. .. versionchanged:: 0.21 Since v0.21, if ``input`` is ``'filename'`` or ``'file'``, the data is first read from the file and then passed to the given callable analyzer. n_features : int, default=(2 ** 20) The number of features (columns) in the output matrices. Small numbers of features are likely to cause hash collisions, but large numbers will cause larger coefficient dimensions in linear learners. binary : bool, default=False If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. norm : {'l1', 'l2'}, default='l2' Norm used to normalize term vectors. None for no normalization. alternate_sign : bool, default=True When True, an alternating sign is added to the features as to approximately conserve the inner product in the hashed space even for small n_features. This approach is similar to sparse random projection. .. versionadded:: 0.19 dtype : type, default=np.float64 Type of the matrix returned by fit_transform() or transform(). See Also -------- CountVectorizer : Convert a collection of text documents to a matrix of token counts. TfidfVectorizer : Convert a collection of raw documents to a matrix of TF-IDF features. Notes ----- This estimator is :term:`stateless` and does not need to be fitted. However, we recommend to call :meth:`fit_transform` instead of :meth:`transform`, as parameter validation is only performed in :meth:`fit`. Examples -------- >>> from sklearn.feature_extraction.text import HashingVectorizer >>> corpus = [ ... 'This is the first document.', ... 'This document is the second document.', ... 'And this is the third one.', ... 'Is this the first document?', ... ] >>> vectorizer = HashingVectorizer(n_features=2**4) >>> X = vectorizer.fit_transform(corpus) >>> print(X.shape) (4, 16) """ _parameter_constraints: dict = { "input": [StrOptions({"filename", "file", "content"})], "encoding": [str], "decode_error": [StrOptions({"strict", "ignore", "replace"})], "strip_accents": [StrOptions({"ascii", "unicode"}), None, callable], "lowercase": ["boolean"], "preprocessor": [callable, None], "tokenizer": [callable, None], "stop_words": [StrOptions({"english"}), list, None], "token_pattern": [str, None], "ngram_range": [tuple], "analyzer": [StrOptions({"word", "char", "char_wb"}), callable], "n_features": [Interval(Integral, 1, np.iinfo(np.int32).max, closed="left")], "binary": ["boolean"], "norm": [StrOptions({"l1", "l2"}), None], "alternate_sign": ["boolean"], "dtype": "no_validation", # delegate to numpy } def __init__( self, *, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=r"(?u)\b\w\w+\b", ngram_range=(1, 1), analyzer="word", n_features=(2**20), binary=False, norm="l2", alternate_sign=True, dtype=np.float64, ): self.input = input self.encoding = encoding self.decode_error = decode_error self.strip_accents = strip_accents self.preprocessor = preprocessor self.tokenizer = tokenizer self.analyzer = analyzer self.lowercase = lowercase self.token_pattern = token_pattern self.stop_words = stop_words self.n_features = n_features self.ngram_range = ngram_range self.binary = binary self.norm = norm self.alternate_sign = alternate_sign self.dtype = dtype @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, X, y=None): """Only validates estimator's parameters. This method allows to: (i) validate the estimator's parameters and (ii) be consistent with the scikit-learn transformer API. Parameters ---------- X : ndarray of shape [n_samples, n_features] Training data. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object HashingVectorizer instance. """ return self @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Only validates estimator's parameters. This method allows to: (i) validate the estimator's parameters and (ii) be consistent with the scikit-learn transformer API. Parameters ---------- X : ndarray of shape [n_samples, n_features] Training data. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object HashingVectorizer instance. """ # triggers a parameter validation if isinstance(X, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._warn_for_unused_params() self._validate_ngram_range() self._get_hasher().fit(X, y=y) return self def transform(self, X): """Transform a sequence of documents to a document-term matrix. Parameters ---------- X : iterable over raw text documents, length = n_samples Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokenized and hashed. Returns ------- X : sparse matrix of shape (n_samples, n_features) Document-term matrix. """ if isinstance(X, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._validate_ngram_range() analyzer = self.build_analyzer() X = self._get_hasher().transform(analyzer(doc) for doc in X) if self.binary: X.data.fill(1) if self.norm is not None: X = normalize(X, norm=self.norm, copy=False) return X def fit_transform(self, X, y=None): """Transform a sequence of documents to a document-term matrix. Parameters ---------- X : iterable over raw text documents, length = n_samples Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokenized and hashed. y : any Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline. Returns ------- X : sparse matrix of shape (n_samples, n_features) Document-term matrix. """ return self.fit(X, y).transform(X) def _get_hasher(self): return FeatureHasher( n_features=self.n_features, input_type="string", dtype=self.dtype, alternate_sign=self.alternate_sign, ) def _more_tags(self): return {"X_types": ["string"]} def _document_frequency(X): """Count the number of non-zero values for each feature in sparse X.""" if sp.issparse(X) and X.format == "csr": return np.bincount(X.indices, minlength=X.shape[1]) else: return np.diff(X.indptr) class CountVectorizer(_VectorizerMixin, BaseEstimator): r"""Convert a collection of text documents to a matrix of token counts. This implementation produces a sparse representation of the counts using scipy.sparse.csr_matrix. If you do not provide an a-priori dictionary and you do not use an analyzer that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analyzing the data. For an efficiency comparision of the different feature extractors, see :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. Read more in the :ref:`User Guide <text_feature_extraction>`. Parameters ---------- input : {'filename', 'file', 'content'}, default='content' - If `'filename'`, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. - If `'file'`, the sequence items must have a'read' method (file-like object) that is called to fetch the bytes in memory. - If `'content'`, the input is expected to be a sequence of items that can be of type string or byte. encoding : str, default='utf-8' If bytes or files are given to analyze, this encoding is used to decode. decode_error : {'strict', 'ignore','replace'}, default='strict' Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. By default, it is 'strict', meaning that a UnicodeDecodeError will be raised. Other values are 'ignore' and'replace'. strip_accents : {'ascii', 'unicode'} or callable, default=None Remove accents and perform other character normalization during the preprocessing step. 'ascii' is a fast method that only works on characters that have a direct ASCII mapping. 'unicode' is a slightly slower method that works on any characters. None (default) means no character normalization is performed. Both 'ascii' and 'unicode' use NFKD normalization from :func:`unicodedata.normalize`. lowercase : bool, default=True Convert all characters to lowercase before tokenizing. preprocessor : callable, default=None Override the preprocessing (strip_accents and lowercase) stage while preserving the tokenizing and n-grams generation steps. Only applies if ``analyzer`` is not callable. tokenizer : callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``. stop_words : {'english'}, list, default=None If 'english', a built-in stop word list for English is used. There are several known issues with 'english' and you should consider an alternative (see :ref:`stop_words`). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. If None, no stop words will be used. In this case, setting `max_df` to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms. token_pattern : str or None, default=r"(?u)\\b\\w\\w+\\b" Regular expression denoting what constitutes a "token", only used if ``analyzer == 'word'``. The default regexp select tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted. ngram_range : tuple (min_n, max_n), default=(1, 1) The lower and upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. All values of n such such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. Only applies if ``analyzer`` is not callable. analyzer : {'word', 'char', 'char_wb'} or callable, default='word' Whether the feature should be made of word n-gram or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. .. versionchanged:: 0.21 Since v0.21, if ``input`` is ``filename`` or ``file``, the data is first read from the file and then passed to the given callable analyzer. max_df : float in range [0.0, 1.0] or int, default=1.0 When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. min_df : float in range [0.0, 1.0] or int, default=1 When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. max_features : int, default=None If not None, build a vocabulary that only consider the top `max_features` ordered by term frequency across the corpus. Otherwise, all features are used. This parameter is ignored if vocabulary is not None. vocabulary : Mapping or iterable, default=None Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms. If not given, a vocabulary is determined from the input documents. Indices in the mapping should not be repeated and should not have any gap between 0 and the largest index. binary : bool, default=False If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. dtype : dtype, default=np.int64 Type of the matrix returned by fit_transform() or transform(). Attributes ---------- vocabulary_ : dict A mapping of terms to feature indices. fixed_vocabulary_ : bool True if a fixed vocabulary of term to indices mapping is provided by the user. stop_words_ : set Terms that were ignored because they either: - occurred in too many documents (`max_df`) - occurred in too few documents (`min_df`) - were cut off by feature selection (`max_features`). This is only available if no vocabulary was given. See Also -------- HashingVectorizer : Convert a collection of text documents to a matrix of token counts. TfidfVectorizer : Convert a collection of raw documents to a matrix of TF-IDF features. Notes ----- The ``stop_words_`` attribute can get large and increase the model size when pickling. This attribute is provided only for introspection and can be safely removed using delattr or set to None before pickling. Examples -------- >>> from sklearn.feature_extraction.text import CountVectorizer >>> corpus = [ ... 'This is the first document.', ... 'This document is the second document.', ... 'And this is the third one.', ... 'Is this the first document?', ... ] >>> vectorizer = CountVectorizer() >>> X = vectorizer.fit_transform(corpus) >>> vectorizer.get_feature_names_out() array(['and', 'document', 'first', 'is', 'one','second', 'the', 'third', 'this'],...) >>> print(X.toarray()) [[0 1 1 1 0 0 1 0 1] [0 2 0 1 0 1 1 0 1] [1 0 0 1 1 0 1 1 1] [0 1 1 1 0 0 1 0 1]] >>> vectorizer2 = CountVectorizer(analyzer='word', ngram_range=(2, 2)) >>> X2 = vectorizer2.fit_transform(corpus) >>> vectorizer2.get_feature_names_out() array(['and this', 'document is', 'first document', 'is the', 'is this', 'second document', 'the first', 'the second', 'the third', 'third one', 'this document', 'this is', 'this the'],...) >>> print(X2.toarray()) [[0 0 1 1 0 0 1 0 0 0 0 1 0] [0 1 0 1 0 1 0 1 0 0 1 0 0] [1 0 0 1 0 0 0 0 1 1 0 1 0] [0 0 1 0 1 0 1 0 0 0 0 0 1]] """ _parameter_constraints: dict = { "input": [StrOptions({"filename", "file", "content"})], "encoding": [str], "decode_error": [StrOptions({"strict", "ignore", "replace"})], "strip_accents": [StrOptions({"ascii", "unicode"}), None, callable], "lowercase": ["boolean"], "preprocessor": [callable, None], "tokenizer": [callable, None], "stop_words": [StrOptions({"english"}), list, None], "token_pattern": [str, None], "ngram_range": [tuple], "analyzer": [StrOptions({"word", "char", "char_wb"}), callable], "max_df": [ Interval(RealNotInt, 0, 1, closed="both"), Interval(Integral, 1, None, closed="left"), ], "min_df": [ Interval(RealNotInt, 0, 1, closed="both"), Interval(Integral, 1, None, closed="left"), ], "max_features": [Interval(Integral, 1, None, closed="left"), None], "vocabulary": [Mapping, HasMethods("__iter__"), None], "binary": ["boolean"], "dtype": "no_validation", # delegate to numpy } def __init__( self, *, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=r"(?u)\b\w\w+\b", ngram_range=(1, 1), analyzer="word", max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=np.int64, ): self.input = input self.encoding = encoding self.decode_error = decode_error self.strip_accents = strip_accents self.preprocessor = preprocessor self.tokenizer = tokenizer self.analyzer = analyzer self.lowercase = lowercase self.token_pattern = token_pattern self.stop_words = stop_words self.max_df = max_df self.min_df = min_df self.max_features = max_features self.ngram_range = ngram_range self.vocabulary = vocabulary self.binary = binary self.dtype = dtype def _sort_features(self, X, vocabulary): """Sort features by name Returns a reordered matrix and modifies the vocabulary in place """ sorted_features = sorted(vocabulary.items()) map_index = np.empty(len(sorted_features), dtype=X.indices.dtype) for new_val, (term, old_val) in enumerate(sorted_features): vocabulary[term] = new_val map_index[old_val] = new_val X.indices = map_index.take(X.indices, mode="clip") return X def _limit_features(self, X, vocabulary, high=None, low=None, limit=None): """Remove too rare or too common features. Prune features that are non zero in more samples than high or less documents than low, modifying the vocabulary, and restricting it to at most the limit most frequent. This does not prune samples with zero features. """ if high is None and low is None and limit is None: return X, set() # Calculate a mask based on document frequencies dfs = _document_frequency(X) mask = np.ones(len(dfs), dtype=bool) if high is not None: mask &= dfs <= high if low is not None: mask &= dfs >= low if limit is not None and mask.sum() > limit: tfs = np.asarray(X.sum(axis=0)).ravel() mask_inds = (-tfs[mask]).argsort()[:limit] new_mask = np.zeros(len(dfs), dtype=bool) new_mask[np.where(mask)[0][mask_inds]] = True mask = new_mask new_indices = np.cumsum(mask) - 1 # maps old indices to new removed_terms = set() for term, old_index in list(vocabulary.items()): if mask[old_index]: vocabulary[term] = new_indices[old_index] else: del vocabulary[term] removed_terms.add(term) kept_indices = np.where(mask)[0] if len(kept_indices) == 0: raise ValueError( "After pruning, no terms remain. Try a lower min_df or a higher max_df." ) return X[:, kept_indices], removed_terms def _count_vocab(self, raw_documents, fixed_vocab): """Create sparse feature matrix, and vocabulary where fixed_vocab=False""" if fixed_vocab: vocabulary = self.vocabulary_ else: # Add a new value when a new vocabulary item is seen vocabulary = defaultdict() vocabulary.default_factory = vocabulary.__len__ analyze = self.build_analyzer() j_indices = [] indptr = [] values = _make_int_array() indptr.append(0) for doc in raw_documents: feature_counter = {} for feature in analyze(doc): try: feature_idx = vocabulary[feature] if feature_idx not in feature_counter: feature_counter[feature_idx] = 1 else: feature_counter[feature_idx] += 1 except KeyError: # Ignore out-of-vocabulary items for fixed_vocab=True continue j_indices.extend(feature_counter.keys()) values.extend(feature_counter.values()) indptr.append(len(j_indices)) if not fixed_vocab: # disable defaultdict behaviour vocabulary = dict(vocabulary) if not vocabulary: raise ValueError( "empty vocabulary; perhaps the documents only contain stop words" ) if indptr[-1] > np.iinfo(np.int32).max: # = 2**31 - 1 if _IS_32BIT: raise ValueError( ( "sparse CSR array has {} non-zero " "elements and requires 64 bit indexing, " "which is unsupported with 32 bit Python." ).format(indptr[-1]) ) indices_dtype = np.int64 else: indices_dtype = np.int32 j_indices = np.asarray(j_indices, dtype=indices_dtype) indptr = np.asarray(indptr, dtype=indices_dtype) values = np.frombuffer(values, dtype=np.intc) X = sp.csr_matrix( (values, j_indices, indptr), shape=(len(indptr) - 1, len(vocabulary)), dtype=self.dtype, ) X.sort_indices() return vocabulary, X def fit(self, raw_documents, y=None): """Learn a vocabulary dictionary of all tokens in the raw documents. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is ignored. Returns ------- self : object Fitted vectorizer. """ self.fit_transform(raw_documents) return self @_fit_context(prefer_skip_nested_validation=True) def fit_transform(self, raw_documents, y=None): """Learn the vocabulary dictionary and return document-term matrix. This is equivalent to fit followed by transform, but more efficiently implemented. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is ignored. Returns ------- X : array of shape (n_samples, n_features) Document-term matrix. """ # We intentionally don't call the transform method to make # fit_transform overridable without unwanted side effects in # TfidfVectorizer. if isinstance(raw_documents, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._validate_ngram_range() self._warn_for_unused_params() self._validate_vocabulary() max_df = self.max_df min_df = self.min_df max_features = self.max_features if self.fixed_vocabulary_ and self.lowercase: for term in self.vocabulary: if any(map(str.isupper, term)): warnings.warn( "Upper case characters found in" " vocabulary while 'lowercase'" " is True. These entries will not" " be matched with any documents" ) break vocabulary, X = self._count_vocab(raw_documents, self.fixed_vocabulary_) if self.binary: X.data.fill(1) if not self.fixed_vocabulary_: n_doc = X.shape[0] max_doc_count = max_df if isinstance(max_df, Integral) else max_df * n_doc min_doc_count = min_df if isinstance(min_df, Integral) else min_df * n_doc if max_doc_count < min_doc_count: raise ValueError("max_df corresponds to < documents than min_df") if max_features is not None: X = self._sort_features(X, vocabulary) X, self.stop_words_ = self._limit_features( X, vocabulary, max_doc_count, min_doc_count, max_features ) if max_features is None: X = self._sort_features(X, vocabulary) self.vocabulary_ = vocabulary return X def transform(self, raw_documents): """Transform documents to document-term matrix. Extract token counts out of raw text documents using the vocabulary fitted with fit or the one provided to the constructor. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. Returns ------- X : sparse matrix of shape (n_samples, n_features) Document-term matrix. """ if isinstance(raw_documents, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._check_vocabulary() # use the same matrix-building strategy as fit_transform _, X = self._count_vocab(raw_documents, fixed_vocab=True) if self.binary: X.data.fill(1) return X def inverse_transform(self, X): """Return terms per document with nonzero entries in X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document-term matrix. Returns ------- X_inv : list of arrays of shape (n_samples,) List of arrays of terms. """ self._check_vocabulary() # We need CSR format for fast row manipulations. X = check_array(X, accept_sparse="csr") n_samples = X.shape[0] terms = np.array(list(self.vocabulary_.keys())) indices = np.array(list(self.vocabulary_.values())) inverse_vocabulary = terms[np.argsort(indices)] if sp.issparse(X): return [ inverse_vocabulary[X[i, :].nonzero()[1]].ravel() for i in range(n_samples) ] else: return [ inverse_vocabulary[np.flatnonzero(X[i, :])].ravel() for i in range(n_samples) ] def get_feature_names_out(self, input_features=None): """Get output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Not used, present here for API consistency by convention. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. """ self._check_vocabulary() return np.asarray( [t for t, i in sorted(self.vocabulary_.items(), key=itemgetter(1))], dtype=object, ) def _more_tags(self): return {"X_types": ["string"]} def _make_int_array(): """Construct an array.array of a type suitable for scipy.sparse indices.""" return array.array(str("i")) class TfidfTransformer( OneToOneFeatureMixin, TransformerMixin, BaseEstimator, auto_wrap_output_keys=None ): """Transform a count matrix to a normalized tf or tf-idf representation. Tf means term-frequency while tf-idf means term-frequency times inverse document-frequency. This is a common term weighting scheme in information retrieval, that has also found good use in document classification. The goal of using tf-idf instead of the raw frequencies of occurrence of a token in a given document is to scale down the impact of tokens that occur very frequently in a given corpus and that are hence empirically less informative than features that occur in a small fraction of the training corpus. The formula that is used to compute the tf-idf for a term t of a document d in a document set is tf-idf(t, d) = tf(t, d) * idf(t), and the idf is computed as idf(t) = log [ n / df(t) ] + 1 (if ``smooth_idf=False``), where n is the total number of documents in the document set and df(t) is the document frequency of t; the document frequency is the number of documents in the document set that contain the term t. The effect of adding "1" to the idf in the equation above is that terms with zero idf, i.e., terms that occur in all documents in a training set, will not be entirely ignored. (Note that the idf formula above differs from the standard textbook notation that defines the idf as idf(t) = log [ n / (df(t) + 1) ]). If ``smooth_idf=True`` (the default), the constant "1" is added to the numerator and denominator of the idf as if an extra document was seen containing every term in the collection exactly once, which prevents zero divisions: idf(t) = log [ (1 + n) / (1 + df(t)) ] + 1. Furthermore, the formulas used to compute tf and idf depend on parameter settings that correspond to the SMART notation used in IR as follows: Tf is "n" (natural) by default, "l" (logarithmic) when ``sublinear_tf=True``. Idf is "t" when use_idf is given, "n" (none) otherwise. Normalization is "c" (cosine) when ``norm='l2'``, "n" (none) when ``norm=None``. Read more in the :ref:`User Guide <text_feature_extraction>`. Parameters ---------- norm : {'l1', 'l2'} or None, default='l2' Each output row will have unit norm, either: - 'l2': Sum of squares of vector elements is 1. The cosine similarity between two vectors is their dot product when l2 norm has been applied. - 'l1': Sum of absolute values of vector elements is 1. See :func:`~sklearn.preprocessing.normalize`. - None: No normalization. use_idf : bool, default=True Enable inverse-document-frequency reweighting. If False, idf(t) = 1. smooth_idf : bool, default=True Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions. sublinear_tf : bool, default=False Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). Attributes ---------- idf_ : array of shape (n_features) The inverse document frequency (IDF) vector; only defined if ``use_idf`` is True. .. versionadded:: 0.20 n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 1.0 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- CountVectorizer : Transforms text into a sparse matrix of n-gram counts. TfidfVectorizer : Convert a collection of raw documents to a matrix of TF-IDF features. HashingVectorizer : Convert a collection of text documents to a matrix of token occurrences. References ---------- .. [Yates2011] R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 68-74. .. [MRS2008] C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 118-120. Examples -------- >>> from sklearn.feature_extraction.text import TfidfTransformer >>> from sklearn.feature_extraction.text import CountVectorizer >>> from sklearn.pipeline import Pipeline >>> corpus = ['this is the first document', ... 'this document is the second document', ... 'and this is the third one', ... 'is this the first document'] >>> vocabulary = ['this', 'document', 'first', 'is','second', 'the', ... 'and', 'one'] >>> pipe = Pipeline([('count', CountVectorizer(vocabulary=vocabulary)), ... ('tfid', TfidfTransformer())]).fit(corpus) >>> pipe['count'].transform(corpus).toarray() array([[1, 1, 1, 1, 0, 1, 0, 0], [1, 2, 0, 1, 1, 1, 0, 0], [1, 0, 0, 1, 0, 1, 1, 1], [1, 1, 1, 1, 0, 1, 0, 0]]) >>> pipe['tfid'].idf_ array([1. , 1.22314355, 1.51082562, 1. , 1.91629073, 1. , 1.91629073, 1.91629073]) >>> pipe.transform(corpus).shape (4, 8) """ _parameter_constraints: dict = { "norm": [StrOptions({"l1", "l2"}), None], "use_idf": ["boolean"], "smooth_idf": ["boolean"], "sublinear_tf": ["boolean"], } def __init__(self, *, norm="l2", use_idf=True, smooth_idf=True, sublinear_tf=False): self.norm = norm self.use_idf = use_idf self.smooth_idf = smooth_idf self.sublinear_tf = sublinear_tf @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Learn the idf vector (global term weights). Parameters ---------- X : sparse matrix of shape n_samples, n_features) A matrix of term/token counts. y : None This parameter is not needed to compute tf-idf. Returns ------- self : object Fitted transformer. """ # large sparse data is not supported for 32bit platforms because # _document_frequency uses np.bincount which works on arrays of # dtype NPY_INTP which is int32 for 32bit platforms. See #20923 X = self._validate_data( X, accept_sparse=("csr", "csc"), accept_large_sparse=not _IS_32BIT ) if not sp.issparse(X): X = sp.csr_matrix(X) dtype = X.dtype if X.dtype in FLOAT_DTYPES else np.float64 if self.use_idf: n_samples, n_features = X.shape df = _document_frequency(X) df = df.astype(dtype, copy=False) # perform idf smoothing if required df += int(self.smooth_idf) n_samples += int(self.smooth_idf) # log+1 instead of log makes sure terms with zero idf don't get # suppressed entirely. idf = np.log(n_samples / df) + 1 self._idf_diag = sp.diags( idf, offsets=0, shape=(n_features, n_features), format="csr", dtype=dtype, ) return self def transform(self, X, copy=True): """Transform a count matrix to a tf or tf-idf representation. Parameters ---------- X : sparse matrix of (n_samples, n_features) A matrix of term/token counts. copy : bool, default=True Whether to copy X and operate on the copy or perform in-place operations. Returns ------- vectors : sparse matrix of shape (n_samples, n_features) Tf-idf-weighted document-term matrix. """ X = self._validate_data( X, accept_sparse="csr", dtype=FLOAT_DTYPES, copy=copy, reset=False ) if not sp.issparse(X): X = sp.csr_matrix(X, dtype=np.float64) if self.sublinear_tf: np.log(X.data, X.data) X.data += 1 if self.use_idf: # idf_ being a property, the automatic attributes detection # does not work as usual and we need to specify the attribute # name: check_is_fitted(self, attributes=["idf_"], msg="idf vector is not fitted") # *= doesn't work X = X * self._idf_diag if self.norm is not None: X = normalize(X, norm=self.norm, copy=False) return X @property def idf_(self): """Inverse document frequency vector, only defined if `use_idf=True`. Returns ------- ndarray of shape (n_features,) """ # if _idf_diag is not set, this will raise an attribute error, # which means hasattr(self, "idf_") is False return np.ravel(self._idf_diag.sum(axis=0)) @idf_.setter def idf_(self, value): value = np.asarray(value, dtype=np.float64) n_features = value.shape[0] self._idf_diag = sp.spdiags( value, diags=0, m=n_features, n=n_features, format="csr" ) def _more_tags(self): return {"X_types": ["2darray", "sparse"]} class TfidfVectorizer(CountVectorizer): r"""Convert a collection of raw documents to a matrix of TF-IDF features. Equivalent to :class:`CountVectorizer` followed by :class:`TfidfTransformer`. For an example of usage, see :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`. For an efficiency comparision of the different feature extractors, see :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. Read more in the :ref:`User Guide <text_feature_extraction>`. Parameters ---------- input : {'filename', 'file', 'content'}, default='content' - If `'filename'`, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. - If `'file'`, the sequence items must have a'read' method (file-like object) that is called to fetch the bytes in memory. - If `'content'`, the input is expected to be a sequence of items that can be of type string or byte. encoding : str, default='utf-8' If bytes or files are given to analyze, this encoding is used to decode. decode_error : {'strict', 'ignore','replace'}, default='strict' Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. By default, it is 'strict', meaning that a UnicodeDecodeError will be raised. Other values are 'ignore' and'replace'. strip_accents : {'ascii', 'unicode'} or callable, default=None Remove accents and perform other character normalization during the preprocessing step. 'ascii' is a fast method that only works on characters that have a direct ASCII mapping. 'unicode' is a slightly slower method that works on any characters. None (default) means no character normalization is performed. Both 'ascii' and 'unicode' use NFKD normalization from :func:`unicodedata.normalize`. lowercase : bool, default=True Convert all characters to lowercase before tokenizing. preprocessor : callable, default=None Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. Only applies if ``analyzer`` is not callable. tokenizer : callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``. analyzer : {'word', 'char', 'char_wb'} or callable, default='word' Whether the feature should be made of word or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. .. versionchanged:: 0.21 Since v0.21, if ``input`` is ``'filename'`` or ``'file'``, the data is first read from the file and then passed to the given callable analyzer. stop_words : {'english'}, list, default=None If a string, it is passed to _check_stop_list and the appropriate stop list is returned. 'english' is currently the only supported string value. There are several known issues with 'english' and you should consider an alternative (see :ref:`stop_words`). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. If None, no stop words will be used. In this case, setting `max_df` to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms. token_pattern : str, default=r"(?u)\\b\\w\\w+\\b" Regular expression denoting what constitutes a "token", only used if ``analyzer == 'word'``. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted. ngram_range : tuple (min_n, max_n), default=(1, 1) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. Only applies if ``analyzer`` is not callable. max_df : float or int, default=1.0 When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float in range [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. min_df : float or int, default=1 When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float in range of [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. max_features : int, default=None If not None, build a vocabulary that only consider the top `max_features` ordered by term frequency across the corpus. Otherwise, all features are used. This parameter is ignored if vocabulary is not None. vocabulary : Mapping or iterable, default=None Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms. If not given, a vocabulary is determined from the input documents. binary : bool, default=False If True, all non-zero term counts are set to 1. This does not mean outputs will have only 0/1 values, only that the tf term in tf-idf is binary. (Set `binary` to True, `use_idf` to False and `norm` to None to get 0/1 outputs). dtype : dtype, default=float64 Type of the matrix returned by fit_transform() or transform(). norm : {'l1', 'l2'} or None, default='l2' Each output row will have unit norm, either: - 'l2': Sum of squares of vector elements is 1. The cosine similarity between two vectors is their dot product when l2 norm has been applied. - 'l1': Sum of absolute values of vector elements is 1. See :func:`~sklearn.preprocessing.normalize`. - None: No normalization. use_idf : bool, default=True Enable inverse-document-frequency reweighting. If False, idf(t) = 1. smooth_idf : bool, default=True Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions. sublinear_tf : bool, default=False Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). Attributes ---------- vocabulary_ : dict A mapping of terms to feature indices. fixed_vocabulary_ : bool True if a fixed vocabulary of term to indices mapping is provided by the user. idf_ : array of shape (n_features,) The inverse document frequency (IDF) vector; only defined if ``use_idf`` is True. stop_words_ : set Terms that were ignored because they either: - occurred in too many documents (`max_df`) - occurred in too few documents (`min_df`) - were cut off by feature selection (`max_features`). This is only available if no vocabulary was given. See Also -------- CountVectorizer : Transforms text into a sparse matrix of n-gram counts. TfidfTransformer : Performs the TF-IDF transformation from a provided matrix of counts. Notes ----- The ``stop_words_`` attribute can get large and increase the model size when pickling. This attribute is provided only for introspection and can be safely removed using delattr or set to None before pickling. Examples -------- >>> from sklearn.feature_extraction.text import TfidfVectorizer >>> corpus = [ ... 'This is the first document.', ... 'This document is the second document.', ... 'And this is the third one.', ... 'Is this the first document?', ... ] >>> vectorizer = TfidfVectorizer() >>> X = vectorizer.fit_transform(corpus) >>> vectorizer.get_feature_names_out() array(['and', 'document', 'first', 'is', 'one','second', 'the', 'third', 'this'],...) >>> print(X.shape) (4, 9) """ _parameter_constraints: dict = {**CountVectorizer._parameter_constraints} _parameter_constraints.update( { "norm": [StrOptions({"l1", "l2"}), None], "use_idf": ["boolean"], "smooth_idf": ["boolean"], "sublinear_tf": ["boolean"], } ) def __init__( self, *, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, analyzer="word", stop_words=None, token_pattern=r"(?u)\b\w\w+\b", ngram_range=(1, 1), max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=np.float64, norm="l2", use_idf=True, smooth_idf=True, sublinear_tf=False, ): super().__init__( input=input, encoding=encoding, decode_error=decode_error, strip_accents=strip_accents, lowercase=lowercase, preprocessor=preprocessor, tokenizer=tokenizer, analyzer=analyzer, stop_words=stop_words, token_pattern=token_pattern, ngram_range=ngram_range, max_df=max_df, min_df=min_df, max_features=max_features, vocabulary=vocabulary, binary=binary, dtype=dtype, ) self.norm = norm self.use_idf = use_idf self.smooth_idf = smooth_idf self.sublinear_tf = sublinear_tf # Broadcast the TF-IDF parameters to the underlying transformer instance # for easy grid search and repr @property def idf_(self): """Inverse document frequency vector, only defined if `use_idf=True`. Returns ------- ndarray of shape (n_features,) """ if not hasattr(self, "_tfidf"): raise NotFittedError( f"{self.__class__.__name__} is not fitted yet. Call 'fit' with " "appropriate arguments before using this attribute." ) return self._tfidf.idf_ @idf_.setter def idf_(self, value): if not self.use_idf: raise ValueError("`idf_` cannot be set when `user_idf=False`.") if not hasattr(self, "_tfidf"): # We should support transferring `idf_` from another `TfidfTransformer` # and therefore, we need to create the transformer instance it does not # exist yet. self._tfidf = TfidfTransformer( norm=self.norm, use_idf=self.use_idf, smooth_idf=self.smooth_idf, sublinear_tf=self.sublinear_tf, ) self._validate_vocabulary() if hasattr(self, "vocabulary_"): if len(self.vocabulary_)!= len(value): raise ValueError( "idf length = %d must be equal to vocabulary size = %d" % (len(value), len(self.vocabulary)) ) self._tfidf.idf_ = value def _check_params(self): if self.dtype not in FLOAT_DTYPES: warnings.warn( "Only {} 'dtype' should be used. {} 'dtype' will " "be converted to np.float64.".format(FLOAT_DTYPES, self.dtype), UserWarning, ) @_fit_context(prefer_skip_nested_validation=True) def fit(self, raw_documents, y=None): """Learn vocabulary and idf from training set. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is not needed to compute tfidf. Returns ------- self : object Fitted vectorizer. """ self._check_params() self._warn_for_unused_params() self._tfidf = TfidfTransformer( norm=self.norm, use_idf=self.use_idf, smooth_idf=self.smooth_idf, sublinear_tf=self.sublinear_tf, ) X = super().fit_transform(raw_documents) self._tfidf.fit(X) return self def fit_transform(self, raw_documents, y=None): """Learn vocabulary and idf, return document-term matrix. This is equivalent to fit followed by transform, but more efficiently implemented. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is ignored. Returns ------- X : sparse matrix of (n_samples, n_features) Tf-idf-weighted document-term matrix. """ self._check_params() self._tfidf = TfidfTransformer( norm=self.norm, use_idf=self.use_idf, smooth_idf=self.smooth_idf, sublinear_tf=self.sublinear_tf, ) X = super().fit_transform(raw_documents) self._tfidf.fit(X) # X is already a transformed view of raw_documents so # we set copy to False return self._tfidf.transform(X, copy=False) def transform(self, raw_documents): """Transform documents to document-term matrix. Uses the vocabulary and document frequencies (df) learned by fit (or fit_transform). Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. Returns ------- X : sparse matrix of (n_samples, n_features) Tf-idf-weighted document-term matrix. """ check_is_fitted(self, msg="The TF-IDF vectorizer is not fitted") X = super().transform(raw_documents) return self._tfidf.transform(X, copy=False) def _more_tags(self): return {"X_types": ["string"], "_skip_test": True} """ The :mod:`sklearn.feature_extraction.image` submodule gathers utilities to extract features from images. """ # Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org> # Gael Varoquaux <gael.varoquaux@normalesup.org> # Olivier Grisel # Vlad Niculae # License: BSD 3 clause from itertools import product from numbers import Integral, Number, Real import numpy as np from numpy.lib.stride_tricks import as_strided from scipy import sparse from..base import BaseEstimator, TransformerMixin, _fit_context from..utils import check_array, check_random_state from..utils._param_validation import Hidden, Interval, RealNotInt, validate_params __all__ = [ "PatchExtractor", "extract_patches_2d", "grid_to_graph", "img_to_graph", "reconstruct_from_patches_2d", ] ############################################################################### # From an image to a graph def _make_edges_3d(n_x, n_y, n_z=1): """Returns a list of edges for a 3D image. Parameters ---------- n_x : int The size of the grid in the x direction. n_y : int The size of the grid in the y direction. n_z : integer, default=1 The size of the grid in the z direction, defaults to 1 """ vertices = np.arange(n_x * n_y * n_z).reshape((n_x, n_y, n_z)) edges_deep = np.vstack((vertices[:, :, :-1].ravel(), vertices[:, :, 1:].ravel())) edges_right = np.vstack((vertices[:, :-1].ravel(), vertices[:, 1:].ravel())) edges_down = np.vstack((vertices[:-1].ravel(), vertices[1:].ravel())) edges = np.hstack((edges_deep, edges_right, edges_down)) return edges def _compute_gradient_3d(edges, img): _, n_y, n_z = img.shape gradient = np.abs( img[ edges[0] // (n_y * n_z), (edges[0] % (n_y * n_z)) // n_z, (edges[0] % (n_y * n_z)) % n_z, ] - img[ edges[1] // (n_y * n_z), (edges[1] % (n_y * n_z)) // n_z, (edges[1] % (n_y * n_z)) % n_z, ] ) return gradient # XXX: Why mask the image after computing the weights? def _mask_edges_weights(mask, edges, weights=None): """Apply a mask to edges (weighted or not)""" inds = np.arange(mask.size) inds = inds[mask.ravel()] ind_mask = np.logical_and(np.isin(edges[0], inds), np.isin(edges[1], inds)) edges = edges[:, ind_mask] if weights is not None: weights = weights[ind_mask] if len(edges.ravel()): maxval = edges.max() else: maxval = 0 order = np.searchsorted(np.flatnonzero(mask), np.arange(maxval + 1)) edges = order[edges] if weights is None: return edges else: return edges, weights def _to_graph( n_x, n_y, n_z, mask=None, img=None, return_as=sparse.coo_matrix, dtype=None ): """Auxiliary function for img_to_graph and grid_to_graph""" edges = _make_edges_3d(n_x, n_y, n_z) if dtype is None: # To not overwrite input dtype if img is None: dtype = int else: dtype = img.dtype if img is not None: img = np.atleast_3d(img) weights = _compute_gradient_3d(edges, img) if mask is not None: edges, weights = _mask_edges_weights(mask, edges, weights) diag = img.squeeze()[mask] else: diag = img.ravel() n_voxels = diag.size else: if mask is not None: mask = mask.astype(dtype=bool, copy=False) edges = _mask_edges_weights(mask, edges) n_voxels = np.sum(mask) else: n_voxels = n_x * n_y * n_z weights = np.ones(edges.shape[1], dtype=dtype) diag = np.ones(n_voxels, dtype=dtype) diag_idx = np.arange(n_voxels) i_idx = np.hstack((edges[0], edges[1])) j_idx = np.hstack((edges[1], edges[0])) graph = sparse.coo_matrix( ( np.hstack((weights, weights, diag)), (np.hstack((i_idx, diag_idx)), np.hstack((j_idx, diag_idx))), ), (n_voxels, n_voxels), dtype=dtype, ) if return_as is np.ndarray: return graph.toarray() return return_as(graph) @validate_params( { "img": ["array-like"], "mask": [None, np.ndarray], "return_as": [type], "dtype": "no_validation", # validation delegated to numpy }, prefer_skip_nested_validation=True, ) def img_to_graph(img, *, mask=None, return_as=sparse.coo_matrix, dtype=None): """Graph of the pixel-to-pixel gradient connections. Edges are weighted with the gradient values. Read more in the :ref:`User Guide <image_feature_extraction>`. Parameters ---------- img : array-like of shape (height, width) or (height, width, channel) 2D or 3D image. mask : ndarray of shape (height, width) or \ (height, width, channel), dtype=bool, default=None An optional mask of the image, to consider only part of the pixels. return_as : np.ndarray or a sparse matrix class, \ default=sparse.coo_matrix The class to use to build the returned adjacency matrix. dtype : dtype, default=None The data of the returned sparse matrix. By default it is the dtype of img. Returns ------- graph : ndarray or a sparse matrix class The computed adjacency matrix. Notes ----- For scikit-learn versions 0.14.1 and prior, return_as=np.ndarray was handled by returning a dense np.matrix instance. Going forward, np.ndarray returns an np.ndarray, as expected. For compatibility, user code relying on this method should wrap its calls in ``np.asarray`` to avoid type issues. """ img = np.atleast_3d(img) n_x, n_y, n_z = img.shape return _to_graph(n_x, n_y, n_z, mask, img, return_as, dtype) @validate_params( { "n_x": [Interval(Integral, left=1, right=None, closed="left")], "n_y": [Interval(Integral, left=1, right=None, closed="left")], "n_z": [Interval(Integral, left=1, right=None, closed="left")], "mask": [None, np.ndarray], "return_as": [type], "dtype": "no_validation", # validation delegated to numpy }, prefer_skip_nested_validation=True, ) def grid_to_graph( n_x, n_y, n_z=1, *, mask=None, return_as=sparse.coo_matrix, dtype=int ): """Graph of the pixel-to-pixel connections. Edges exist if 2 voxels are connected. Parameters ---------- n_x : int Dimension in x axis. n_y : int Dimension in y axis. n_z : int, default=1 Dimension in z axis. mask : ndarray of shape (n_x, n_y, n_z), dtype=bool, default=None An optional mask of the image, to consider only part of the pixels. return_as : np.ndarray or a sparse matrix class, \ default=sparse.coo_matrix The class to use to build the returned adjacency matrix. dtype : dtype, default=int The data of the returned sparse matrix. By default it is int. Returns ------- graph : np.ndarray or a sparse matrix class The computed adjacency matrix. Notes ----- For scikit-learn versions 0.14.1 and prior, return_as=np.ndarray was handled by returning a dense np.matrix instance. Going forward, np.ndarray returns an np.ndarray, as expected. For compatibility, user code relying on this method should wrap its calls in ``np.asarray`` to avoid type issues. """ return _to_graph(n_x, n_y, n_z, mask=mask, return_as=return_as, dtype=dtype) ############################################################################### # From an image to a set of small image patches def _compute_n_patches(i_h, i_w, p_h, p_w, max_patches=None): """Compute the number of patches that will be extracted in an image. Read more in the :ref:`User Guide <image_feature_extraction>`. Parameters ---------- i_h : int The image height i_w : int The image with p_h : int The height of a patch p_w : int The width of a patch max_patches : int or float, default=None The maximum number of patches to extract. If `max_patches` is a float between 0 and 1, it is taken to be a proportion of the total number of patches. If `max_patches` is None, all possible patches are extracted. """ n_h = i_h - p_h + 1 n_w = i_w - p_w + 1 all_patches = n_h * n_w if max_patches: if isinstance(max_patches, (Integral)) and max_patches < all_patches: return max_patches elif isinstance(max_patches, (Integral)) and max_patches >= all_patches: return all_patches elif isinstance(max_patches, (Real)) and 0 < max_patches < 1: return int(max_patches * all_patches) else: raise ValueError("Invalid value for max_patches: %r" % max_patches) else: return all_patches def _extract_patches(arr, patch_shape=8, extraction_step=1): """Extracts patches of any n-dimensional array in place using strides. Given an n-dimensional array it will return a 2n-dimensional array with the first n dimensions indexing patch position and the last n indexing the patch content. This operation is immediate (O(1)). A reshape performed on the first n dimensions will cause numpy to copy data, leading to a list of extracted patches. Read more in the :ref:`User Guide <image_feature_extraction>`. Parameters ---------- arr : ndarray n-dimensional array of which patches are to be extracted patch_shape : int or tuple of length arr.ndim.default=8 Indicates the shape of the patches to be extracted. If an integer is given, the shape will be a hypercube of sidelength given by its value. extraction_step : int or tuple of length arr.ndim, default=1 Indicates step size at which extraction shall be performed. If integer is given, then the step is uniform in all dimensions. Returns ------- patches : strided ndarray 2n-dimensional array indexing patches on first n dimensions and containing patches on the last n dimensions. These dimensions are fake, but this way no data is copied. A simple reshape invokes a copying operation to obtain a list of patches: result.reshape([-1] + list(patch_shape)) """ arr_ndim = arr.ndim if isinstance(patch_shape, Number): patch_shape = tuple([patch_shape] * arr_ndim) if isinstance(extraction_step, Number): extraction_step = tuple([extraction_step] * arr_ndim) patch_strides = arr.strides slices = tuple(slice(None, None, st) for st in extraction_step) indexing_strides = arr[slices].strides patch_indices_shape = ( (np.array(arr.shape) - np.array(patch_shape)) // np.array(extraction_step) ) + 1 shape = tuple(list(patch_indices_shape) + list(patch_shape)) strides = tuple(list(indexing_strides) + list(patch_strides)) patches = as_strided(arr, shape=shape, strides=strides) return patches @validate_params( { "image": [np.ndarray], "patch_size": [tuple, list], "max_patches": [ Interval(RealNotInt, 0, 1, closed="neither"), Interval(Integral, 1, None, closed="left"), None, ], "random_state": ["random_state"], }, prefer_skip_nested_validation=True, ) def extract_patches_2d(image, patch_size, *, max_patches=None, random_state=None): """Reshape a 2D image into a collection of patches. The resulting patches are allocated in a dedicated array. Read more in the :ref:`User Guide <image_feature_extraction>`. Parameters ---------- image : ndarray of shape (image_height, image_width) or \ (image_height, image_width, n_channels) The original image data. For color images, the last dimension specifies the channel: a RGB image would have `n_channels=3`. patch_size : tuple of int (patch_height, patch_width) The dimensions of one patch. max_patches : int or float, default=None The maximum number of patches to extract. If `max_patches` is a float between 0 and 1, it is taken to be a proportion of the total number of patches. If `max_patches` is None it corresponds to the total number of patches that can be extracted. random_state : int, RandomState instance, default=None Determines the random number generator used for random sampling when `max_patches` is not None. Use an int to make the randomness deterministic. See :term:`Glossary <random_state>`. Returns ------- patches : array of shape (n_patches, patch_height, patch_width) or \ (n_patches, patch_height, patch_width, n_channels) The collection of patches extracted from the image, where `n_patches` is either `max_patches` or the total number of patches that can be extracted. Examples -------- >>> from sklearn.datasets import load_sample_image >>> from sklearn.feature_extraction import image >>> # Use the array data from the first image in this dataset: >>> one_image = load_sample_image("china.jpg") >>> print('Image shape: {}'.format(one_image.shape)) Image shape: (427, 640, 3) >>> patches = image.extract_patches_2d(one_image, (2, 2)) >>> print('Patches shape: {}'.format(patches.shape)) Patches shape: (272214, 2, 2, 3) >>> # Here are just two of these patches: >>> print(patches[1]) [[[174 201 231] [174 201 231]] [[173 200 230] [173 200 230]]] >>> print(patches[800]) [[[187 214 243] [188 215 244]] [[187 214 243] [188 215 244]]] """ i_h, i_w = image.shape[:2] p_h, p_w = patch_size if p_h > i_h: raise ValueError( "Height of the patch should be less than the height of the image." ) if p_w > i_w: raise ValueError( "Width of the patch should be less than the width of the image." ) image = check_array(image, allow_nd=True) image = image.reshape((i_h, i_w, -1)) n_colors = image.shape[-1] extracted_patches = _extract_patches( image, patch_shape=(p_h, p_w, n_colors), extraction_step=1 ) n_patches = _compute_n_patches(i_h, i_w, p_h, p_w, max_patches) if max_patches: rng = check_random_state(random_state) i_s = rng.randint(i_h - p_h + 1, size=n_patches) j_s = rng.randint(i_w - p_w + 1, size=n_patches) patches = extracted_patches[i_s, j_s, 0] else: patches = extracted_patches patches = patches.reshape(-1, p_h, p_w, n_colors) # remove the color dimension if useless if patches.shape[-1] == 1: return patches.reshape((n_patches, p_h, p_w)) else: return patches @validate_params( {"patches": [np.ndarray], "image_size": [tuple, Hidden(list)]}, prefer_skip_nested_validation=True, ) def reconstruct_from_patches_2d(patches, image_size): """Reconstruct the image from all of its patches. Patches are assumed to overlap and the image is constructed by filling in the patches from left to right, top to bottom, averaging the overlapping regions. Read more in the :ref:`User Guide <image_feature_extraction>`. Parameters ---------- patches : ndarray of shape (n_patches, patch_height, patch_width) or \ (n_patches, patch_height, patch_width, n_channels) The complete set of patches. If the patches contain colour information, channels are indexed along the last dimension: RGB patches would have `n_channels=3`. image_size : tuple of int (image_height, image_width) or \ (image_height, image_width, n_channels) The size of the image that will be reconstructed. Returns ------- image : ndarray of shape image_size The reconstructed image. """ i_h, i_w = image_size[:2] p_h, p_w = patches.shape[1:3] img = np.zeros(image_size) # compute the dimensions of the patches array n_h = i_h - p_h + 1 n_w = i_w - p_w + 1 for p, (i, j) in zip(patches, product(range(n_h), range(n_w))): img[i : i + p_h, j : j + p_w] += p for i in range(i_h): for j in range(i_w): # divide by the amount of overlap # XXX: is this the most efficient way? memory-wise yes, cpu wise? img[i, j] /= float(min(i + 1, p_h, i_h - i) * min(j + 1, p_w, i_w - j)) return img class PatchExtractor(TransformerMixin, BaseEstimator): """Extracts patches from a collection of images. Read more in the :ref:`User Guide <image_feature_extraction>`. .. versionadded:: 0.9 Parameters ---------- patch_size : tuple of int (patch_height, patch_width), default=None The dimensions of one patch. If set to None, the patch size will be automatically set to `(img_height // 10, img_width // 10)`, where `img_height` and `img_width` are the dimensions of the input images. max_patches : int or float, default=None The maximum number of patches per image to extract. If `max_patches` is a float in (0, 1), it is taken to mean a proportion of the total number of patches. If set to None, extract all possible patches. random_state : int, RandomState instance, default=None Determines the random number generator used for random sampling when `max_patches is not None`. Use an int to make the randomness deterministic. See :term:`Glossary <random_state>`. See Also -------- reconstruct_from_patches_2d : Reconstruct image from all of its patches. Notes ----- This estimator is stateless and does not need to be fitted. However, we recommend to call :meth:`fit_transform` instead of :meth:`transform`, as parameter validation is only performed in :meth:`fit`. Examples -------- >>> from sklearn.datasets import load_sample_images >>> from sklearn.feature_extraction import image >>> # Use the array data from the second image in this dataset: >>> X = load_sample_images().images[1] >>> X = X[None,...] >>> print(f"Image shape: {X.shape}") Image shape: (1, 427, 640, 3) >>> pe = image.PatchExtractor(patch_size=(10, 10)) >>> pe_trans = pe.transform(X) >>> print(f"Patches shape: {pe_trans.shape}") Patches shape: (263758, 10, 10, 3) >>> X_reconstructed = image.reconstruct_from_patches_2d(pe_trans, X.shape[1:]) >>> print(f"Reconstructed shape: {X_reconstructed.shape}") Reconstructed shape: (427, 640, 3) """ _parameter_constraints: dict = { "patch_size": [tuple, None], "max_patches": [ None, Interval(RealNotInt, 0, 1, closed="neither"), Interval(Integral, 1, None, closed="left"), ], "random_state": ["random_state"], } def __init__(self, *, patch_size=None, max_patches=None, random_state=None): self.patch_size = patch_size self.max_patches = max_patches self.random_state = random_state @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Only validate the parameters of the estimator. This method allows to: (i) validate the parameters of the estimator and (ii) be consistent with the scikit-learn transformer API. Parameters ---------- X : ndarray of shape (n_samples, image_height, image_width) or \ (n_samples, image_height, image_width, n_channels) Array of images from which to extract patches. For color images, the last dimension specifies the channel: a RGB image would have `n_channels=3`. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object Returns the instance itself. """ return self def transform(self, X): """Transform the image samples in `X` into a matrix of patch data. Parameters ---------- X : ndarray of shape (n_samples, image_height, image_width) or \ (n_samples, image_height, image_width, n_channels) Array of images from which to extract patches. For color images, the last dimension specifies the channel: a RGB image would have `n_channels=3`. Returns ------- patches : array of shape (n_patches, patch_height, patch_width) or \ (n_patches, patch_height, patch_width, n_channels) The collection of patches extracted from the images, where `n_patches` is either `n_samples * max_patches` or the total number of patches that can be extracted. """ X = self._validate_data( X=X, ensure_2d=False, allow_nd=True, ensure_min_samples=1, ensure_min_features=1, reset=False, ) random_state = check_random_state(self.random_state) n_imgs, img_height, img_width = X.shape[:3] if self.patch_size is None: patch_size = img_height // 10, img_width // 10 else: if len(self.patch_size)!= 2: raise ValueError( "patch_size must be a tuple of two integers. Got" f" {self.patch_size} instead." ) patch_size = self.patch_size n_imgs, img_height, img_width = X.shape[:3] X = np.reshape(X, (n_imgs, img_height, img_width, -1)) n_channels = X.shape[-1] # compute the dimensions of the patches array patch_height, patch_width = patch_size n_patches = _compute_n_patches( img_height, img_width, patch_height, patch_width, self.max_patches ) patches_shape = (n_imgs * n_patches,) + patch_size if n_channels > 1: patches_shape += (n_channels,) # extract the patches patches = np.empty(patches_shape) for ii, image in enumerate(X): patches[ii * n_patches : (ii + 1) * n_patches] = extract_patches_2d( image, patch_size, max_patches=self.max_patches, random_state=random_state, ) return patches def _more_tags(self): return {"X_types": ["3darray"], "stateless": True}

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gaussian_process.rst

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scikit-learn__scikit-learn/doc/modules/gaussian_process.rst

scikit-learn__scikit-learn/sklearn/gaussian_process

Gaussian Processes Gaussian Processes (GP) are a generic supervised learning method designed to solve regression and probabilistic classification problems. The advantages of Gaussian processes are: - The prediction interpolates the observations (at least for regular kernels). - The prediction is probabilistic (Gaussian) so that one can compute empirical confidence intervals and decide based on those if one should refit (online fitting, adaptive fitting) the prediction in some region of interest. - Versatile: different kernels <gp_kernels> can be specified. Common kernels are provided, but it is also possible to specify custom kernels. The disadvantages of Gaussian processes include: - They are not sparse, i.e., they use the whole samples/features information to perform the prediction. - They lose efficiency in high dimensional spaces -- namely when the number of features exceeds a few dozens. Gaussian Process Regression (GPR) The GaussianProcessRegressor implements Gaussian processes (GP) for regression purposes. For this, the prior of the GP needs to be specified. The prior mean is assumed to be constant and zero (for normalize_y=False) or the training data's mean (for normalize_y=True). The prior's covariance is specified by passing a kernel <gp_kernels> object. The hyperparameters of the kernel are optimized during fitting of GaussianProcessRegressor by maximizing the log-marginal-likelihood (LML) based on the passed optimizer. As the LML may have multiple local optima, the optimizer can be started repeatedly by specifying n_restarts_optimizer. The first run is always conducted starting from the initial hyperparameter values of the kernel; subsequent runs are conducted from hyperparameter values that have been chosen randomly from the range of allowed values. If the initial hyperparameters should be kept fixed, None can be passed as optimizer. The noise level in the targets can be specified by passing it via the parameter alpha, either globally as a scalar or per datapoint. Note that a moderate noise level can also be helpful for dealing with numeric issues during fitting as it is effectively implemented as Tikhonov regularization, i.e., by adding it to the diagonal of the kernel matrix. An alternative to specifying the noise level explicitly is to include a WhiteKernel component into the kernel, which can estimate the global noise level from the data (see example below). The implementation is based on Algorithm 2.1 of [RW2006]. In addition to the API of standard scikit-learn estimators, GaussianProcessRegressor: - allows prediction without prior fitting (based on the GP prior) - provides an additional method sample_y(X), which evaluates samples drawn from the GPR (prior or posterior) at given inputs - exposes a method log_marginal_likelihood(theta), which can be used externally for other ways of selecting hyperparameters, e.g., via Markov chain Monte Carlo. GPR examples GPR with noise-level estimation This example illustrates that GPR with a sum-kernel including a WhiteKernel can estimate the noise level of data. An illustration of the log-marginal-likelihood (LML) landscape shows that there exist two local maxima of LML. The first corresponds to a model with a high noise level and a large length scale, which explains all variations in the data by noise. The second one has a smaller noise level and shorter length scale, which explains most of the variation by the noise-free functional relationship. The second model has a higher likelihood; however, depending on the initial value for the hyperparameters, the gradient-based optimization might also converge to the high-noise solution. It is thus important to repeat the optimization several times for different initializations. Comparison of GPR and Kernel Ridge Regression Both kernel ridge regression (KRR) and GPR learn a target function by employing internally the "kernel trick". KRR learns a linear function in the space induced by the respective kernel which corresponds to a non-linear function in the original space. The linear function in the kernel space is chosen based on the mean-squared error loss with ridge regularization. GPR uses the kernel to define the covariance of a prior distribution over the target functions and uses the observed training data to define a likelihood function. Based on Bayes theorem, a (Gaussian) posterior distribution over target functions is defined, whose mean is used for prediction. A major difference is that GPR can choose the kernel's hyperparameters based on gradient-ascent on the marginal likelihood function while KRR needs to perform a grid search on a cross-validated loss function (mean-squared error loss). A further difference is that GPR learns a generative, probabilistic model of the target function and can thus provide meaningful confidence intervals and posterior samples along with the predictions while KRR only provides predictions. The following figure illustrates both methods on an artificial dataset, which consists of a sinusoidal target function and strong noise. The figure compares the learned model of KRR and GPR based on a ExpSineSquared kernel, which is suited for learning periodic functions. The kernel's hyperparameters control the smoothness (length_scale) and periodicity of the kernel (periodicity). Moreover, the noise level of the data is learned explicitly by GPR by an additional WhiteKernel component in the kernel and by the regularization parameter alpha of KRR. The figure shows that both methods learn reasonable models of the target function. GPR provides reasonable confidence bounds on the prediction which are not available for KRR. A major difference between the two methods is the time required for fitting and predicting: while fitting KRR is fast in principle, the grid-search for hyperparameter optimization scales exponentially with the number of hyperparameters ("curse of dimensionality"). The gradient-based optimization of the parameters in GPR does not suffer from this exponential scaling and is thus considerably faster on this example with 3-dimensional hyperparameter space. The time for predicting is similar; however, generating the variance of the predictive distribution of GPR takes considerably longer than just predicting the mean. GPR on Mauna Loa CO2 data This example is based on Section 5.4.3 of [RW2006]. It illustrates an example of complex kernel engineering and hyperparameter optimization using gradient ascent on the log-marginal-likelihood. The data consists of the monthly average atmospheric CO2 concentrations (in parts per million by volume (ppmv)) collected at the Mauna Loa Observatory in Hawaii, between 1958 and 1997. The objective is to model the CO2 concentration as a function of the time t. The kernel is composed of several terms that are responsible for explaining different properties of the signal: - a long term, smooth rising trend is to be explained by an RBF kernel. The RBF kernel with a large length-scale enforces this component to be smooth; it is not enforced that the trend is rising which leaves this choice to the GP. The specific length-scale and the amplitude are free hyperparameters. - a seasonal component, which is to be explained by the periodic ExpSineSquared kernel with a fixed periodicity of 1 year. The length-scale of this periodic component, controlling its smoothness, is a free parameter. In order to allow decaying away from exact periodicity, the product with an RBF kernel is taken. The length-scale of this RBF component controls the decay time and is a further free parameter. - smaller, medium term irregularities are to be explained by a RationalQuadratic kernel component, whose length-scale and alpha parameter, which determines the diffuseness of the length-scales, are to be determined. According to [RW2006], these irregularities can better be explained by a RationalQuadratic than an RBF kernel component, probably because it can accommodate several length-scales. - a "noise" term, consisting of an RBF kernel contribution, which shall explain the correlated noise components such as local weather phenomena, and a WhiteKernel contribution for the white noise. The relative amplitudes and the RBF's length scale are further free parameters. Maximizing the log-marginal-likelihood after subtracting the target's mean yields the following kernel with an LML of -83.214: 34.4**2 * RBF(length_scale=41.8) + 3.27**2 * RBF(length_scale=180) * ExpSineSquared(length_scale=1.44, periodicity=1) + 0.446**2 * RationalQuadratic(alpha=17.7, length_scale=0.957) + 0.197**2 * RBF(length_scale=0.138) + WhiteKernel(noise_level=0.0336) Thus, most of the target signal (34.4ppm) is explained by a long-term rising trend (length-scale 41.8 years). The periodic component has an amplitude of 3.27ppm, a decay time of 180 years and a length-scale of 1.44. The long decay time indicates that we have a locally very close to periodic seasonal component. The correlated noise has an amplitude of 0.197ppm with a length scale of 0.138 years and a white-noise contribution of 0.197ppm. Thus, the overall noise level is very small, indicating that the data can be very well explained by the model. The figure shows also that the model makes very confident predictions until around 2015 Gaussian Process Classification (GPC) The GaussianProcessClassifier implements Gaussian processes (GP) for classification purposes, more specifically for probabilistic classification, where test predictions take the form of class probabilities. GaussianProcessClassifier places a GP prior on a latent function f, which is then squashed through a link function to obtain the probabilistic classification. The latent function f is a so-called nuisance function, whose values are not observed and are not relevant by themselves. Its purpose is to allow a convenient formulation of the model, and f is removed (integrated out) during prediction. GaussianProcessClassifier implements the logistic link function, for which the integral cannot be computed analytically but is easily approximated in the binary case. In contrast to the regression setting, the posterior of the latent function f is not Gaussian even for a GP prior since a Gaussian likelihood is inappropriate for discrete class labels. Rather, a non-Gaussian likelihood corresponding to the logistic link function (logit) is used. GaussianProcessClassifier approximates the non-Gaussian posterior with a Gaussian based on the Laplace approximation. More details can be found in Chapter 3 of [RW2006]. The GP prior mean is assumed to be zero. The prior's covariance is specified by passing a kernel <gp_kernels> object. The hyperparameters of the kernel are optimized during fitting of GaussianProcessRegressor by maximizing the log-marginal-likelihood (LML) based on the passed optimizer. As the LML may have multiple local optima, the optimizer can be started repeatedly by specifying n_restarts_optimizer. The first run is always conducted starting from the initial hyperparameter values of the kernel; subsequent runs are conducted from hyperparameter values that have been chosen randomly from the range of allowed values. If the initial hyperparameters should be kept fixed, None can be passed as optimizer. GaussianProcessClassifier supports multi-class classification by performing either one-versus-rest or one-versus-one based training and prediction. In one-versus-rest, one binary Gaussian process classifier is fitted for each class, which is trained to separate this class from the rest. In "one_vs_one", one binary Gaussian process classifier is fitted for each pair of classes, which is trained to separate these two classes. The predictions of these binary predictors are combined into multi-class predictions. See the section on multi-class classification <multiclass> for more details. In the case of Gaussian process classification, "one_vs_one" might be computationally cheaper since it has to solve many problems involving only a subset of the whole training set rather than fewer problems on the whole dataset. Since Gaussian process classification scales cubically with the size of the dataset, this might be considerably faster. However, note that "one_vs_one" does not support predicting probability estimates but only plain predictions. Moreover, note that GaussianProcessClassifier does not (yet) implement a true multi-class Laplace approximation internally, but as discussed above is based on solving several binary classification tasks internally, which are combined using one-versus-rest or one-versus-one. GPC examples Probabilistic predictions with GPC This example illustrates the predicted probability of GPC for an RBF kernel with different choices of the hyperparameters. The first figure shows the predicted probability of GPC with arbitrarily chosen hyperparameters and with the hyperparameters corresponding to the maximum log-marginal-likelihood (LML). While the hyperparameters chosen by optimizing LML have a considerably larger LML, they perform slightly worse according to the log-loss on test data. The figure shows that this is because they exhibit a steep change of the class probabilities at the class boundaries (which is good) but have predicted probabilities close to 0.5 far away from the class boundaries (which is bad) This undesirable effect is caused by the Laplace approximation used internally by GPC. The second figure shows the log-marginal-likelihood for different choices of the kernel's hyperparameters, highlighting the two choices of the hyperparameters used in the first figure by black dots. Illustration of GPC on the XOR dataset This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains considerably better results because the class-boundaries are linear and coincide with the coordinate axes. In practice, however, stationary kernels such as RBF often obtain better results. Gaussian process classification (GPC) on iris dataset This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF kernel on a two-dimensional version for the iris-dataset. This illustrates the applicability of GPC to non-binary classification. The anisotropic RBF kernel obtains slightly higher log-marginal-likelihood by assigning different length-scales to the two feature dimensions. Kernels for Gaussian Processes Kernels (also called "covariance functions" in the context of GPs) are a crucial ingredient of GPs which determine the shape of prior and posterior of the GP. They encode the assumptions on the function being learned by defining the "similarity" of two datapoints combined with the assumption that similar datapoints should have similar target values. Two categories of kernels can be distinguished: stationary kernels depend only on the distance of two datapoints and not on their absolute values k(x_(i),x_(j)) = k(d(x_(i),x_(j))) and are thus invariant to translations in the input space, while non-stationary kernels depend also on the specific values of the datapoints. Stationary kernels can further be subdivided into isotropic and anisotropic kernels, where isotropic kernels are also invariant to rotations in the input space. For more details, we refer to Chapter 4 of [RW2006]. For guidance on how to best combine different kernels, we refer to [Duv2014]. Gaussian Process Kernel API The main usage of a Kernel is to compute the GP's covariance between datapoints. For this, the method __call__ of the kernel can be called. This method can either be used to compute the "auto-covariance" of all pairs of datapoints in a 2d array X, or the "cross-covariance" of all combinations of datapoints of a 2d array X with datapoints in a 2d array Y. The following identity holds true for all kernels k (except for the WhiteKernel): k(X) == K(X, Y=X) If only the diagonal of the auto-covariance is being used, the method diag() of a kernel can be called, which is more computationally efficient than the equivalent call to __call__: np.diag(k(X, X)) == k.diag(X) Kernels are parameterized by a vector θ of hyperparameters. These hyperparameters can for instance control length-scales or periodicity of a kernel (see below). All kernels support computing analytic gradients of the kernel's auto-covariance with respect to log(θ) via setting eval_gradient=True in the __call__ method. That is, a (len(X), len(X), len(theta)) array is returned where the entry [i, j, l] contains $\frac{\partial k_\theta(x_i, x_j)}{\partial log(\theta_l)}$. This gradient is used by the Gaussian process (both regressor and classifier) in computing the gradient of the log-marginal-likelihood, which in turn is used to determine the value of θ, which maximizes the log-marginal-likelihood, via gradient ascent. For each hyperparameter, the initial value and the bounds need to be specified when creating an instance of the kernel. The current value of θ can be get and set via the property theta of the kernel object. Moreover, the bounds of the hyperparameters can be accessed by the property bounds of the kernel. Note that both properties (theta and bounds) return log-transformed values of the internally used values since those are typically more amenable to gradient-based optimization. The specification of each hyperparameter is stored in the form of an instance of Hyperparameter in the respective kernel. Note that a kernel using a hyperparameter with name "x" must have the attributes self.x and self.x_bounds. The abstract base class for all kernels is Kernel. Kernel implements a similar interface as ~sklearn.base.BaseEstimator, providing the methods get_params(), set_params(), and clone(). This allows setting kernel values also via meta-estimators such as ~sklearn.pipeline.Pipeline or ~sklearn.model_selection.GridSearchCV. Note that due to the nested structure of kernels (by applying kernel operators, see below), the names of kernel parameters might become relatively complicated. In general, for a binary kernel operator, parameters of the left operand are prefixed with k1__ and parameters of the right operand with k2__. An additional convenience method is clone_with_theta(theta), which returns a cloned version of the kernel but with the hyperparameters set to theta. An illustrative example: >>> from sklearn.gaussian_process.kernels import ConstantKernel, RBF >>> kernel = ConstantKernel(constant_value=1.0, constant_value_bounds=(0.0, 10.0)) * RBF(length_scale=0.5, length_scale_bounds=(0.0, 10.0)) + RBF(length_scale=2.0, length_scale_bounds=(0.0, 10.0)) >>> for hyperparameter in kernel.hyperparameters: print(hyperparameter) Hyperparameter(name='k1__k1__constant_value', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) Hyperparameter(name='k1__k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) Hyperparameter(name='k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) >>> params = kernel.get_params() >>> for key in sorted(params): print("%s : %s" % (key, params[key])) k1 : 1*2 RBF(length_scale=0.5) k1__k1 : 1**2 k1__k1__constant_value : 1.0 k1__k1__constant_value_bounds : (0.0, 10.0) k1__k2 : RBF(length_scale=0.5) k1__k2__length_scale : 0.5 k1__k2__length_scale_bounds : (0.0, 10.0) k2 : RBF(length_scale=2) k2__length_scale : 2.0 k2__length_scale_bounds : (0.0, 10.0) >>> print(kernel.theta) # Note: log-transformed [ 0. -0.69314718 0.69314718] >>> print(kernel.bounds) # Note: log-transformed [[ -inf 2.30258509] [ -inf 2.30258509] [ -inf 2.30258509]] All Gaussian process kernels are interoperable with sklearn.metrics.pairwise and vice versa: instances of subclasses of Kernel can be passed as metric to pairwise_kernels from sklearn.metrics.pairwise. Moreover, kernel functions from pairwise can be used as GP kernels by using the wrapper class PairwiseKernel. The only caveat is that the gradient of the hyperparameters is not analytic but numeric and all those kernels support only isotropic distances. The parameter gamma is considered to be a hyperparameter and may be optimized. The other kernel parameters are set directly at initialization and are kept fixed. Basic kernels The ConstantKernel kernel can be used as part of a Product kernel where it scales the magnitude of the other factor (kernel) or as part of a Sum kernel, where it modifies the mean of the Gaussian process. It depends on a parameter constant_value. It is defined as: k(x_(i),x_(j)) = constant_value ∀ x₁, x₂ The main use-case of the WhiteKernel kernel is as part of a sum-kernel where it explains the noise-component of the signal. Tuning its parameter noise_level corresponds to estimating the noise-level. It is defined as: k(x_(i),x_(j)) = noise_level if x_(i) =  = x_(j) else 0

"""Kernels for Gaussian process regression and classification. The kernels in this module allow kernel-engineering, i.e., they can be combined via the "+" and "*" operators or be exponentiated with a scalar via "**". These sum and product expressions can also contain scalar values, which are automatically converted to a constant kernel. All kernels allow (analytic) gradient-based hyperparameter optimization. The space of hyperparameters can be specified by giving lower und upper boundaries for the value of each hyperparameter (the search space is thus rectangular). Instead of specifying bounds, hyperparameters can also be declared to be "fixed", which causes these hyperparameters to be excluded from optimization. """ # Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de> # License: BSD 3 clause # Note: this module is strongly inspired by the kernel module of the george # package. import math import warnings from abc import ABCMeta, abstractmethod from collections import namedtuple from inspect import signature import numpy as np from scipy.spatial.distance import cdist, pdist, squareform from scipy.special import gamma, kv from..base import clone from..exceptions import ConvergenceWarning from..metrics.pairwise import pairwise_kernels from..utils.validation import _num_samples def _check_length_scale(X, length_scale): length_scale = np.squeeze(length_scale).astype(float) if np.ndim(length_scale) > 1: raise ValueError("length_scale cannot be of dimension greater than 1") if np.ndim(length_scale) == 1 and X.shape[1]!= length_scale.shape[0]: raise ValueError( "Anisotropic kernel must have the same number of " "dimensions as data (%d!=%d)" % (length_scale.shape[0], X.shape[1]) ) return length_scale class Hyperparameter( namedtuple( "Hyperparameter", ("name", "value_type", "bounds", "n_elements", "fixed") ) ): """A kernel hyperparameter's specification in form of a namedtuple. .. versionadded:: 0.18 Attributes ---------- name : str The name of the hyperparameter. Note that a kernel using a hyperparameter with name "x" must have the attributes self.x and self.x_bounds value_type : str The type of the hyperparameter. Currently, only "numeric" hyperparameters are supported. bounds : pair of floats >= 0 or "fixed" The lower and upper bound on the parameter. If n_elements>1, a pair of 1d array with n_elements each may be given alternatively. If the string "fixed" is passed as bounds, the hyperparameter's value cannot be changed. n_elements : int, default=1 The number of elements of the hyperparameter value. Defaults to 1, which corresponds to a scalar hyperparameter. n_elements > 1 corresponds to a hyperparameter which is vector-valued, such as, e.g., anisotropic length-scales. fixed : bool, default=None Whether the value of this hyperparameter is fixed, i.e., cannot be changed during hyperparameter tuning. If None is passed, the "fixed" is derived based on the given bounds. Examples -------- >>> from sklearn.gaussian_process.kernels import ConstantKernel >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import Hyperparameter >>> X, y = make_friedman2(n_samples=50, noise=0, random_state=0) >>> kernel = ConstantKernel(constant_value=1.0, ... constant_value_bounds=(0.0, 10.0)) We can access each hyperparameter: >>> for hyperparameter in kernel.hyperparameters: ... print(hyperparameter) Hyperparameter(name='constant_value', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) >>> params = kernel.get_params() >>> for key in sorted(params): print(f"{key} : {params[key]}") constant_value : 1.0 constant_value_bounds : (0.0, 10.0) """ # A raw namedtuple is very memory efficient as it packs the attributes # in a struct to get rid of the __dict__ of attributes in particular it # does not copy the string for the keys on each instance. # By deriving a namedtuple class just to introduce the __init__ method we # would also reintroduce the __dict__ on the instance. By telling the # Python interpreter that this subclass uses static __slots__ instead of # dynamic attributes. Furthermore we don't need any additional slot in the # subclass so we set __slots__ to the empty tuple. __slots__ = () def __new__(cls, name, value_type, bounds, n_elements=1, fixed=None): if not isinstance(bounds, str) or bounds!= "fixed": bounds = np.atleast_2d(bounds) if n_elements > 1: # vector-valued parameter if bounds.shape[0] == 1: bounds = np.repeat(bounds, n_elements, 0) elif bounds.shape[0]!= n_elements: raise ValueError( "Bounds on %s should have either 1 or " "%d dimensions. Given are %d" % (name, n_elements, bounds.shape[0]) ) if fixed is None: fixed = isinstance(bounds, str) and bounds == "fixed" return super(Hyperparameter, cls).__new__( cls, name, value_type, bounds, n_elements, fixed ) # This is mainly a testing utility to check that two hyperparameters # are equal. def __eq__(self, other): return ( self.name == other.name and self.value_type == other.value_type and np.all(self.bounds == other.bounds) and self.n_elements == other.n_elements and self.fixed == other.fixed ) class Kernel(metaclass=ABCMeta): """Base class for all kernels. .. versionadded:: 0.18 """ def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. """ params = dict() # introspect the constructor arguments to find the model parameters # to represent cls = self.__class__ init = getattr(cls.__init__, "deprecated_original", cls.__init__) init_sign = signature(init) args, varargs = [], [] for parameter in init_sign.parameters.values(): if parameter.kind!= parameter.VAR_KEYWORD and parameter.name!= "self": args.append(parameter.name) if parameter.kind == parameter.VAR_POSITIONAL: varargs.append(parameter.name) if len(varargs)!= 0: raise RuntimeError( "scikit-learn kernels should always " "specify their parameters in the signature" " of their __init__ (no varargs)." " %s doesn't follow this convention." % (cls,) ) for arg in args: params[arg] = getattr(self, arg) return params def set_params(self, **params): """Set the parameters of this kernel. The method works on simple kernels as well as on nested kernels. The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object. Returns ------- self """ if not params: # Simple optimisation to gain speed (inspect is slow) return self valid_params = self.get_params(deep=True) for key, value in params.items(): split = key.split("__", 1) if len(split) > 1: # nested objects case name, sub_name = split if name not in valid_params: raise ValueError( "Invalid parameter %s for kernel %s. " "Check the list of available parameters " "with `kernel.get_params().keys()`." % (name, self) ) sub_object = valid_params[name] sub_object.set_params(**{sub_name: value}) else: # simple objects case if key not in valid_params: raise ValueError( "Invalid parameter %s for kernel %s. " "Check the list of available parameters " "with `kernel.get_params().keys()`." % (key, self.__class__.__name__) ) setattr(self, key, value) return self def clone_with_theta(self, theta): """Returns a clone of self with given hyperparameters theta. Parameters ---------- theta : ndarray of shape (n_dims,) The hyperparameters """ cloned = clone(self) cloned.theta = theta return cloned @property def n_dims(self): """Returns the number of non-fixed hyperparameters of the kernel.""" return self.theta.shape[0] @property def hyperparameters(self): """Returns a list of all hyperparameter specifications.""" r = [ getattr(self, attr) for attr in dir(self) if attr.startswith("hyperparameter_") ] return r @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : ndarray of shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ theta = [] params = self.get_params() for hyperparameter in self.hyperparameters: if not hyperparameter.fixed: theta.append(params[hyperparameter.name]) if len(theta) > 0: return np.log(np.hstack(theta)) else: return np.array([]) @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : ndarray of shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ params = self.get_params() i = 0 for hyperparameter in self.hyperparameters: if hyperparameter.fixed: continue if hyperparameter.n_elements > 1: # vector-valued parameter params[hyperparameter.name] = np.exp( theta[i : i + hyperparameter.n_elements] ) i += hyperparameter.n_elements else: params[hyperparameter.name] = np.exp(theta[i]) i += 1 if i!= len(theta): raise ValueError( "theta has not the correct number of entries." " Should be %d; given are %d" % (i, len(theta)) ) self.set_params(**params) @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : ndarray of shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ bounds = [ hyperparameter.bounds for hyperparameter in self.hyperparameters if not hyperparameter.fixed ] if len(bounds) > 0: return np.log(np.vstack(bounds)) else: return np.array([]) def __add__(self, b): if not isinstance(b, Kernel): return Sum(self, ConstantKernel(b)) return Sum(self, b) def __radd__(self, b): if not isinstance(b, Kernel): return Sum(ConstantKernel(b), self) return Sum(b, self) def __mul__(self, b): if not isinstance(b, Kernel): return Product(self, ConstantKernel(b)) return Product(self, b) def __rmul__(self, b): if not isinstance(b, Kernel): return Product(ConstantKernel(b), self) return Product(b, self) def __pow__(self, b): return Exponentiation(self, b) def __eq__(self, b): if type(self)!= type(b): return False params_a = self.get_params() params_b = b.get_params() for key in set(list(params_a.keys()) + list(params_b.keys())): if np.any(params_a.get(key, None)!= params_b.get(key, None)): return False return True def __repr__(self): return "{0}({1})".format( self.__class__.__name__, ", ".join(map("{0:.3g}".format, self.theta)) ) @abstractmethod def __call__(self, X, Y=None, eval_gradient=False): """Evaluate the kernel.""" @abstractmethod def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array-like of shape (n_samples,) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : ndarray of shape (n_samples_X,) Diagonal of kernel k(X, X) """ @abstractmethod def is_stationary(self): """Returns whether the kernel is stationary.""" @property def requires_vector_input(self): """Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility.""" return True def _check_bounds_params(self): """Called after fitting to warn if bounds may have been too tight.""" list_close = np.isclose(self.bounds, np.atleast_2d(self.theta).T) idx = 0 for hyp in self.hyperparameters: if hyp.fixed: continue for dim in range(hyp.n_elements): if list_close[idx, 0]: warnings.warn( "The optimal value found for " "dimension %s of parameter %s is " "close to the specified lower " "bound %s. Decreasing the bound and" " calling fit again may find a " "better value." % (dim, hyp.name, hyp.bounds[dim][0]), ConvergenceWarning, ) elif list_close[idx, 1]: warnings.warn( "The optimal value found for " "dimension %s of parameter %s is " "close to the specified upper " "bound %s. Increasing the bound and" " calling fit again may find a " "better value." % (dim, hyp.name, hyp.bounds[dim][1]), ConvergenceWarning, ) idx += 1 class NormalizedKernelMixin: """Mixin for kernels which are normalized: k(X, X)=1. .. versionadded:: 0.18 """ def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : ndarray of shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : ndarray of shape (n_samples_X,) Diagonal of kernel k(X, X) """ return np.ones(X.shape[0]) class StationaryKernelMixin: """Mixin for kernels which are stationary: k(X, Y)= f(X-Y). .. versionadded:: 0.18 """ def is_stationary(self): """Returns whether the kernel is stationary.""" return True class GenericKernelMixin: """Mixin for kernels which operate on generic objects such as variable- length sequences, trees, and graphs. .. versionadded:: 0.22 """ @property def requires_vector_input(self): """Whether the kernel works only on fixed-length feature vectors.""" return False class CompoundKernel(Kernel): """Kernel which is composed of a set of other kernels. .. versionadded:: 0.18 Parameters ---------- kernels : list of Kernels The other kernels Examples -------- >>> from sklearn.gaussian_process.kernels import WhiteKernel >>> from sklearn.gaussian_process.kernels import RBF >>> from sklearn.gaussian_process.kernels import CompoundKernel >>> kernel = CompoundKernel( ... [WhiteKernel(noise_level=3.0), RBF(length_scale=2.0)]) >>> print(kernel.bounds) [[-11.51292546 11.51292546] [-11.51292546 11.51292546]] >>> print(kernel.n_dims) 2 >>> print(kernel.theta) [1.09861229 0.69314718] """ def __init__(self, kernels): self.kernels = kernels def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. """ return dict(kernels=self.kernels) @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : ndarray of shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ return np.hstack([kernel.theta for kernel in self.kernels]) @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : array of shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ k_dims = self.k1.n_dims for i, kernel in enumerate(self.kernels): kernel.theta = theta[i * k_dims : (i + 1) * k_dims] @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : array of shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ return np.vstack([kernel.bounds for kernel in self.kernels]) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Note that this compound kernel returns the results of all simple kernel stacked along an additional axis. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object, \ default=None Left argument of the returned kernel k(X, Y) Y : array-like of shape (n_samples_X, n_features) or list of object, \ default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y, n_kernels) Kernel k(X, Y) K_gradient : ndarray of shape \ (n_samples_X, n_samples_X, n_dims, n_kernels), optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when `eval_gradient` is True. """ if eval_gradient: K = [] K_grad = [] for kernel in self.kernels: K_single, K_grad_single = kernel(X, Y, eval_gradient) K.append(K_single) K_grad.append(K_grad_single[..., np.newaxis]) return np.dstack(K), np.concatenate(K_grad, 3) else: return np.dstack([kernel(X, Y, eval_gradient) for kernel in self.kernels]) def __eq__(self, b): if type(self)!= type(b) or len(self.kernels)!= len(b.kernels): return False return np.all( [self.kernels[i] == b.kernels[i] for i in range(len(self.kernels))] ) def is_stationary(self): """Returns whether the kernel is stationary.""" return np.all([kernel.is_stationary() for kernel in self.kernels]) @property def requires_vector_input(self): """Returns whether the kernel is defined on discrete structures.""" return np.any([kernel.requires_vector_input for kernel in self.kernels]) def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to `np.diag(self(X))`; however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Argument to the kernel. Returns ------- K_diag : ndarray of shape (n_samples_X, n_kernels) Diagonal of kernel k(X, X) """ return np.vstack([kernel.diag(X) for kernel in self.kernels]).T class KernelOperator(Kernel): """Base class for all kernel operators. .. versionadded:: 0.18 """ def __init__(self, k1, k2): self.k1 = k1 self.k2 = k2 def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. """ params = dict(k1=self.k1, k2=self.k2) if deep: deep_items = self.k1.get_params().items() params.update(("k1__" + k, val) for k, val in deep_items) deep_items = self.k2.get_params().items() params.update(("k2__" + k, val) for k, val in deep_items) return params @property def hyperparameters(self): """Returns a list of all hyperparameter.""" r = [ Hyperparameter( "k1__" + hyperparameter.name, hyperparameter.value_type, hyperparameter.bounds, hyperparameter.n_elements, ) for hyperparameter in self.k1.hyperparameters ] for hyperparameter in self.k2.hyperparameters: r.append( Hyperparameter( "k2__" + hyperparameter.name, hyperparameter.value_type, hyperparameter.bounds, hyperparameter.n_elements, ) ) return r @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : ndarray of shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ return np.append(self.k1.theta, self.k2.theta) @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : ndarray of shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ k1_dims = self.k1.n_dims self.k1.theta = theta[:k1_dims] self.k2.theta = theta[k1_dims:] @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : ndarray of shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ if self.k1.bounds.size == 0: return self.k2.bounds if self.k2.bounds.size == 0: return self.k1.bounds return np.vstack((self.k1.bounds, self.k2.bounds)) def __eq__(self, b): if type(self)!= type(b): return False return (self.k1 == b.k1 and self.k2 == b.k2) or ( self.k1 == b.k2 and self.k2 == b.k1 ) def is_stationary(self): """Returns whether the kernel is stationary.""" return self.k1.is_stationary() and self.k2.is_stationary() @property def requires_vector_input(self): """Returns whether the kernel is stationary.""" return self.k1.requires_vector_input or self.k2.requires_vector_input class Sum(KernelOperator): """The `Sum` kernel takes two kernels :math:`k_1` and :math:`k_2` and combines them via .. math:: k_{sum}(X, Y) = k_1(X, Y) + k_2(X, Y) Note that the `__add__` magic method is overridden, so `Sum(RBF(), RBF())` is equivalent to using the + operator with `RBF() + RBF()`. Read more in the :ref:`User Guide <gp_kernels>`. .. versionadded:: 0.18 Parameters ---------- k1 : Kernel The first base-kernel of the sum-kernel k2 : Kernel The second base-kernel of the sum-kernel Examples -------- >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import RBF, Sum, ConstantKernel >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = Sum(ConstantKernel(2), RBF()) >>> gpr = GaussianProcessRegressor(kernel=kernel, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 1.0 >>> kernel 1.41**2 + RBF(length_scale=1) """ def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Left argument of the returned kernel k(X, Y) Y : array-like of shape (n_samples_X, n_features) or list of object,\ default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims),\ optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when `eval_gradient` is True. """ if eval_gradient: K1, K1_gradient = self.k1(X, Y, eval_gradient=True) K2, K2_gradient = self.k2(X, Y, eval_gradient=True) return K1 + K2, np.dstack((K1_gradient, K2_gradient)) else: return self.k1(X, Y) + self.k2(X, Y) def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to `np.diag(self(X))`; however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Argument to the kernel. Returns ------- K_diag : ndarray of shape (n_samples_X,) Diagonal of kernel k(X, X) """ return self.k1.diag(X) + self.k2.diag(X) def __repr__(self): return "{0} + {1}".format(self.k1, self.k2) class Product(KernelOperator): """The `Product` kernel takes two kernels :math:`k_1` and :math:`k_2` and combines them via .. math:: k_{prod}(X, Y) = k_1(X, Y) * k_2(X, Y) Note that the `__mul__` magic method is overridden, so `Product(RBF(), RBF())` is equivalent to using the * operator with `RBF() * RBF()`. Read more in the :ref:`User Guide <gp_kernels>`. .. versionadded:: 0.18 Parameters ---------- k1 : Kernel The first base-kernel of the product-kernel k2 : Kernel The second base-kernel of the product-kernel Examples -------- >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import (RBF, Product, ... ConstantKernel) >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = Product(ConstantKernel(2), RBF()) >>> gpr = GaussianProcessRegressor(kernel=kernel, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 1.0 >>> kernel 1.41**2 * RBF(length_scale=1) """ def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Left argument of the returned kernel k(X, Y) Y : array-like of shape (n_samples_Y, n_features) or list of object,\ default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims), \ optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when `eval_gradient` is True. """ if eval_gradient: K1, K1_gradient = self.k1(X, Y, eval_gradient=True) K2, K2_gradient = self.k2(X, Y, eval_gradient=True) return K1 * K2, np.dstack( (K1_gradient * K2[:, :, np.newaxis], K2_gradient * K1[:, :, np.newaxis]) ) else: return self.k1(X, Y) * self.k2(X, Y) def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Argument to the kernel. Returns ------- K_diag : ndarray of shape (n_samples_X,) Diagonal of kernel k(X, X) """ return self.k1.diag(X) * self.k2.diag(X) def __repr__(self): return "{0} * {1}".format(self.k1, self.k2) class Exponentiation(Kernel): """The Exponentiation kernel takes one base kernel and a scalar parameter :math:`p` and combines them via .. math:: k_{exp}(X, Y) = k(X, Y) ^p Note that the `__pow__` magic method is overridden, so `Exponentiation(RBF(), 2)` is equivalent to using the ** operator with `RBF() ** 2`. Read more in the :ref:`User Guide <gp_kernels>`. .. versionadded:: 0.18 Parameters ---------- kernel : Kernel The base kernel exponent : float The exponent for the base kernel Examples -------- >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import (RationalQuadratic, ... Exponentiation) >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = Exponentiation(RationalQuadratic(), exponent=2) >>> gpr = GaussianProcessRegressor(kernel=kernel, alpha=5, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 0.419... >>> gpr.predict(X[:1,:], return_std=True) (array([635.5...]), array([0.559...])) """ def __init__(self, kernel, exponent): self.kernel = kernel self.exponent = exponent def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. """ params = dict(kernel=self.kernel, exponent=self.exponent) if deep: deep_items = self.kernel.get_params().items() params.update(("kernel__" + k, val) for k, val in deep_items) return params @property def hyperparameters(self): """Returns a list of all hyperparameter.""" r = [] for hyperparameter in self.kernel.hyperparameters: r.append( Hyperparameter( "kernel__" + hyperparameter.name, hyperparameter.value_type, hyperparameter.bounds, hyperparameter.n_elements, ) ) return r @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : ndarray of shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ return self.kernel.theta @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : ndarray of shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ self.kernel.theta = theta @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : ndarray of shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ return self.kernel.bounds def __eq__(self, b): if type(self)!= type(b): return False return self.kernel == b.kernel and self.exponent == b.exponent def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Left argument of the returned kernel k(X, Y) Y : array-like of shape (n_samples_Y, n_features) or list of object,\ default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims),\ optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when `eval_gradient` is True. """ if eval_gradient: K, K_gradient = self.kernel(X, Y, eval_gradient=True) K_gradient *= self.exponent * K[:, :, np.newaxis] ** (self.exponent - 1) return K**self.exponent, K_gradient else: K = self.kernel(X, Y, eval_gradient=False) return K**self.exponent def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Argument to the kernel. Returns ------- K_diag : ndarray of shape (n_samples_X,) Diagonal of kernel k(X, X) """ return self.kernel.diag(X) ** self.exponent def __repr__(self): return "{0} ** {1}".format(self.kernel, self.exponent) def is_stationary(self): """Returns whether the kernel is stationary.""" return self.kernel.is_stationary() @property def requires_vector_input(self): """Returns whether the kernel is defined on discrete structures.""" return self.kernel.requires_vector_input class ConstantKernel(StationaryKernelMixin, GenericKernelMixin, Kernel): """Constant kernel. Can be used as part of a product-kernel where it scales the magnitude of the other factor (kernel) or as part of a sum-kernel, where it modifies the mean of the Gaussian process. .. math:: k(x_1, x_2) = constant\\_value \\;\\forall\\; x_1, x_2 Adding a constant kernel is equivalent to adding a constant:: kernel = RBF() + ConstantKernel(constant_value=2) is the same as:: kernel = RBF() + 2 Read more in the :ref:`User Guide <gp_kernels>`. .. versionadded:: 0.18 Parameters ---------- constant_value : float, default=1.0 The constant value which defines the covariance: k(x_1, x_2) = constant_value constant_value_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5) The lower and upper bound on `constant_value`. If set to "fixed", `constant_value` cannot be changed during hyperparameter tuning. Examples -------- >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import RBF, ConstantKernel >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = RBF() + ConstantKernel(constant_value=2) >>> gpr = GaussianProcessRegressor(kernel=kernel, alpha=5, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 0.3696... >>> gpr.predict(X[:1,:], return_std=True) (array([606.1...]), array([0.24...])) """ def __init__(self, constant_value=1.0, constant_value_bounds=(1e-5, 1e5)): self.constant_value = constant_value self.constant_value_bounds = constant_value_bounds @property def hyperparameter_constant_value(self): return Hyperparameter("constant_value", "numeric", self.constant_value_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Left argument of the returned kernel k(X, Y) Y : array-like of shape (n_samples_X, n_features) or list of object, \ default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is None. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims), \ optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if Y is None: Y = X elif eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") K = np.full( (_num_samples(X), _num_samples(Y)), self.constant_value, dtype=np.array(self.constant_value).dtype, ) if eval_gradient: if not self.hyperparameter_constant_value.fixed: return ( K, np.full( (_num_samples(X), _num_samples(X), 1), self.constant_value, dtype=np.array(self.constant_value).dtype, ), ) else: return K, np.empty((_num_samples(X), _num_samples(X), 0)) else: return K def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Argument to the kernel. Returns ------- K_diag : ndarray of shape (n_samples_X,) Diagonal of kernel k(X, X) """ return np.full( _num_samples(X), self.constant_value, dtype=np.array(self.constant_value).dtype, ) def __repr__(self): return "{0:.3g}**2".format(np.sqrt(self.constant_value)) class WhiteKernel(StationaryKernelMixin, GenericKernelMixin, Kernel): """White kernel. The main use-case of this kernel is as part of a sum-kernel where it explains the noise of the signal as independently and identically normally-distributed. The parameter noise_level equals the variance of this noise. .. math:: k(x_1, x_2) = noise\\_level \\text{ if } x_i == x_j \\text{ else } 0 Read more in the :ref:`User Guide <gp_kernels>`. .. versionadded:: 0.18 Parameters ---------- noise_level : float, default=1.0 Parameter controlling the noise level (variance) noise_level_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5) The lower and upper bound on 'noise_level'. If set to "fixed", 'noise_level' cannot be changed during hyperparameter tuning. Examples -------- >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = DotProduct() + WhiteKernel(noise_level=0.5) >>> gpr = GaussianProcessRegressor(kernel=kernel, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 0.3680... >>> gpr.predict(X[:2,:], return_std=True) (array([653.0..., 592.1... ]), array([316.6..., 316.6...])) """ def __init__(self, noise_level=1.0, noise_level_bounds=(1e-5, 1e5)): self.noise_level = noise_level self.noise_level_bounds = noise_level_bounds @property def hyperparameter_noise_level(self): return Hyperparameter("noise_level", "numeric", self.noise_level_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Left argument of the returned kernel k(X, Y) Y : array-like of shape (n_samples_X, n_features) or list of object,\ default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is None. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims),\ optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if Y is not None and eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") if Y is None: K = self.noise_level * np.eye(_num_samples(X)) if eval_gradient: if not self.hyperparameter_noise_level.fixed: return ( K, self.noise_level * np.eye(_num_samples(X))[:, :, np.newaxis], ) else: return K, np.empty((_num_samples(X), _num_samples(X), 0)) else: return K else: return np.zeros((_num_samples(X), _num_samples(Y))) def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array-like of shape (n_samples_X, n_features) or list of object Argument to the kernel. Returns ------- K_diag : ndarray of shape (n_samples_X,) Diagonal of kernel k(X, X) """ return np.full( _num_samples(X), self.noise_level, dtype=np.array(self.noise_level).dtype ) def __repr__(self): return "{0}(noise_level={1:.3g})".format( self.__class__.__name__, self.noise_level ) class RBF(StationaryKernelMixin, NormalizedKernelMixin, Kernel): """Radial basis function kernel (aka squared-exponential kernel). The RBF kernel is a stationary kernel. It is also known as the "squared exponential" kernel. It is parameterized by a length scale parameter :math:`l>0`, which can either be a scalar (isotropic variant of the kernel) or a vector with the same number of dimensions as the inputs X (anisotropic variant of the kernel). The kernel is given by: .. math:: k(x_i, x_j) = \\exp\\left(- \\frac{d(x_i, x_j)^2}{2l^2} \\right) where :math:`l` is the length scale of the kernel and :math:`d(\\cdot,\\cdot)` is the Euclidean distance. For advice on how to set the length scale parameter, see e.g. [1]_. This kernel is infinitely differentiable, which implies that GPs with this kernel as covariance function have mean square derivatives of all orders, and are thus very smooth. See [2]_, Chapter 4, Section 4.2, for further details of the RBF kernel. Read more in the :ref:`User Guide <gp_kernels>`. .. versionadded:: 0.18 Parameters ---------- length_scale : float or ndarray of shape (n_features,), default=1.0 The length scale of the kernel. If a float, an isotropic kernel is used. If an array, an anisotropic kernel is used where each dimension of l defines the length-scale of the respective feature dimension. length_scale_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5) The lower and upper bound on 'length_scale'. If set to "fixed", 'length_scale' cannot be changed during hyperparameter tuning. References ---------- .. [1] `David Duvenaud (2014). "The Kernel Cookbook: Advice on Covariance functions". <https://www.cs.toronto.edu/~duvenaud/cookbook/>`_ .. [2] `Carl Edward Rasmussen, Christopher K. I. Williams (2006). "Gaussian Processes for Machine Learning". The MIT Press. <http://www.gaussianprocess.org/gpml/>`_ Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.gaussian_process import GaussianProcessClassifier >>> from sklearn.gaussian_process.kernels import RBF >>> X, y = load_iris(return_X_y=True) >>> kernel = 1.0 * RBF(1.0) >>> gpc = GaussianProcessClassifier(kernel=kernel, ... random_state=0).fit(X, y) >>> gpc.score(X, y) 0.9866... >>> gpc.predict_proba(X[:2,:]) array([[0.8354..., 0.03228..., 0.1322...], [0.7906..., 0.0652..., 0.1441...]]) """ def __init__(self, length_scale=1.0, length_scale_bounds=(1e-5, 1e5)): self.length_scale = length_scale self.length_scale_bounds = length_scale_bounds @property def anisotropic(self): return np.iterable(self.length_scale) and len(self.length_scale) > 1 @property def hyperparameter_length_scale(self): if self.anisotropic: return Hyperparameter( "length_scale", "numeric", self.length_scale_bounds, len(self.length_scale), ) return Hyperparameter("length_scale", "numeric", self.length_scale_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : ndarray of shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : ndarray of shape (n_samples_Y, n_features), default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is None. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims), \ optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when `eval_gradient` is True. """ X = np.atleast_2d(X) length_scale = _check_length_scale(X, self.length_scale) if Y is None: dists = pdist(X / length_scale, metric="sqeuclidean") K = np.exp(-0.5 * dists) # convert from upper-triangular matrix to square matrix K = squareform(K) np.fill_diagonal(K, 1) else: if eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") dists = cdist(X / length_scale, Y / length_scale, metric="sqeuclidean") K = np.exp(-0.5 * dists) if eval_gradient: if self.hyperparameter_length_scale.fixed: # Hyperparameter l kept fixed return K, np.empty((X.shape[0], X.shape[0], 0)) elif not self.anisotropic or length_scale.shape[0] == 1: K_gradient = (K * squareform(dists))[:, :, np.newaxis] return K, K_gradient elif self.anisotropic: # We need to recompute the pairwise dimension-wise distances K_gradient = (X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2 / ( length_scale**2 ) K_gradient *= K[..., np.newaxis] return K, K_gradient else: return K def __repr__(self): if self.anisotropic: return "{0}(length_scale=[{1}])".format( self.__class__.__name__, ", ".join(map("{0:.3g}".format, self.length_scale)), ) else: # isotropic return "{0}(length_scale={1:.3g})".format( self.__class__.__name__, np.ravel(self.length_scale)[0] ) class Matern(RBF): """Matern kernel. The class of Matern kernels is a generalization of the :class:`RBF`. It has an additional parameter :math:`\\nu` which controls the smoothness of the resulting function. The smaller :math:`\\nu`, the less smooth the approximated function is. As :math:`\\nu\\rightarrow\\infty`, the kernel becomes equivalent to the :class:`RBF` kernel. When :math:`\\nu = 1/2`, the Matérn kernel becomes identical to the absolute exponential kernel. Important intermediate values are :math:`\\nu=1.5` (once differentiable functions) and :math:`\\nu=2.5` (twice differentiable functions). The kernel is given by: .. math:: k(x_i, x_j) = \\frac{1}{\\Gamma(\\nu)2^{\\nu-1}}\\Bigg( \\frac{\\sqrt{2\\nu}}{l} d(x_i, x_j ) \\Bigg)^\\nu K_\\nu\\Bigg( \\frac{\\sqrt{2\\nu}}{l} d(x_i, x_j )\\Bigg) where :math:`d(\\cdot,\\cdot)` is the Euclidean distance, :math:`K_{\\nu}(\\cdot)` is a modified Bessel function and :math:`\\Gamma(\\cdot)` is the gamma function. See [1]_, Chapter 4, Section 4.2, for details regarding the different variants of the Matern kernel. Read more in the :ref:`User Guide <gp_kernels>`. .. versionadded:: 0.18 Parameters ---------- length_scale : float or ndarray of shape (n_features,), default=1.0 The length scale of the kernel. If a float, an isotropic kernel is used. If an array, an anisotropic kernel is used where each dimension of l defines the length-scale of the respective feature dimension. length_scale_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5) The lower and upper bound on 'length_scale'. If set to "fixed", 'length_scale' cannot be changed during hyperparameter tuning. nu : float, default=1.5 The parameter nu controlling the smoothness of the learned function. The smaller nu, the less smooth the approximated function is. For nu=inf, the kernel becomes equivalent to the RBF kernel and for nu=0.5 to the absolute exponential kernel. Important intermediate values are nu=1.5 (once differentiable functions) and nu=2.5 (twice differentiable functions). Note that values of nu not in [0.5, 1.5, 2.5, inf] incur a considerably higher computational cost (appr. 10 times higher) since they require to evaluate the modified Bessel function. Furthermore, in contrast to l, nu is kept fixed to its initial value and not optimized. References ---------- .. [1] `Carl Edward Rasmussen, Christopher K. I. Williams (2006). "Gaussian Processes for Machine Learning". The MIT Press. <http://www.gaussianprocess.org/gpml/>`_ Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.gaussian_process import GaussianProcessClassifier >>> from sklearn.gaussian_process.kernels import Matern >>> X, y = load_iris(return_X_y=True) >>> kernel = 1.0 * Matern(length_scale=1.0, nu=1.5) >>> gpc = GaussianProcessClassifier(kernel=kernel, ... random_state=0).fit(X, y) >>> gpc.score(X, y) 0.9866... >>> gpc.predict_proba(X[:2,:]) array([[0.8513..., 0.0368..., 0.1117...], [0.8086..., 0.0693..., 0.1220...]]) """ def __init__(self, length_scale=1.0, length_scale_bounds=(1e-5, 1e5), nu=1.5): super().__init__(length_scale, length_scale_bounds) self.nu = nu def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : ndarray of shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : ndarray of shape (n_samples_Y, n_features), default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is None. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims), \ optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when `eval_gradient` is True. """ X = np.atleast_2d(X) length_scale = _check_length_scale(X, self.length_scale) if Y is None: dists = pdist(X / length_scale, metric="euclidean") else: if eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") dists = cdist(X / length_scale, Y / length_scale, metric="euclidean") if self.nu == 0.5: K = np.exp(-dists) elif self.nu == 1.5: K = dists * math.sqrt(3) K = (1.0 + K) * np.exp(-K) elif self.nu == 2.5: K = dists * math.sqrt(5) K = (1.0 + K + K**2 / 3.0) * np.exp(-K) elif self.nu == np.inf: K = np.exp(-(dists**2) / 2.0) else: # general case; expensive to evaluate K = dists K[K == 0.0] += np.finfo(float).eps # strict zeros result in nan tmp = math.sqrt(2 * self.nu) * K K.fill((2 ** (1.0 - self.nu)) / gamma(self.nu)) K *= tmp**self.nu K *= kv(self.nu, tmp) if Y is None: # convert from upper-triangular matrix to square matrix K = squareform(K) np.fill_diagonal(K, 1) if eval_gradient: if self.hyperparameter_length_scale.fixed: # Hyperparameter l kept fixed K_gradient = np.empty((X.shape[0], X.shape[0], 0)) return K, K_gradient # We need to recompute the pairwise dimension-wise distances if self.anisotropic: D = (X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2 / ( length_scale**2 ) else: D = squareform(dists**2)[:, :, np.newaxis] if self.nu == 0.5: denominator = np.sqrt(D.sum(axis=2))[:, :, np.newaxis] divide_result = np.zeros_like(D) np.divide( D, denominator, out=divide_result, where=denominator!= 0, ) K_gradient = K[..., np.newaxis] * divide_result elif self.nu == 1.5: K_gradient = 3 * D * np.exp(-np.sqrt(3 * D.sum(-1)))[..., np.newaxis] elif self.nu == 2.5: tmp = np.sqrt(5 * D.sum(-1))[..., np.newaxis] K_gradient = 5.0 / 3.0 * D * (tmp + 1) * np.exp(-tmp) elif self.nu == np.inf: K_gradient = D * K[..., np.newaxis] else: # approximate gradient numerically def f(theta): # helper function return self.clone_with_theta(theta)(X, Y) return K, _approx_fprime(self.theta, f, 1e-10) if not self.anisotropic: return K, K_gradient[:, :].sum(-1)[:, :, np.newaxis] else: return K, K_gradient else: return K def __repr__(self): if self.anisotropic: return "{0}(length_scale=[{1}], nu={2:.3g})".format( self.__class__.__name__, ", ".join(map("{0:.3g}".format, self.length_scale)), self.nu, ) else: return "{0}(length_scale={1:.3g}, nu={2:.3g})".format( self.__class__.__name__, np.ravel(self.length_scale)[0], self.nu ) class RationalQuadratic(StationaryKernelMixin, NormalizedKernelMixin, Kernel): """Rational Quadratic kernel. The RationalQuadratic kernel can be seen as a scale mixture (an infinite sum) of RBF kernels with different characteristic length scales. It is parameterized by a length scale parameter :math:`l>0` and a scale mixture parameter :math:`\\alpha>0`. Only the isotropic variant where length_scale :math:`l` is a scalar is supported at the moment. The kernel is given by: .. math:: k(x_i, x_j) = \\left( 1 + \\frac{d(x_i, x_j)^2 }{ 2\\alpha l^2}\\right)^{-\\alpha} where :math:`\\alpha` is the scale mixture parameter, :math:`l` is the length scale of the kernel and :math:`d(\\cdot,\\cdot)` is the Euclidean distance. For advice on how to set the parameters, see e.g. [1]_. Read more in the :ref:`User Guide <gp_kernels>`. .. versionadded:: 0.18 Parameters ---------- length_scale : float > 0, default=1.0 The length scale of the kernel. alpha : float > 0, default=1.0 Scale mixture parameter length_scale_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5) The lower and upper bound on 'length_scale'. If set to "fixed", 'length_scale' cannot be changed during hyperparameter tuning. alpha_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5) The lower and upper bound on 'alpha'. If set to "fixed", 'alpha' cannot be changed during hyperparameter tuning. References ---------- .. [1] `David Duvenaud (2014). "The Kernel Cookbook: Advice on Covariance functions". <https://www.cs.toronto.edu/~duvenaud/cookbook/>`_ Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.gaussian_process import GaussianProcessClassifier >>> from sklearn.gaussian_process.kernels import RationalQuadratic >>> X, y = load_iris(return_X_y=True) >>> kernel = RationalQuadratic(length_scale=1.0, alpha=1.5) >>> gpc = GaussianProcessClassifier(kernel=kernel, ... random_state=0).fit(X, y) >>> gpc.score(X, y) 0.9733... >>> gpc.predict_proba(X[:2,:]) array([[0.8881..., 0.0566..., 0.05518...], [0.8678..., 0.0707..., 0.0614...]]) """ def __init__( self, length_scale=1.0, alpha=1.0, length_scale_bounds=(1e-5, 1e5), alpha_bounds=(1e-5, 1e5), ): self.length_scale = length_scale self.alpha = alpha self.length_scale_bounds = length_scale_bounds self.alpha_bounds = alpha_bounds @property def hyperparameter_length_scale(self): return Hyperparameter("length_scale", "numeric", self.length_scale_bounds) @property def hyperparameter_alpha(self): return Hyperparameter("alpha", "numeric", self.alpha_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : ndarray of shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : ndarray of shape (n_samples_Y, n_features), default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is None. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if len(np.atleast_1d(self.length_scale)) > 1: raise AttributeError( "RationalQuadratic kernel only supports isotropic version, " "please use a single scalar for length_scale" ) X = np.atleast_2d(X) if Y is None: dists = squareform(pdist(X, metric="sqeuclidean")) tmp = dists / (2 * self.alpha * self.length_scale**2) base = 1 + tmp K = base**-self.alpha np.fill_diagonal(K, 1) else: if eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") dists = cdist(X, Y, metric="sqeuclidean") K = (1 + dists / (2 * self.alpha * self.length_scale**2)) ** -self.alpha if eval_gradient: # gradient with respect to length_scale if not self.hyperparameter_length_scale.fixed: length_scale_gradient = dists * K / (self.length_scale**2 * base) length_scale_gradient = length_scale_gradient[:, :, np.newaxis] else: # l is kept fixed length_scale_gradient = np.empty((K.shape[0], K.shape[1], 0)) # gradient with respect to alpha if not self.hyperparameter_alpha.fixed: alpha_gradient = K * ( -self.alpha * np.log(base) + dists / (2 * self.length_scale**2 * base) ) alpha_gradient = alpha_gradient[:, :, np.newaxis] else: # alpha is kept fixed alpha_gradient = np.empty((K.shape[0], K.shape[1], 0)) return K, np.dstack((alpha_gradient, length_scale_gradient)) else: return K def __repr__(self): return "{0}(alpha={1:.3g}, length_scale={2:.3g})".format( self.__class__.__name__, self.alpha, self.length_scale ) class ExpSineSquared(StationaryKernelMixin, NormalizedKernelMixin, Kernel): r"""Exp-Sine-Squared kernel (aka periodic kernel). The ExpSineSquared kernel allows one to model functions which repeat themselves exactly. It is parameterized by a length scale parameter :math:`l>0` and a periodicity parameter :math:`p>0`. Only the isotropic variant where :math:`l` is a scalar is supported at the moment. The kernel is given by: .. math:: k(x_i, x_j) = \text{exp}\left(- \frac{ 2\sin^2(\pi d(x_i, x_j)/p) }{ l^ 2} \right) where :math:`l` is the length scale of the kernel, :math:`p` the periodicity of the kernel and :math:`d(\\cdot,\\cdot)` is the Euclidean distance. Read more in the :ref:`User Guide <gp_kernels>`. .. versionadded:: 0.18 Parameters ---------- length_scale : float > 0, default=1.0 The length scale of the kernel. periodicity : float > 0, default=1.0 The periodicity of the kernel. length_scale_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5) The lower and upper bound on 'length_scale'. If set to "fixed", 'length_scale' cannot be changed during hyperparameter tuning. periodicity_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5) The lower and upper bound on 'periodicity'. If set to "fixed", 'periodicity' cannot be changed during hyperparameter tuning. Examples -------- >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import ExpSineSquared >>> X, y = make_friedman2(n_samples=50, noise=0, random_state=0) >>> kernel = ExpSineSquared(length_scale=1, periodicity=1) >>> gpr = GaussianProcessRegressor(kernel=kernel, alpha=5, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 0.0144... >>> gpr.predict(X[:2,:], return_std=True) (array([425.6..., 457.5...]), array([0.3894..., 0.3467...])) """ def __init__( self, length_scale=1.0, periodicity=1.0, length_scale_bounds=(1e-5, 1e5), periodicity_bounds=(1e-5, 1e5), ): self.length_scale = length_scale self.periodicity = periodicity self.length_scale_bounds = length_scale_bounds self.periodicity_bounds = periodicity_bounds @property def hyperparameter_length_scale(self): """Returns the length scale""" return Hyperparameter("length_scale", "numeric", self.length_scale_bounds) @property def hyperparameter_periodicity(self): return Hyperparameter("periodicity", "numeric", self.periodicity_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : ndarray of shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : ndarray of shape (n_samples_Y, n_features), default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is None. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims), \ optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when `eval_gradient` is True. """ X = np.atleast_2d(X) if Y is None: dists = squareform(pdist(X, metric="euclidean")) arg = np.pi * dists / self.periodicity sin_of_arg = np.sin(arg) K = np.exp(-2 * (sin_of_arg / self.length_scale) ** 2) else: if eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") dists = cdist(X, Y, metric="euclidean") K = np.exp( -2 * (np.sin(np.pi / self.periodicity * dists) / self.length_scale) ** 2 ) if eval_gradient: cos_of_arg = np.cos(arg) # gradient with respect to length_scale if not self.hyperparameter_length_scale.fixed: length_scale_gradient = 4 / self.length_scale**2 * sin_of_arg**2 * K length_scale_gradient = length_scale_gradient[:, :, np.newaxis] else: # length_scale is kept fixed length_scale_gradient = np.empty((K.shape[0], K.shape[1], 0)) # gradient with respect to p if not self.hyperparameter_periodicity.fixed: periodicity_gradient = ( 4 * arg / self.length_scale**2 * cos_of_arg * sin_of_arg * K ) periodicity_gradient = periodicity_gradient[:, :, np.newaxis] else: # p is kept fixed periodicity_gradient = np.empty((K.shape[0], K.shape[1], 0)) return K, np.dstack((length_scale_gradient, periodicity_gradient)) else: return K def __repr__(self): return "{0}(length_scale={1:.3g}, periodicity={2:.3g})".format( self.__class__.__name__, self.length_scale, self.periodicity ) class DotProduct(Kernel): r"""Dot-Product kernel. The DotProduct kernel is non-stationary and can be obtained from linear regression by putting :math:`N(0, 1)` priors on the coefficients of :math:`x_d (d = 1,..., D)` and a prior of :math:`N(0, \sigma_0^2)` on the bias. The DotProduct kernel is invariant to a rotation of the coordinates about the origin, but not translations. It is parameterized by a parameter sigma_0 :math:`\sigma` which controls the inhomogenity of the kernel. For :math:`\sigma_0^2 =0`, the kernel is called the homogeneous linear kernel, otherwise it is inhomogeneous. The kernel is given by .. math:: k(x_i, x_j) = \sigma_0 ^ 2 + x_i \cdot x_j The DotProduct kernel is commonly combined with exponentiation. See [1]_, Chapter 4, Section 4.2, for further details regarding the DotProduct kernel. Read more in the :ref:`User Guide <gp_kernels>`. .. versionadded:: 0.18 Parameters ---------- sigma_0 : float >= 0, default=1.0 Parameter controlling the inhomogenity of the kernel. If sigma_0=0, the kernel is homogeneous. sigma_0_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5) The lower and upper bound on'sigma_0'. If set to "fixed",'sigma_0' cannot be changed during hyperparameter tuning. References ---------- .. [1] `Carl Edward Rasmussen, Christopher K. I. Williams (2006). "Gaussian Processes for Machine Learning". The MIT Press. <http://www.gaussianprocess.org/gpml/>`_ Examples -------- >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = DotProduct() + WhiteKernel() >>> gpr = GaussianProcessRegressor(kernel=kernel, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 0.3680... >>> gpr.predict(X[:2,:], return_std=True) (array([653.0..., 592.1...]), array([316.6..., 316.6...])) """ def __init__(self, sigma_0=1.0, sigma_0_bounds=(1e-5, 1e5)): self.sigma_0 = sigma_0 self.sigma_0_bounds = sigma_0_bounds @property def hyperparameter_sigma_0(self): return Hyperparameter("sigma_0", "numeric", self.sigma_0_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : ndarray of shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : ndarray of shape (n_samples_Y, n_features), default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is None. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims),\ optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when `eval_gradient` is True. """ X = np.atleast_2d(X) if Y is None: K = np.inner(X, X) + self.sigma_0**2 else: if eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") K = np.inner(X, Y) + self.sigma_0**2 if eval_gradient: if not self.hyperparameter_sigma_0.fixed: K_gradient = np.empty((K.shape[0], K.shape[1], 1)) K_gradient[..., 0] = 2 * self.sigma_0**2 return K, K_gradient else: return K, np.empty((X.shape[0], X.shape[0], 0)) else: return K def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : ndarray of shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y). Returns ------- K_diag : ndarray of shape (n_samples_X,) Diagonal of kernel k(X, X). """ return np.einsum("ij,ij->i", X, X) + self.sigma_0**2 def is_stationary(self): """Returns whether the kernel is stationary.""" return False def __repr__(self): return "{0}(sigma_0={1:.3g})".format(self.__class__.__name__, self.sigma_0) # adapted from scipy/optimize/optimize.py for functions with 2d output def _approx_fprime(xk, f, epsilon, args=()): f0 = f(*((xk,) + args)) grad = np.zeros((f0.shape[0], f0.shape[1], len(xk)), float) ei = np.zeros((len(xk),), float) for k in range(len(xk)): ei[k] = 1.0 d = epsilon * ei grad[:, :, k] = (f(*((xk + d,) + args)) - f0) / d[k] ei[k] = 0.0 return grad class PairwiseKernel(Kernel): """Wrapper for kernels in sklearn.metrics.pairwise. A thin wrapper around the functionality of the kernels in sklearn.metrics.pairwise. Note: Evaluation of eval_gradient is not analytic but numeric and all kernels support only isotropic distances. The parameter gamma is considered to be a hyperparameter and may be optimized. The other kernel parameters are set directly at initialization and are kept fixed. .. versionadded:: 0.18 Parameters ---------- gamma : float, default=1.0 Parameter gamma of the pairwise kernel specified by metric. It should be positive. gamma_bounds : pair of floats >= 0 or "fixed", default=(1e-5, 1e5) The lower and upper bound on 'gamma'. If set to "fixed", 'gamma' cannot be changed during hyperparameter tuning. metric : {"linear", "additive_chi2", "chi2", "poly", "polynomial", \ "rbf", "laplacian", "sigmoid", "cosine"} or callable, \ default="linear" The metric to use when calculating kernel between instances in a feature array. If metric is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS. If metric is "precomputed", X is assumed to be a kernel matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. pairwise_kernels_kwargs : dict, default=None All entries of this dict (if any) are passed as keyword arguments to the pairwise kernel function. Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.gaussian_process import GaussianProcessClassifier >>> from sklearn.gaussian_process.kernels import PairwiseKernel >>> X, y = load_iris(return_X_y=True) >>> kernel = PairwiseKernel(metric='rbf') >>> gpc = GaussianProcessClassifier(kernel=kernel, ... random_state=0).fit(X, y) >>> gpc.score(X, y) 0.9733... >>> gpc.predict_proba(X[:2,:]) array([[0.8880..., 0.05663..., 0.05532...], [0.8676..., 0.07073..., 0.06165...]]) """ def __init__( self, gamma=1.0, gamma_bounds=(1e-5, 1e5), metric="linear", pairwise_kernels_kwargs=None, ): self.gamma = gamma self.gamma_bounds = gamma_bounds self.metric = metric self.pairwise_kernels_kwargs = pairwise_kernels_kwargs @property def hyperparameter_gamma(self): return Hyperparameter("gamma", "numeric", self.gamma_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : ndarray of shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : ndarray of shape (n_samples_Y, n_features), default=None Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool, default=False Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is None. Returns ------- K : ndarray of shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims),\ optional The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when `eval_gradient` is True. """ pairwise_kernels_kwargs = self.pairwise_kernels_kwargs if self.pairwise_kernels_kwargs is None: pairwise_kernels_kwargs = {} X = np.atleast_2d(X) K = pairwise_kernels( X, Y, metric=self.metric, gamma=self.gamma, filter_params=True, **pairwise_kernels_kwargs, ) if eval_gradient: if self.hyperparameter_gamma.fixed: return K, np.empty((X.shape[0], X.shape[0], 0)) else: # approximate gradient numerically def f(gamma): # helper function return pairwise_kernels( X, Y, metric=self.metric, gamma=np.exp(gamma), filter_params=True, **pairwise_kernels_kwargs, ) return K, _approx_fprime(self.theta, f, 1e-10) else: return K def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : ndarray of shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : ndarray of shape (n_samples_X,) Diagonal of kernel k(X, X) """ # We have to fall back to slow way of computing diagonal return np.apply_along_axis(self, 1, X).ravel() def is_stationary(self): """Returns whether the kernel is stationary.""" return self.metric in ["rbf"] def __repr__(self): return "{0}(gamma={1}, metric={2})".format( self.__class__.__name__, self.gamma, self.metric )

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scikit-learn__scikit-learn/doc/modules/isotonic.rst

Isotonic regression The class IsotonicRegression fits a non-decreasing real function to 1-dimensional data. It solves the following problem: minimize ∑_(i)w_(i)(y_(i)−ŷ_(i))² subject to ŷ_(i) ≤ ŷ_(j) whenever X_(i) ≤ X_(j), where the weights w_(i) are strictly positive, and both X and y are arbitrary real quantities. The increasing parameter changes the constraint to ŷ_(i) ≥ ŷ_(j) whenever X_(i) ≤ X_(j). Setting it to 'auto' will automatically choose the constraint based on Spearman's rank correlation coefficient. IsotonicRegression produces a series of predictions ŷ_(i) for the training data which are the closest to the targets y in terms of mean squared error. These predictions are interpolated for predicting to unseen data. The predictions of IsotonicRegression thus form a function that is piecewise linear.

# Authors: Fabian Pedregosa <fabian@fseoane.net> # Alexandre Gramfort <alexandre.gramfort@inria.fr> # Nelle Varoquaux <nelle.varoquaux@gmail.com> # License: BSD 3 clause import math import warnings from numbers import Real import numpy as np from scipy import interpolate from scipy.stats import spearmanr from._isotonic import _inplace_contiguous_isotonic_regression, _make_unique from.base import BaseEstimator, RegressorMixin, TransformerMixin, _fit_context from.utils import check_array, check_consistent_length from.utils._param_validation import Interval, StrOptions, validate_params from.utils.validation import _check_sample_weight, check_is_fitted __all__ = ["check_increasing", "isotonic_regression", "IsotonicRegression"] @validate_params( { "x": ["array-like"], "y": ["array-like"], }, prefer_skip_nested_validation=True, ) def check_increasing(x, y): """Determine whether y is monotonically correlated with x. y is found increasing or decreasing with respect to x based on a Spearman correlation test. Parameters ---------- x : array-like of shape (n_samples,) Training data. y : array-like of shape (n_samples,) Training target. Returns ------- increasing_bool : boolean Whether the relationship is increasing or decreasing. Notes ----- The Spearman correlation coefficient is estimated from the data, and the sign of the resulting estimate is used as the result. In the event that the 95% confidence interval based on Fisher transform spans zero, a warning is raised. References ---------- Fisher transformation. Wikipedia. https://en.wikipedia.org/wiki/Fisher_transformation """ # Calculate Spearman rho estimate and set return accordingly. rho, _ = spearmanr(x, y) increasing_bool = rho >= 0 # Run Fisher transform to get the rho CI, but handle rho=+/-1 if rho not in [-1.0, 1.0] and len(x) > 3: F = 0.5 * math.log((1.0 + rho) / (1.0 - rho)) F_se = 1 / math.sqrt(len(x) - 3) # Use a 95% CI, i.e., +/-1.96 S.E. # https://en.wikipedia.org/wiki/Fisher_transformation rho_0 = math.tanh(F - 1.96 * F_se) rho_1 = math.tanh(F + 1.96 * F_se) # Warn if the CI spans zero. if np.sign(rho_0)!= np.sign(rho_1): warnings.warn( "Confidence interval of the Spearman " "correlation coefficient spans zero. " "Determination of ``increasing`` may be " "suspect." ) return increasing_bool @validate_params( { "y": ["array-like"], "sample_weight": ["array-like", None], "y_min": [Interval(Real, None, None, closed="both"), None], "y_max": [Interval(Real, None, None, closed="both"), None], "increasing": ["boolean"], }, prefer_skip_nested_validation=True, ) def isotonic_regression( y, *, sample_weight=None, y_min=None, y_max=None, increasing=True ): """Solve the isotonic regression model. Read more in the :ref:`User Guide <isotonic>`. Parameters ---------- y : array-like of shape (n_samples,) The data. sample_weight : array-like of shape (n_samples,), default=None Weights on each point of the regression. If None, weight is set to 1 (equal weights). y_min : float, default=None Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf. y_max : float, default=None Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf. increasing : bool, default=True Whether to compute ``y_`` is increasing (if set to True) or decreasing (if set to False). Returns ------- y_ : ndarray of shape (n_samples,) Isotonic fit of y. References ---------- "Active set algorithms for isotonic regression; A unifying framework" by Michael J. Best and Nilotpal Chakravarti, section 3. """ order = np.s_[:] if increasing else np.s_[::-1] y = check_array(y, ensure_2d=False, input_name="y", dtype=[np.float64, np.float32]) y = np.array(y[order], dtype=y.dtype) sample_weight = _check_sample_weight(sample_weight, y, dtype=y.dtype, copy=True) sample_weight = np.ascontiguousarray(sample_weight[order]) _inplace_contiguous_isotonic_regression(y, sample_weight) if y_min is not None or y_max is not None: # Older versions of np.clip don't accept None as a bound, so use np.inf if y_min is None: y_min = -np.inf if y_max is None: y_max = np.inf np.clip(y, y_min, y_max, y) return y[order] class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator): """Isotonic regression model. Read more in the :ref:`User Guide <isotonic>`. .. versionadded:: 0.13 Parameters ---------- y_min : float, default=None Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf. y_max : float, default=None Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf. increasing : bool or 'auto', default=True Determines whether the predictions should be constrained to increase or decrease with `X`. 'auto' will decide based on the Spearman correlation estimate's sign. out_of_bounds : {'nan', 'clip', 'raise'}, default='nan' Handles how `X` values outside of the training domain are handled during prediction. - 'nan', predictions will be NaN. - 'clip', predictions will be set to the value corresponding to the nearest train interval endpoint. - 'raise', a `ValueError` is raised. Attributes ---------- X_min_ : float Minimum value of input array `X_` for left bound. X_max_ : float Maximum value of input array `X_` for right bound. X_thresholds_ : ndarray of shape (n_thresholds,) Unique ascending `X` values used to interpolate the y = f(X) monotonic function. .. versionadded:: 0.24 y_thresholds_ : ndarray of shape (n_thresholds,) De-duplicated `y` values suitable to interpolate the y = f(X) monotonic function. .. versionadded:: 0.24 f_ : function The stepwise interpolating function that covers the input domain ``X``. increasing_ : bool Inferred value for ``increasing``. See Also -------- sklearn.linear_model.LinearRegression : Ordinary least squares Linear Regression. sklearn.ensemble.HistGradientBoostingRegressor : Gradient boosting that is a non-parametric model accepting monotonicity constraints. isotonic_regression : Function to solve the isotonic regression model. Notes ----- Ties are broken using the secondary method from de Leeuw, 1977. References ---------- Isotonic Median Regression: A Linear Programming Approach Nilotpal Chakravarti Mathematics of Operations Research Vol. 14, No. 2 (May, 1989), pp. 303-308 Isotone Optimization in R : Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods de Leeuw, Hornik, Mair Journal of Statistical Software 2009 Correctness of Kruskal's algorithms for monotone regression with ties de Leeuw, Psychometrica, 1977 Examples -------- >>> from sklearn.datasets import make_regression >>> from sklearn.isotonic import IsotonicRegression >>> X, y = make_regression(n_samples=10, n_features=1, random_state=41) >>> iso_reg = IsotonicRegression().fit(X, y) >>> iso_reg.predict([.1,.2]) array([1.8628..., 3.7256...]) """ _parameter_constraints: dict = { "y_min": [Interval(Real, None, None, closed="both"), None], "y_max": [Interval(Real, None, None, closed="both"), None], "increasing": ["boolean", StrOptions({"auto"})], "out_of_bounds": [StrOptions({"nan", "clip", "raise"})], } def __init__(self, *, y_min=None, y_max=None, increasing=True, out_of_bounds="nan"): self.y_min = y_min self.y_max = y_max self.increasing = increasing self.out_of_bounds = out_of_bounds def _check_input_data_shape(self, X): if not (X.ndim == 1 or (X.ndim == 2 and X.shape[1] == 1)): msg = ( "Isotonic regression input X should be a 1d array or " "2d array with 1 feature" ) raise ValueError(msg) def _build_f(self, X, y): """Build the f_ interp1d function.""" bounds_error = self.out_of_bounds == "raise" if len(y) == 1: # single y, constant prediction self.f_ = lambda x: y.repeat(x.shape) else: self.f_ = interpolate.interp1d( X, y, kind="linear", bounds_error=bounds_error ) def _build_y(self, X, y, sample_weight, trim_duplicates=True): """Build the y_ IsotonicRegression.""" self._check_input_data_shape(X) X = X.reshape(-1) # use 1d view # Determine increasing if auto-determination requested if self.increasing == "auto": self.increasing_ = check_increasing(X, y) else: self.increasing_ = self.increasing # If sample_weights is passed, removed zero-weight values and clean # order sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) mask = sample_weight > 0 X, y, sample_weight = X[mask], y[mask], sample_weight[mask] order = np.lexsort((y, X)) X, y, sample_weight = [array[order] for array in [X, y, sample_weight]] unique_X, unique_y, unique_sample_weight = _make_unique(X, y, sample_weight) X = unique_X y = isotonic_regression( unique_y, sample_weight=unique_sample_weight, y_min=self.y_min, y_max=self.y_max, increasing=self.increasing_, ) # Handle the left and right bounds on X self.X_min_, self.X_max_ = np.min(X), np.max(X) if trim_duplicates: # Remove unnecessary points for faster prediction keep_data = np.ones((len(y),), dtype=bool) # Aside from the 1st and last point, remove points whose y values # are equal to both the point before and the point after it. keep_data[1:-1] = np.logical_or( np.not_equal(y[1:-1], y[:-2]), np.not_equal(y[1:-1], y[2:]) ) return X[keep_data], y[keep_data] else: # The ability to turn off trim_duplicates is only used to it make # easier to unit test that removing duplicates in y does not have # any impact the resulting interpolation function (besides # prediction speed). return X, y @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y, sample_weight=None): """Fit the model using X, y as training data. Parameters ---------- X : array-like of shape (n_samples,) or (n_samples, 1) Training data. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. y : array-like of shape (n_samples,) Training target. sample_weight : array-like of shape (n_samples,), default=None Weights. If set to None, all weights will be set to 1 (equal weights). Returns ------- self : object Returns an instance of self. Notes ----- X is stored for future use, as :meth:`transform` needs X to interpolate new input data. """ check_params = dict(accept_sparse=False, ensure_2d=False) X = check_array( X, input_name="X", dtype=[np.float64, np.float32], **check_params ) y = check_array(y, input_name="y", dtype=X.dtype, **check_params) check_consistent_length(X, y, sample_weight) # Transform y by running the isotonic regression algorithm and # transform X accordingly. X, y = self._build_y(X, y, sample_weight) # It is necessary to store the non-redundant part of the training set # on the model to make it possible to support model persistence via # the pickle module as the object built by scipy.interp1d is not # picklable directly. self.X_thresholds_, self.y_thresholds_ = X, y # Build the interpolation function self._build_f(X, y) return self def _transform(self, T): """`_transform` is called by both `transform` and `predict` methods. Since `transform` is wrapped to output arrays of specific types (e.g. NumPy arrays, pandas DataFrame), we cannot make `predict` call `transform` directly. The above behaviour could be changed in the future, if we decide to output other type of arrays when calling `predict`. """ if hasattr(self, "X_thresholds_"): dtype = self.X_thresholds_.dtype else: dtype = np.float64 T = check_array(T, dtype=dtype, ensure_2d=False) self._check_input_data_shape(T) T = T.reshape(-1) # use 1d view if self.out_of_bounds == "clip": T = np.clip(T, self.X_min_, self.X_max_) res = self.f_(T) # on scipy 0.17, interp1d up-casts to float64, so we cast back res = res.astype(T.dtype) return res def transform(self, T): """Transform new data by linear interpolation. Parameters ---------- T : array-like of shape (n_samples,) or (n_samples, 1) Data to transform. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. Returns ------- y_pred : ndarray of shape (n_samples,) The transformed data. """ return self._transform(T) def predict(self, T): """Predict new data by linear interpolation. Parameters ---------- T : array-like of shape (n_samples,) or (n_samples, 1) Data to transform. Returns ------- y_pred : ndarray of shape (n_samples,) Transformed data. """ return self._transform(T) # We implement get_feature_names_out here instead of using # `ClassNamePrefixFeaturesOutMixin`` because `input_features` are ignored. # `input_features` are ignored because `IsotonicRegression` accepts 1d # arrays and the semantics of `feature_names_in_` are not clear for 1d arrays. def get_feature_names_out(self, input_features=None): """Get output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Ignored. Returns ------- feature_names_out : ndarray of str objects An ndarray with one string i.e. ["isotonicregression0"]. """ check_is_fitted(self, "f_") class_name = self.__class__.__name__.lower() return np.asarray([f"{class_name}0"], dtype=object) def __getstate__(self): """Pickle-protocol - return state of the estimator.""" state = super().__getstate__() # remove interpolation method state.pop("f_", None) return state def __setstate__(self, state): """Pickle-protocol - set state of the estimator. We need to rebuild the interpolation function. """ super().__setstate__(state) if hasattr(self, "X_thresholds_") and hasattr(self, "y_thresholds_"): self._build_f(self.X_thresholds_, self.y_thresholds_) def _more_tags(self): return {"X_types": ["1darray"]}

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kernel_approximation.rst

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scikit-learn__scikit-learn/doc/modules/kernel_approximation.rst

scikit-learn__scikit-learn/sklearn/linear_model

Kernel Approximation This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines (see svm). The following feature functions perform non-linear transformations of the input, which can serve as a basis for linear classification or other algorithms. The advantage of using approximate explicit feature maps compared to the kernel trick, which makes use of feature maps implicitly, is that explicit mappings can be better suited for online learning and can significantly reduce the cost of learning with very large datasets. Standard kernelized SVMs do not scale well to large datasets, but using an approximate kernel map it is possible to use much more efficient linear SVMs. In particular, the combination of kernel map approximations with SGDClassifier can make non-linear learning on large datasets possible. Since there has not been much empirical work using approximate embeddings, it is advisable to compare results against exact kernel methods when possible. polynomial_regression for an exact polynomial transformation. Nystroem Method for Kernel Approximation The Nystroem method, as implemented in Nystroem is a general method for low-rank approximations of kernels. It achieves this by essentially subsampling the data on which the kernel is evaluated. By default Nystroem uses the rbf kernel, but it can use any kernel function or a precomputed kernel matrix. The number of samples used - which is also the dimensionality of the features computed -is given by the parameter n_components. Radial Basis Function Kernel The RBFSampler constructs an approximate mapping for the radial basis function kernel, also known as Random Kitchen Sinks [RR2007]. This transformation can be used to explicitly model a kernel map, prior to applying a linear algorithm, for example a linear SVM: >>> from sklearn.kernel_approximation import RBFSampler >>> from sklearn.linear_model import SGDClassifier >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]] >>> y = [0, 0, 1, 1] >>> rbf_feature = RBFSampler(gamma=1, random_state=1) >>> X_features = rbf_feature.fit_transform(X) >>> clf = SGDClassifier(max_iter=5) >>> clf.fit(X_features, y) SGDClassifier(max_iter=5) >>> clf.score(X_features, y) 1.0 The mapping relies on a Monte Carlo approximation to the kernel values. The fit function performs the Monte Carlo sampling, whereas the transform method performs the mapping of the data. Because of the inherent randomness of the process, results may vary between different calls to the fit function. The fit function takes two arguments: n_components, which is the target dimensionality of the feature transform, and gamma, the parameter of the RBF-kernel. A higher n_components will result in a better approximation of the kernel and will yield results more similar to those produced by a kernel SVM. Note that "fitting" the feature function does not actually depend on the data given to the fit function. Only the dimensionality of the data is used. Details on the method can be found in [RR2007]. For a given value of n_components RBFSampler is often less accurate as Nystroem. RBFSampler is cheaper to compute, though, making use of larger feature spaces more efficient. Additive Chi Squared Kernel The additive chi squared kernel is a kernel on histograms, often used in computer vision. The additive chi squared kernel as used here is given by $$k(x, y) = \sum_i \frac{2x_iy_i}{x_i+y_i}$$ This is not exactly the same as sklearn.metrics.pairwise.additive_chi2_kernel. The authors of [VZ2010] prefer the version above as it is always positive definite. Since the kernel is additive, it is possible to treat all components x_(i) separately for embedding. This makes it possible to sample the Fourier transform in regular intervals, instead of approximating using Monte Carlo sampling. The class AdditiveChi2Sampler implements this component wise deterministic sampling. Each component is sampled n times, yielding 2n + 1 dimensions per input dimension (the multiple of two stems from the real and complex part of the Fourier transform). In the literature, n is usually chosen to be 1 or 2, transforming the dataset to size n_samples * 5 * n_features (in the case of n = 2). The approximate feature map provided by AdditiveChi2Sampler can be combined with the approximate feature map provided by RBFSampler to yield an approximate feature map for the exponentiated chi squared kernel. See the [VZ2010] for details and [VVZ2010] for combination with the RBFSampler. Skewed Chi Squared Kernel The skewed chi squared kernel is given by: $$k(x,y) = \prod_i \frac{2\sqrt{x_i+c}\sqrt{y_i+c}}{x_i + y_i + 2c}$$ It has properties that are similar to the exponentiated chi squared kernel often used in computer vision, but allows for a simple Monte Carlo approximation of the feature map. The usage of the SkewedChi2Sampler is the same as the usage described above for the RBFSampler. The only difference is in the free parameter, that is called c. For a motivation for this mapping and the mathematical details see [LS2010]. Polynomial Kernel Approximation via Tensor Sketch The polynomial kernel <polynomial_kernel> is a popular type of kernel function given by: k(x,y) = (γx^(⊤)y+c₀)^(d) where: - x, y are the input vectors - d is the kernel degree Intuitively, the feature space of the polynomial kernel of degree d consists of all possible degree-d products among input features, which enables learning algorithms using this kernel to account for interactions between features. The TensorSketch [PP2013] method, as implemented in PolynomialCountSketch, is a scalable, input data independent method for polynomial kernel approximation. It is based on the concept of Count sketch [WIKICS] [CCF2002] , a dimensionality reduction technique similar to feature hashing, which instead uses several independent hash functions. TensorSketch obtains a Count Sketch of the outer product of two vectors (or a vector with itself), which can be used as an approximation of the polynomial kernel feature space. In particular, instead of explicitly computing the outer product, TensorSketch computes the Count Sketch of the vectors and then uses polynomial multiplication via the Fast Fourier Transform to compute the Count Sketch of their outer product. Conveniently, the training phase of TensorSketch simply consists of initializing some random variables. It is thus independent of the input data, i.e. it only depends on the number of input features, but not the data values. In addition, this method can transform samples in 𝒪(n_(samples)(n_(features)+n_(components)log(n_(components)))) time, where n_(components) is the desired output dimension, determined by n_components. Mathematical Details Kernel methods like support vector machines or kernelized PCA rely on a property of reproducing kernel Hilbert spaces. For any positive definite kernel function k (a so called Mercer kernel), it is guaranteed that there exists a mapping ϕ into a Hilbert space ℋ, such that k(x,y) = ⟨ϕ(x), ϕ(y)⟩ Where ⟨⋅, ⋅ ⟩ denotes the inner product in the Hilbert space. If an algorithm, such as a linear support vector machine or PCA, relies only on the scalar product of data points x_(i), one may use the value of k(x_(i),x_(j)), which corresponds to applying the algorithm to the mapped data points ϕ(x_(i)). The advantage of using k is that the mapping ϕ never has to be calculated explicitly, allowing for arbitrary large features (even infinite). One drawback of kernel methods is, that it might be necessary to store many kernel values k(x_(i),x_(j)) during optimization. If a kernelized classifier is applied to new data y_(j), k(x_(i),y_(j)) needs to be computed to make predictions, possibly for many different x_(i) in the training set. The classes in this submodule allow to approximate the embedding ϕ, thereby working explicitly with the representations ϕ(x_(i)), which obviates the need to apply the kernel or store training examples.

""" The :mod:`sklearn.kernel_approximation` module implements several approximate kernel feature maps based on Fourier transforms and Count Sketches. """ # Author: Andreas Mueller <amueller@ais.uni-bonn.de> # Daniel Lopez-Sanchez (TensorSketch) <lope@usal.es> # License: BSD 3 clause import warnings from numbers import Integral, Real import numpy as np import scipy.sparse as sp from scipy.linalg import svd try: from scipy.fft import fft, ifft except ImportError: # scipy < 1.4 from scipy.fftpack import fft, ifft from.base import ( BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context, ) from.metrics.pairwise import KERNEL_PARAMS, PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels from.utils import check_random_state, deprecated from.utils._param_validation import Interval, StrOptions from.utils.extmath import safe_sparse_dot from.utils.validation import ( _check_feature_names_in, check_is_fitted, check_non_negative, ) class PolynomialCountSketch( ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator ): """Polynomial kernel approximation via Tensor Sketch. Implements Tensor Sketch, which approximates the feature map of the polynomial kernel:: K(X, Y) = (gamma * <X, Y> + coef0)^degree by efficiently computing a Count Sketch of the outer product of a vector with itself using Fast Fourier Transforms (FFT). Read more in the :ref:`User Guide <polynomial_kernel_approx>`. .. versionadded:: 0.24 Parameters ---------- gamma : float, default=1.0 Parameter of the polynomial kernel whose feature map will be approximated. degree : int, default=2 Degree of the polynomial kernel whose feature map will be approximated. coef0 : int, default=0 Constant term of the polynomial kernel whose feature map will be approximated. n_components : int, default=100 Dimensionality of the output feature space. Usually, `n_components` should be greater than the number of features in input samples in order to achieve good performance. The optimal score / run time balance is typically achieved around `n_components` = 10 * `n_features`, but this depends on the specific dataset being used. random_state : int, RandomState instance, default=None Determines random number generation for indexHash and bitHash initialization. Pass an int for reproducible results across multiple function calls. See :term:`Glossary <random_state>`. Attributes ---------- indexHash_ : ndarray of shape (degree, n_features), dtype=int64 Array of indexes in range [0, n_components) used to represent the 2-wise independent hash functions for Count Sketch computation. bitHash_ : ndarray of shape (degree, n_features), dtype=float32 Array with random entries in {+1, -1}, used to represent the 2-wise independent hash functions for Count Sketch computation. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- AdditiveChi2Sampler : Approximate feature map for additive chi2 kernel. Nystroem : Approximate a kernel map using a subset of the training data. RBFSampler : Approximate a RBF kernel feature map using random Fourier features. SkewedChi2Sampler : Approximate feature map for "skewed chi-squared" kernel. sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels. Examples -------- >>> from sklearn.kernel_approximation import PolynomialCountSketch >>> from sklearn.linear_model import SGDClassifier >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]] >>> y = [0, 0, 1, 1] >>> ps = PolynomialCountSketch(degree=3, random_state=1) >>> X_features = ps.fit_transform(X) >>> clf = SGDClassifier(max_iter=10, tol=1e-3) >>> clf.fit(X_features, y) SGDClassifier(max_iter=10) >>> clf.score(X_features, y) 1.0 """ _parameter_constraints: dict = { "gamma": [Interval(Real, 0, None, closed="left")], "degree": [Interval(Integral, 1, None, closed="left")], "coef0": [Interval(Real, None, None, closed="neither")], "n_components": [Interval(Integral, 1, None, closed="left")], "random_state": ["random_state"], } def __init__( self, *, gamma=1.0, degree=2, coef0=0, n_components=100, random_state=None ): self.gamma = gamma self.degree = degree self.coef0 = coef0 self.n_components = n_components self.random_state = random_state @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Fit the model with X. Initializes the internal variables. The method needs no information about the distribution of data, so we only care about n_features in X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) or (n_samples, n_outputs), \ default=None Target values (None for unsupervised transformations). Returns ------- self : object Returns the instance itself. """ X = self._validate_data(X, accept_sparse="csc") random_state = check_random_state(self.random_state) n_features = X.shape[1] if self.coef0!= 0: n_features += 1 self.indexHash_ = random_state.randint( 0, high=self.n_components, size=(self.degree, n_features) ) self.bitHash_ = random_state.choice(a=[-1, 1], size=(self.degree, n_features)) self._n_features_out = self.n_components return self def transform(self, X): """Generate the feature map approximation for X. Parameters ---------- X : {array-like}, shape (n_samples, n_features) New data, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- X_new : array-like, shape (n_samples, n_components) Returns the instance itself. """ check_is_fitted(self) X = self._validate_data(X, accept_sparse="csc", reset=False) X_gamma = np.sqrt(self.gamma) * X if sp.issparse(X_gamma) and self.coef0!= 0: X_gamma = sp.hstack( [X_gamma, np.sqrt(self.coef0) * np.ones((X_gamma.shape[0], 1))], format="csc", ) elif not sp.issparse(X_gamma) and self.coef0!= 0: X_gamma = np.hstack( [X_gamma, np.sqrt(self.coef0) * np.ones((X_gamma.shape[0], 1))] ) if X_gamma.shape[1]!= self.indexHash_.shape[1]: raise ValueError( "Number of features of test samples does not" " match that of training samples." ) count_sketches = np.zeros((X_gamma.shape[0], self.degree, self.n_components)) if sp.issparse(X_gamma): for j in range(X_gamma.shape[1]): for d in range(self.degree): iHashIndex = self.indexHash_[d, j] iHashBit = self.bitHash_[d, j] count_sketches[:, d, iHashIndex] += ( (iHashBit * X_gamma[:, j]).toarray().ravel() ) else: for j in range(X_gamma.shape[1]): for d in range(self.degree): iHashIndex = self.indexHash_[d, j] iHashBit = self.bitHash_[d, j] count_sketches[:, d, iHashIndex] += iHashBit * X_gamma[:, j] # For each same, compute a count sketch of phi(x) using the polynomial # multiplication (via FFT) of p count sketches of x. count_sketches_fft = fft(count_sketches, axis=2, overwrite_x=True) count_sketches_fft_prod = np.prod(count_sketches_fft, axis=1) data_sketch = np.real(ifft(count_sketches_fft_prod, overwrite_x=True)) return data_sketch class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): """Approximate a RBF kernel feature map using random Fourier features. It implements a variant of Random Kitchen Sinks.[1] Read more in the :ref:`User Guide <rbf_kernel_approx>`. Parameters ---------- gamma :'scale' or float, default=1.0 Parameter of RBF kernel: exp(-gamma * x^2). If ``gamma='scale'`` is passed then it uses 1 / (n_features * X.var()) as value of gamma. .. versionadded:: 1.2 The option `"scale"` was added in 1.2. n_components : int, default=100 Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space. random_state : int, RandomState instance or None, default=None Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. Attributes ---------- random_offset_ : ndarray of shape (n_components,), dtype={np.float64, np.float32} Random offset used to compute the projection in the `n_components` dimensions of the feature space. random_weights_ : ndarray of shape (n_features, n_components),\ dtype={np.float64, np.float32} Random projection directions drawn from the Fourier transform of the RBF kernel. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- AdditiveChi2Sampler : Approximate feature map for additive chi2 kernel. Nystroem : Approximate a kernel map using a subset of the training data. PolynomialCountSketch : Polynomial kernel approximation via Tensor Sketch. SkewedChi2Sampler : Approximate feature map for "skewed chi-squared" kernel. sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels. Notes ----- See "Random Features for Large-Scale Kernel Machines" by A. Rahimi and Benjamin Recht. [1] "Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning" by A. Rahimi and Benjamin Recht. (https://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf) Examples -------- >>> from sklearn.kernel_approximation import RBFSampler >>> from sklearn.linear_model import SGDClassifier >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]] >>> y = [0, 0, 1, 1] >>> rbf_feature = RBFSampler(gamma=1, random_state=1) >>> X_features = rbf_feature.fit_transform(X) >>> clf = SGDClassifier(max_iter=5, tol=1e-3) >>> clf.fit(X_features, y) SGDClassifier(max_iter=5) >>> clf.score(X_features, y) 1.0 """ _parameter_constraints: dict = { "gamma": [ StrOptions({"scale"}), Interval(Real, 0.0, None, closed="left"), ], "n_components": [Interval(Integral, 1, None, closed="left")], "random_state": ["random_state"], } def __init__(self, *, gamma=1.0, n_components=100, random_state=None): self.gamma = gamma self.n_components = n_components self.random_state = random_state @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Fit the model with X. Samples random projection according to n_features. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like, shape (n_samples,) or (n_samples, n_outputs), \ default=None Target values (None for unsupervised transformations). Returns ------- self : object Returns the instance itself. """ X = self._validate_data(X, accept_sparse="csr") random_state = check_random_state(self.random_state) n_features = X.shape[1] sparse = sp.issparse(X) if self.gamma == "scale": # var = E[X^2] - E[X]^2 if sparse X_var = (X.multiply(X)).mean() - (X.mean()) ** 2 if sparse else X.var() self._gamma = 1.0 / (n_features * X_var) if X_var!= 0 else 1.0 else: self._gamma = self.gamma self.random_weights_ = (2.0 * self._gamma) ** 0.5 * random_state.normal( size=(n_features, self.n_components) ) self.random_offset_ = random_state.uniform(0, 2 * np.pi, size=self.n_components) if X.dtype == np.float32: # Setting the data type of the fitted attribute will ensure the # output data type during `transform`. self.random_weights_ = self.random_weights_.astype(X.dtype, copy=False) self.random_offset_ = self.random_offset_.astype(X.dtype, copy=False) self._n_features_out = self.n_components return self def transform(self, X): """Apply the approximate feature map to X. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) New data, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- X_new : array-like, shape (n_samples, n_components) Returns the instance itself. """ check_is_fitted(self) X = self._validate_data(X, accept_sparse="csr", reset=False) projection = safe_sparse_dot(X, self.random_weights_) projection += self.random_offset_ np.cos(projection, projection) projection *= (2.0 / self.n_components) ** 0.5 return projection def _more_tags(self): return {"preserves_dtype": [np.float64, np.float32]} class SkewedChi2Sampler( ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator ): """Approximate feature map for "skewed chi-squared" kernel. Read more in the :ref:`User Guide <skewed_chi_kernel_approx>`. Parameters ---------- skewedness : float, default=1.0 "skewedness" parameter of the kernel. Needs to be cross-validated. n_components : int, default=100 Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space. random_state : int, RandomState instance or None, default=None Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. Attributes ---------- random_weights_ : ndarray of shape (n_features, n_components) Weight array, sampled from a secant hyperbolic distribution, which will be used to linearly transform the log of the data. random_offset_ : ndarray of shape (n_features, n_components) Bias term, which will be added to the data. It is uniformly distributed between 0 and 2*pi. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- AdditiveChi2Sampler : Approximate feature map for additive chi2 kernel. Nystroem : Approximate a kernel map using a subset of the training data. RBFSampler : Approximate a RBF kernel feature map using random Fourier features. SkewedChi2Sampler : Approximate feature map for "skewed chi-squared" kernel. sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel. sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels. References ---------- See "Random Fourier Approximations for Skewed Multiplicative Histogram Kernels" by Fuxin Li, Catalin Ionescu and Cristian Sminchisescu. Examples -------- >>> from sklearn.kernel_approximation import SkewedChi2Sampler >>> from sklearn.linear_model import SGDClassifier >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]] >>> y = [0, 0, 1, 1] >>> chi2_feature = SkewedChi2Sampler(skewedness=.01, ... n_components=10, ... random_state=0) >>> X_features = chi2_feature.fit_transform(X, y) >>> clf = SGDClassifier(max_iter=10, tol=1e-3) >>> clf.fit(X_features, y) SGDClassifier(max_iter=10) >>> clf.score(X_features, y) 1.0 """ _parameter_constraints: dict = { "skewedness": [Interval(Real, None, None, closed="neither")], "n_components": [Interval(Integral, 1, None, closed="left")], "random_state": ["random_state"], } def __init__(self, *, skewedness=1.0, n_components=100, random_state=None): self.skewedness = skewedness self.n_components = n_components self.random_state = random_state @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Fit the model with X. Samples random projection according to n_features. Parameters ---------- X : array-like, shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like, shape (n_samples,) or (n_samples, n_outputs), \ default=None Target values (None for unsupervised transformations). Returns ------- self : object Returns the instance itself. """ X = self._validate_data(X) random_state = check_random_state(self.random_state) n_features = X.shape[1] uniform = random_state.uniform(size=(n_features, self.n_components)) # transform by inverse CDF of sech self.random_weights_ = 1.0 / np.pi * np.log(np.tan(np.pi / 2.0 * uniform)) self.random_offset_ = random_state.uniform(0, 2 * np.pi, size=self.n_components) if X.dtype == np.float32: # Setting the data type of the fitted attribute will ensure the # output data type during `transform`. self.random_weights_ = self.random_weights_.astype(X.dtype, copy=False) self.random_offset_ = self.random_offset_.astype(X.dtype, copy=False) self._n_features_out = self.n_components return self def transform(self, X): """Apply the approximate feature map to X. Parameters ---------- X : array-like, shape (n_samples, n_features) New data, where `n_samples` is the number of samples and `n_features` is the number of features. All values of X must be strictly greater than "-skewedness". Returns ------- X_new : array-like, shape (n_samples, n_components) Returns the instance itself. """ check_is_fitted(self) X = self._validate_data( X, copy=True, dtype=[np.float64, np.float32], reset=False ) if (X <= -self.skewedness).any(): raise ValueError("X may not contain entries smaller than -skewedness.") X += self.skewedness np.log(X, X) projection = safe_sparse_dot(X, self.random_weights_) projection += self.random_offset_ np.cos(projection, projection) projection *= np.sqrt(2.0) / np.sqrt(self.n_components) return projection def _more_tags(self): return {"preserves_dtype": [np.float64, np.float32]} class AdditiveChi2Sampler(TransformerMixin, BaseEstimator): """Approximate feature map for additive chi2 kernel. Uses sampling the fourier transform of the kernel characteristic at regular intervals. Since the kernel that is to be approximated is additive, the components of the input vectors can be treated separately. Each entry in the original space is transformed into 2*sample_steps-1 features, where sample_steps is a parameter of the method. Typical values of sample_steps include 1, 2 and 3. Optimal choices for the sampling interval for certain data ranges can be computed (see the reference). The default values should be reasonable. Read more in the :ref:`User Guide <additive_chi_kernel_approx>`. Parameters ---------- sample_steps : int, default=2 Gives the number of (complex) sampling points. sample_interval : float, default=None Sampling interval. Must be specified when sample_steps not in {1,2,3}. Attributes ---------- sample_interval_ : float Stored sampling interval. Specified as a parameter if `sample_steps` not in {1,2,3}. .. deprecated:: 1.3 `sample_interval_` serves internal purposes only and will be removed in 1.5. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- SkewedChi2Sampler : A Fourier-approximation to a non-additive variant of the chi squared kernel. sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel. sklearn.metrics.pairwise.additive_chi2_kernel : The exact additive chi squared kernel. Notes ----- This estimator approximates a slightly different version of the additive chi squared kernel then ``metric.additive_chi2`` computes. This estimator is stateless and does not need to be fitted. However, we recommend to call :meth:`fit_transform` instead of :meth:`transform`, as parameter validation is only performed in :meth:`fit`. References ---------- See `"Efficient additive kernels via explicit feature maps" <http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>`_ A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence, 2011 Examples -------- >>> from sklearn.datasets import load_digits >>> from sklearn.linear_model import SGDClassifier >>> from sklearn.kernel_approximation import AdditiveChi2Sampler >>> X, y = load_digits(return_X_y=True) >>> chi2sampler = AdditiveChi2Sampler(sample_steps=2) >>> X_transformed = chi2sampler.fit_transform(X, y) >>> clf = SGDClassifier(max_iter=5, random_state=0, tol=1e-3) >>> clf.fit(X_transformed, y) SGDClassifier(max_iter=5, random_state=0) >>> clf.score(X_transformed, y) 0.9499... """ _parameter_constraints: dict = { "sample_steps": [Interval(Integral, 1, None, closed="left")], "sample_interval": [Interval(Real, 0, None, closed="left"), None], } def __init__(self, *, sample_steps=2, sample_interval=None): self.sample_steps = sample_steps self.sample_interval = sample_interval @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Only validates estimator's parameters. This method allows to: (i) validate the estimator's parameters and (ii) be consistent with the scikit-learn transformer API. Parameters ---------- X : array-like, shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like, shape (n_samples,) or (n_samples, n_outputs), \ default=None Target values (None for unsupervised transformations). Returns ------- self : object Returns the transformer. """ X = self._validate_data(X, accept_sparse="csr") check_non_negative(X, "X in AdditiveChi2Sampler.fit") # TODO(1.5): remove the setting of _sample_interval from fit if self.sample_interval is None: # See figure 2 c) of "Efficient additive kernels via explicit feature maps" # <http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf> # A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence, # 2011 if self.sample_steps == 1: self._sample_interval = 0.8 elif self.sample_steps == 2: self._sample_interval = 0.5 elif self.sample_steps == 3: self._sample_interval = 0.4 else: raise ValueError( "If sample_steps is not in [1, 2, 3]," " you need to provide sample_interval" ) else: self._sample_interval = self.sample_interval return self # TODO(1.5): remove @deprecated( # type: ignore "The ``sample_interval_`` attribute was deprecated in version 1.3 and " "will be removed 1.5." ) @property def sample_interval_(self): return self._sample_interval def transform(self, X): """Apply approximate feature map to X. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- X_new : {ndarray, sparse matrix}, \ shape = (n_samples, n_features * (2*sample_steps - 1)) Whether the return value is an array or sparse matrix depends on the type of the input X. """ X = self._validate_data(X, accept_sparse="csr", reset=False) check_non_negative(X, "X in AdditiveChi2Sampler.transform") sparse = sp.issparse(X) if hasattr(self, "_sample_interval"): # TODO(1.5): remove this branch sample_interval = self._sample_interval else: if self.sample_interval is None: # See figure 2 c) of "Efficient additive kernels via explicit feature maps" # noqa # <http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf> # A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence, # noqa # 2011 if self.sample_steps == 1: sample_interval = 0.8 elif self.sample_steps == 2: sample_interval = 0.5 elif self.sample_steps == 3: sample_interval = 0.4 else: raise ValueError( "If sample_steps is not in [1, 2, 3]," " you need to provide sample_interval" ) else: sample_interval = self.sample_interval # zeroth component # 1/cosh = sech # cosh(0) = 1.0 transf = self._transform_sparse if sparse else self._transform_dense return transf(X, self.sample_steps, sample_interval) def get_feature_names_out(self, input_features=None): """Get output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Only used to validate feature names with the names seen in :meth:`fit`. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. """ check_is_fitted(self, "n_features_in_") input_features = _check_feature_names_in( self, input_features, generate_names=True ) est_name = self.__class__.__name__.lower() names_list = [f"{est_name}_{name}_sqrt" for name in input_features] for j in range(1, self.sample_steps): cos_names = [f"{est_name}_{name}_cos{j}" for name in input_features] sin_names = [f"{est_name}_{name}_sin{j}" for name in input_features] names_list.extend(cos_names + sin_names) return np.asarray(names_list, dtype=object) @staticmethod def _transform_dense(X, sample_steps, sample_interval): non_zero = X!= 0.0 X_nz = X[non_zero] X_step = np.zeros_like(X) X_step[non_zero] = np.sqrt(X_nz * sample_interval) X_new = [X_step] log_step_nz = sample_interval * np.log(X_nz) step_nz = 2 * X_nz * sample_interval for j in range(1, sample_steps): factor_nz = np.sqrt(step_nz / np.cosh(np.pi * j * sample_interval)) X_step = np.zeros_like(X) X_step[non_zero] = factor_nz * np.cos(j * log_step_nz) X_new.append(X_step) X_step = np.zeros_like(X) X_step[non_zero] = factor_nz * np.sin(j * log_step_nz) X_new.append(X_step) return np.hstack(X_new) @staticmethod def _transform_sparse(X, sample_steps, sample_interval): indices = X.indices.copy() indptr = X.indptr.copy() data_step = np.sqrt(X.data * sample_interval) X_step = sp.csr_matrix( (data_step, indices, indptr), shape=X.shape, dtype=X.dtype, copy=False ) X_new = [X_step] log_step_nz = sample_interval * np.log(X.data) step_nz = 2 * X.data * sample_interval for j in range(1, sample_steps): factor_nz = np.sqrt(step_nz / np.cosh(np.pi * j * sample_interval)) data_step = factor_nz * np.cos(j * log_step_nz) X_step = sp.csr_matrix( (data_step, indices, indptr), shape=X.shape, dtype=X.dtype, copy=False ) X_new.append(X_step) data_step = factor_nz * np.sin(j * log_step_nz) X_step = sp.csr_matrix( (data_step, indices, indptr), shape=X.shape, dtype=X.dtype, copy=False ) X_new.append(X_step) return sp.hstack(X_new) def _more_tags(self): return {"stateless": True, "requires_positive_X": True} class Nystroem(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): """Approximate a kernel map using a subset of the training data. Constructs an approximate feature map for an arbitrary kernel using a subset of the data as basis. Read more in the :ref:`User Guide <nystroem_kernel_approx>`. .. versionadded:: 0.13 Parameters ---------- kernel : str or callable, default='rbf' Kernel map to be approximated. A callable should accept two arguments and the keyword arguments passed to this object as `kernel_params`, and should return a floating point number. gamma : float, default=None Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels. coef0 : float, default=None Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels. degree : float, default=None Degree of the polynomial kernel. Ignored by other kernels. kernel_params : dict, default=None Additional parameters (keyword arguments) for kernel function passed as callable object. n_components : int, default=100 Number of features to construct. How many data points will be used to construct the mapping. random_state : int, RandomState instance or None, default=None Pseudo-random number generator to control the uniform sampling without replacement of `n_components` of the training data to construct the basis kernel. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. n_jobs : int, default=None The number of jobs to use for the computation. This works by breaking down the kernel matrix into `n_jobs` even slices and computing them in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. .. versionadded:: 0.24 Attributes ---------- components_ : ndarray of shape (n_components, n_features) Subset of training points used to construct the feature map. component_indices_ : ndarray of shape (n_components) Indices of ``components_`` in the training set. normalization_ : ndarray of shape (n_components, n_components) Normalization matrix needed for embedding. Square root of the kernel matrix on ``components_``. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- AdditiveChi2Sampler : Approximate feature map for additive chi2 kernel. PolynomialCountSketch : Polynomial kernel approximation via Tensor Sketch. RBFSampler : Approximate a RBF kernel feature map using random Fourier features. SkewedChi2Sampler : Approximate feature map for "skewed chi-squared" kernel. sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels. References ---------- * Williams, C.K.I. and Seeger, M. "Using the Nystroem method to speed up kernel machines", Advances in neural information processing systems 2001 * T. Yang, Y. Li, M. Mahdavi, R. Jin and Z. Zhou "Nystroem Method vs Random Fourier Features: A Theoretical and Empirical Comparison", Advances in Neural Information Processing Systems 2012 Examples -------- >>> from sklearn import datasets, svm >>> from sklearn.kernel_approximation import Nystroem >>> X, y = datasets.load_digits(n_class=9, return_X_y=True) >>> data = X / 16. >>> clf = svm.LinearSVC(dual="auto") >>> feature_map_nystroem = Nystroem(gamma=.2, ... random_state=1, ... n_components=300) >>> data_transformed = feature_map_nystroem.fit_transform(data) >>> clf.fit(data_transformed, y) LinearSVC(dual='auto') >>> clf.score(data_transformed, y) 0.9987... """ _parameter_constraints: dict = { "kernel": [ StrOptions(set(PAIRWISE_KERNEL_FUNCTIONS.keys()) | {"precomputed"}), callable, ], "gamma": [Interval(Real, 0, None, closed="left"), None], "coef0": [Interval(Real, None, None, closed="neither"), None], "degree": [Interval(Real, 1, None, closed="left"), None], "kernel_params": [dict, None], "n_components": [Interval(Integral, 1, None, closed="left")], "random_state": ["random_state"], "n_jobs": [Integral, None], } def __init__( self, kernel="rbf", *, gamma=None, coef0=None, degree=None, kernel_params=None, n_components=100, random_state=None, n_jobs=None, ): self.kernel = kernel self.gamma = gamma self.coef0 = coef0 self.degree = degree self.kernel_params = kernel_params self.n_components = n_components self.random_state = random_state self.n_jobs = n_jobs @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Fit estimator to data. Samples a subset of training points, computes kernel on these and computes normalization matrix. Parameters ---------- X : array-like, shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like, shape (n_samples,) or (n_samples, n_outputs), \ default=None Target values (None for unsupervised transformations). Returns ------- self : object Returns the instance itself. """ X = self._validate_data(X, accept_sparse="csr") rnd = check_random_state(self.random_state) n_samples = X.shape[0] # get basis vectors if self.n_components > n_samples: # XXX should we just bail? n_components = n_samples warnings.warn( "n_components > n_samples. This is not possible.\n" "n_components was set to n_samples, which results" " in inefficient evaluation of the full kernel." ) else: n_components = self.n_components n_components = min(n_samples, n_components) inds = rnd.permutation(n_samples) basis_inds = inds[:n_components] basis = X[basis_inds] basis_kernel = pairwise_kernels( basis, metric=self.kernel, filter_params=True, n_jobs=self.n_jobs, **self._get_kernel_params(), ) # sqrt of kernel matrix on basis vectors U, S, V = svd(basis_kernel) S = np.maximum(S, 1e-12) self.normalization_ = np.dot(U / np.sqrt(S), V) self.components_ = basis self.component_indices_ = basis_inds self._n_features_out = n_components return self def transform(self, X): """Apply feature map to X. Computes an approximate feature map using the kernel between some training points and X. Parameters ---------- X : array-like of shape (n_samples, n_features) Data to transform. Returns ------- X_transformed : ndarray of shape (n_samples, n_components) Transformed data. """ check_is_fitted(self) X = self._validate_data(X, accept_sparse="csr", reset=False) kernel_params = self._get_kernel_params() embedded = pairwise_kernels( X, self.components_, metric=self.kernel, filter_params=True, n_jobs=self.n_jobs, **kernel_params, ) return np.dot(embedded, self.normalization_.T) def _get_kernel_params(self): params = self.kernel_params if params is None: params = {} if not callable(self.kernel) and self.kernel!= "precomputed": for param in KERNEL_PARAMS[self.kernel]: if getattr(self, param) is not None: params[param] = getattr(self, param) else: if ( self.gamma is not None or self.coef0 is not None or self.degree is not None ): raise ValueError( "Don't pass gamma, coef0 or degree to " "Nystroem if using a callable " "or precomputed kernel" ) return params def _more_tags(self): return { "_xfail_checks": { "check_transformer_preserve_dtypes": ( "dtypes are preserved but not at a close enough precision" ) }, "preserves_dtype": [np.float64, np.float32], }

scikit-learn__scikit-learn

kernel_ridge.rst

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scikit-learn__scikit-learn/doc/modules/kernel_ridge.rst

Kernel ridge regression Kernel ridge regression (KRR) [M2012] combines ridge_regression (linear least squares with l2-norm regularization) with the kernel trick. It thus learns a linear function in the space induced by the respective kernel and the data. For non-linear kernels, this corresponds to a non-linear function in the original space. The form of the model learned by KernelRidge is identical to support vector regression (~sklearn.svm.SVR). However, different loss functions are used: KRR uses squared error loss while support vector regression uses ϵ-insensitive loss, both combined with l2 regularization. In contrast to ~sklearn.svm.SVR, fitting KernelRidge can be done in closed-form and is typically faster for medium-sized datasets. On the other hand, the learned model is non-sparse and thus slower than ~sklearn.svm.SVR, which learns a sparse model for ϵ > 0, at prediction-time. The following figure compares KernelRidge and ~sklearn.svm.SVR on an artificial dataset, which consists of a sinusoidal target function and strong noise added to every fifth datapoint. The learned model of KernelRidge and ~sklearn.svm.SVR is plotted, where both complexity/regularization and bandwidth of the RBF kernel have been optimized using grid-search. The learned functions are very similar; however, fitting KernelRidge is approximately seven times faster than fitting ~sklearn.svm.SVR (both with grid-search). However, prediction of 100000 target values is more than three times faster with ~sklearn.svm.SVR since it has learned a sparse model using only approximately 1/3 of the 100 training datapoints as support vectors. The next figure compares the time for fitting and prediction of KernelRidge and ~sklearn.svm.SVR for different sizes of the training set. Fitting KernelRidge is faster than ~sklearn.svm.SVR for medium-sized training sets (less than 1000 samples); however, for larger training sets ~sklearn.svm.SVR scales better. With regard to prediction time, ~sklearn.svm.SVR is faster than KernelRidge for all sizes of the training set because of the learned sparse solution. Note that the degree of sparsity and thus the prediction time depends on the parameters ϵ and C of the ~sklearn.svm.SVR; ϵ = 0 would correspond to a dense model.

"""Module :mod:`sklearn.kernel_ridge` implements kernel ridge regression.""" # Authors: Mathieu Blondel <mathieu@mblondel.org> # Jan Hendrik Metzen <jhm@informatik.uni-bremen.de> # License: BSD 3 clause from numbers import Integral, Real import numpy as np from.base import BaseEstimator, MultiOutputMixin, RegressorMixin, _fit_context from.linear_model._ridge import _solve_cholesky_kernel from.metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels from.utils._param_validation import Interval, StrOptions from.utils.validation import _check_sample_weight, check_is_fitted class KernelRidge(MultiOutputMixin, RegressorMixin, BaseEstimator): """Kernel ridge regression. Kernel ridge regression (KRR) combines ridge regression (linear least squares with l2-norm regularization) with the kernel trick. It thus learns a linear function in the space induced by the respective kernel and the data. For non-linear kernels, this corresponds to a non-linear function in the original space. The form of the model learned by KRR is identical to support vector regression (SVR). However, different loss functions are used: KRR uses squared error loss while support vector regression uses epsilon-insensitive loss, both combined with l2 regularization. In contrast to SVR, fitting a KRR model can be done in closed-form and is typically faster for medium-sized datasets. On the other hand, the learned model is non-sparse and thus slower than SVR, which learns a sparse model for epsilon > 0, at prediction-time. This estimator has built-in support for multi-variate regression (i.e., when y is a 2d-array of shape [n_samples, n_targets]). Read more in the :ref:`User Guide <kernel_ridge>`. Parameters ---------- alpha : float or array-like of shape (n_targets,), default=1.0 Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to ``1 / (2C)`` in other linear models such as :class:`~sklearn.linear_model.LogisticRegression` or :class:`~sklearn.svm.LinearSVC`. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number. See :ref:`ridge_regression` for formula. kernel : str or callable, default="linear" Kernel mapping used internally. This parameter is directly passed to :class:`~sklearn.metrics.pairwise.pairwise_kernels`. If `kernel` is a string, it must be one of the metrics in `pairwise.PAIRWISE_KERNEL_FUNCTIONS` or "precomputed". If `kernel` is "precomputed", X is assumed to be a kernel matrix. Alternatively, if `kernel` is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two rows from X as input and return the corresponding kernel value as a single number. This means that callables from :mod:`sklearn.metrics.pairwise` are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead. gamma : float, default=None Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels. degree : int, default=3 Degree of the polynomial kernel. Ignored by other kernels. coef0 : float, default=1 Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels. kernel_params : dict, default=None Additional parameters (keyword arguments) for kernel function passed as callable object. Attributes ---------- dual_coef_ : ndarray of shape (n_samples,) or (n_samples, n_targets) Representation of weight vector(s) in kernel space X_fit_ : {ndarray, sparse matrix} of shape (n_samples, n_features) Training data, which is also required for prediction. If kernel == "precomputed" this is instead the precomputed training matrix, of shape (n_samples, n_samples). n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- sklearn.gaussian_process.GaussianProcessRegressor : Gaussian Process regressor providing automatic kernel hyperparameters tuning and predictions uncertainty. sklearn.linear_model.Ridge : Linear ridge regression. sklearn.linear_model.RidgeCV : Ridge regression with built-in cross-validation. sklearn.svm.SVR : Support Vector Regression accepting a large variety of kernels. References ---------- * Kevin P. Murphy "Machine Learning: A Probabilistic Perspective", The MIT Press chapter 14.4.3, pp. 492-493 Examples -------- >>> from sklearn.kernel_ridge import KernelRidge >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> rng = np.random.RandomState(0) >>> y = rng.randn(n_samples) >>> X = rng.randn(n_samples, n_features) >>> krr = KernelRidge(alpha=1.0) >>> krr.fit(X, y) KernelRidge(alpha=1.0) """ _parameter_constraints: dict = { "alpha": [Interval(Real, 0, None, closed="left"), "array-like"], "kernel": [ StrOptions(set(PAIRWISE_KERNEL_FUNCTIONS.keys()) | {"precomputed"}), callable, ], "gamma": [Interval(Real, 0, None, closed="left"), None], "degree": [Interval(Integral, 0, None, closed="left")], "coef0": [Interval(Real, None, None, closed="neither")], "kernel_params": [dict, None], } def __init__( self, alpha=1, *, kernel="linear", gamma=None, degree=3, coef0=1, kernel_params=None, ): self.alpha = alpha self.kernel = kernel self.gamma = gamma self.degree = degree self.coef0 = coef0 self.kernel_params = kernel_params def _get_kernel(self, X, Y=None): if callable(self.kernel): params = self.kernel_params or {} else: params = {"gamma": self.gamma, "degree": self.degree, "coef0": self.coef0} return pairwise_kernels(X, Y, metric=self.kernel, filter_params=True, **params) def _more_tags(self): return {"pairwise": self.kernel == "precomputed"} @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y, sample_weight=None): """Fit Kernel Ridge regression model. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. If kernel == "precomputed" this is instead a precomputed kernel matrix, of shape (n_samples, n_samples). y : array-like of shape (n_samples,) or (n_samples, n_targets) Target values. sample_weight : float or array-like of shape (n_samples,), default=None Individual weights for each sample, ignored if None is passed. Returns ------- self : object Returns the instance itself. """ # Convert data X, y = self._validate_data( X, y, accept_sparse=("csr", "csc"), multi_output=True, y_numeric=True ) if sample_weight is not None and not isinstance(sample_weight, float): sample_weight = _check_sample_weight(sample_weight, X) K = self._get_kernel(X) alpha = np.atleast_1d(self.alpha) ravel = False if len(y.shape) == 1: y = y.reshape(-1, 1) ravel = True copy = self.kernel == "precomputed" self.dual_coef_ = _solve_cholesky_kernel(K, y, alpha, sample_weight, copy) if ravel: self.dual_coef_ = self.dual_coef_.ravel() self.X_fit_ = X return self def predict(self, X): """Predict using the kernel ridge model. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Samples. If kernel == "precomputed" this is instead a precomputed kernel matrix, shape = [n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for this estimator. Returns ------- C : ndarray of shape (n_samples,) or (n_samples, n_targets) Returns predicted values. """ check_is_fitted(self) X = self._validate_data(X, accept_sparse=("csr", "csc"), reset=False) K = self._get_kernel(X, self.X_fit_) return np.dot(K, self.dual_coef_)

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metrics.rst

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scikit-learn__scikit-learn/sklearn/metrics

Pairwise metrics, Affinities and Kernels The sklearn.metrics.pairwise submodule implements utilities to evaluate pairwise distances or affinity of sets of samples. This module contains both distance metrics and kernels. A brief summary is given on the two here. Distance metrics are functions d(a, b) such that d(a, b) < d(a, c) if objects a and b are considered "more similar" than objects a and c. Two objects exactly alike would have a distance of zero. One of the most popular examples is Euclidean distance. To be a 'true' metric, it must obey the following four conditions: 1. d(a, b) >= 0, for all a and b 2. d(a, b) == 0, if and only if a = b, positive definiteness 3. d(a, b) == d(b, a), symmetry 4. d(a, c) <= d(a, b) + d(b, c), the triangle inequality Kernels are measures of similarity, i.e. s(a, b) > s(a, c) if objects a and b are considered "more similar" than objects a and c. A kernel must also be positive semi-definite. There are a number of ways to convert between a distance metric and a similarity measure, such as a kernel. Let D be the distance, and S be the kernel: 1. S = np.exp(-D * gamma), where one heuristic for choosing gamma is 1 / num_features 2. S = 1. / (D / np.max(D)) The distances between the row vectors of X and the row vectors of Y can be evaluated using pairwise_distances. If Y is omitted the pairwise distances of the row vectors of X are calculated. Similarly, pairwise.pairwise_kernels can be used to calculate the kernel between X and Y using different kernel functions. See the API reference for more details. >>> import numpy as np >>> from sklearn.metrics import pairwise_distances >>> from sklearn.metrics.pairwise import pairwise_kernels >>> X = np.array([[2, 3], [3, 5], [5, 8]]) >>> Y = np.array([[1, 0], [2, 1]]) >>> pairwise_distances(X, Y, metric='manhattan') array([[ 4., 2.], [ 7., 5.], [12., 10.]]) >>> pairwise_distances(X, metric='manhattan') array([[0., 3., 8.], [3., 0., 5.], [8., 5., 0.]]) >>> pairwise_kernels(X, Y, metric='linear') array([[ 2., 7.], [ 3., 11.], [ 5., 18.]]) Cosine similarity cosine_similarity computes the L2-normalized dot product of vectors. That is, if x and y are row vectors, their cosine similarity k is defined as: $$k(x, y) = \frac{x y^\top}{\|x\| \|y\|}$$ This is called cosine similarity, because Euclidean (L2) normalization projects the vectors onto the unit sphere, and their dot product is then the cosine of the angle between the points denoted by the vectors. This kernel is a popular choice for computing the similarity of documents represented as tf-idf vectors. cosine_similarity accepts scipy.sparse matrices. (Note that the tf-idf functionality in sklearn.feature_extraction.text can produce normalized vectors, in which case cosine_similarity is equivalent to linear_kernel, only slower.) Linear kernel The function linear_kernel computes the linear kernel, that is, a special case of polynomial_kernel with degree=1 and coef0=0 (homogeneous). If x and y are column vectors, their linear kernel is: k(x,y) = x^(⊤)y Polynomial kernel The function polynomial_kernel computes the degree-d polynomial kernel between two vectors. The polynomial kernel represents the similarity between two vectors. Conceptually, the polynomial kernels considers not only the similarity between vectors under the same dimension, but also across dimensions. When used in machine learning algorithms, this allows to account for feature interaction. The polynomial kernel is defined as: k(x,y) = (γx^(⊤)y+c₀)^(d) where: - x, y are the input vectors - d is the kernel degree If c₀ = 0 the kernel is said to be homogeneous. Sigmoid kernel The function sigmoid_kernel computes the sigmoid kernel between two vectors. The sigmoid kernel is also known as hyperbolic tangent, or Multilayer Perceptron (because, in the neural network field, it is often used as neuron activation function). It is defined as: k(x,y) = tanh (γx^(⊤)y+c₀) where: - x, y are the input vectors - γ is known as slope - c₀ is known as intercept RBF kernel The function rbf_kernel computes the radial basis function (RBF) kernel between two vectors. This kernel is defined as: k(x,y) = exp (−γ∥x−y∥²) where x and y are the input vectors. If γ = σ⁻² the kernel is known as the Gaussian kernel of variance σ². Laplacian kernel The function laplacian_kernel is a variant on the radial basis function kernel defined as: k(x,y) = exp (−γ∥x−y∥₁) where x and y are the input vectors and ∥x − y∥₁ is the Manhattan distance between the input vectors. It has proven useful in ML applied to noiseless data. See e.g. Machine learning for quantum mechanics in a nutshell. Chi-squared kernel The chi-squared kernel is a very popular choice for training non-linear SVMs in computer vision applications. It can be computed using chi2_kernel and then passed to an ~sklearn.svm.SVC with kernel="precomputed": >>> from sklearn.svm import SVC >>> from sklearn.metrics.pairwise import chi2_kernel >>> X = [[0, 1], [1, 0], [.2, .8], [.7, .3]] >>> y = [0, 1, 0, 1] >>> K = chi2_kernel(X, gamma=.5) >>> K array([[1. , 0.36787944, 0.89483932, 0.58364548], [0.36787944, 1. , 0.51341712, 0.83822343], [0.89483932, 0.51341712, 1. , 0.7768366 ], [0.58364548, 0.83822343, 0.7768366 , 1. ]]) >>> svm = SVC(kernel='precomputed').fit(K, y) >>> svm.predict(K) array([0, 1, 0, 1]) It can also be directly used as the kernel argument: >>> svm = SVC(kernel=chi2_kernel).fit(X, y) >>> svm.predict(X) array([0, 1, 0, 1]) The chi squared kernel is given by $$k(x, y) = \exp \left (-\gamma \sum_i \frac{(x[i] - y[i]) ^ 2}{x[i] + y[i]} \right )$$ The data is assumed to be non-negative, and is often normalized to have an L1-norm of one. The normalization is rationalized with the connection to the chi squared distance, which is a distance between discrete probability distributions. The chi squared kernel is most commonly used on histograms (bags) of visual words.

# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr> # Mathieu Blondel <mathieu@mblondel.org> # Robert Layton <robertlayton@gmail.com> # Andreas Mueller <amueller@ais.uni-bonn.de> # Philippe Gervais <philippe.gervais@inria.fr> # Lars Buitinck # Joel Nothman <joel.nothman@gmail.com> # License: BSD 3 clause import itertools import warnings from functools import partial from numbers import Integral, Real import numpy as np from joblib import effective_n_jobs from scipy.sparse import csr_matrix, issparse from scipy.spatial import distance from.. import config_context from..exceptions import DataConversionWarning from..preprocessing import normalize from..utils import ( check_array, gen_batches, gen_even_slices, get_chunk_n_rows, is_scalar_nan, ) from..utils._mask import _get_mask from..utils._param_validation import ( Hidden, Interval, MissingValues, Options, StrOptions, validate_params, ) from..utils.extmath import row_norms, safe_sparse_dot from..utils.fixes import parse_version, sp_base_version from..utils.parallel import Parallel, delayed from..utils.validation import _num_samples, check_non_negative from._pairwise_distances_reduction import ArgKmin from._pairwise_fast import _chi2_kernel_fast, _sparse_manhattan # Utility Functions def _return_float_dtype(X, Y): """ 1. If dtype of X and Y is float32, then dtype float32 is returned. 2. Else dtype float is returned. """ if not issparse(X) and not isinstance(X, np.ndarray): X = np.asarray(X) if Y is None: Y_dtype = X.dtype elif not issparse(Y) and not isinstance(Y, np.ndarray): Y = np.asarray(Y) Y_dtype = Y.dtype else: Y_dtype = Y.dtype if X.dtype == Y_dtype == np.float32: dtype = np.float32 else: dtype = float return X, Y, dtype def check_pairwise_arrays( X, Y, *, precomputed=False, dtype=None, accept_sparse="csr", force_all_finite=True, copy=False, ): """Set X and Y appropriately and checks inputs. If Y is None, it is set as a pointer to X (i.e. not a copy). If Y is given, this does not happen. All distance metrics should use this function first to assert that the given parameters are correct and safe to use. Specifically, this function first ensures that both X and Y are arrays, then checks that they are at least two dimensional while ensuring that their elements are floats (or dtype if provided). Finally, the function checks that the size of the second dimension of the two arrays is equal, or the equivalent check for a precomputed distance matrix. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features) precomputed : bool, default=False True if X is to be treated as precomputed distances to the samples in Y. dtype : str, type, list of type, default=None Data type required for X and Y. If None, the dtype will be an appropriate float type selected by _return_float_dtype. .. versionadded:: 0.18 accept_sparse : str, bool or list/tuple of str, default='csr' String[s] representing allowed sparse matrix formats, such as 'csc', 'csr', etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error. force_all_finite : bool or 'allow-nan', default=True Whether to raise an error on np.inf, np.nan, pd.NA in array. The possibilities are: - True: Force all values of array to be finite. - False: accepts np.inf, np.nan, pd.NA in array. - 'allow-nan': accepts only np.nan and pd.NA values in array. Values cannot be infinite. .. versionadded:: 0.22 ``force_all_finite`` accepts the string ``'allow-nan'``. .. versionchanged:: 0.23 Accepts `pd.NA` and converts it into `np.nan`. copy : bool, default=False Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. .. versionadded:: 0.22 Returns ------- safe_X : {array-like, sparse matrix} of shape (n_samples_X, n_features) An array equal to X, guaranteed to be a numpy array. safe_Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features) An array equal to Y if Y was not None, guaranteed to be a numpy array. If Y was None, safe_Y will be a pointer to X. """ X, Y, dtype_float = _return_float_dtype(X, Y) estimator = "check_pairwise_arrays" if dtype is None: dtype = dtype_float if Y is X or Y is None: X = Y = check_array( X, accept_sparse=accept_sparse, dtype=dtype, copy=copy, force_all_finite=force_all_finite, estimator=estimator, ) else: X = check_array( X, accept_sparse=accept_sparse, dtype=dtype, copy=copy, force_all_finite=force_all_finite, estimator=estimator, ) Y = check_array( Y, accept_sparse=accept_sparse, dtype=dtype, copy=copy, force_all_finite=force_all_finite, estimator=estimator, ) if precomputed: if X.shape[1]!= Y.shape[0]: raise ValueError( "Precomputed metric requires shape " "(n_queries, n_indexed). Got (%d, %d) " "for %d indexed." % (X.shape[0], X.shape[1], Y.shape[0]) ) elif X.shape[1]!= Y.shape[1]: raise ValueError( "Incompatible dimension for X and Y matrices: " "X.shape[1] == %d while Y.shape[1] == %d" % (X.shape[1], Y.shape[1]) ) return X, Y def check_paired_arrays(X, Y): """Set X and Y appropriately and checks inputs for paired distances. All paired distance metrics should use this function first to assert that the given parameters are correct and safe to use. Specifically, this function first ensures that both X and Y are arrays, then checks that they are at least two dimensional while ensuring that their elements are floats. Finally, the function checks that the size of the dimensions of the two arrays are equal. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features) Returns ------- safe_X : {array-like, sparse matrix} of shape (n_samples_X, n_features) An array equal to X, guaranteed to be a numpy array. safe_Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features) An array equal to Y if Y was not None, guaranteed to be a numpy array. If Y was None, safe_Y will be a pointer to X. """ X, Y = check_pairwise_arrays(X, Y) if X.shape!= Y.shape: raise ValueError( "X and Y should be of same shape. They were respectively %r and %r long." % (X.shape, Y.shape) ) return X, Y # Pairwise distances @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "Y_norm_squared": ["array-like", None], "squared": ["boolean"], "X_norm_squared": ["array-like", None], }, prefer_skip_nested_validation=True, ) def euclidean_distances( X, Y=None, *, Y_norm_squared=None, squared=False, X_norm_squared=None ): """ Compute the distance matrix between each pair from a vector array X and Y. For efficiency reasons, the euclidean distance between a pair of row vector x and y is computed as:: dist(x, y) = sqrt(dot(x, x) - 2 * dot(x, y) + dot(y, y)) This formulation has two advantages over other ways of computing distances. First, it is computationally efficient when dealing with sparse data. Second, if one argument varies but the other remains unchanged, then `dot(x, x)` and/or `dot(y, y)` can be pre-computed. However, this is not the most precise way of doing this computation, because this equation potentially suffers from "catastrophic cancellation". Also, the distance matrix returned by this function may not be exactly symmetric as required by, e.g., ``scipy.spatial.distance`` functions. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) An array where each row is a sample and each column is a feature. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), \ default=None An array where each row is a sample and each column is a feature. If `None`, method uses `Y=X`. Y_norm_squared : array-like of shape (n_samples_Y,) or (n_samples_Y, 1) \ or (1, n_samples_Y), default=None Pre-computed dot-products of vectors in Y (e.g., ``(Y**2).sum(axis=1)``) May be ignored in some cases, see the note below. squared : bool, default=False Return squared Euclidean distances. X_norm_squared : array-like of shape (n_samples_X,) or (n_samples_X, 1) \ or (1, n_samples_X), default=None Pre-computed dot-products of vectors in X (e.g., ``(X**2).sum(axis=1)``) May be ignored in some cases, see the note below. Returns ------- distances : ndarray of shape (n_samples_X, n_samples_Y) Returns the distances between the row vectors of `X` and the row vectors of `Y`. See Also -------- paired_distances : Distances between pairs of elements of X and Y. Notes ----- To achieve a better accuracy, `X_norm_squared` and `Y_norm_squared` may be unused if they are passed as `np.float32`. Examples -------- >>> from sklearn.metrics.pairwise import euclidean_distances >>> X = [[0, 1], [1, 1]] >>> # distance between rows of X >>> euclidean_distances(X, X) array([[0., 1.], [1., 0.]]) >>> # get distance to origin >>> euclidean_distances(X, [[0, 0]]) array([[1. ], [1.41421356]]) """ X, Y = check_pairwise_arrays(X, Y) if X_norm_squared is not None: X_norm_squared = check_array(X_norm_squared, ensure_2d=False) original_shape = X_norm_squared.shape if X_norm_squared.shape == (X.shape[0],): X_norm_squared = X_norm_squared.reshape(-1, 1) if X_norm_squared.shape == (1, X.shape[0]): X_norm_squared = X_norm_squared.T if X_norm_squared.shape!= (X.shape[0], 1): raise ValueError( f"Incompatible dimensions for X of shape {X.shape} and " f"X_norm_squared of shape {original_shape}." ) if Y_norm_squared is not None: Y_norm_squared = check_array(Y_norm_squared, ensure_2d=False) original_shape = Y_norm_squared.shape if Y_norm_squared.shape == (Y.shape[0],): Y_norm_squared = Y_norm_squared.reshape(1, -1) if Y_norm_squared.shape == (Y.shape[0], 1): Y_norm_squared = Y_norm_squared.T if Y_norm_squared.shape!= (1, Y.shape[0]): raise ValueError( f"Incompatible dimensions for Y of shape {Y.shape} and " f"Y_norm_squared of shape {original_shape}." ) return _euclidean_distances(X, Y, X_norm_squared, Y_norm_squared, squared) def _euclidean_distances(X, Y, X_norm_squared=None, Y_norm_squared=None, squared=False): """Computational part of euclidean_distances Assumes inputs are already checked. If norms are passed as float32, they are unused. If arrays are passed as float32, norms needs to be recomputed on upcast chunks. TODO: use a float64 accumulator in row_norms to avoid the latter. """ if X_norm_squared is not None: if X_norm_squared.dtype == np.float32: XX = None else: XX = X_norm_squared.reshape(-1, 1) elif X.dtype == np.float32: XX = None else: XX = row_norms(X, squared=True)[:, np.newaxis] if Y is X: YY = None if XX is None else XX.T else: if Y_norm_squared is not None: if Y_norm_squared.dtype == np.float32: YY = None else: YY = Y_norm_squared.reshape(1, -1) elif Y.dtype == np.float32: YY = None else: YY = row_norms(Y, squared=True)[np.newaxis, :] if X.dtype == np.float32: # To minimize precision issues with float32, we compute the distance # matrix on chunks of X and Y upcast to float64 distances = _euclidean_distances_upcast(X, XX, Y, YY) else: # if dtype is already float64, no need to chunk and upcast distances = -2 * safe_sparse_dot(X, Y.T, dense_output=True) distances += XX distances += YY np.maximum(distances, 0, out=distances) # Ensure that distances between vectors and themselves are set to 0.0. # This may not be the case due to floating point rounding errors. if X is Y: np.fill_diagonal(distances, 0) return distances if squared else np.sqrt(distances, out=distances) @validate_params( { "X": ["array-like"], "Y": ["array-like", None], "squared": ["boolean"], "missing_values": [MissingValues(numeric_only=True)], "copy": ["boolean"], }, prefer_skip_nested_validation=True, ) def nan_euclidean_distances( X, Y=None, *, squared=False, missing_values=np.nan, copy=True ): """Calculate the euclidean distances in the presence of missing values. Compute the euclidean distance between each pair of samples in X and Y, where Y=X is assumed if Y=None. When calculating the distance between a pair of samples, this formulation ignores feature coordinates with a missing value in either sample and scales up the weight of the remaining coordinates: dist(x,y) = sqrt(weight * sq. distance from present coordinates) where, weight = Total # of coordinates / # of present coordinates For example, the distance between ``[3, na, na, 6]`` and ``[1, na, 4, 5]`` is: .. math:: \\sqrt{\\frac{4}{2}((3-1)^2 + (6-5)^2)} If all the coordinates are missing or if there are no common present coordinates then NaN is returned for that pair. Read more in the :ref:`User Guide <metrics>`. .. versionadded:: 0.22 Parameters ---------- X : array-like of shape (n_samples_X, n_features) An array where each row is a sample and each column is a feature. Y : array-like of shape (n_samples_Y, n_features), default=None An array where each row is a sample and each column is a feature. If `None`, method uses `Y=X`. squared : bool, default=False Return squared Euclidean distances. missing_values : np.nan, float or int, default=np.nan Representation of missing value. copy : bool, default=True Make and use a deep copy of X and Y (if Y exists). Returns ------- distances : ndarray of shape (n_samples_X, n_samples_Y) Returns the distances between the row vectors of `X` and the row vectors of `Y`. See Also -------- paired_distances : Distances between pairs of elements of X and Y. References ---------- * John K. Dixon, "Pattern Recognition with Partly Missing Data", IEEE Transactions on Systems, Man, and Cybernetics, Volume: 9, Issue: 10, pp. 617 - 621, Oct. 1979. http://ieeexplore.ieee.org/abstract/document/4310090/ Examples -------- >>> from sklearn.metrics.pairwise import nan_euclidean_distances >>> nan = float("NaN") >>> X = [[0, 1], [1, nan]] >>> nan_euclidean_distances(X, X) # distance between rows of X array([[0. , 1.41421356], [1.41421356, 0. ]]) >>> # get distance to origin >>> nan_euclidean_distances(X, [[0, 0]]) array([[1. ], [1.41421356]]) """ force_all_finite = "allow-nan" if is_scalar_nan(missing_values) else True X, Y = check_pairwise_arrays( X, Y, accept_sparse=False, force_all_finite=force_all_finite, copy=copy ) # Get missing mask for X missing_X = _get_mask(X, missing_values) # Get missing mask for Y missing_Y = missing_X if Y is X else _get_mask(Y, missing_values) # set missing values to zero X[missing_X] = 0 Y[missing_Y] = 0 distances = euclidean_distances(X, Y, squared=True) # Adjust distances for missing values XX = X * X YY = Y * Y distances -= np.dot(XX, missing_Y.T) distances -= np.dot(missing_X, YY.T) np.clip(distances, 0, None, out=distances) if X is Y: # Ensure that distances between vectors and themselves are set to 0.0. # This may not be the case due to floating point rounding errors. np.fill_diagonal(distances, 0.0) present_X = 1 - missing_X present_Y = present_X if Y is X else ~missing_Y present_count = np.dot(present_X, present_Y.T) distances[present_count == 0] = np.nan # avoid divide by zero np.maximum(1, present_count, out=present_count) distances /= present_count distances *= X.shape[1] if not squared: np.sqrt(distances, out=distances) return distances def _euclidean_distances_upcast(X, XX=None, Y=None, YY=None, batch_size=None): """Euclidean distances between X and Y. Assumes X and Y have float32 dtype. Assumes XX and YY have float64 dtype or are None. X and Y are upcast to float64 by chunks, which size is chosen to limit memory increase by approximately 10% (at least 10MiB). """ n_samples_X = X.shape[0] n_samples_Y = Y.shape[0] n_features = X.shape[1] distances = np.empty((n_samples_X, n_samples_Y), dtype=np.float32) if batch_size is None: x_density = X.nnz / np.prod(X.shape) if issparse(X) else 1 y_density = Y.nnz / np.prod(Y.shape) if issparse(Y) else 1 # Allow 10% more memory than X, Y and the distance matrix take (at # least 10MiB) maxmem = max( ( (x_density * n_samples_X + y_density * n_samples_Y) * n_features + (x_density * n_samples_X * y_density * n_samples_Y) ) / 10, 10 * 2**17, ) # The increase amount of memory in 8-byte blocks is: # - x_density * batch_size * n_features (copy of chunk of X) # - y_density * batch_size * n_features (copy of chunk of Y) # - batch_size * batch_size (chunk of distance matrix) # Hence x² + (xd+yd)kx = M, where x=batch_size, k=n_features, M=maxmem # xd=x_density and yd=y_density tmp = (x_density + y_density) * n_features batch_size = (-tmp + np.sqrt(tmp**2 + 4 * maxmem)) / 2 batch_size = max(int(batch_size), 1) x_batches = gen_batches(n_samples_X, batch_size) for i, x_slice in enumerate(x_batches): X_chunk = X[x_slice].astype(np.float64) if XX is None: XX_chunk = row_norms(X_chunk, squared=True)[:, np.newaxis] else: XX_chunk = XX[x_slice] y_batches = gen_batches(n_samples_Y, batch_size) for j, y_slice in enumerate(y_batches): if X is Y and j < i: # when X is Y the distance matrix is symmetric so we only need # to compute half of it. d = distances[y_slice, x_slice].T else: Y_chunk = Y[y_slice].astype(np.float64) if YY is None: YY_chunk = row_norms(Y_chunk, squared=True)[np.newaxis, :] else: YY_chunk = YY[:, y_slice] d = -2 * safe_sparse_dot(X_chunk, Y_chunk.T, dense_output=True) d += XX_chunk d += YY_chunk distances[x_slice, y_slice] = d.astype(np.float32, copy=False) return distances def _argmin_min_reduce(dist, start): # `start` is specified in the signature but not used. This is because the higher # order `pairwise_distances_chunked` function needs reduction functions that are # passed as argument to have a two arguments signature. indices = dist.argmin(axis=1) values = dist[np.arange(dist.shape[0]), indices] return indices, values def _argmin_reduce(dist, start): # `start` is specified in the signature but not used. This is because the higher # order `pairwise_distances_chunked` function needs reduction functions that are # passed as argument to have a two arguments signature. return dist.argmin(axis=1) _VALID_METRICS = [ "euclidean", "l2", "l1", "manhattan", "cityblock", "braycurtis", "canberra", "chebyshev", "correlation", "cosine", "dice", "hamming", "jaccard", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "yule", "wminkowski", "nan_euclidean", "haversine", ] if sp_base_version < parse_version("1.11"): # pragma: no cover # Deprecated in SciPy 1.9 and removed in SciPy 1.11 _VALID_METRICS += ["kulsinski"] if sp_base_version < parse_version("1.9"): # Deprecated in SciPy 1.0 and removed in SciPy 1.9 _VALID_METRICS += ["matching"] _NAN_METRICS = ["nan_euclidean"] @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix"], "axis": [Options(Integral, {0, 1})], "metric": [ StrOptions(set(_VALID_METRICS).union(ArgKmin.valid_metrics())), callable, ], "metric_kwargs": [dict, None], }, prefer_skip_nested_validation=False, # metric is not validated yet ) def pairwise_distances_argmin_min( X, Y, *, axis=1, metric="euclidean", metric_kwargs=None ): """Compute minimum distances between one point and a set of points. This function computes for each row in X, the index of the row of Y which is closest (according to the specified distance). The minimal distances are also returned. This is mostly equivalent to calling: (pairwise_distances(X, Y=Y, metric=metric).argmin(axis=axis), pairwise_distances(X, Y=Y, metric=metric).min(axis=axis)) but uses much less memory, and is faster for large arrays. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) Array containing points. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features) Array containing points. axis : int, default=1 Axis along which the argmin and distances are to be computed. metric : str or callable, default='euclidean' Metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. Distance matrices are not supported. Valid values for metric are: - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan'] - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis','minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean','sokalmichener','sokalsneath','sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. .. note:: `'kulsinski'` is deprecated from SciPy 1.9 and will be removed in SciPy 1.11. .. note:: `'matching'` has been removed in SciPy 1.9 (use `'hamming'` instead). metric_kwargs : dict, default=None Keyword arguments to pass to specified metric function. Returns ------- argmin : ndarray Y[argmin[i], :] is the row in Y that is closest to X[i, :]. distances : ndarray The array of minimum distances. `distances[i]` is the distance between the i-th row in X and the argmin[i]-th row in Y. See Also -------- pairwise_distances : Distances between every pair of samples of X and Y. pairwise_distances_argmin : Same as `pairwise_distances_argmin_min` but only returns the argmins. """ X, Y = check_pairwise_arrays(X, Y) if axis == 0: X, Y = Y, X if metric_kwargs is None: metric_kwargs = {} if ArgKmin.is_usable_for(X, Y, metric): # This is an adaptor for one "sqeuclidean" specification. # For this backend, we can directly use "sqeuclidean". if metric_kwargs.get("squared", False) and metric == "euclidean": metric = "sqeuclidean" metric_kwargs = {} values, indices = ArgKmin.compute( X=X, Y=Y, k=1, metric=metric, metric_kwargs=metric_kwargs, strategy="auto", return_distance=True, ) values = values.flatten() indices = indices.flatten() else: # Joblib-based backend, which is used when user-defined callable # are passed for metric. # This won't be used in the future once PairwiseDistancesReductions support: # - DistanceMetrics which work on supposedly binary data # - CSR-dense and dense-CSR case if 'euclidean' in metric. # Turn off check for finiteness because this is costly and because arrays # have already been validated. with config_context(assume_finite=True): indices, values = zip( *pairwise_distances_chunked( X, Y, reduce_func=_argmin_min_reduce, metric=metric, **metric_kwargs ) ) indices = np.concatenate(indices) values = np.concatenate(values) return indices, values @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix"], "axis": [Options(Integral, {0, 1})], "metric": [ StrOptions(set(_VALID_METRICS).union(ArgKmin.valid_metrics())), callable, ], "metric_kwargs": [dict, None], }, prefer_skip_nested_validation=False, # metric is not validated yet ) def pairwise_distances_argmin(X, Y, *, axis=1, metric="euclidean", metric_kwargs=None): """Compute minimum distances between one point and a set of points. This function computes for each row in X, the index of the row of Y which is closest (according to the specified distance). This is mostly equivalent to calling: pairwise_distances(X, Y=Y, metric=metric).argmin(axis=axis) but uses much less memory, and is faster for large arrays. This function works with dense 2D arrays only. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) Array containing points. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features) Arrays containing points. axis : int, default=1 Axis along which the argmin and distances are to be computed. metric : str or callable, default="euclidean" Metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. Distance matrices are not supported. Valid values for metric are: - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan'] - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis','minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean','sokalmichener','sokalsneath','sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. .. note:: `'kulsinski'` is deprecated from SciPy 1.9 and will be removed in SciPy 1.11. .. note:: `'matching'` has been removed in SciPy 1.9 (use `'hamming'` instead). metric_kwargs : dict, default=None Keyword arguments to pass to specified metric function. Returns ------- argmin : numpy.ndarray Y[argmin[i], :] is the row in Y that is closest to X[i, :]. See Also -------- pairwise_distances : Distances between every pair of samples of X and Y. pairwise_distances_argmin_min : Same as `pairwise_distances_argmin` but also returns the distances. """ if metric_kwargs is None: metric_kwargs = {} X, Y = check_pairwise_arrays(X, Y) if axis == 0: X, Y = Y, X if metric_kwargs is None: metric_kwargs = {} if ArgKmin.is_usable_for(X, Y, metric): # This is an adaptor for one "sqeuclidean" specification. # For this backend, we can directly use "sqeuclidean". if metric_kwargs.get("squared", False) and metric == "euclidean": metric = "sqeuclidean" metric_kwargs = {} indices = ArgKmin.compute( X=X, Y=Y, k=1, metric=metric, metric_kwargs=metric_kwargs, strategy="auto", return_distance=False, ) indices = indices.flatten() else: # Joblib-based backend, which is used when user-defined callable # are passed for metric. # This won't be used in the future once PairwiseDistancesReductions support: # - DistanceMetrics which work on supposedly binary data # - CSR-dense and dense-CSR case if 'euclidean' in metric. # Turn off check for finiteness because this is costly and because arrays # have already been validated. with config_context(assume_finite=True): indices = np.concatenate( list( # This returns a np.ndarray generator whose arrays we need # to flatten into one. pairwise_distances_chunked( X, Y, reduce_func=_argmin_reduce, metric=metric, **metric_kwargs ) ) ) return indices @validate_params( {"X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None]}, prefer_skip_nested_validation=True, ) def haversine_distances(X, Y=None): """Compute the Haversine distance between samples in X and Y. The Haversine (or great circle) distance is the angular distance between two points on the surface of a sphere. The first coordinate of each point is assumed to be the latitude, the second is the longitude, given in radians. The dimension of the data must be 2. .. math:: D(x, y) = 2\\arcsin[\\sqrt{\\sin^2((x_{lat} - y_{lat}) / 2) + \\cos(x_{lat})\\cos(y_{lat})\\ sin^2((x_{lon} - y_{lon}) / 2)}] Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, 2) A feature array. Y : {array-like, sparse matrix} of shape (n_samples_Y, 2), default=None An optional second feature array. If `None`, uses `Y=X`. Returns ------- distance : ndarray of shape (n_samples_X, n_samples_Y) The distance matrix. Notes ----- As the Earth is nearly spherical, the haversine formula provides a good approximation of the distance between two points of the Earth surface, with a less than 1% error on average. Examples -------- We want to calculate the distance between the Ezeiza Airport (Buenos Aires, Argentina) and the Charles de Gaulle Airport (Paris, France). >>> from sklearn.metrics.pairwise import haversine_distances >>> from math import radians >>> bsas = [-34.83333, -58.5166646] >>> paris = [49.0083899664, 2.53844117956] >>> bsas_in_radians = [radians(_) for _ in bsas] >>> paris_in_radians = [radians(_) for _ in paris] >>> result = haversine_distances([bsas_in_radians, paris_in_radians]) >>> result * 6371000/1000 # multiply by Earth radius to get kilometers array([[ 0. , 11099.54035582], [11099.54035582, 0. ]]) """ from..metrics import DistanceMetric return DistanceMetric.get_metric("haversine").pairwise(X, Y) @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "sum_over_features": ["boolean", Hidden(StrOptions({"deprecated"}))], }, prefer_skip_nested_validation=True, ) def manhattan_distances(X, Y=None, *, sum_over_features="deprecated"): """Compute the L1 distances between the vectors in X and Y. With sum_over_features equal to False it returns the componentwise distances. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) An array where each row is a sample and each column is a feature. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None An array where each row is a sample and each column is a feature. If `None`, method uses `Y=X`. sum_over_features : bool, default=True If True the function returns the pairwise distance matrix else it returns the componentwise L1 pairwise-distances. Not supported for sparse matrix inputs. .. deprecated:: 1.2 ``sum_over_features`` was deprecated in version 1.2 and will be removed in 1.4. Returns ------- D : ndarray of shape (n_samples_X * n_samples_Y, n_features) or \ (n_samples_X, n_samples_Y) If sum_over_features is False shape is (n_samples_X * n_samples_Y, n_features) and D contains the componentwise L1 pairwise-distances (ie. absolute difference), else shape is (n_samples_X, n_samples_Y) and D contains the pairwise L1 distances. Notes ----- When X and/or Y are CSR sparse matrices and they are not already in canonical format, this function modifies them in-place to make them canonical. Examples -------- >>> from sklearn.metrics.pairwise import manhattan_distances >>> manhattan_distances([[3]], [[3]]) array([[0.]]) >>> manhattan_distances([[3]], [[2]]) array([[1.]]) >>> manhattan_distances([[2]], [[3]]) array([[1.]]) >>> manhattan_distances([[1, 2], [3, 4]],\ [[1, 2], [0, 3]]) array([[0., 2.], [4., 4.]]) """ # TODO(1.4): remove sum_over_features if sum_over_features!= "deprecated": warnings.warn( ( "`sum_over_features` is deprecated in version 1.2 and will be" " removed in version 1.4." ), FutureWarning, ) else: sum_over_features = True X, Y = check_pairwise_arrays(X, Y) if issparse(X) or issparse(Y): if not sum_over_features: raise TypeError( "sum_over_features=%r not supported for sparse matrices" % sum_over_features ) X = csr_matrix(X, copy=False) Y = csr_matrix(Y, copy=False) X.sum_duplicates() # this also sorts indices in-place Y.sum_duplicates() D = np.zeros((X.shape[0], Y.shape[0])) _sparse_manhattan(X.data, X.indices, X.indptr, Y.data, Y.indices, Y.indptr, D) return D if sum_over_features: return distance.cdist(X, Y, "cityblock") D = X[:, np.newaxis, :] - Y[np.newaxis, :, :] D = np.abs(D, D) return D.reshape((-1, X.shape[1])) @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], }, prefer_skip_nested_validation=True, ) def cosine_distances(X, Y=None): """Compute cosine distance between samples in X and Y. Cosine distance is defined as 1.0 minus the cosine similarity. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) Matrix `X`. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), \ default=None Matrix `Y`. Returns ------- distance matrix : ndarray of shape (n_samples_X, n_samples_Y) Returns the cosine distance between samples in X and Y. See Also -------- cosine_similarity : Compute cosine similarity between samples in X and Y. scipy.spatial.distance.cosine : Dense matrices only. """ # 1.0 - cosine_similarity(X, Y) without copy S = cosine_similarity(X, Y) S *= -1 S += 1 np.clip(S, 0, 2, out=S) if X is Y or Y is None: # Ensure that distances between vectors and themselves are set to 0.0. # This may not be the case due to floating point rounding errors. S[np.diag_indices_from(S)] = 0.0 return S # Paired distances @validate_params( {"X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix"]}, prefer_skip_nested_validation=True, ) def paired_euclidean_distances(X, Y): """Compute the paired euclidean distances between X and Y. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Input array/matrix X. Y : {array-like, sparse matrix} of shape (n_samples, n_features) Input array/matrix Y. Returns ------- distances : ndarray of shape (n_samples,) Output array/matrix containing the calculated paired euclidean distances. """ X, Y = check_paired_arrays(X, Y) return row_norms(X - Y) @validate_params( {"X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix"]}, prefer_skip_nested_validation=True, ) def paired_manhattan_distances(X, Y): """Compute the paired L1 distances between X and Y. Distances are calculated between (X[0], Y[0]), (X[1], Y[1]),..., (X[n_samples], Y[n_samples]). Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) An array-like where each row is a sample and each column is a feature. Y : {array-like, sparse matrix} of shape (n_samples, n_features) An array-like where each row is a sample and each column is a feature. Returns ------- distances : ndarray of shape (n_samples,) L1 paired distances between the row vectors of `X` and the row vectors of `Y`. Examples -------- >>> from sklearn.metrics.pairwise import paired_manhattan_distances >>> import numpy as np >>> X = np.array([[1, 1, 0], [0, 1, 0], [0, 0, 1]]) >>> Y = np.array([[0, 1, 0], [0, 0, 1], [0, 0, 0]]) >>> paired_manhattan_distances(X, Y) array([1., 2., 1.]) """ X, Y = check_paired_arrays(X, Y) diff = X - Y if issparse(diff): diff.data = np.abs(diff.data) return np.squeeze(np.array(diff.sum(axis=1))) else: return np.abs(diff).sum(axis=-1) @validate_params( {"X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix"]}, prefer_skip_nested_validation=True, ) def paired_cosine_distances(X, Y): """ Compute the paired cosine distances between X and Y. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) An array where each row is a sample and each column is a feature. Y : {array-like, sparse matrix} of shape (n_samples, n_features) An array where each row is a sample and each column is a feature. Returns ------- distances : ndarray of shape (n_samples,) Returns the distances between the row vectors of `X` and the row vectors of `Y`, where `distances[i]` is the distance between `X[i]` and `Y[i]`. Notes ----- The cosine distance is equivalent to the half the squared euclidean distance if each sample is normalized to unit norm. """ X, Y = check_paired_arrays(X, Y) return 0.5 * row_norms(normalize(X) - normalize(Y), squared=True) PAIRED_DISTANCES = { "cosine": paired_cosine_distances, "euclidean": paired_euclidean_distances, "l2": paired_euclidean_distances, "l1": paired_manhattan_distances, "manhattan": paired_manhattan_distances, "cityblock": paired_manhattan_distances, } @validate_params( { "X": ["array-like"], "Y": ["array-like"], "metric": [StrOptions(set(PAIRED_DISTANCES)), callable], }, prefer_skip_nested_validation=True, ) def paired_distances(X, Y, *, metric="euclidean", **kwds): """ Compute the paired distances between X and Y. Compute the distances between (X[0], Y[0]), (X[1], Y[1]), etc... Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : ndarray of shape (n_samples, n_features) Array 1 for distance computation. Y : ndarray of shape (n_samples, n_features) Array 2 for distance computation. metric : str or callable, default="euclidean" The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options specified in PAIRED_DISTANCES, including "euclidean", "manhattan", or "cosine". Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from `X` as input and return a value indicating the distance between them. **kwds : dict Unused parameters. Returns ------- distances : ndarray of shape (n_samples,) Returns the distances between the row vectors of `X` and the row vectors of `Y`. See Also -------- sklearn.metrics.pairwise_distances : Computes the distance between every pair of samples. Examples -------- >>> from sklearn.metrics.pairwise import paired_distances >>> X = [[0, 1], [1, 1]] >>> Y = [[0, 1], [2, 1]] >>> paired_distances(X, Y) array([0., 1.]) """ if metric in PAIRED_DISTANCES: func = PAIRED_DISTANCES[metric] return func(X, Y) elif callable(metric): # Check the matrix first (it is usually done by the metric) X, Y = check_paired_arrays(X, Y) distances = np.zeros(len(X)) for i in range(len(X)): distances[i] = metric(X[i], Y[i]) return distances # Kernels @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "dense_output": ["boolean"], }, prefer_skip_nested_validation=True, ) def linear_kernel(X, Y=None, dense_output=True): """ Compute the linear kernel between X and Y. Read more in the :ref:`User Guide <linear_kernel>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) A feature array. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None An optional second feature array. If `None`, uses `Y=X`. dense_output : bool, default=True Whether to return dense output even when the input is sparse. If ``False``, the output is sparse if both input arrays are sparse. .. versionadded:: 0.20 Returns ------- Gram matrix : ndarray of shape (n_samples_X, n_samples_Y) The Gram matrix of the linear kernel, i.e. `X @ Y.T`. """ X, Y = check_pairwise_arrays(X, Y) return safe_sparse_dot(X, Y.T, dense_output=dense_output) @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "degree": [Interval(Real, 1, None, closed="left")], "gamma": [ Interval(Real, 0, None, closed="left"), None, Hidden(np.ndarray), ], "coef0": [Interval(Real, None, None, closed="neither")], }, prefer_skip_nested_validation=True, ) def polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1): """ Compute the polynomial kernel between X and Y. K(X, Y) = (gamma <X, Y> + coef0) ^ degree Read more in the :ref:`User Guide <polynomial_kernel>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) A feature array. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None An optional second feature array. If `None`, uses `Y=X`. degree : float, default=3 Kernel degree. gamma : float, default=None Coefficient of the vector inner product. If None, defaults to 1.0 / n_features. coef0 : float, default=1 Constant offset added to scaled inner product. Returns ------- Gram matrix : ndarray of shape (n_samples_X, n_samples_Y) The polynomial kernel. """ X, Y = check_pairwise_arrays(X, Y) if gamma is None: gamma = 1.0 / X.shape[1] K = safe_sparse_dot(X, Y.T, dense_output=True) K *= gamma K += coef0 K **= degree return K @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "gamma": [ Interval(Real, 0, None, closed="left"), None, Hidden(np.ndarray), ], "coef0": [Interval(Real, None, None, closed="neither")], }, prefer_skip_nested_validation=True, ) def sigmoid_kernel(X, Y=None, gamma=None, coef0=1): """Compute the sigmoid kernel between X and Y. K(X, Y) = tanh(gamma <X, Y> + coef0) Read more in the :ref:`User Guide <sigmoid_kernel>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) A feature array. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None An optional second feature array. If `None`, uses `Y=X`. gamma : float, default=None Coefficient of the vector inner product. If None, defaults to 1.0 / n_features. coef0 : float, default=1 Constant offset added to scaled inner product. Returns ------- Gram matrix : ndarray of shape (n_samples_X, n_samples_Y) Sigmoid kernel between two arrays. """ X, Y = check_pairwise_arrays(X, Y) if gamma is None: gamma = 1.0 / X.shape[1] K = safe_sparse_dot(X, Y.T, dense_output=True) K *= gamma K += coef0 np.tanh(K, K) # compute tanh in-place return K @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "gamma": [ Interval(Real, 0, None, closed="left"), None, Hidden(np.ndarray), ], }, prefer_skip_nested_validation=True, ) def rbf_kernel(X, Y=None, gamma=None): """Compute the rbf (gaussian) kernel between X and Y. K(x, y) = exp(-gamma ||x-y||^2) for each pair of rows x in X and y in Y. Read more in the :ref:`User Guide <rbf_kernel>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) A feature array. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None An optional second feature array. If `None`, uses `Y=X`. gamma : float, default=None If None, defaults to 1.0 / n_features. Returns ------- kernel_matrix : ndarray of shape (n_samples_X, n_samples_Y) The RBF kernel. """ X, Y = check_pairwise_arrays(X, Y) if gamma is None: gamma = 1.0 / X.shape[1] K = euclidean_distances(X, Y, squared=True) K *= -gamma np.exp(K, K) # exponentiate K in-place return K @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "gamma": [ Interval(Real, 0, None, closed="neither"), Hidden(np.ndarray), None, ], }, prefer_skip_nested_validation=True, ) def laplacian_kernel(X, Y=None, gamma=None): """Compute the laplacian kernel between X and Y. The laplacian kernel is defined as:: K(x, y) = exp(-gamma ||x-y||_1) for each pair of rows x in X and y in Y. Read more in the :ref:`User Guide <laplacian_kernel>`. .. versionadded:: 0.17 Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) A feature array. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None An optional second feature array. If `None`, uses `Y=X`. gamma : float, default=None If None, defaults to 1.0 / n_features. Otherwise it should be strictly positive. Returns ------- kernel_matrix : ndarray of shape (n_samples_X, n_samples_Y) The kernel matrix. """ X, Y = check_pairwise_arrays(X, Y) if gamma is None: gamma = 1.0 / X.shape[1] K = -gamma * manhattan_distances(X, Y) np.exp(K, K) # exponentiate K in-place return K @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "dense_output": ["boolean"], }, prefer_skip_nested_validation=True, ) def cosine_similarity(X, Y=None, dense_output=True): """Compute cosine similarity between samples in X and Y. Cosine similarity, or the cosine kernel, computes similarity as the normalized dot product of X and Y: K(X, Y) = <X, Y> / (||X||*||Y||) On L2-normalized data, this function is equivalent to linear_kernel. Read more in the :ref:`User Guide <cosine_similarity>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_features) Input data. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), \ default=None Input data. If ``None``, the output will be the pairwise similarities between all samples in ``X``. dense_output : bool, default=True Whether to return dense output even when the input is sparse. If ``False``, the output is sparse if both input arrays are sparse. .. versionadded:: 0.17 parameter ``dense_output`` for dense output. Returns ------- kernel matrix : ndarray of shape (n_samples_X, n_samples_Y) Returns the cosine similarity between samples in X and Y. """ # to avoid recursive import X, Y = check_pairwise_arrays(X, Y) X_normalized = normalize(X, copy=True) if X is Y: Y_normalized = X_normalized else: Y_normalized = normalize(Y, copy=True) K = safe_sparse_dot(X_normalized, Y_normalized.T, dense_output=dense_output) return K @validate_params( {"X": ["array-like"], "Y": ["array-like", None]}, prefer_skip_nested_validation=True, ) def additive_chi2_kernel(X, Y=None): """Compute the additive chi-squared kernel between observations in X and Y. The chi-squared kernel is computed between each pair of rows in X and Y. X and Y have to be non-negative. This kernel is most commonly applied to histograms. The chi-squared kernel is given by:: k(x, y) = -Sum [(x - y)^2 / (x + y)] It can be interpreted as a weighted difference per entry. Read more in the :ref:`User Guide <chi2_kernel>`. Parameters ---------- X : array-like of shape (n_samples_X, n_features) A feature array. Y : array-like of shape (n_samples_Y, n_features), default=None An optional second feature array. If `None`, uses `Y=X`. Returns ------- kernel_matrix : ndarray of shape (n_samples_X, n_samples_Y) The kernel matrix. See Also -------- chi2_kernel : The exponentiated version of the kernel, which is usually preferable. sklearn.kernel_approximation.AdditiveChi2Sampler : A Fourier approximation to this kernel. Notes ----- As the negative of a distance, this kernel is only conditionally positive definite. References ---------- * Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C. Local features and kernels for classification of texture and object categories: A comprehensive study International Journal of Computer Vision 2007 https://hal.archives-ouvertes.fr/hal-00171412/document """ X, Y = check_pairwise_arrays(X, Y, accept_sparse=False) if (X < 0).any(): raise ValueError("X contains negative values.") if Y is not X and (Y < 0).any(): raise ValueError("Y contains negative values.") result = np.zeros((X.shape[0], Y.shape[0]), dtype=X.dtype) _chi2_kernel_fast(X, Y, result) return result @validate_params( { "X": ["array-like"], "Y": ["array-like", None], "gamma": [Interval(Real, 0, None, closed="neither"), Hidden(np.ndarray)], }, prefer_skip_nested_validation=True, ) def chi2_kernel(X, Y=None, gamma=1.0): """Compute the exponential chi-squared kernel between X and Y. The chi-squared kernel is computed between each pair of rows in X and Y. X and Y have to be non-negative. This kernel is most commonly applied to histograms. The chi-squared kernel is given by:: k(x, y) = exp(-gamma Sum [(x - y)^2 / (x + y)]) It can be interpreted as a weighted difference per entry. Read more in the :ref:`User Guide <chi2_kernel>`. Parameters ---------- X : array-like of shape (n_samples_X, n_features) A feature array. Y : array-like of shape (n_samples_Y, n_features), default=None An optional second feature array. If `None`, uses `Y=X`. gamma : float, default=1 Scaling parameter of the chi2 kernel. Returns ------- kernel_matrix : ndarray of shape (n_samples_X, n_samples_Y) The kernel matrix. See Also -------- additive_chi2_kernel : The additive version of this kernel. sklearn.kernel_approximation.AdditiveChi2Sampler : A Fourier approximation to the additive version of this kernel. References ---------- * Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C. Local features and kernels for classification of texture and object categories: A comprehensive study International Journal of Computer Vision 2007 https://hal.archives-ouvertes.fr/hal-00171412/document """ K = additive_chi2_kernel(X, Y) K *= gamma return np.exp(K, K) # Helper functions - distance PAIRWISE_DISTANCE_FUNCTIONS = { # If updating this dictionary, update the doc in both distance_metrics() # and also in pairwise_distances()! "cityblock": manhattan_distances, "cosine": cosine_distances, "euclidean": euclidean_distances, "haversine": haversine_distances, "l2": euclidean_distances, "l1": manhattan_distances, "manhattan": manhattan_distances, "precomputed": None, # HACK: precomputed is always allowed, never called "nan_euclidean": nan_euclidean_distances, } def distance_metrics(): """Valid metrics for pairwise_distances. This function simply returns the valid pairwise distance metrics. It exists to allow for a description of the mapping for each of the valid strings. The valid distance metrics, and the function they map to, are: =============== ======================================== metric Function =============== ======================================== 'cityblock' metrics.pairwise.manhattan_distances 'cosine' metrics.pairwise.cosine_distances 'euclidean' metrics.pairwise.euclidean_distances 'haversine' metrics.pairwise.haversine_distances 'l1' metrics.pairwise.manhattan_distances 'l2' metrics.pairwise.euclidean_distances 'manhattan' metrics.pairwise.manhattan_distances 'nan_euclidean' metrics.pairwise.nan_euclidean_distances =============== ======================================== Read more in the :ref:`User Guide <metrics>`. Returns ------- distance_metrics : dict Returns valid metrics for pairwise_distances. """ return PAIRWISE_DISTANCE_FUNCTIONS def _dist_wrapper(dist_func, dist_matrix, slice_, *args, **kwargs): """Write in-place to a slice of a distance matrix.""" dist_matrix[:, slice_] = dist_func(*args, **kwargs) def _parallel_pairwise(X, Y, func, n_jobs, **kwds): """Break the pairwise matrix in n_jobs even slices and compute them in parallel.""" if Y is None: Y = X X, Y, dtype = _return_float_dtype(X, Y) if effective_n_jobs(n_jobs) == 1: return func(X, Y, **kwds) # enforce a threading backend to prevent data communication overhead fd = delayed(_dist_wrapper) ret = np.empty((X.shape[0], Y.shape[0]), dtype=dtype, order="F") Parallel(backend="threading", n_jobs=n_jobs)( fd(func, ret, s, X, Y[s], **kwds) for s in gen_even_slices(_num_samples(Y), effective_n_jobs(n_jobs)) ) if (X is Y or Y is None) and func is euclidean_distances: # zeroing diagonal for euclidean norm. # TODO: do it also for other norms. np.fill_diagonal(ret, 0) return ret def _pairwise_callable(X, Y, metric, force_all_finite=True, **kwds): """Handle the callable case for pairwise_{distances,kernels}.""" X, Y = check_pairwise_arrays(X, Y, force_all_finite=force_all_finite) if X is Y: # Only calculate metric for upper triangle out = np.zeros((X.shape[0], Y.shape[0]), dtype="float") iterator = itertools.combinations(range(X.shape[0]), 2) for i, j in iterator: out[i, j] = metric(X[i], Y[j], **kwds) # Make symmetric # NB: out += out.T will produce incorrect results out = out + out.T # Calculate diagonal # NB: nonzero diagonals are allowed for both metrics and kernels for i in range(X.shape[0]): x = X[i] out[i, i] = metric(x, x, **kwds) else: # Calculate all cells out = np.empty((X.shape[0], Y.shape[0]), dtype="float") iterator = itertools.product(range(X.shape[0]), range(Y.shape[0])) for i, j in iterator: out[i, j] = metric(X[i], Y[j], **kwds) return out def _check_chunk_size(reduced, chunk_size): """Checks chunk is a sequence of expected size or a tuple of same.""" if reduced is None: return is_tuple = isinstance(reduced, tuple) if not is_tuple: reduced = (reduced,) if any(isinstance(r, tuple) or not hasattr(r, "__iter__") for r in reduced): raise TypeError( "reduce_func returned %r. Expected sequence(s) of length %d." % (reduced if is_tuple else reduced[0], chunk_size) ) if any(_num_samples(r)!= chunk_size for r in reduced): actual_size = tuple(_num_samples(r) for r in reduced) raise ValueError( "reduce_func returned object of length %s. " "Expected same length as input: %d." % (actual_size if is_tuple else actual_size[0], chunk_size) ) def _precompute_metric_params(X, Y, metric=None, **kwds): """Precompute data-derived metric parameters if not provided.""" if metric == "seuclidean" and "V" not in kwds: if X is Y: V = np.var(X, axis=0, ddof=1) else: raise ValueError( "The 'V' parameter is required for the seuclidean metric " "when Y is passed." ) return {"V": V} if metric == "mahalanobis" and "VI" not in kwds: if X is Y: VI = np.linalg.inv(np.cov(X.T)).T else: raise ValueError( "The 'VI' parameter is required for the mahalanobis metric " "when Y is passed." ) return {"VI": VI} return {} @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "reduce_func": [callable, None], "metric": [StrOptions({"precomputed"}.union(_VALID_METRICS)), callable], "n_jobs": [Integral, None], "working_memory": [Interval(Real, 0, None, closed="left"), None], }, prefer_skip_nested_validation=False, # metric is not validated yet ) def pairwise_distances_chunked( X, Y=None, *, reduce_func=None, metric="euclidean", n_jobs=None, working_memory=None, **kwds, ): """Generate a distance matrix chunk by chunk with optional reduction. In cases where not all of a pairwise distance matrix needs to be stored at once, this is used to calculate pairwise distances in ``working_memory``-sized chunks. If ``reduce_func`` is given, it is run on each chunk and its return values are concatenated into lists, arrays or sparse matrices. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_samples_X) or \ (n_samples_X, n_features) Array of pairwise distances between samples, or a feature array. The shape the array should be (n_samples_X, n_samples_X) if metric='precomputed' and (n_samples_X, n_features) otherwise. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None An optional second feature array. Only allowed if metric!= "precomputed". reduce_func : callable, default=None The function which is applied on each chunk of the distance matrix, reducing it to needed values. ``reduce_func(D_chunk, start)`` is called repeatedly, where ``D_chunk`` is a contiguous vertical slice of the pairwise distance matrix, starting at row ``start``. It should return one of: None; an array, a list, or a sparse matrix of length ``D_chunk.shape[0]``; or a tuple of such objects. Returning None is useful for in-place operations, rather than reductions. If None, pairwise_distances_chunked returns a generator of vertical chunks of the distance matrix. metric : str or callable, default='euclidean' The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. If metric is "precomputed", X is assumed to be a distance matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. n_jobs : int, default=None The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. working_memory : float, default=None The sought maximum memory for temporary distance matrix chunks. When None (default), the value of ``sklearn.get_config()['working_memory']`` is used. **kwds : optional keyword parameters Any further parameters are passed directly to the distance function. If using a scipy.spatial.distance metric, the parameters are still metric dependent. See the scipy docs for usage examples. Yields ------ D_chunk : {ndarray, sparse matrix} A contiguous slice of distance matrix, optionally processed by ``reduce_func``. Examples -------- Without reduce_func: >>> import numpy as np >>> from sklearn.metrics import pairwise_distances_chunked >>> X = np.random.RandomState(0).rand(5, 3) >>> D_chunk = next(pairwise_distances_chunked(X)) >>> D_chunk array([[0. ..., 0.29..., 0.41..., 0.19..., 0.57...], [0.29..., 0. ..., 0.57..., 0.41..., 0.76...], [0.41..., 0.57..., 0. ..., 0.44..., 0.90...], [0.19..., 0.41..., 0.44..., 0. ..., 0.51...], [0.57..., 0.76..., 0.90..., 0.51..., 0. ...]]) Retrieve all neighbors and average distance within radius r: >>> r =.2 >>> def reduce_func(D_chunk, start): ... neigh = [np.flatnonzero(d < r) for d in D_chunk] ... avg_dist = (D_chunk * (D_chunk < r)).mean(axis=1) ... return neigh, avg_dist >>> gen = pairwise_distances_chunked(X, reduce_func=reduce_func) >>> neigh, avg_dist = next(gen) >>> neigh [array([0, 3]), array([1]), array([2]), array([0, 3]), array([4])] >>> avg_dist array([0.039..., 0. , 0. , 0.039..., 0. ]) Where r is defined per sample, we need to make use of ``start``: >>> r = [.2,.4,.4,.3,.1] >>> def reduce_func(D_chunk, start): ... neigh = [np.flatnonzero(d < r[i]) ... for i, d in enumerate(D_chunk, start)] ... return neigh >>> neigh = next(pairwise_distances_chunked(X, reduce_func=reduce_func)) >>> neigh [array([0, 3]), array([0, 1]), array([2]), array([0, 3]), array([4])] Force row-by-row generation by reducing ``working_memory``: >>> gen = pairwise_distances_chunked(X, reduce_func=reduce_func, ... working_memory=0) >>> next(gen) [array([0, 3])] >>> next(gen) [array([0, 1])] """ n_samples_X = _num_samples(X) if metric == "precomputed": slices = (slice(0, n_samples_X),) else: if Y is None: Y = X # We get as many rows as possible within our working_memory budget to # store len(Y) distances in each row of output. # # Note: # - this will get at least 1 row, even if 1 row of distances will # exceed working_memory. # - this does not account for any temporary memory usage while # calculating distances (e.g. difference of vectors in manhattan # distance. chunk_n_rows = get_chunk_n_rows( row_bytes=8 * _num_samples(Y), max_n_rows=n_samples_X, working_memory=working_memory, ) slices = gen_batches(n_samples_X, chunk_n_rows) # precompute data-derived metric params params = _precompute_metric_params(X, Y, metric=metric, **kwds) kwds.update(**params) for sl in slices: if sl.start == 0 and sl.stop == n_samples_X: X_chunk = X # enable optimised paths for X is Y else: X_chunk = X[sl] D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds) if (X is Y or Y is None) and PAIRWISE_DISTANCE_FUNCTIONS.get( metric, None ) is euclidean_distances: # zeroing diagonal, taking care of aliases of "euclidean", # i.e. "l2" D_chunk.flat[sl.start :: _num_samples(X) + 1] = 0 if reduce_func is not None: chunk_size = D_chunk.shape[0] D_chunk = reduce_func(D_chunk, sl.start) _check_chunk_size(D_chunk, chunk_size) yield D_chunk @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "metric": [StrOptions(set(_VALID_METRICS) | {"precomputed"}), callable], "n_jobs": [Integral, None], "force_all_finite": ["boolean", StrOptions({"allow-nan"})], }, prefer_skip_nested_validation=True, ) def pairwise_distances( X, Y=None, metric="euclidean", *, n_jobs=None, force_all_finite=True, **kwds ): """Compute the distance matrix from a vector array X and optional Y. This method takes either a vector array or a distance matrix, and returns a distance matrix. If the input is a vector array, the distances are computed. If the input is a distances matrix, it is returned instead. This method provides a safe way to take a distance matrix as input, while preserving compatibility with many other algorithms that take a vector array. If Y is given (default is None), then the returned matrix is the pairwise distance between the arrays from both X and Y. Valid values for metric are: - From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan']. These metrics support sparse matrix inputs. ['nan_euclidean'] but it does not yet support sparse matrices. - From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski','mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao','seuclidean', 'sokalmichener','sokalsneath','sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. These metrics do not support sparse matrix inputs. .. note:: `'kulsinski'` is deprecated from SciPy 1.9 and will be removed in SciPy 1.11. .. note:: `'matching'` has been removed in SciPy 1.9 (use `'hamming'` instead). Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are valid scipy.spatial.distance metrics), the scikit-learn implementation will be used, which is faster and has support for sparse matrices (except for 'cityblock'). For a verbose description of the metrics from scikit-learn, see :func:`sklearn.metrics.pairwise.distance_metrics` function. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_samples_X) or \ (n_samples_X, n_features) Array of pairwise distances between samples, or a feature array. The shape of the array should be (n_samples_X, n_samples_X) if metric == "precomputed" and (n_samples_X, n_features) otherwise. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None An optional second feature array. Only allowed if metric!= "precomputed". metric : str or callable, default='euclidean' The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in ``pairwise.PAIRWISE_DISTANCE_FUNCTIONS``. If metric is "precomputed", X is assumed to be a distance matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. n_jobs : int, default=None The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. force_all_finite : bool or 'allow-nan', default=True Whether to raise an error on np.inf, np.nan, pd.NA in array. Ignored for a metric listed in ``pairwise.PAIRWISE_DISTANCE_FUNCTIONS``. The possibilities are: - True: Force all values of array to be finite. - False: accepts np.inf, np.nan, pd.NA in array. - 'allow-nan': accepts only np.nan and pd.NA values in array. Values cannot be infinite. .. versionadded:: 0.22 ``force_all_finite`` accepts the string ``'allow-nan'``. .. versionchanged:: 0.23 Accepts `pd.NA` and converts it into `np.nan`. **kwds : optional keyword parameters Any further parameters are passed directly to the distance function. If using a scipy.spatial.distance metric, the parameters are still metric dependent. See the scipy docs for usage examples. Returns ------- D : ndarray of shape (n_samples_X, n_samples_X) or \ (n_samples_X, n_samples_Y) A distance matrix D such that D_{i, j} is the distance between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then D_{i, j} is the distance between the ith array from X and the jth array from Y. See Also -------- pairwise_distances_chunked : Performs the same calculation as this function, but returns a generator of chunks of the distance matrix, in order to limit memory usage. sklearn.metrics.pairwise.paired_distances : Computes the distances between corresponding elements of two arrays. """ if metric == "precomputed": X, _ = check_pairwise_arrays( X, Y, precomputed=True, force_all_finite=force_all_finite ) whom = ( "`pairwise_distances`. Precomputed distance " " need to have non-negative values." ) check_non_negative(X, whom=whom) return X elif metric in PAIRWISE_DISTANCE_FUNCTIONS: func = PAIRWISE_DISTANCE_FUNCTIONS[metric] elif callable(metric): func = partial( _pairwise_callable, metric=metric, force_all_finite=force_all_finite, **kwds ) else: if issparse(X) or issparse(Y): raise TypeError("scipy distance metrics do not support sparse matrices.") dtype = bool if metric in PAIRWISE_BOOLEAN_FUNCTIONS else None if dtype == bool and (X.dtype!= bool or (Y is not None and Y.dtype!= bool)): msg = "Data was converted to boolean for metric %s" % metric warnings.warn(msg, DataConversionWarning) X, Y = check_pairwise_arrays( X, Y, dtype=dtype, force_all_finite=force_all_finite ) # precompute data-derived metric params params = _precompute_metric_params(X, Y, metric=metric, **kwds) kwds.update(**params) if effective_n_jobs(n_jobs) == 1 and X is Y: return distance.squareform(distance.pdist(X, metric=metric, **kwds)) func = partial(distance.cdist, metric=metric, **kwds) return _parallel_pairwise(X, Y, func, n_jobs, **kwds) # These distances require boolean arrays, when using scipy.spatial.distance PAIRWISE_BOOLEAN_FUNCTIONS = [ "dice", "jaccard", "rogerstanimoto", "russellrao", "sokalmichener", "sokalsneath", "yule", ] if sp_base_version < parse_version("1.11"): # Deprecated in SciPy 1.9 and removed in SciPy 1.11 PAIRWISE_BOOLEAN_FUNCTIONS += ["kulsinski"] if sp_base_version < parse_version("1.9"): # Deprecated in SciPy 1.0 and removed in SciPy 1.9 PAIRWISE_BOOLEAN_FUNCTIONS += ["matching"] # Helper functions - distance PAIRWISE_KERNEL_FUNCTIONS = { # If updating this dictionary, update the doc in both distance_metrics() # and also in pairwise_distances()! "additive_chi2": additive_chi2_kernel, "chi2": chi2_kernel, "linear": linear_kernel, "polynomial": polynomial_kernel, "poly": polynomial_kernel, "rbf": rbf_kernel, "laplacian": laplacian_kernel, "sigmoid": sigmoid_kernel, "cosine": cosine_similarity, } def kernel_metrics(): """Valid metrics for pairwise_kernels. This function simply returns the valid pairwise distance metrics. It exists, however, to allow for a verbose description of the mapping for each of the valid strings. The valid distance metrics, and the function they map to, are: =============== ======================================== metric Function =============== ======================================== 'additive_chi2' sklearn.pairwise.additive_chi2_kernel 'chi2' sklearn.pairwise.chi2_kernel 'linear' sklearn.pairwise.linear_kernel 'poly' sklearn.pairwise.polynomial_kernel 'polynomial' sklearn.pairwise.polynomial_kernel 'rbf' sklearn.pairwise.rbf_kernel 'laplacian' sklearn.pairwise.laplacian_kernel 'sigmoid' sklearn.pairwise.sigmoid_kernel 'cosine' sklearn.pairwise.cosine_similarity =============== ======================================== Read more in the :ref:`User Guide <metrics>`. Returns ------- kernel_metrics : dict Returns valid metrics for pairwise_kernels. """ return PAIRWISE_KERNEL_FUNCTIONS KERNEL_PARAMS = { "additive_chi2": (), "chi2": frozenset(["gamma"]), "cosine": (), "linear": (), "poly": frozenset(["gamma", "degree", "coef0"]), "polynomial": frozenset(["gamma", "degree", "coef0"]), "rbf": frozenset(["gamma"]), "laplacian": frozenset(["gamma"]), "sigmoid": frozenset(["gamma", "coef0"]), } @validate_params( { "X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix", None], "metric": [ StrOptions(set(PAIRWISE_KERNEL_FUNCTIONS) | {"precomputed"}), callable, ], "filter_params": ["boolean"], "n_jobs": [Integral, None], }, prefer_skip_nested_validation=True, ) def pairwise_kernels( X, Y=None, metric="linear", *, filter_params=False, n_jobs=None, **kwds ): """Compute the kernel between arrays X and optional array Y. This method takes either a vector array or a kernel matrix, and returns a kernel matrix. If the input is a vector array, the kernels are computed. If the input is a kernel matrix, it is returned instead. This method provides a safe way to take a kernel matrix as input, while preserving compatibility with many other algorithms that take a vector array. If Y is given (default is None), then the returned matrix is the pairwise kernel between the arrays from both X and Y. Valid values for metric are: ['additive_chi2', 'chi2', 'linear', 'poly', 'polynomial', 'rbf', 'laplacian','sigmoid', 'cosine'] Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples_X, n_samples_X) or \ (n_samples_X, n_features) Array of pairwise kernels between samples, or a feature array. The shape of the array should be (n_samples_X, n_samples_X) if metric == "precomputed" and (n_samples_X, n_features) otherwise. Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None A second feature array only if X has shape (n_samples_X, n_features). metric : str or callable, default="linear" The metric to use when calculating kernel between instances in a feature array. If metric is a string, it must be one of the metrics in ``pairwise.PAIRWISE_KERNEL_FUNCTIONS``. If metric is "precomputed", X is assumed to be a kernel matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two rows from X as input and return the corresponding kernel value as a single number. This means that callables from :mod:`sklearn.metrics.pairwise` are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead. filter_params : bool, default=False Whether to filter invalid parameters or not. n_jobs : int, default=None The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. **kwds : optional keyword parameters Any further parameters are passed directly to the kernel function. Returns ------- K : ndarray of shape (n_samples_X, n_samples_X) or (n_samples_X, n_samples_Y) A kernel matrix K such that K_{i, j} is the kernel between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then K_{i, j} is the kernel between the ith array from X and the jth array from Y. Notes ----- If metric is 'precomputed', Y is ignored and X is returned. """ # import GPKernel locally to prevent circular imports from..gaussian_process.kernels import Kernel as GPKernel if metric == "precomputed": X, _ = check_pairwise_arrays(X, Y, precomputed=True) return X elif isinstance(metric, GPKernel): func = metric.__call__ elif metric in PAIRWISE_KERNEL_FUNCTIONS: if filter_params: kwds = {k: kwds[k] for k in kwds if k in KERNEL_PARAMS[metric]} func = PAIRWISE_KERNEL_FUNCTIONS[metric] elif callable(metric): func = partial(_pairwise_callable, metric=metric, **kwds) return _parallel_pairwise(X, Y, func, n_jobs, **kwds)

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naive_bayes.rst

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scikit-learn__scikit-learn/doc/modules/naive_bayes.rst

Naive Bayes Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes' theorem with the "naive" assumption of conditional independence between every pair of features given the value of the class variable. Bayes' theorem states the following relationship, given class variable y and dependent feature vector x₁ through x_(n), : $$P(y \mid x_1, \dots, x_n) = \frac{P(y) P(x_1, \dots, x_n \mid y)} {P(x_1, \dots, x_n)}$$ Using the naive conditional independence assumption that P(x_(i)|y,x₁,…,x_(i − 1),x_(i + 1),…,x_(n)) = P(x_(i)|y), for all i, this relationship is simplified to $$P(y \mid x_1, \dots, x_n) = \frac{P(y) \prod_{i=1}^{n} P(x_i \mid y)} {P(x_1, \dots, x_n)}$$ Since P(x₁,…,x_(n)) is constant given the input, we can use the following classification rule: $$P(y \mid x_1, \dots, x_n) \propto P(y) \prod_{i=1}^{n} P(x_i \mid y)$$ ⇓ $$\hat{y} = \arg\max_y P(y) \prod_{i=1}^{n} P(x_i \mid y),$$ and we can use Maximum A Posteriori (MAP) estimation to estimate P(y) and P(x_(i)∣y); the former is then the relative frequency of class y in the training set. The different naive Bayes classifiers differ mainly by the assumptions they make regarding the distribution of P(x_(i)∣y). In spite of their apparently over-simplified assumptions, naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They require a small amount of training data to estimate the necessary parameters. (For theoretical reasons why naive Bayes works well, and on which types of data it does, see the references below.) Naive Bayes learners and classifiers can be extremely fast compared to more sophisticated methods. The decoupling of the class conditional feature distributions means that each distribution can be independently estimated as a one dimensional distribution. This in turn helps to alleviate problems stemming from the curse of dimensionality. On the flip side, although naive Bayes is known as a decent classifier, it is known to be a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously. Gaussian Naive Bayes GaussianNB implements the Gaussian Naive Bayes algorithm for classification. The likelihood of the features is assumed to be Gaussian: $$P(x_i \mid y) = \frac{1}{\sqrt{2\pi\sigma^2_y}} \exp\left(-\frac{(x_i - \mu_y)^2}{2\sigma^2_y}\right)$$ The parameters σ_(y) and μ_(y) are estimated using maximum likelihood. >>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import train_test_split >>> from sklearn.naive_bayes import GaussianNB >>> X, y = load_iris(return_X_y=True) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0) >>> gnb = GaussianNB() >>> y_pred = gnb.fit(X_train, y_train).predict(X_test) >>> print("Number of mislabeled points out of a total %d points : %d" ... % (X_test.shape[0], (y_test != y_pred).sum())) Number of mislabeled points out of a total 75 points : 4 Multinomial Naive Bayes MultinomialNB implements the naive Bayes algorithm for multinomially distributed data, and is one of the two classic naive Bayes variants used in text classification (where the data are typically represented as word vector counts, although tf-idf vectors are also known to work well in practice). The distribution is parametrized by vectors θ_(y) = (θ_(y1),…,θ_(yn)) for each class y, where n is the number of features (in text classification, the size of the vocabulary) and θ_(yi) is the probability P(x_(i)∣y) of feature i appearing in a sample belonging to class y. The parameters θ_(y) is estimated by a smoothed version of maximum likelihood, i.e. relative frequency counting: $$\hat{\theta}_{yi} = \frac{ N_{yi} + \alpha}{N_y + \alpha n}$$ where N_(yi) = ∑_(x ∈ T)x_(i) is the number of times feature i appears in a sample of class y in the training set T, and $N_{y} = \sum_{i=1}^{n} N_{yi}$ is the total count of all features for class y. The smoothing priors α ≥ 0 accounts for features not present in the learning samples and prevents zero probabilities in further computations. Setting α = 1 is called Laplace smoothing, while α < 1 is called Lidstone smoothing. Complement Naive Bayes ComplementNB implements the complement naive Bayes (CNB) algorithm. CNB is an adaptation of the standard multinomial naive Bayes (MNB) algorithm that is particularly suited for imbalanced data sets. Specifically, CNB uses statistics from the complement of each class to compute the model's weights. The inventors of CNB show empirically that the parameter estimates for CNB are more stable than those for MNB. Further, CNB regularly outperforms MNB (often by a considerable margin) on text classification tasks. The procedure for calculating the weights is as follows: $$\hat{\theta}_{ci} = \frac{\alpha_i + \sum_{j:y_j \neq c} d_{ij}} {\alpha + \sum_{j:y_j \neq c} \sum_{k} d_{kj}}$$ w_(ci) = log θ̂_(ci) $$w_{ci} = \frac{w_{ci}}{\sum_{j} |w_{cj}|}$$ where the summations are over all documents j not in class c, d_(ij) is either the count or tf-idf value of term i in document j, α_(i) is a smoothing hyperparameter like that found in MNB, and α = ∑_(i)α_(i). The second normalization addresses the tendency for longer documents to dominate parameter estimates in MNB. The classification rule is: ĉ = arg min_(c)∑_(i)t_(i)w_(ci) i.e., a document is assigned to the class that is the poorest complement match. Bernoulli Naive Bayes BernoulliNB implements the naive Bayes training and classification algorithms for data that is distributed according to multivariate Bernoulli distributions; i.e., there may be multiple features but each one is assumed to be a binary-valued (Bernoulli, boolean) variable. Therefore, this class requires samples to be represented as binary-valued feature vectors; if handed any other kind of data, a BernoulliNB instance may binarize its input (depending on the binarize parameter). The decision rule for Bernoulli naive Bayes is based on P(x_(i)∣y) = P(x_(i)=1∣y)x_(i) + (1−P(x_(i)=1∣y))(1−x_(i)) which differs from multinomial NB's rule in that it explicitly penalizes the non-occurrence of a feature i that is an indicator for class y, where the multinomial variant would simply ignore a non-occurring feature. In the case of text classification, word occurrence vectors (rather than word count vectors) may be used to train and use this classifier. BernoulliNB might perform better on some datasets, especially those with shorter documents. It is advisable to evaluate both models, if time permits. References: - C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 234-265. - A. McCallum and K. Nigam (1998). A comparison of event models for Naive Bayes text classification. Proc. AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48. - V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with Naive Bayes -- Which Naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS). Categorical Naive Bayes CategoricalNB implements the categorical naive Bayes algorithm for categorically distributed data. It assumes that each feature, which is described by the index i, has its own categorical distribution. For each feature i in the training set X, CategoricalNB estimates a categorical distribution for each feature i of X conditioned on the class y. The index set of the samples is defined as J = {1, …, m}, with m as the number of samples. The probability of category t in feature i given class c is estimated as: $$P(x_i = t \mid y = c \: ;\, \alpha) = \frac{ N_{tic} + \alpha}{N_{c} + \alpha n_i},$$ where N_(tic) = |{j∈J∣x_(ij)=t,y_(j)=c}| is the number of times category t appears in the samples x_(i), which belong to class c, N_(c) = |{j∈J∣y_(j)=c}| is the number of samples with class c, α is a smoothing parameter and n_(i) is the number of available categories of feature i. CategoricalNB assumes that the sample matrix X is encoded (for instance with the help of ~sklearn.preprocessing.OrdinalEncoder) such that all categories for each feature i are represented with numbers 0, ..., n_(i) − 1 where n_(i) is the number of available categories of feature i. Out-of-core naive Bayes model fitting Naive Bayes models can be used to tackle large scale classification problems for which the full training set might not fit in memory. To handle this case, MultinomialNB, BernoulliNB, and GaussianNB expose a partial_fit method that can be used incrementally as done with other classifiers as demonstrated in sphx_glr_auto_examples_applications_plot_out_of_core_classification.py. All naive Bayes classifiers support sample weighting. Contrary to the fit method, the first call to partial_fit needs to be passed the list of all the expected class labels. For an overview of available strategies in scikit-learn, see also the out-of-core learning <scaling_strategies> documentation. Note The partial_fit method call of naive Bayes models introduces some computational overhead. It is recommended to use data chunk sizes that are as large as possible, that is as the available RAM allows.

""" The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These are supervised learning methods based on applying Bayes' theorem with strong (naive) feature independence assumptions. """ # Author: Vincent Michel <vincent.michel@inria.fr> # Minor fixes by Fabian Pedregosa # Amit Aides <amitibo@tx.technion.ac.il> # Yehuda Finkelstein <yehudaf@tx.technion.ac.il> # Lars Buitinck # Jan Hendrik Metzen <jhm@informatik.uni-bremen.de> # (parts based on earlier work by Mathieu Blondel) # # License: BSD 3 clause import warnings from abc import ABCMeta, abstractmethod from numbers import Integral, Real import numpy as np from scipy.special import logsumexp from.base import BaseEstimator, ClassifierMixin, _fit_context from.preprocessing import LabelBinarizer, binarize, label_binarize from.utils._param_validation import Hidden, Interval, StrOptions from.utils.extmath import safe_sparse_dot from.utils.multiclass import _check_partial_fit_first_call from.utils.validation import _check_sample_weight, check_is_fitted, check_non_negative __all__ = [ "BernoulliNB", "GaussianNB", "MultinomialNB", "ComplementNB", "CategoricalNB", ] class _BaseNB(ClassifierMixin, BaseEstimator, metaclass=ABCMeta): """Abstract base class for naive Bayes estimators""" @abstractmethod def _joint_log_likelihood(self, X): """Compute the unnormalized posterior log probability of X I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of shape (n_samples, n_classes). Public methods predict, predict_proba, predict_log_proba, and predict_joint_log_proba pass the input through _check_X before handing it over to _joint_log_likelihood. The term "joint log likelihood" is used interchangibly with "joint log probability". """ @abstractmethod def _check_X(self, X): """To be overridden in subclasses with the actual checks. Only used in predict* methods. """ def predict_joint_log_proba(self, X): """Return joint log probability estimates for the test vector X. For each row x of X and class y, the joint log probability is given by ``log P(x, y) = log P(y) + log P(x|y),`` where ``log P(y)`` is the class prior probability and ``log P(x|y)`` is the class-conditional probability. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- C : ndarray of shape (n_samples, n_classes) Returns the joint log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`. """ check_is_fitted(self) X = self._check_X(X) return self._joint_log_likelihood(X) def predict(self, X): """ Perform classification on an array of test vectors X. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- C : ndarray of shape (n_samples,) Predicted target values for X. """ check_is_fitted(self) X = self._check_X(X) jll = self._joint_log_likelihood(X) return self.classes_[np.argmax(jll, axis=1)] def predict_log_proba(self, X): """ Return log-probability estimates for the test vector X. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- C : array-like of shape (n_samples, n_classes) Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`. """ check_is_fitted(self) X = self._check_X(X) jll = self._joint_log_likelihood(X) # normalize by P(x) = P(f_1,..., f_n) log_prob_x = logsumexp(jll, axis=1) return jll - np.atleast_2d(log_prob_x).T def predict_proba(self, X): """ Return probability estimates for the test vector X. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- C : array-like of shape (n_samples, n_classes) Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`. """ return np.exp(self.predict_log_proba(X)) class GaussianNB(_BaseNB): """ Gaussian Naive Bayes (GaussianNB). Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque: http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf Read more in the :ref:`User Guide <gaussian_naive_bayes>`. Parameters ---------- priors : array-like of shape (n_classes,), default=None Prior probabilities of the classes. If specified, the priors are not adjusted according to the data. var_smoothing : float, default=1e-9 Portion of the largest variance of all features that is added to variances for calculation stability. .. versionadded:: 0.20 Attributes ---------- class_count_ : ndarray of shape (n_classes,) number of training samples observed in each class. class_prior_ : ndarray of shape (n_classes,) probability of each class. classes_ : ndarray of shape (n_classes,) class labels known to the classifier. epsilon_ : float absolute additive value to variances. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 var_ : ndarray of shape (n_classes, n_features) Variance of each feature per class. .. versionadded:: 1.0 theta_ : ndarray of shape (n_classes, n_features) mean of each feature per class. See Also -------- BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models. CategoricalNB : Naive Bayes classifier for categorical features. ComplementNB : Complement Naive Bayes classifier. MultinomialNB : Naive Bayes classifier for multinomial models. Examples -------- >>> import numpy as np >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> Y = np.array([1, 1, 1, 2, 2, 2]) >>> from sklearn.naive_bayes import GaussianNB >>> clf = GaussianNB() >>> clf.fit(X, Y) GaussianNB() >>> print(clf.predict([[-0.8, -1]])) [1] >>> clf_pf = GaussianNB() >>> clf_pf.partial_fit(X, Y, np.unique(Y)) GaussianNB() >>> print(clf_pf.predict([[-0.8, -1]])) [1] """ _parameter_constraints: dict = { "priors": ["array-like", None], "var_smoothing": [Interval(Real, 0, None, closed="left")], } def __init__(self, *, priors=None, var_smoothing=1e-9): self.priors = priors self.var_smoothing = var_smoothing @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y, sample_weight=None): """Fit Gaussian Naive Bayes according to X, y. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). .. versionadded:: 0.17 Gaussian Naive Bayes supports fitting with *sample_weight*. Returns ------- self : object Returns the instance itself. """ y = self._validate_data(y=y) return self._partial_fit( X, y, np.unique(y), _refit=True, sample_weight=sample_weight ) def _check_X(self, X): """Validate X, used only in predict* methods.""" return self._validate_data(X, reset=False) @staticmethod def _update_mean_variance(n_past, mu, var, X, sample_weight=None): """Compute online update of Gaussian mean and variance. Given starting sample count, mean, and variance, a new set of points X, and optionally sample weights, return the updated mean and variance. (NB - each dimension (column) in X is treated as independent -- you get variance, not covariance). Can take scalar mean and variance, or vector mean and variance to simultaneously update a number of independent Gaussians. See Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque: http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf Parameters ---------- n_past : int Number of samples represented in old mean and variance. If sample weights were given, this should contain the sum of sample weights represented in old mean and variance. mu : array-like of shape (number of Gaussians,) Means for Gaussians in original set. var : array-like of shape (number of Gaussians,) Variances for Gaussians in original set. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- total_mu : array-like of shape (number of Gaussians,) Updated mean for each Gaussian over the combined set. total_var : array-like of shape (number of Gaussians,) Updated variance for each Gaussian over the combined set. """ if X.shape[0] == 0: return mu, var # Compute (potentially weighted) mean and variance of new datapoints if sample_weight is not None: n_new = float(sample_weight.sum()) if np.isclose(n_new, 0.0): return mu, var new_mu = np.average(X, axis=0, weights=sample_weight) new_var = np.average((X - new_mu) ** 2, axis=0, weights=sample_weight) else: n_new = X.shape[0] new_var = np.var(X, axis=0) new_mu = np.mean(X, axis=0) if n_past == 0: return new_mu, new_var n_total = float(n_past + n_new) # Combine mean of old and new data, taking into consideration # (weighted) number of observations total_mu = (n_new * new_mu + n_past * mu) / n_total # Combine variance of old and new data, taking into consideration # (weighted) number of observations. This is achieved by combining # the sum-of-squared-differences (ssd) old_ssd = n_past * var new_ssd = n_new * new_var total_ssd = old_ssd + new_ssd + (n_new * n_past / n_total) * (mu - new_mu) ** 2 total_var = total_ssd / n_total return total_mu, total_var @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, X, y, classes=None, sample_weight=None): """Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning. This is especially useful when the whole dataset is too big to fit in memory at once. This method has some performance and numerical stability overhead, hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). .. versionadded:: 0.17 Returns ------- self : object Returns the instance itself. """ return self._partial_fit( X, y, classes, _refit=False, sample_weight=sample_weight ) def _partial_fit(self, X, y, classes=None, _refit=False, sample_weight=None): """Actual implementation of Gaussian NB fitting. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls. _refit : bool, default=False If true, act as though this were the first time we called _partial_fit (ie, throw away any past fitting and start over). sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- self : object """ if _refit: self.classes_ = None first_call = _check_partial_fit_first_call(self, classes) X, y = self._validate_data(X, y, reset=first_call) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X) # If the ratio of data variance between dimensions is too small, it # will cause numerical errors. To address this, we artificially # boost the variance by epsilon, a small fraction of the standard # deviation of the largest dimension. self.epsilon_ = self.var_smoothing * np.var(X, axis=0).max() if first_call: # This is the first call to partial_fit: # initialize various cumulative counters n_features = X.shape[1] n_classes = len(self.classes_) self.theta_ = np.zeros((n_classes, n_features)) self.var_ = np.zeros((n_classes, n_features)) self.class_count_ = np.zeros(n_classes, dtype=np.float64) # Initialise the class prior # Take into account the priors if self.priors is not None: priors = np.asarray(self.priors) # Check that the provided prior matches the number of classes if len(priors)!= n_classes: raise ValueError("Number of priors must match number of classes.") # Check that the sum is 1 if not np.isclose(priors.sum(), 1.0): raise ValueError("The sum of the priors should be 1.") # Check that the priors are non-negative if (priors < 0).any(): raise ValueError("Priors must be non-negative.") self.class_prior_ = priors else: # Initialize the priors to zeros for each class self.class_prior_ = np.zeros(len(self.classes_), dtype=np.float64) else: if X.shape[1]!= self.theta_.shape[1]: msg = "Number of features %d does not match previous data %d." raise ValueError(msg % (X.shape[1], self.theta_.shape[1])) # Put epsilon back in each time self.var_[:, :] -= self.epsilon_ classes = self.classes_ unique_y = np.unique(y) unique_y_in_classes = np.isin(unique_y, classes) if not np.all(unique_y_in_classes): raise ValueError( "The target label(s) %s in y do not exist in the initial classes %s" % (unique_y[~unique_y_in_classes], classes) ) for y_i in unique_y: i = classes.searchsorted(y_i) X_i = X[y == y_i, :] if sample_weight is not None: sw_i = sample_weight[y == y_i] N_i = sw_i.sum() else: sw_i = None N_i = X_i.shape[0] new_theta, new_sigma = self._update_mean_variance( self.class_count_[i], self.theta_[i, :], self.var_[i, :], X_i, sw_i ) self.theta_[i, :] = new_theta self.var_[i, :] = new_sigma self.class_count_[i] += N_i self.var_[:, :] += self.epsilon_ # Update if only no priors is provided if self.priors is None: # Empirical prior, with sample_weight taken into account self.class_prior_ = self.class_count_ / self.class_count_.sum() return self def _joint_log_likelihood(self, X): joint_log_likelihood = [] for i in range(np.size(self.classes_)): jointi = np.log(self.class_prior_[i]) n_ij = -0.5 * np.sum(np.log(2.0 * np.pi * self.var_[i, :])) n_ij -= 0.5 * np.sum(((X - self.theta_[i, :]) ** 2) / (self.var_[i, :]), 1) joint_log_likelihood.append(jointi + n_ij) joint_log_likelihood = np.array(joint_log_likelihood).T return joint_log_likelihood class _BaseDiscreteNB(_BaseNB): """Abstract base class for naive Bayes on discrete/categorical data Any estimator based on this class should provide: __init__ _joint_log_likelihood(X) as per _BaseNB _update_feature_log_prob(alpha) _count(X, Y) """ _parameter_constraints: dict = { "alpha": [Interval(Real, 0, None, closed="left"), "array-like"], "fit_prior": ["boolean"], "class_prior": ["array-like", None], "force_alpha": ["boolean", Hidden(StrOptions({"warn"}))], } def __init__(self, alpha=1.0, fit_prior=True, class_prior=None, force_alpha="warn"): self.alpha = alpha self.fit_prior = fit_prior self.class_prior = class_prior self.force_alpha = force_alpha @abstractmethod def _count(self, X, Y): """Update counts that are used to calculate probabilities. The counts make up a sufficient statistic extracted from the data. Accordingly, this method is called each time `fit` or `partial_fit` update the model. `class_count_` and `feature_count_` must be updated here along with any model specific counts. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The input samples. Y : ndarray of shape (n_samples, n_classes) Binarized class labels. """ @abstractmethod def _update_feature_log_prob(self, alpha): """Update feature log probabilities based on counts. This method is called each time `fit` or `partial_fit` update the model. Parameters ---------- alpha : float smoothing parameter. See :meth:`_check_alpha`. """ def _check_X(self, X): """Validate X, used only in predict* methods.""" return self._validate_data(X, accept_sparse="csr", reset=False) def _check_X_y(self, X, y, reset=True): """Validate X and y in fit methods.""" return self._validate_data(X, y, accept_sparse="csr", reset=reset) def _update_class_log_prior(self, class_prior=None): """Update class log priors. The class log priors are based on `class_prior`, class count or the number of classes. This method is called each time `fit` or `partial_fit` update the model. """ n_classes = len(self.classes_) if class_prior is not None: if len(class_prior)!= n_classes: raise ValueError("Number of priors must match number of classes.") self.class_log_prior_ = np.log(class_prior) elif self.fit_prior: with warnings.catch_warnings(): # silence the warning when count is 0 because class was not yet # observed warnings.simplefilter("ignore", RuntimeWarning) log_class_count = np.log(self.class_count_) # empirical prior, with sample_weight taken into account self.class_log_prior_ = log_class_count - np.log(self.class_count_.sum()) else: self.class_log_prior_ = np.full(n_classes, -np.log(n_classes)) def _check_alpha(self): alpha = ( np.asarray(self.alpha) if not isinstance(self.alpha, Real) else self.alpha ) alpha_min = np.min(alpha) if isinstance(alpha, np.ndarray): if not alpha.shape[0] == self.n_features_in_: raise ValueError( "When alpha is an array, it should contains `n_features`. " f"Got {alpha.shape[0]} elements instead of {self.n_features_in_}." ) # check that all alpha are positive if alpha_min < 0: raise ValueError("All values in alpha must be greater than 0.") alpha_lower_bound = 1e-10 # TODO(1.4): Replace w/ deprecation of self.force_alpha # See gh #22269 _force_alpha = self.force_alpha if _force_alpha == "warn" and alpha_min < alpha_lower_bound: _force_alpha = False warnings.warn( ( "The default value for `force_alpha` will change to `True` in 1.4." " To suppress this warning, manually set the value of" " `force_alpha`." ), FutureWarning, ) if alpha_min < alpha_lower_bound and not _force_alpha: warnings.warn( "alpha too small will result in numeric errors, setting alpha =" f" {alpha_lower_bound:.1e}. Use `force_alpha=True` to keep alpha" " unchanged." ) return np.maximum(alpha, alpha_lower_bound) return alpha @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, X, y, classes=None, sample_weight=None): """Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning. This is especially useful when the whole dataset is too big to fit in memory at once. This method has some performance overhead hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- self : object Returns the instance itself. """ first_call = not hasattr(self, "classes_") X, y = self._check_X_y(X, y, reset=first_call) _, n_features = X.shape if _check_partial_fit_first_call(self, classes): # This is the first call to partial_fit: # initialize various cumulative counters n_classes = len(classes) self._init_counters(n_classes, n_features) Y = label_binarize(y, classes=self.classes_) if Y.shape[1] == 1: if len(self.classes_) == 2: Y = np.concatenate((1 - Y, Y), axis=1) else: # degenerate case: just one class Y = np.ones_like(Y) if X.shape[0]!= Y.shape[0]: msg = "X.shape[0]=%d and y.shape[0]=%d are incompatible." raise ValueError(msg % (X.shape[0], y.shape[0])) # label_binarize() returns arrays with dtype=np.int64. # We convert it to np.float64 to support sample_weight consistently Y = Y.astype(np.float64, copy=False) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X) sample_weight = np.atleast_2d(sample_weight) Y *= sample_weight.T class_prior = self.class_prior # Count raw events from data before updating the class log prior # and feature log probas self._count(X, Y) # XXX: OPTIM: we could introduce a public finalization method to # be called by the user explicitly just once after several consecutive # calls to partial_fit and prior any call to predict[_[log_]proba] # to avoid computing the smooth log probas at each call to partial fit alpha = self._check_alpha() self._update_feature_log_prob(alpha) self._update_class_log_prior(class_prior=class_prior) return self @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y, sample_weight=None): """Fit Naive Bayes classifier according to X, y. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- self : object Returns the instance itself. """ X, y = self._check_X_y(X, y) _, n_features = X.shape labelbin = LabelBinarizer() Y = labelbin.fit_transform(y) self.classes_ = labelbin.classes_ if Y.shape[1] == 1: if len(self.classes_) == 2: Y = np.concatenate((1 - Y, Y), axis=1) else: # degenerate case: just one class Y = np.ones_like(Y) # LabelBinarizer().fit_transform() returns arrays with dtype=np.int64. # We convert it to np.float64 to support sample_weight consistently; # this means we also don't have to cast X to floating point if sample_weight is not None: Y = Y.astype(np.float64, copy=False) sample_weight = _check_sample_weight(sample_weight, X) sample_weight = np.atleast_2d(sample_weight) Y *= sample_weight.T class_prior = self.class_prior # Count raw events from data before updating the class log prior # and feature log probas n_classes = Y.shape[1] self._init_counters(n_classes, n_features) self._count(X, Y) alpha = self._check_alpha() self._update_feature_log_prob(alpha) self._update_class_log_prior(class_prior=class_prior) return self def _init_counters(self, n_classes, n_features): self.class_count_ = np.zeros(n_classes, dtype=np.float64) self.feature_count_ = np.zeros((n_classes, n_features), dtype=np.float64) def _more_tags(self): return {"poor_score": True} class MultinomialNB(_BaseDiscreteNB): """ Naive Bayes classifier for multinomial models. The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work. Read more in the :ref:`User Guide <multinomial_naive_bayes>`. Parameters ---------- alpha : float or array-like of shape (n_features,), default=1.0 Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing). force_alpha : bool, default=False If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0. .. versionadded:: 1.2 .. deprecated:: 1.2 The default value of `force_alpha` will change to `True` in v1.4. fit_prior : bool, default=True Whether to learn class prior probabilities or not. If false, a uniform prior will be used. class_prior : array-like of shape (n_classes,), default=None Prior probabilities of the classes. If specified, the priors are not adjusted according to the data. Attributes ---------- class_count_ : ndarray of shape (n_classes,) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided. class_log_prior_ : ndarray of shape (n_classes,) Smoothed empirical log probability for each class. classes_ : ndarray of shape (n_classes,) Class labels known to the classifier feature_count_ : ndarray of shape (n_classes, n_features) Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided. feature_log_prob_ : ndarray of shape (n_classes, n_features) Empirical log probability of features given a class, ``P(x_i|y)``. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models. CategoricalNB : Naive Bayes classifier for categorical features. ComplementNB : Complement Naive Bayes classifier. GaussianNB : Gaussian Naive Bayes. References ---------- C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 234-265. https://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html Examples -------- >>> import numpy as np >>> rng = np.random.RandomState(1) >>> X = rng.randint(5, size=(6, 100)) >>> y = np.array([1, 2, 3, 4, 5, 6]) >>> from sklearn.naive_bayes import MultinomialNB >>> clf = MultinomialNB(force_alpha=True) >>> clf.fit(X, y) MultinomialNB(force_alpha=True) >>> print(clf.predict(X[2:3])) [3] """ def __init__( self, *, alpha=1.0, force_alpha="warn", fit_prior=True, class_prior=None ): super().__init__( alpha=alpha, fit_prior=fit_prior, class_prior=class_prior, force_alpha=force_alpha, ) def _more_tags(self): return {"requires_positive_X": True} def _count(self, X, Y): """Count and smooth feature occurrences.""" check_non_negative(X, "MultinomialNB (input X)") self.feature_count_ += safe_sparse_dot(Y.T, X) self.class_count_ += Y.sum(axis=0) def _update_feature_log_prob(self, alpha): """Apply smoothing to raw counts and recompute log probabilities""" smoothed_fc = self.feature_count_ + alpha smoothed_cc = smoothed_fc.sum(axis=1) self.feature_log_prob_ = np.log(smoothed_fc) - np.log( smoothed_cc.reshape(-1, 1) ) def _joint_log_likelihood(self, X): """Calculate the posterior log probability of the samples X""" return safe_sparse_dot(X, self.feature_log_prob_.T) + self.class_log_prior_ class ComplementNB(_BaseDiscreteNB): """The Complement Naive Bayes classifier described in Rennie et al. (2003). The Complement Naive Bayes classifier was designed to correct the "severe assumptions" made by the standard Multinomial Naive Bayes classifier. It is particularly suited for imbalanced data sets. Read more in the :ref:`User Guide <complement_naive_bayes>`. .. versionadded:: 0.20 Parameters ---------- alpha : float or array-like of shape (n_features,), default=1.0 Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing). force_alpha : bool, default=False If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0. .. versionadded:: 1.2 .. deprecated:: 1.2 The default value of `force_alpha` will change to `True` in v1.4. fit_prior : bool, default=True Only used in edge case with a single class in the training set. class_prior : array-like of shape (n_classes,), default=None Prior probabilities of the classes. Not used. norm : bool, default=False Whether or not a second normalization of the weights is performed. The default behavior mirrors the implementations found in Mahout and Weka, which do not follow the full algorithm described in Table 9 of the paper. Attributes ---------- class_count_ : ndarray of shape (n_classes,) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided. class_log_prior_ : ndarray of shape (n_classes,) Smoothed empirical log probability for each class. Only used in edge case with a single class in the training set. classes_ : ndarray of shape (n_classes,) Class labels known to the classifier feature_all_ : ndarray of shape (n_features,) Number of samples encountered for each feature during fitting. This value is weighted by the sample weight when provided. feature_count_ : ndarray of shape (n_classes, n_features) Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided. feature_log_prob_ : ndarray of shape (n_classes, n_features) Empirical weights for class complements. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models. CategoricalNB : Naive Bayes classifier for categorical features. GaussianNB : Gaussian Naive Bayes. MultinomialNB : Naive Bayes classifier for multinomial models. References ---------- Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of naive bayes text classifiers. In ICML (Vol. 3, pp. 616-623). https://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf Examples -------- >>> import numpy as np >>> rng = np.random.RandomState(1) >>> X = rng.randint(5, size=(6, 100)) >>> y = np.array([1, 2, 3, 4, 5, 6]) >>> from sklearn.naive_bayes import ComplementNB >>> clf = ComplementNB(force_alpha=True) >>> clf.fit(X, y) ComplementNB(force_alpha=True) >>> print(clf.predict(X[2:3])) [3] """ _parameter_constraints: dict = { **_BaseDiscreteNB._parameter_constraints, "norm": ["boolean"], } def __init__( self, *, alpha=1.0, force_alpha="warn", fit_prior=True, class_prior=None, norm=False, ): super().__init__( alpha=alpha, force_alpha=force_alpha, fit_prior=fit_prior, class_prior=class_prior, ) self.norm = norm def _more_tags(self): return {"requires_positive_X": True} def _count(self, X, Y): """Count feature occurrences.""" check_non_negative(X, "ComplementNB (input X)") self.feature_count_ += safe_sparse_dot(Y.T, X) self.class_count_ += Y.sum(axis=0) self.feature_all_ = self.feature_count_.sum(axis=0) def _update_feature_log_prob(self, alpha): """Apply smoothing to raw counts and compute the weights.""" comp_count = self.feature_all_ + alpha - self.feature_count_ logged = np.log(comp_count / comp_count.sum(axis=1, keepdims=True)) # _BaseNB.predict uses argmax, but ComplementNB operates with argmin. if self.norm: summed = logged.sum(axis=1, keepdims=True) feature_log_prob = logged / summed else: feature_log_prob = -logged self.feature_log_prob_ = feature_log_prob def _joint_log_likelihood(self, X): """Calculate the class scores for the samples in X.""" jll = safe_sparse_dot(X, self.feature_log_prob_.T) if len(self.classes_) == 1: jll += self.class_log_prior_ return jll class BernoulliNB(_BaseDiscreteNB): """Naive Bayes classifier for multivariate Bernoulli models. Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features. Read more in the :ref:`User Guide <bernoulli_naive_bayes>`. Parameters ---------- alpha : float or array-like of shape (n_features,), default=1.0 Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing). force_alpha : bool, default=False If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0. .. versionadded:: 1.2 .. deprecated:: 1.2 The default value of `force_alpha` will change to `True` in v1.4. binarize : float or None, default=0.0 Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors. fit_prior : bool, default=True Whether to learn class prior probabilities or not. If false, a uniform prior will be used. class_prior : array-like of shape (n_classes,), default=None Prior probabilities of the classes. If specified, the priors are not adjusted according to the data. Attributes ---------- class_count_ : ndarray of shape (n_classes,) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided. class_log_prior_ : ndarray of shape (n_classes,) Log probability of each class (smoothed). classes_ : ndarray of shape (n_classes,) Class labels known to the classifier feature_count_ : ndarray of shape (n_classes, n_features) Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided. feature_log_prob_ : ndarray of shape (n_classes, n_features) Empirical log probability of features given a class, P(x_i|y). n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- CategoricalNB : Naive Bayes classifier for categorical features. ComplementNB : The Complement Naive Bayes classifier described in Rennie et al. (2003). GaussianNB : Gaussian Naive Bayes (GaussianNB). MultinomialNB : Naive Bayes classifier for multinomial models. References ---------- C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 234-265. https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html A. McCallum and K. Nigam (1998). A comparison of event models for naive Bayes text classification. Proc. AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48. V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS). Examples -------- >>> import numpy as np >>> rng = np.random.RandomState(1) >>> X = rng.randint(5, size=(6, 100)) >>> Y = np.array([1, 2, 3, 4, 4, 5]) >>> from sklearn.naive_bayes import BernoulliNB >>> clf = BernoulliNB(force_alpha=True) >>> clf.fit(X, Y) BernoulliNB(force_alpha=True) >>> print(clf.predict(X[2:3])) [3] """ _parameter_constraints: dict = { **_BaseDiscreteNB._parameter_constraints, "binarize": [None, Interval(Real, 0, None, closed="left")], } def __init__( self, *, alpha=1.0, force_alpha="warn", binarize=0.0, fit_prior=True, class_prior=None, ): super().__init__( alpha=alpha, fit_prior=fit_prior, class_prior=class_prior, force_alpha=force_alpha, ) self.binarize = binarize def _check_X(self, X): """Validate X, used only in predict* methods.""" X = super()._check_X(X) if self.binarize is not None: X = binarize(X, threshold=self.binarize) return X def _check_X_y(self, X, y, reset=True): X, y = super()._check_X_y(X, y, reset=reset) if self.binarize is not None: X = binarize(X, threshold=self.binarize) return X, y def _count(self, X, Y): """Count and smooth feature occurrences.""" self.feature_count_ += safe_sparse_dot(Y.T, X) self.class_count_ += Y.sum(axis=0) def _update_feature_log_prob(self, alpha): """Apply smoothing to raw counts and recompute log probabilities""" smoothed_fc = self.feature_count_ + alpha smoothed_cc = self.class_count_ + alpha * 2 self.feature_log_prob_ = np.log(smoothed_fc) - np.log( smoothed_cc.reshape(-1, 1) ) def _joint_log_likelihood(self, X): """Calculate the posterior log probability of the samples X""" n_features = self.feature_log_prob_.shape[1] n_features_X = X.shape[1] if n_features_X!= n_features: raise ValueError( "Expected input with %d features, got %d instead" % (n_features, n_features_X) ) neg_prob = np.log(1 - np.exp(self.feature_log_prob_)) # Compute neg_prob · (1 - X).T as ∑neg_prob - X · neg_prob jll = safe_sparse_dot(X, (self.feature_log_prob_ - neg_prob).T) jll += self.class_log_prior_ + neg_prob.sum(axis=1) return jll class CategoricalNB(_BaseDiscreteNB): """Naive Bayes classifier for categorical features. The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution. Read more in the :ref:`User Guide <categorical_naive_bayes>`. Parameters ---------- alpha : float, default=1.0 Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing). force_alpha : bool, default=False If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0. .. versionadded:: 1.2 .. deprecated:: 1.2 The default value of `force_alpha` will change to `True` in v1.4. fit_prior : bool, default=True Whether to learn class prior probabilities or not. If false, a uniform prior will be used. class_prior : array-like of shape (n_classes,), default=None Prior probabilities of the classes. If specified, the priors are not adjusted according to the data. min_categories : int or array-like of shape (n_features,), default=None Minimum number of categories per feature. - integer: Sets the minimum number of categories per feature to `n_categories` for each features. - array-like: shape (n_features,) where `n_categories[i]` holds the minimum number of categories for the ith column of the input. - None (default): Determines the number of categories automatically from the training data. .. versionadded:: 0.24 Attributes ---------- category_count_ : list of arrays of shape (n_features,) Holds arrays of shape (n_classes, n_categories of respective feature) for each feature. Each array provides the number of samples encountered for each class and category of the specific feature. class_count_ : ndarray of shape (n_classes,) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided. class_log_prior_ : ndarray of shape (n_classes,) Smoothed empirical log probability for each class. classes_ : ndarray of shape (n_classes,) Class labels known to the classifier feature_log_prob_ : list of arrays of shape (n_features,) Holds arrays of shape (n_classes, n_categories of respective feature) for each feature. Each array provides the empirical log probability of categories given the respective feature and class, ``P(x_i|y)``. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_categories_ : ndarray of shape (n_features,), dtype=np.int64 Number of categories for each feature. This value is inferred from the data or set by the minimum number of categories. .. versionadded:: 0.24 See Also -------- BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models. ComplementNB : Complement Naive Bayes classifier. GaussianNB : Gaussian Naive Bayes. MultinomialNB : Naive Bayes classifier for multinomial models. Examples -------- >>> import numpy as np >>> rng = np.random.RandomState(1) >>> X = rng.randint(5, size=(6, 100)) >>> y = np.array([1, 2, 3, 4, 5, 6]) >>> from sklearn.naive_bayes import CategoricalNB >>> clf = CategoricalNB(force_alpha=True) >>> clf.fit(X, y) CategoricalNB(force_alpha=True) >>> print(clf.predict(X[2:3])) [3] """ _parameter_constraints: dict = { **_BaseDiscreteNB._parameter_constraints, "min_categories": [ None, "array-like", Interval(Integral, 1, None, closed="left"), ], "alpha": [Interval(Real, 0, None, closed="left")], } def __init__( self, *, alpha=1.0, force_alpha="warn", fit_prior=True, class_prior=None, min_categories=None, ): super().__init__( alpha=alpha, force_alpha=force_alpha, fit_prior=fit_prior, class_prior=class_prior, ) self.min_categories = min_categories def fit(self, X, y, sample_weight=None): """Fit Naive Bayes classifier according to X, y. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0,..., n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- self : object Returns the instance itself. """ return super().fit(X, y, sample_weight=sample_weight) def partial_fit(self, X, y, classes=None, sample_weight=None): """Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning. This is especially useful when the whole dataset is too big to fit in memory at once. This method has some performance overhead hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0,..., n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder. y : array-like of shape (n_samples,) Target values. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls. sample_weight : array-like of shape (n_samples,), default=None Weights applied to individual samples (1. for unweighted). Returns ------- self : object Returns the instance itself. """ return super().partial_fit(X, y, classes, sample_weight=sample_weight) def _more_tags(self): return {"requires_positive_X": True} def _check_X(self, X): """Validate X, used only in predict* methods.""" X = self._validate_data( X, dtype="int", accept_sparse=False, force_all_finite=True, reset=False ) check_non_negative(X, "CategoricalNB (input X)") return X def _check_X_y(self, X, y, reset=True): X, y = self._validate_data( X, y, dtype="int", accept_sparse=False, force_all_finite=True, reset=reset ) check_non_negative(X, "CategoricalNB (input X)") return X, y def _init_counters(self, n_classes, n_features): self.class_count_ = np.zeros(n_classes, dtype=np.float64) self.category_count_ = [np.zeros((n_classes, 0)) for _ in range(n_features)] @staticmethod def _validate_n_categories(X, min_categories): # rely on max for n_categories categories are encoded between 0...n-1 n_categories_X = X.max(axis=0) + 1 min_categories_ = np.array(min_categories) if min_categories is not None: if not np.issubdtype(min_categories_.dtype, np.signedinteger): raise ValueError( "'min_categories' should have integral type. Got " f"{min_categories_.dtype} instead." ) n_categories_ = np.maximum(n_categories_X, min_categories_, dtype=np.int64) if n_categories_.shape!= n_categories_X.shape: raise ValueError( f"'min_categories' should have shape ({X.shape[1]}," ") when an array-like is provided. Got" f" {min_categories_.shape} instead." ) return n_categories_ else: return n_categories_X def _count(self, X, Y): def _update_cat_count_dims(cat_count, highest_feature): diff = highest_feature + 1 - cat_count.shape[1] if diff > 0: # we append a column full of zeros for each new category return np.pad(cat_count, [(0, 0), (0, diff)], "constant") return cat_count def _update_cat_count(X_feature, Y, cat_count, n_classes): for j in range(n_classes): mask = Y[:, j].astype(bool) if Y.dtype.type == np.int64: weights = None else: weights = Y[mask, j] counts = np.bincount(X_feature[mask], weights=weights) indices = np.nonzero(counts)[0] cat_count[j, indices] += counts[indices] self.class_count_ += Y.sum(axis=0) self.n_categories_ = self._validate_n_categories(X, self.min_categories) for i in range(self.n_features_in_): X_feature = X[:, i] self.category_count_[i] = _update_cat_count_dims( self.category_count_[i], self.n_categories_[i] - 1 ) _update_cat_count( X_feature, Y, self.category_count_[i], self.class_count_.shape[0] ) def _update_feature_log_prob(self, alpha): feature_log_prob = [] for i in range(self.n_features_in_): smoothed_cat_count = self.category_count_[i] + alpha smoothed_class_count = smoothed_cat_count.sum(axis=1) feature_log_prob.append( np.log(smoothed_cat_count) - np.log(smoothed_class_count.reshape(-1, 1)) ) self.feature_log_prob_ = feature_log_prob def _joint_log_likelihood(self, X): self._check_n_features(X, reset=False) jll = np.zeros((X.shape[0], self.class_count_.shape[0])) for i in range(self.n_features_in_): indices = X[:, i] jll += self.feature_log_prob_[i][:, indices].T total_ll = jll + self.class_log_prior_ return total_ll

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random_projection.rst

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scikit-learn__scikit-learn/doc/modules/random_projection.rst

Random Projection The sklearn.random_projection module implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional variance) for faster processing times and smaller model sizes. This module implements two types of unstructured random matrix: Gaussian random matrix <gaussian_random_matrix> and sparse random matrix <sparse_random_matrix>. The dimensions and distribution of random projections matrices are controlled so as to preserve the pairwise distances between any two samples of the dataset. Thus random projection is a suitable approximation technique for distance based method. The Johnson-Lindenstrauss lemma The main theoretical result behind the efficiency of random projection is the Johnson-Lindenstrauss lemma (quoting Wikipedia): In mathematics, the Johnson-Lindenstrauss lemma is a result concerning low-distortion embeddings of points from high-dimensional into low-dimensional Euclidean space. The lemma states that a small set of points in a high-dimensional space can be embedded into a space of much lower dimension in such a way that distances between the points are nearly preserved. The map used for the embedding is at least Lipschitz, and can even be taken to be an orthogonal projection. Knowing only the number of samples, the johnson_lindenstrauss_min_dim estimates conservatively the minimal size of the random subspace to guarantee a bounded distortion introduced by the random projection: >>> from sklearn.random_projection import johnson_lindenstrauss_min_dim >>> johnson_lindenstrauss_min_dim(n_samples=1e6, eps=0.5) 663 >>> johnson_lindenstrauss_min_dim(n_samples=1e6, eps=[0.5, 0.1, 0.01]) array([ 663, 11841, 1112658]) >>> johnson_lindenstrauss_min_dim(n_samples=[1e4, 1e5, 1e6], eps=0.1) array([ 7894, 9868, 11841]) Gaussian random projection The GaussianRandomProjection reduces the dimensionality by projecting the original input space on a randomly generated matrix where components are drawn from the following distribution $N(0, \frac{1}{n_{components}})$. Here a small excerpt which illustrates how to use the Gaussian random projection transformer: >>> import numpy as np >>> from sklearn import random_projection >>> X = np.random.rand(100, 10000) >>> transformer = random_projection.GaussianRandomProjection() >>> X_new = transformer.fit_transform(X) >>> X_new.shape (100, 3947) Sparse random projection The SparseRandomProjection reduces the dimensionality by projecting the original input space using a sparse random matrix. Sparse random matrices are an alternative to dense Gaussian random projection matrix that guarantees similar embedding quality while being much more memory efficient and allowing faster computation of the projected data. If we define s = 1 / density, the elements of the random matrix are drawn from $$\begin{aligned} \left\{ \begin{array}{c c l} -\sqrt{\frac{s}{n_{\text{components}}}} & & 1 / 2s\\ 0 &\text{with probability} & 1 - 1 / s \\ +\sqrt{\frac{s}{n_{\text{components}}}} & & 1 / 2s\\ \end{array} \right. \end{aligned}$$ where n_(components) is the size of the projected subspace. By default the density of non zero elements is set to the minimum density as recommended by Ping Li et al.: $1 / \sqrt{n_{\text{features}}}$. Here a small excerpt which illustrates how to use the sparse random projection transformer: >>> import numpy as np >>> from sklearn import random_projection >>> X = np.random.rand(100, 10000) >>> transformer = random_projection.SparseRandomProjection() >>> X_new = transformer.fit_transform(X) >>> X_new.shape (100, 3947) Inverse Transform The random projection transformers have compute_inverse_components parameter. When set to True, after creating the random components_ matrix during fitting, the transformer computes the pseudo-inverse of this matrix and stores it as inverse_components_. The inverse_components_ matrix has shape n_(features) × n_(components), and it is always a dense matrix, regardless of whether the components matrix is sparse or dense. So depending on the number of features and components, it may use a lot of memory. When the inverse_transform method is called, it computes the product of the input X and the transpose of the inverse components. If the inverse components have been computed during fit, they are reused at each call to inverse_transform. Otherwise they are recomputed each time, which can be costly. The result is always dense, even if X is sparse. Here a small code example which illustrates how to use the inverse transform feature: >>> import numpy as np >>> from sklearn.random_projection import SparseRandomProjection >>> X = np.random.rand(100, 10000) >>> transformer = SparseRandomProjection( ... compute_inverse_components=True ... ) ... >>> X_new = transformer.fit_transform(X) >>> X_new.shape (100, 3947) >>> X_new_inversed = transformer.inverse_transform(X_new) >>> X_new_inversed.shape (100, 10000) >>> X_new_again = transformer.transform(X_new_inversed) >>> np.allclose(X_new, X_new_again) True

"""Random Projection transformers. Random Projections are a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional variance) for faster processing times and smaller model sizes. The dimensions and distribution of Random Projections matrices are controlled so as to preserve the pairwise distances between any two samples of the dataset. The main theoretical result behind the efficiency of random projection is the `Johnson-Lindenstrauss lemma (quoting Wikipedia) <https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma>`_: In mathematics, the Johnson-Lindenstrauss lemma is a result concerning low-distortion embeddings of points from high-dimensional into low-dimensional Euclidean space. The lemma states that a small set of points in a high-dimensional space can be embedded into a space of much lower dimension in such a way that distances between the points are nearly preserved. The map used for the embedding is at least Lipschitz, and can even be taken to be an orthogonal projection. """ # Authors: Olivier Grisel <olivier.grisel@ensta.org>, # Arnaud Joly <a.joly@ulg.ac.be> # License: BSD 3 clause import warnings from abc import ABCMeta, abstractmethod from numbers import Integral, Real import numpy as np import scipy.sparse as sp from scipy import linalg from.base import ( BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context, ) from.exceptions import DataDimensionalityWarning from.utils import check_random_state from.utils._param_validation import Interval, StrOptions, validate_params from.utils.extmath import safe_sparse_dot from.utils.random import sample_without_replacement from.utils.validation import check_array, check_is_fitted __all__ = [ "SparseRandomProjection", "GaussianRandomProjection", "johnson_lindenstrauss_min_dim", ] @validate_params( { "n_samples": ["array-like", Interval(Real, 1, None, closed="left")], "eps": ["array-like", Interval(Real, 0, 1, closed="neither")], }, prefer_skip_nested_validation=True, ) def johnson_lindenstrauss_min_dim(n_samples, *, eps=0.1): """Find a'safe' number of components to randomly project to. The distortion introduced by a random projection `p` only changes the distance between two points by a factor (1 +- eps) in a euclidean space with good probability. The projection `p` is an eps-embedding as defined by: (1 - eps) ||u - v||^2 < ||p(u) - p(v)||^2 < (1 + eps) ||u - v||^2 Where u and v are any rows taken from a dataset of shape (n_samples, n_features), eps is in ]0, 1[ and p is a projection by a random Gaussian N(0, 1) matrix of shape (n_components, n_features) (or a sparse Achlioptas matrix). The minimum number of components to guarantee the eps-embedding is given by: n_components >= 4 log(n_samples) / (eps^2 / 2 - eps^3 / 3) Note that the number of dimensions is independent of the original number of features but instead depends on the size of the dataset: the larger the dataset, the higher is the minimal dimensionality of an eps-embedding. Read more in the :ref:`User Guide <johnson_lindenstrauss>`. Parameters ---------- n_samples : int or array-like of int Number of samples that should be an integer greater than 0. If an array is given, it will compute a safe number of components array-wise. eps : float or array-like of shape (n_components,), dtype=float, \ default=0.1 Maximum distortion rate in the range (0, 1) as defined by the Johnson-Lindenstrauss lemma. If an array is given, it will compute a safe number of components array-wise. Returns ------- n_components : int or ndarray of int The minimal number of components to guarantee with good probability an eps-embedding with n_samples. References ---------- .. [1] https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma .. [2] `Sanjoy Dasgupta and Anupam Gupta, 1999, "An elementary proof of the Johnson-Lindenstrauss Lemma." <https://citeseerx.ist.psu.edu/doc_view/pid/95cd464d27c25c9c8690b378b894d337cdf021f9>`_ Examples -------- >>> from sklearn.random_projection import johnson_lindenstrauss_min_dim >>> johnson_lindenstrauss_min_dim(1e6, eps=0.5) 663 >>> johnson_lindenstrauss_min_dim(1e6, eps=[0.5, 0.1, 0.01]) array([ 663, 11841, 1112658]) >>> johnson_lindenstrauss_min_dim([1e4, 1e5, 1e6], eps=0.1) array([ 7894, 9868, 11841]) """ eps = np.asarray(eps) n_samples = np.asarray(n_samples) if np.any(eps <= 0.0) or np.any(eps >= 1): raise ValueError("The JL bound is defined for eps in ]0, 1[, got %r" % eps) if np.any(n_samples <= 0): raise ValueError( "The JL bound is defined for n_samples greater than zero, got %r" % n_samples ) denominator = (eps**2 / 2) - (eps**3 / 3) return (4 * np.log(n_samples) / denominator).astype(np.int64) def _check_density(density, n_features): """Factorize density check according to Li et al.""" if density == "auto": density = 1 / np.sqrt(n_features) elif density <= 0 or density > 1: raise ValueError("Expected density in range ]0, 1], got: %r" % density) return density def _check_input_size(n_components, n_features): """Factorize argument checking for random matrix generation.""" if n_components <= 0: raise ValueError( "n_components must be strictly positive, got %d" % n_components ) if n_features <= 0: raise ValueError("n_features must be strictly positive, got %d" % n_features) def _gaussian_random_matrix(n_components, n_features, random_state=None): """Generate a dense Gaussian random matrix. The components of the random matrix are drawn from N(0, 1.0 / n_components). Read more in the :ref:`User Guide <gaussian_random_matrix>`. Parameters ---------- n_components : int, Dimensionality of the target projection space. n_features : int, Dimensionality of the original source space. random_state : int, RandomState instance or None, default=None Controls the pseudo random number generator used to generate the matrix at fit time. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. Returns ------- components : ndarray of shape (n_components, n_features) The generated Gaussian random matrix. See Also -------- GaussianRandomProjection """ _check_input_size(n_components, n_features) rng = check_random_state(random_state) components = rng.normal( loc=0.0, scale=1.0 / np.sqrt(n_components), size=(n_components, n_features) ) return components def _sparse_random_matrix(n_components, n_features, density="auto", random_state=None): """Generalized Achlioptas random sparse matrix for random projection. Setting density to 1 / 3 will yield the original matrix by Dimitris Achlioptas while setting a lower value will yield the generalization by Ping Li et al. If we note :math:`s = 1 / density`, the components of the random matrix are drawn from: - -sqrt(s) / sqrt(n_components) with probability 1 / 2s - 0 with probability 1 - 1 / s - +sqrt(s) / sqrt(n_components) with probability 1 / 2s Read more in the :ref:`User Guide <sparse_random_matrix>`. Parameters ---------- n_components : int, Dimensionality of the target projection space. n_features : int, Dimensionality of the original source space. density : float or 'auto', default='auto' Ratio of non-zero component in the random projection matrix in the range `(0, 1]` If density = 'auto', the value is set to the minimum density as recommended by Ping Li et al.: 1 / sqrt(n_features). Use density = 1 / 3.0 if you want to reproduce the results from Achlioptas, 2001. random_state : int, RandomState instance or None, default=None Controls the pseudo random number generator used to generate the matrix at fit time. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. Returns ------- components : {ndarray, sparse matrix} of shape (n_components, n_features) The generated Gaussian random matrix. Sparse matrix will be of CSR format. See Also -------- SparseRandomProjection References ---------- .. [1] Ping Li, T. Hastie and K. W. Church, 2006, "Very Sparse Random Projections". https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf .. [2] D. Achlioptas, 2001, "Database-friendly random projections", https://cgi.di.uoa.gr/~optas/papers/jl.pdf """ _check_input_size(n_components, n_features) density = _check_density(density, n_features) rng = check_random_state(random_state) if density == 1: # skip index generation if totally dense components = rng.binomial(1, 0.5, (n_components, n_features)) * 2 - 1 return 1 / np.sqrt(n_components) * components else: # Generate location of non zero elements indices = [] offset = 0 indptr = [offset] for _ in range(n_components): # find the indices of the non-zero components for row i n_nonzero_i = rng.binomial(n_features, density) indices_i = sample_without_replacement( n_features, n_nonzero_i, random_state=rng ) indices.append(indices_i) offset += n_nonzero_i indptr.append(offset) indices = np.concatenate(indices) # Among non zero components the probability of the sign is 50%/50% data = rng.binomial(1, 0.5, size=np.size(indices)) * 2 - 1 # build the CSR structure by concatenating the rows components = sp.csr_matrix( (data, indices, indptr), shape=(n_components, n_features) ) return np.sqrt(1 / density) / np.sqrt(n_components) * components class BaseRandomProjection( TransformerMixin, BaseEstimator, ClassNamePrefixFeaturesOutMixin, metaclass=ABCMeta ): """Base class for random projections. Warning: This class should not be used directly. Use derived classes instead. """ _parameter_constraints: dict = { "n_components": [ Interval(Integral, 1, None, closed="left"), StrOptions({"auto"}), ], "eps": [Interval(Real, 0, None, closed="neither")], "compute_inverse_components": ["boolean"], "random_state": ["random_state"], } @abstractmethod def __init__( self, n_components="auto", *, eps=0.1, compute_inverse_components=False, random_state=None, ): self.n_components = n_components self.eps = eps self.compute_inverse_components = compute_inverse_components self.random_state = random_state @abstractmethod def _make_random_matrix(self, n_components, n_features): """Generate the random projection matrix. Parameters ---------- n_components : int, Dimensionality of the target projection space. n_features : int, Dimensionality of the original source space. Returns ------- components : {ndarray, sparse matrix} of shape (n_components, n_features) The generated random matrix. Sparse matrix will be of CSR format. """ def _compute_inverse_components(self): """Compute the pseudo-inverse of the (densified) components.""" components = self.components_ if sp.issparse(components): components = components.toarray() return linalg.pinv(components, check_finite=False) @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Generate a sparse random projection matrix. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) Training set: only the shape is used to find optimal random matrix dimensions based on the theory referenced in the afore mentioned papers. y : Ignored Not used, present here for API consistency by convention. Returns ------- self : object BaseRandomProjection class instance. """ X = self._validate_data( X, accept_sparse=["csr", "csc"], dtype=[np.float64, np.float32] ) n_samples, n_features = X.shape if self.n_components == "auto": self.n_components_ = johnson_lindenstrauss_min_dim( n_samples=n_samples, eps=self.eps ) if self.n_components_ <= 0: raise ValueError( "eps=%f and n_samples=%d lead to a target dimension of " "%d which is invalid" % (self.eps, n_samples, self.n_components_) ) elif self.n_components_ > n_features: raise ValueError( "eps=%f and n_samples=%d lead to a target dimension of " "%d which is larger than the original space with " "n_features=%d" % (self.eps, n_samples, self.n_components_, n_features) ) else: if self.n_components > n_features: warnings.warn( "The number of components is higher than the number of" " features: n_features < n_components (%s < %s)." "The dimensionality of the problem will not be reduced." % (n_features, self.n_components), DataDimensionalityWarning, ) self.n_components_ = self.n_components # Generate a projection matrix of size [n_components, n_features] self.components_ = self._make_random_matrix( self.n_components_, n_features ).astype(X.dtype, copy=False) if self.compute_inverse_components: self.inverse_components_ = self._compute_inverse_components() # Required by ClassNamePrefixFeaturesOutMixin.get_feature_names_out. self._n_features_out = self.n_components return self def inverse_transform(self, X): """Project data back to its original space. Returns an array X_original whose transform would be X. Note that even if X is sparse, X_original is dense: this may use a lot of RAM. If `compute_inverse_components` is False, the inverse of the components is computed during each call to `inverse_transform` which can be costly. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_components) Data to be transformed back. Returns ------- X_original : ndarray of shape (n_samples, n_features) Reconstructed data. """ check_is_fitted(self) X = check_array(X, dtype=[np.float64, np.float32], accept_sparse=("csr", "csc")) if self.compute_inverse_components: return X @ self.inverse_components_.T inverse_components = self._compute_inverse_components() return X @ inverse_components.T def _more_tags(self): return { "preserves_dtype": [np.float64, np.float32], } class GaussianRandomProjection(BaseRandomProjection): """Reduce dimensionality through Gaussian random projection. The components of the random matrix are drawn from N(0, 1 / n_components). Read more in the :ref:`User Guide <gaussian_random_matrix>`. .. versionadded:: 0.13 Parameters ---------- n_components : int or 'auto', default='auto' Dimensionality of the target projection space. n_components can be automatically adjusted according to the number of samples in the dataset and the bound given by the Johnson-Lindenstrauss lemma. In that case the quality of the embedding is controlled by the ``eps`` parameter. It should be noted that Johnson-Lindenstrauss lemma can yield very conservative estimated of the required number of components as it makes no assumption on the structure of the dataset. eps : float, default=0.1 Parameter to control the quality of the embedding according to the Johnson-Lindenstrauss lemma when `n_components` is set to 'auto'. The value should be strictly positive. Smaller values lead to better embedding and higher number of dimensions (n_components) in the target projection space. compute_inverse_components : bool, default=False Learn the inverse transform by computing the pseudo-inverse of the components during fit. Note that computing the pseudo-inverse does not scale well to large matrices. random_state : int, RandomState instance or None, default=None Controls the pseudo random number generator used to generate the projection matrix at fit time. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. Attributes ---------- n_components_ : int Concrete number of components computed when n_components="auto". components_ : ndarray of shape (n_components, n_features) Random matrix used for the projection. inverse_components_ : ndarray of shape (n_features, n_components) Pseudo-inverse of the components, only computed if `compute_inverse_components` is True. .. versionadded:: 1.1 n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- SparseRandomProjection : Reduce dimensionality through sparse random projection. Examples -------- >>> import numpy as np >>> from sklearn.random_projection import GaussianRandomProjection >>> rng = np.random.RandomState(42) >>> X = rng.rand(25, 3000) >>> transformer = GaussianRandomProjection(random_state=rng) >>> X_new = transformer.fit_transform(X) >>> X_new.shape (25, 2759) """ def __init__( self, n_components="auto", *, eps=0.1, compute_inverse_components=False, random_state=None, ): super().__init__( n_components=n_components, eps=eps, compute_inverse_components=compute_inverse_components, random_state=random_state, ) def _make_random_matrix(self, n_components, n_features): """Generate the random projection matrix. Parameters ---------- n_components : int, Dimensionality of the target projection space. n_features : int, Dimensionality of the original source space. Returns ------- components : ndarray of shape (n_components, n_features) The generated random matrix. """ random_state = check_random_state(self.random_state) return _gaussian_random_matrix( n_components, n_features, random_state=random_state ) def transform(self, X): """Project the data by using matrix product with the random matrix. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The input data to project into a smaller dimensional space. Returns ------- X_new : ndarray of shape (n_samples, n_components) Projected array. """ check_is_fitted(self) X = self._validate_data( X, accept_sparse=["csr", "csc"], reset=False, dtype=[np.float64, np.float32] ) return X @ self.components_.T class SparseRandomProjection(BaseRandomProjection): """Reduce dimensionality through sparse random projection. Sparse random matrix is an alternative to dense random projection matrix that guarantees similar embedding quality while being much more memory efficient and allowing faster computation of the projected data. If we note `s = 1 / density` the components of the random matrix are drawn from: - -sqrt(s) / sqrt(n_components) with probability 1 / 2s - 0 with probability 1 - 1 / s - +sqrt(s) / sqrt(n_components) with probability 1 / 2s Read more in the :ref:`User Guide <sparse_random_matrix>`. .. versionadded:: 0.13 Parameters ---------- n_components : int or 'auto', default='auto' Dimensionality of the target projection space. n_components can be automatically adjusted according to the number of samples in the dataset and the bound given by the Johnson-Lindenstrauss lemma. In that case the quality of the embedding is controlled by the ``eps`` parameter. It should be noted that Johnson-Lindenstrauss lemma can yield very conservative estimated of the required number of components as it makes no assumption on the structure of the dataset. density : float or 'auto', default='auto' Ratio in the range (0, 1] of non-zero component in the random projection matrix. If density = 'auto', the value is set to the minimum density as recommended by Ping Li et al.: 1 / sqrt(n_features). Use density = 1 / 3.0 if you want to reproduce the results from Achlioptas, 2001. eps : float, default=0.1 Parameter to control the quality of the embedding according to the Johnson-Lindenstrauss lemma when n_components is set to 'auto'. This value should be strictly positive. Smaller values lead to better embedding and higher number of dimensions (n_components) in the target projection space. dense_output : bool, default=False If True, ensure that the output of the random projection is a dense numpy array even if the input and random projection matrix are both sparse. In practice, if the number of components is small the number of zero components in the projected data will be very small and it will be more CPU and memory efficient to use a dense representation. If False, the projected data uses a sparse representation if the input is sparse. compute_inverse_components : bool, default=False Learn the inverse transform by computing the pseudo-inverse of the components during fit. Note that the pseudo-inverse is always a dense array, even if the training data was sparse. This means that it might be necessary to call `inverse_transform` on a small batch of samples at a time to avoid exhausting the available memory on the host. Moreover, computing the pseudo-inverse does not scale well to large matrices. random_state : int, RandomState instance or None, default=None Controls the pseudo random number generator used to generate the projection matrix at fit time. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. Attributes ---------- n_components_ : int Concrete number of components computed when n_components="auto". components_ : sparse matrix of shape (n_components, n_features) Random matrix used for the projection. Sparse matrix will be of CSR format. inverse_components_ : ndarray of shape (n_features, n_components) Pseudo-inverse of the components, only computed if `compute_inverse_components` is True. .. versionadded:: 1.1 density_ : float in range 0.0 - 1.0 Concrete density computed from when density = "auto". n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- GaussianRandomProjection : Reduce dimensionality through Gaussian random projection. References ---------- .. [1] Ping Li, T. Hastie and K. W. Church, 2006, "Very Sparse Random Projections". https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf .. [2] D. Achlioptas, 2001, "Database-friendly random projections", https://cgi.di.uoa.gr/~optas/papers/jl.pdf Examples -------- >>> import numpy as np >>> from sklearn.random_projection import SparseRandomProjection >>> rng = np.random.RandomState(42) >>> X = rng.rand(25, 3000) >>> transformer = SparseRandomProjection(random_state=rng) >>> X_new = transformer.fit_transform(X) >>> X_new.shape (25, 2759) >>> # very few components are non-zero >>> np.mean(transformer.components_!= 0) 0.0182... """ _parameter_constraints: dict = { **BaseRandomProjection._parameter_constraints, "density": [Interval(Real, 0.0, 1.0, closed="right"), StrOptions({"auto"})], "dense_output": ["boolean"], } def __init__( self, n_components="auto", *, density="auto", eps=0.1, dense_output=False, compute_inverse_components=False, random_state=None, ): super().__init__( n_components=n_components, eps=eps, compute_inverse_components=compute_inverse_components, random_state=random_state, ) self.dense_output = dense_output self.density = density def _make_random_matrix(self, n_components, n_features): """Generate the random projection matrix Parameters ---------- n_components : int Dimensionality of the target projection space. n_features : int Dimensionality of the original source space. Returns ------- components : sparse matrix of shape (n_components, n_features) The generated random matrix in CSR format. """ random_state = check_random_state(self.random_state) self.density_ = _check_density(self.density, n_features) return _sparse_random_matrix( n_components, n_features, density=self.density_, random_state=random_state ) def transform(self, X): """Project the data by using matrix product with the random matrix. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The input data to project into a smaller dimensional space. Returns ------- X_new : {ndarray, sparse matrix} of shape (n_samples, n_components) Projected array. It is a sparse matrix only when the input is sparse and `dense_output = False`. """ check_is_fitted(self) X = self._validate_data( X, accept_sparse=["csr", "csc"], reset=False, dtype=[np.float64, np.float32] ) return safe_sparse_dot(X, self.components_.T, dense_output=self.dense_output)

scikit-learn__scikit-learn

working_with_text_data.rst

Tutorial

Generate tutorial about work with text data

BSD 3-Clause New or Revised License

scikit-learn__scikit-learn/doc/tutorial/text_analytics/working_with_text_data.rst

Working With Text Data The goal of this guide is to explore some of the main scikit-learn tools on a single practical task: analyzing a collection of text documents (newsgroups posts) on twenty different topics. In this section we will see how to: - load the file contents and the categories - extract feature vectors suitable for machine learning - train a linear model to perform categorization - use a grid search strategy to find a good configuration of both the feature extraction components and the classifier Tutorial setup To get started with this tutorial, you must first install scikit-learn and all of its required dependencies. Please refer to the installation instructions <installation-instructions> page for more information and for system-specific instructions. The source of this tutorial can be found within your scikit-learn folder: scikit-learn/doc/tutorial/text_analytics/ The source can also be found on Github. The tutorial folder should contain the following sub-folders: - *.rst files - the source of the tutorial document written with sphinx - data - folder to put the datasets used during the tutorial - skeletons - sample incomplete scripts for the exercises - solutions - solutions of the exercises You can already copy the skeletons into a new folder somewhere on your hard-drive named sklearn_tut_workspace, where you will edit your own files for the exercises while keeping the original skeletons intact: bash $ cp -r skeletons work_directory/sklearn_tut_workspace Machine learning algorithms need data. Go to each $TUTORIAL_HOME/data sub-folder and run the fetch_data.py script from there (after having read them first). For instance: bash $ cd $TUTORIAL_HOME/data/languages less fetch_data.py python fetch_data.py Loading the 20 newsgroups dataset The dataset is called "Twenty Newsgroups". Here is the official description, quoted from the website: The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of our knowledge, it was originally collected by Ken Lang, probably for his paper "Newsweeder: Learning to filter netnews," though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering. In the following we will use the built-in dataset loader for 20 newsgroups from scikit-learn. Alternatively, it is possible to download the dataset manually from the website and use the sklearn.datasets.load_files function by pointing it to the 20news-bydate-train sub-folder of the uncompressed archive folder. In order to get faster execution times for this first example, we will work on a partial dataset with only 4 categories out of the 20 available in the dataset: >>> categories = ['alt.atheism', 'soc.religion.christian', ... 'comp.graphics', 'sci.med'] We can now load the list of files matching those categories as follows: >>> from sklearn.datasets import fetch_20newsgroups >>> twenty_train = fetch_20newsgroups(subset='train', ... categories=categories, shuffle=True, random_state=42) The returned dataset is a scikit-learn "bunch": a simple holder object with fields that can be both accessed as python dict keys or object attributes for convenience, for instance the target_names holds the list of the requested category names: >>> twenty_train.target_names ['alt.atheism', 'comp.graphics', 'sci.med', 'soc.religion.christian'] The files themselves are loaded in memory in the data attribute. For reference the filenames are also available: >>> len(twenty_train.data) 2257 >>> len(twenty_train.filenames) 2257 Let's print the first lines of the first loaded file: >>> print("\n".join(twenty_train.data[0].split("\n")[:3])) From: sd345@city.ac.uk (Michael Collier) Subject: Converting images to HP LaserJet III? Nntp-Posting-Host: hampton >>> print(twenty_train.target_names[twenty_train.target[0]]) comp.graphics Supervised learning algorithms will require a category label for each document in the training set. In this case the category is the name of the newsgroup which also happens to be the name of the folder holding the individual documents. For speed and space efficiency reasons, scikit-learn loads the target attribute as an array of integers that corresponds to the index of the category name in the target_names list. The category integer id of each sample is stored in the target attribute: >>> twenty_train.target[:10] array([1, 1, 3, 3, 3, 3, 3, 2, 2, 2]) It is possible to get back the category names as follows: >>> for t in twenty_train.target[:10]: ... print(twenty_train.target_names[t]) ... comp.graphics comp.graphics soc.religion.christian soc.religion.christian soc.religion.christian soc.religion.christian soc.religion.christian sci.med sci.med sci.med You might have noticed that the samples were shuffled randomly when we called fetch_20newsgroups(..., shuffle=True, random_state=42): this is useful if you wish to select only a subset of samples to quickly train a model and get a first idea of the results before re-training on the complete dataset later. Extracting features from text files In order to perform machine learning on text documents, we first need to turn the text content into numerical feature vectors. sklearn.feature_extraction.text Bags of words The most intuitive way to do so is to use a bags of words representation: 1. Assign a fixed integer id to each word occurring in any document of the training set (for instance by building a dictionary from words to integer indices). 2. For each document #i, count the number of occurrences of each word w and store it in X[i, j] as the value of feature #j where j is the index of word w in the dictionary. The bags of words representation implies that n_features is the number of distinct words in the corpus: this number is typically larger than 100,000. If n_samples == 10000, storing X as a NumPy array of type float32 would require 10000 x 100000 x 4 bytes = 4GB in RAM which is barely manageable on today's computers. Fortunately, most values in X will be zeros since for a given document less than a few thousand distinct words will be used. For this reason we say that bags of words are typically high-dimensional sparse datasets. We can save a lot of memory by only storing the non-zero parts of the feature vectors in memory. scipy.sparse matrices are data structures that do exactly this, and scikit-learn has built-in support for these structures. Tokenizing text with scikit-learn Text preprocessing, tokenizing and filtering of stopwords are all included in CountVectorizer, which builds a dictionary of features and transforms documents to feature vectors: >>> from sklearn.feature_extraction.text import CountVectorizer >>> count_vect = CountVectorizer() >>> X_train_counts = count_vect.fit_transform(twenty_train.data) >>> X_train_counts.shape (2257, 35788) CountVectorizer supports counts of N-grams of words or consecutive characters. Once fitted, the vectorizer has built a dictionary of feature indices: >>> count_vect.vocabulary_.get(u'algorithm') 4690 The index value of a word in the vocabulary is linked to its frequency in the whole training corpus. The method count_vect.fit_transform performs two actions: it learns the vocabulary and transforms the documents into count vectors. It's possible to separate these steps by calling count_vect.fit(twenty_train.data) followed by X_train_counts = count_vect.transform(twenty_train.data), but doing so would tokenize and vectorize each text file twice. From occurrences to frequencies Occurrence count is a good start but there is an issue: longer documents will have higher average count values than shorter documents, even though they might talk about the same topics. To avoid these potential discrepancies it suffices to divide the number of occurrences of each word in a document by the total number of words in the document: these new features are called tf for Term Frequencies. Another refinement on top of tf is to downscale weights for words that occur in many documents in the corpus and are therefore less informative than those that occur only in a smaller portion of the corpus. This downscaling is called tf–idf for "Term Frequency times Inverse Document Frequency". Both tf and tf–idf can be computed as follows using TfidfTransformer: >>> from sklearn.feature_extraction.text import TfidfTransformer >>> tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts) >>> X_train_tf = tf_transformer.transform(X_train_counts) >>> X_train_tf.shape (2257, 35788) In the above example-code, we firstly use the fit(..) method to fit our estimator to the data and secondly the transform(..) method to transform our count-matrix to a tf-idf representation. These two steps can be combined to achieve the same end result faster by skipping redundant processing. This is done through using the fit_transform(..) method as shown below, and as mentioned in the note in the previous section: >>> tfidf_transformer = TfidfTransformer() >>> X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts) >>> X_train_tfidf.shape (2257, 35788) Training a classifier Now that we have our features, we can train a classifier to try to predict the category of a post. Let's start with a naïve Bayes <naive_bayes> classifier, which provides a nice baseline for this task. scikit-learn includes several variants of this classifier, and the one most suitable for word counts is the multinomial variant: >>> from sklearn.naive_bayes import MultinomialNB >>> clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target) To try to predict the outcome on a new document we need to extract the features using almost the same feature extracting chain as before. The difference is that we call transform instead of fit_transform on the transformers, since they have already been fit to the training set: >>> docs_new = ['God is love', 'OpenGL on the GPU is fast'] >>> X_new_counts = count_vect.transform(docs_new) >>> X_new_tfidf = tfidf_transformer.transform(X_new_counts) >>> predicted = clf.predict(X_new_tfidf) >>> for doc, category in zip(docs_new, predicted): ... print('%r => %s' % (doc, twenty_train.target_names[category])) ... 'God is love' => soc.religion.christian 'OpenGL on the GPU is fast' => comp.graphics Building a pipeline In order to make the vectorizer => transformer => classifier easier to work with, scikit-learn provides a ~sklearn.pipeline.Pipeline class that behaves like a compound classifier: >>> from sklearn.pipeline import Pipeline >>> text_clf = Pipeline([ ... ('vect', CountVectorizer()), ... ('tfidf', TfidfTransformer()), ... ('clf', MultinomialNB()), ... ]) The names vect, tfidf and clf (classifier) are arbitrary. We will use them to perform grid search for suitable hyperparameters below. We can now train the model with a single command: >>> text_clf.fit(twenty_train.data, twenty_train.target) Pipeline(...) Evaluation of the performance on the test set Evaluating the predictive accuracy of the model is equally easy: >>> import numpy as np >>> twenty_test = fetch_20newsgroups(subset='test', ... categories=categories, shuffle=True, random_state=42) >>> docs_test = twenty_test.data >>> predicted = text_clf.predict(docs_test) >>> np.mean(predicted == twenty_test.target) 0.8348... We achieved 83.5% accuracy. Let's see if we can do better with a linear support vector machine (SVM) <svm>, which is widely regarded as one of the best text classification algorithms (although it's also a bit slower than naïve Bayes). We can change the learner by simply plugging a different classifier object into our pipeline: >>> from sklearn.linear_model import SGDClassifier >>> text_clf = Pipeline([ ... ('vect', CountVectorizer()), ... ('tfidf', TfidfTransformer()), ... ('clf', SGDClassifier(loss='hinge', penalty='l2', ... alpha=1e-3, random_state=42, ... max_iter=5, tol=None)), ... ]) >>> text_clf.fit(twenty_train.data, twenty_train.target) Pipeline(...) >>> predicted = text_clf.predict(docs_test) >>> np.mean(predicted == twenty_test.target) 0.9101... We achieved 91.3% accuracy using the SVM. scikit-learn provides further utilities for more detailed performance analysis of the results: >>> from sklearn import metrics >>> print(metrics.classification_report(twenty_test.target, predicted, ... target_names=twenty_test.target_names)) precision recall f1-score support <BLANKLINE> alt.atheism 0.95 0.80 0.87 319 comp.graphics 0.87 0.98 0.92 389 sci.med 0.94 0.89 0.91 396 soc.religion.christian 0.90 0.95 0.93 398 <BLANKLINE> accuracy 0.91 1502 macro avg 0.91 0.91 0.91 1502 weighted avg 0.91 0.91 0.91 1502 <BLANKLINE> >>> metrics.confusion_matrix(twenty_test.target, predicted) array([[256, 11, 16, 36], [ 4, 380, 3, 2], [ 5, 35, 353, 3], [ 5, 11, 4, 378]]) As expected the confusion matrix shows that posts from the newsgroups on atheism and Christianity are more often confused for one another than with computer graphics. SGD stands for Stochastic Gradient Descent. This is a simple optimization algorithms that is known to be scalable when the dataset has many samples. By setting loss="hinge" and penalty="l2" we are configuring the classifier model to tune its parameters for the linear Support Vector Machine cost function. Alternatively we could have used sklearn.svm.LinearSVC (Linear Support Vector Machine Classifier) that provides an alternative optimizer for the same cost function based on the liblinear C++ library. Parameter tuning using grid search We've already encountered some parameters such as use_idf in the TfidfTransformer. Classifiers tend to have many parameters as well; e.g., MultinomialNB includes a smoothing parameter alpha and SGDClassifier has a penalty parameter alpha and configurable loss and penalty terms in the objective function (see the module documentation, or use the Python help function to get a description of these). Instead of tweaking the parameters of the various components of the chain, it is possible to run an exhaustive search of the best parameters on a grid of possible values. We try out all classifiers on either words or bigrams, with or without idf, and with a penalty parameter of either 0.01 or 0.001 for the linear SVM: >>> from sklearn.model_selection import GridSearchCV >>> parameters = { ... 'vect__ngram_range': [(1, 1), (1, 2)], ... 'tfidf__use_idf': (True, False), ... 'clf__alpha': (1e-2, 1e-3), ... } Obviously, such an exhaustive search can be expensive. If we have multiple CPU cores at our disposal, we can tell the grid searcher to try these eight parameter combinations in parallel with the n_jobs parameter. If we give this parameter a value of -1, grid search will detect how many cores are installed and use them all: >>> gs_clf = GridSearchCV(text_clf, parameters, cv=5, n_jobs=-1) The grid search instance behaves like a normal scikit-learn model. Let's perform the search on a smaller subset of the training data to speed up the computation: >>> gs_clf = gs_clf.fit(twenty_train.data[:400], twenty_train.target[:400]) The result of calling fit on a GridSearchCV object is a classifier that we can use to predict: >>> twenty_train.target_names[gs_clf.predict(['God is love'])[0]] 'soc.religion.christian' The object's best_score_ and best_params_ attributes store the best mean score and the parameters setting corresponding to that score: >>> gs_clf.best_score_ 0.9... >>> for param_name in sorted(parameters.keys()): ... print("%s: %r" % (param_name, gs_clf.best_params_[param_name])) ... clf__alpha: 0.001 tfidf__use_idf: True vect__ngram_range: (1, 1) A more detailed summary of the search is available at gs_clf.cv_results_. The cv_results_ parameter can be easily imported into pandas as a DataFrame for further inspection. A GridSearchCV object also stores the best classifier that it trained as its best_estimator_ attribute. In this case, that isn't much use as we trained on a small, 400-document subset of our full training set. Exercises To do the exercises, copy the content of the 'skeletons' folder as a new folder named 'workspace': bash $ cp -r skeletons workspace You can then edit the content of the workspace without fear of losing the original exercise instructions. Then fire an ipython shell and run the work-in-progress script with: [1] %run workspace/exercise_XX_script.py arg1 arg2 arg3 If an exception is triggered, use %debug to fire-up a post mortem ipdb session. Refine the implementation and iterate until the exercise is solved. For each exercise, the skeleton file provides all the necessary import statements, boilerplate code to load the data and sample code to evaluate the predictive accuracy of the model. Exercise 1: Language identification - Write a text classification pipeline using a custom preprocessor and TfidfVectorizer set up to use character based n-grams, using data from Wikipedia articles as the training set. - Evaluate the performance on some held out test set. ipython command line: %run workspace/exercise_01_language_train_model.py data/languages/paragraphs/ Exercise 2: Sentiment Analysis on movie reviews - Write a text classification pipeline to classify movie reviews as either positive or negative. - Find a good set of parameters using grid search. - Evaluate the performance on a held out test set. ipython command line: %run workspace/exercise_02_sentiment.py data/movie_reviews/txt_sentoken/ Exercise 3: CLI text classification utility Using the results of the previous exercises and the cPickle module of the standard library, write a command line utility that detects the language of some text provided on stdin and estimate the polarity (positive or negative) if the text is written in English. Bonus point if the utility is able to give a confidence level for its predictions. Where to from here Here are a few suggestions to help further your scikit-learn intuition upon the completion of this tutorial: - Try playing around with the analyzer and token normalisation under CountVectorizer. - If you don't have labels, try using Clustering <sphx_glr_auto_examples_text_plot_document_clustering.py> on your problem. - If you have multiple labels per document, e.g categories, have a look at the Multiclass and multilabel section <multiclass>. - Try using Truncated SVD <LSA> for latent semantic analysis. - Have a look at using Out-of-core Classification <sphx_glr_auto_examples_applications_plot_out_of_core_classification.py> to learn from data that would not fit into the computer main memory. - Have a look at the Hashing Vectorizer <hashing_vectorizer> as a memory efficient alternative to CountVectorizer.

# Authors: Olivier Grisel <olivier.grisel@ensta.org> # Mathieu Blondel <mathieu@mblondel.org> # Lars Buitinck # Robert Layton <robertlayton@gmail.com> # Jochen Wersdörfer <jochen@wersdoerfer.de> # Roman Sinayev <roman.sinayev@gmail.com> # # License: BSD 3 clause """ The :mod:`sklearn.feature_extraction.text` submodule gathers utilities to build feature vectors from text documents. """ import array import re import unicodedata import warnings from collections import defaultdict from collections.abc import Mapping from functools import partial from numbers import Integral from operator import itemgetter import numpy as np import scipy.sparse as sp from..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context from..exceptions import NotFittedError from..preprocessing import normalize from..utils import _IS_32BIT from..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions from..utils.validation import FLOAT_DTYPES, check_array, check_is_fitted from._hash import FeatureHasher from._stop_words import ENGLISH_STOP_WORDS __all__ = [ "HashingVectorizer", "CountVectorizer", "ENGLISH_STOP_WORDS", "TfidfTransformer", "TfidfVectorizer", "strip_accents_ascii", "strip_accents_unicode", "strip_tags", ] def _preprocess(doc, accent_function=None, lower=False): """Chain together an optional series of text preprocessing steps to apply to a document. Parameters ---------- doc: str The string to preprocess accent_function: callable, default=None Function for handling accented characters. Common strategies include normalizing and removing. lower: bool, default=False Whether to use str.lower to lowercase all of the text Returns ------- doc: str preprocessed string """ if lower: doc = doc.lower() if accent_function is not None: doc = accent_function(doc) return doc def _analyze( doc, analyzer=None, tokenizer=None, ngrams=None, preprocessor=None, decoder=None, stop_words=None, ): """Chain together an optional series of text processing steps to go from a single document to ngrams, with or without tokenizing or preprocessing. If analyzer is used, only the decoder argument is used, as the analyzer is intended to replace the preprocessor, tokenizer, and ngrams steps. Parameters ---------- analyzer: callable, default=None tokenizer: callable, default=None ngrams: callable, default=None preprocessor: callable, default=None decoder: callable, default=None stop_words: list, default=None Returns ------- ngrams: list A sequence of tokens, possibly with pairs, triples, etc. """ if decoder is not None: doc = decoder(doc) if analyzer is not None: doc = analyzer(doc) else: if preprocessor is not None: doc = preprocessor(doc) if tokenizer is not None: doc = tokenizer(doc) if ngrams is not None: if stop_words is not None: doc = ngrams(doc, stop_words) else: doc = ngrams(doc) return doc def strip_accents_unicode(s): """Transform accentuated unicode symbols into their simple counterpart. Warning: the python-level loop and join operations make this implementation 20 times slower than the strip_accents_ascii basic normalization. Parameters ---------- s : str The string to strip. Returns ------- s : str The stripped string. See Also -------- strip_accents_ascii : Remove accentuated char for any unicode symbol that has a direct ASCII equivalent. """ try: # If `s` is ASCII-compatible, then it does not contain any accented # characters and we can avoid an expensive list comprehension s.encode("ASCII", errors="strict") return s except UnicodeEncodeError: normalized = unicodedata.normalize("NFKD", s) return "".join([c for c in normalized if not unicodedata.combining(c)]) def strip_accents_ascii(s): """Transform accentuated unicode symbols into ascii or nothing. Warning: this solution is only suited for languages that have a direct transliteration to ASCII symbols. Parameters ---------- s : str The string to strip. Returns ------- s : str The stripped string. See Also -------- strip_accents_unicode : Remove accentuated char for any unicode symbol. """ nkfd_form = unicodedata.normalize("NFKD", s) return nkfd_form.encode("ASCII", "ignore").decode("ASCII") def strip_tags(s): """Basic regexp based HTML / XML tag stripper function. For serious HTML/XML preprocessing you should rather use an external library such as lxml or BeautifulSoup. Parameters ---------- s : str The string to strip. Returns ------- s : str The stripped string. """ return re.compile(r"<([^>]+)>", flags=re.UNICODE).sub(" ", s) def _check_stop_list(stop): if stop == "english": return ENGLISH_STOP_WORDS elif isinstance(stop, str): raise ValueError("not a built-in stop list: %s" % stop) elif stop is None: return None else: # assume it's a collection return frozenset(stop) class _VectorizerMixin: """Provides common code for text vectorizers (tokenization logic).""" _white_spaces = re.compile(r"\s\s+") def decode(self, doc): """Decode the input into a string of unicode symbols. The decoding strategy depends on the vectorizer parameters. Parameters ---------- doc : bytes or str The string to decode. Returns ------- doc: str A string of unicode symbols. """ if self.input == "filename": with open(doc, "rb") as fh: doc = fh.read() elif self.input == "file": doc = doc.read() if isinstance(doc, bytes): doc = doc.decode(self.encoding, self.decode_error) if doc is np.nan: raise ValueError( "np.nan is an invalid document, expected byte or unicode string." ) return doc def _word_ngrams(self, tokens, stop_words=None): """Turn tokens into a sequence of n-grams after stop words filtering""" # handle stop words if stop_words is not None: tokens = [w for w in tokens if w not in stop_words] # handle token n-grams min_n, max_n = self.ngram_range if max_n!= 1: original_tokens = tokens if min_n == 1: # no need to do any slicing for unigrams # just iterate through the original tokens tokens = list(original_tokens) min_n += 1 else: tokens = [] n_original_tokens = len(original_tokens) # bind method outside of loop to reduce overhead tokens_append = tokens.append space_join = " ".join for n in range(min_n, min(max_n + 1, n_original_tokens + 1)): for i in range(n_original_tokens - n + 1): tokens_append(space_join(original_tokens[i : i + n])) return tokens def _char_ngrams(self, text_document): """Tokenize text_document into a sequence of character n-grams""" # normalize white spaces text_document = self._white_spaces.sub(" ", text_document) text_len = len(text_document) min_n, max_n = self.ngram_range if min_n == 1: # no need to do any slicing for unigrams # iterate through the string ngrams = list(text_document) min_n += 1 else: ngrams = [] # bind method outside of loop to reduce overhead ngrams_append = ngrams.append for n in range(min_n, min(max_n + 1, text_len + 1)): for i in range(text_len - n + 1): ngrams_append(text_document[i : i + n]) return ngrams def _char_wb_ngrams(self, text_document): """Whitespace sensitive char-n-gram tokenization. Tokenize text_document into a sequence of character n-grams operating only inside word boundaries. n-grams at the edges of words are padded with space.""" # normalize white spaces text_document = self._white_spaces.sub(" ", text_document) min_n, max_n = self.ngram_range ngrams = [] # bind method outside of loop to reduce overhead ngrams_append = ngrams.append for w in text_document.split(): w = " " + w + " " w_len = len(w) for n in range(min_n, max_n + 1): offset = 0 ngrams_append(w[offset : offset + n]) while offset + n < w_len: offset += 1 ngrams_append(w[offset : offset + n]) if offset == 0: # count a short word (w_len < n) only once break return ngrams def build_preprocessor(self): """Return a function to preprocess the text before tokenization. Returns ------- preprocessor: callable A function to preprocess the text before tokenization. """ if self.preprocessor is not None: return self.preprocessor # accent stripping if not self.strip_accents: strip_accents = None elif callable(self.strip_accents): strip_accents = self.strip_accents elif self.strip_accents == "ascii": strip_accents = strip_accents_ascii elif self.strip_accents == "unicode": strip_accents = strip_accents_unicode else: raise ValueError( 'Invalid value for "strip_accents": %s' % self.strip_accents ) return partial(_preprocess, accent_function=strip_accents, lower=self.lowercase) def build_tokenizer(self): """Return a function that splits a string into a sequence of tokens. Returns ------- tokenizer: callable A function to split a string into a sequence of tokens. """ if self.tokenizer is not None: return self.tokenizer token_pattern = re.compile(self.token_pattern) if token_pattern.groups > 1: raise ValueError( "More than 1 capturing group in token pattern. Only a single " "group should be captured." ) return token_pattern.findall def get_stop_words(self): """Build or fetch the effective stop words list. Returns ------- stop_words: list or None A list of stop words. """ return _check_stop_list(self.stop_words) def _check_stop_words_consistency(self, stop_words, preprocess, tokenize): """Check if stop words are consistent Returns ------- is_consistent : True if stop words are consistent with the preprocessor and tokenizer, False if they are not, None if the check was previously performed, "error" if it could not be performed (e.g. because of the use of a custom preprocessor / tokenizer) """ if id(self.stop_words) == getattr(self, "_stop_words_id", None): # Stop words are were previously validated return None # NB: stop_words is validated, unlike self.stop_words try: inconsistent = set() for w in stop_words or (): tokens = list(tokenize(preprocess(w))) for token in tokens: if token not in stop_words: inconsistent.add(token) self._stop_words_id = id(self.stop_words) if inconsistent: warnings.warn( "Your stop_words may be inconsistent with " "your preprocessing. Tokenizing the stop " "words generated tokens %r not in " "stop_words." % sorted(inconsistent) ) return not inconsistent except Exception: # Failed to check stop words consistency (e.g. because a custom # preprocessor or tokenizer was used) self._stop_words_id = id(self.stop_words) return "error" def build_analyzer(self): """Return a callable to process input data. The callable handles preprocessing, tokenization, and n-grams generation. Returns ------- analyzer: callable A function to handle preprocessing, tokenization and n-grams generation. """ if callable(self.analyzer): return partial(_analyze, analyzer=self.analyzer, decoder=self.decode) preprocess = self.build_preprocessor() if self.analyzer == "char": return partial( _analyze, ngrams=self._char_ngrams, preprocessor=preprocess, decoder=self.decode, ) elif self.analyzer == "char_wb": return partial( _analyze, ngrams=self._char_wb_ngrams, preprocessor=preprocess, decoder=self.decode, ) elif self.analyzer == "word": stop_words = self.get_stop_words() tokenize = self.build_tokenizer() self._check_stop_words_consistency(stop_words, preprocess, tokenize) return partial( _analyze, ngrams=self._word_ngrams, tokenizer=tokenize, preprocessor=preprocess, decoder=self.decode, stop_words=stop_words, ) else: raise ValueError( "%s is not a valid tokenization scheme/analyzer" % self.analyzer ) def _validate_vocabulary(self): vocabulary = self.vocabulary if vocabulary is not None: if isinstance(vocabulary, set): vocabulary = sorted(vocabulary) if not isinstance(vocabulary, Mapping): vocab = {} for i, t in enumerate(vocabulary): if vocab.setdefault(t, i)!= i: msg = "Duplicate term in vocabulary: %r" % t raise ValueError(msg) vocabulary = vocab else: indices = set(vocabulary.values()) if len(indices)!= len(vocabulary): raise ValueError("Vocabulary contains repeated indices.") for i in range(len(vocabulary)): if i not in indices: msg = "Vocabulary of size %d doesn't contain index %d." % ( len(vocabulary), i, ) raise ValueError(msg) if not vocabulary: raise ValueError("empty vocabulary passed to fit") self.fixed_vocabulary_ = True self.vocabulary_ = dict(vocabulary) else: self.fixed_vocabulary_ = False def _check_vocabulary(self): """Check if vocabulary is empty or missing (not fitted)""" if not hasattr(self, "vocabulary_"): self._validate_vocabulary() if not self.fixed_vocabulary_: raise NotFittedError("Vocabulary not fitted or provided") if len(self.vocabulary_) == 0: raise ValueError("Vocabulary is empty") def _validate_ngram_range(self): """Check validity of ngram_range parameter""" min_n, max_m = self.ngram_range if min_n > max_m: raise ValueError( "Invalid value for ngram_range=%s " "lower boundary larger than the upper boundary." % str(self.ngram_range) ) def _warn_for_unused_params(self): if self.tokenizer is not None and self.token_pattern is not None: warnings.warn( "The parameter 'token_pattern' will not be used" " since 'tokenizer' is not None'" ) if self.preprocessor is not None and callable(self.analyzer): warnings.warn( "The parameter 'preprocessor' will not be used" " since 'analyzer' is callable'" ) if ( self.ngram_range!= (1, 1) and self.ngram_range is not None and callable(self.analyzer) ): warnings.warn( "The parameter 'ngram_range' will not be used" " since 'analyzer' is callable'" ) if self.analyzer!= "word" or callable(self.analyzer): if self.stop_words is not None: warnings.warn( "The parameter'stop_words' will not be used" " since 'analyzer'!= 'word'" ) if ( self.token_pattern is not None and self.token_pattern!= r"(?u)\b\w\w+\b" ): warnings.warn( "The parameter 'token_pattern' will not be used" " since 'analyzer'!= 'word'" ) if self.tokenizer is not None: warnings.warn( "The parameter 'tokenizer' will not be used" " since 'analyzer'!= 'word'" ) class HashingVectorizer( TransformerMixin, _VectorizerMixin, BaseEstimator, auto_wrap_output_keys=None ): r"""Convert a collection of text documents to a matrix of token occurrences. It turns a collection of text documents into a scipy.sparse matrix holding token occurrence counts (or binary occurrence information), possibly normalized as token frequencies if norm='l1' or projected on the euclidean unit sphere if norm='l2'. This text vectorizer implementation uses the hashing trick to find the token string name to feature integer index mapping. This strategy has several advantages: - it is very low memory scalable to large datasets as there is no need to store a vocabulary dictionary in memory. - it is fast to pickle and un-pickle as it holds no state besides the constructor parameters. - it can be used in a streaming (partial fit) or parallel pipeline as there is no state computed during fit. There are also a couple of cons (vs using a CountVectorizer with an in-memory vocabulary): - there is no way to compute the inverse transform (from feature indices to string feature names) which can be a problem when trying to introspect which features are most important to a model. - there can be collisions: distinct tokens can be mapped to the same feature index. However in practice this is rarely an issue if n_features is large enough (e.g. 2 ** 18 for text classification problems). - no IDF weighting as this would render the transformer stateful. The hash function employed is the signed 32-bit version of Murmurhash3. For an efficiency comparision of the different feature extractors, see :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. Read more in the :ref:`User Guide <text_feature_extraction>`. Parameters ---------- input : {'filename', 'file', 'content'}, default='content' - If `'filename'`, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. - If `'file'`, the sequence items must have a'read' method (file-like object) that is called to fetch the bytes in memory. - If `'content'`, the input is expected to be a sequence of items that can be of type string or byte. encoding : str, default='utf-8' If bytes or files are given to analyze, this encoding is used to decode. decode_error : {'strict', 'ignore','replace'}, default='strict' Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. By default, it is 'strict', meaning that a UnicodeDecodeError will be raised. Other values are 'ignore' and'replace'. strip_accents : {'ascii', 'unicode'} or callable, default=None Remove accents and perform other character normalization during the preprocessing step. 'ascii' is a fast method that only works on characters that have a direct ASCII mapping. 'unicode' is a slightly slower method that works on any character. None (default) means no character normalization is performed. Both 'ascii' and 'unicode' use NFKD normalization from :func:`unicodedata.normalize`. lowercase : bool, default=True Convert all characters to lowercase before tokenizing. preprocessor : callable, default=None Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. Only applies if ``analyzer`` is not callable. tokenizer : callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``. stop_words : {'english'}, list, default=None If 'english', a built-in stop word list for English is used. There are several known issues with 'english' and you should consider an alternative (see :ref:`stop_words`). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. token_pattern : str or None, default=r"(?u)\\b\\w\\w+\\b" Regular expression denoting what constitutes a "token", only used if ``analyzer == 'word'``. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted. ngram_range : tuple (min_n, max_n), default=(1, 1) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. Only applies if ``analyzer`` is not callable. analyzer : {'word', 'char', 'char_wb'} or callable, default='word' Whether the feature should be made of word or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. .. versionchanged:: 0.21 Since v0.21, if ``input`` is ``'filename'`` or ``'file'``, the data is first read from the file and then passed to the given callable analyzer. n_features : int, default=(2 ** 20) The number of features (columns) in the output matrices. Small numbers of features are likely to cause hash collisions, but large numbers will cause larger coefficient dimensions in linear learners. binary : bool, default=False If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. norm : {'l1', 'l2'}, default='l2' Norm used to normalize term vectors. None for no normalization. alternate_sign : bool, default=True When True, an alternating sign is added to the features as to approximately conserve the inner product in the hashed space even for small n_features. This approach is similar to sparse random projection. .. versionadded:: 0.19 dtype : type, default=np.float64 Type of the matrix returned by fit_transform() or transform(). See Also -------- CountVectorizer : Convert a collection of text documents to a matrix of token counts. TfidfVectorizer : Convert a collection of raw documents to a matrix of TF-IDF features. Notes ----- This estimator is :term:`stateless` and does not need to be fitted. However, we recommend to call :meth:`fit_transform` instead of :meth:`transform`, as parameter validation is only performed in :meth:`fit`. Examples -------- >>> from sklearn.feature_extraction.text import HashingVectorizer >>> corpus = [ ... 'This is the first document.', ... 'This document is the second document.', ... 'And this is the third one.', ... 'Is this the first document?', ... ] >>> vectorizer = HashingVectorizer(n_features=2**4) >>> X = vectorizer.fit_transform(corpus) >>> print(X.shape) (4, 16) """ _parameter_constraints: dict = { "input": [StrOptions({"filename", "file", "content"})], "encoding": [str], "decode_error": [StrOptions({"strict", "ignore", "replace"})], "strip_accents": [StrOptions({"ascii", "unicode"}), None, callable], "lowercase": ["boolean"], "preprocessor": [callable, None], "tokenizer": [callable, None], "stop_words": [StrOptions({"english"}), list, None], "token_pattern": [str, None], "ngram_range": [tuple], "analyzer": [StrOptions({"word", "char", "char_wb"}), callable], "n_features": [Interval(Integral, 1, np.iinfo(np.int32).max, closed="left")], "binary": ["boolean"], "norm": [StrOptions({"l1", "l2"}), None], "alternate_sign": ["boolean"], "dtype": "no_validation", # delegate to numpy } def __init__( self, *, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=r"(?u)\b\w\w+\b", ngram_range=(1, 1), analyzer="word", n_features=(2**20), binary=False, norm="l2", alternate_sign=True, dtype=np.float64, ): self.input = input self.encoding = encoding self.decode_error = decode_error self.strip_accents = strip_accents self.preprocessor = preprocessor self.tokenizer = tokenizer self.analyzer = analyzer self.lowercase = lowercase self.token_pattern = token_pattern self.stop_words = stop_words self.n_features = n_features self.ngram_range = ngram_range self.binary = binary self.norm = norm self.alternate_sign = alternate_sign self.dtype = dtype @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, X, y=None): """Only validates estimator's parameters. This method allows to: (i) validate the estimator's parameters and (ii) be consistent with the scikit-learn transformer API. Parameters ---------- X : ndarray of shape [n_samples, n_features] Training data. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object HashingVectorizer instance. """ return self @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Only validates estimator's parameters. This method allows to: (i) validate the estimator's parameters and (ii) be consistent with the scikit-learn transformer API. Parameters ---------- X : ndarray of shape [n_samples, n_features] Training data. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object HashingVectorizer instance. """ # triggers a parameter validation if isinstance(X, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._warn_for_unused_params() self._validate_ngram_range() self._get_hasher().fit(X, y=y) return self def transform(self, X): """Transform a sequence of documents to a document-term matrix. Parameters ---------- X : iterable over raw text documents, length = n_samples Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokenized and hashed. Returns ------- X : sparse matrix of shape (n_samples, n_features) Document-term matrix. """ if isinstance(X, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._validate_ngram_range() analyzer = self.build_analyzer() X = self._get_hasher().transform(analyzer(doc) for doc in X) if self.binary: X.data.fill(1) if self.norm is not None: X = normalize(X, norm=self.norm, copy=False) return X def fit_transform(self, X, y=None): """Transform a sequence of documents to a document-term matrix. Parameters ---------- X : iterable over raw text documents, length = n_samples Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokenized and hashed. y : any Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline. Returns ------- X : sparse matrix of shape (n_samples, n_features) Document-term matrix. """ return self.fit(X, y).transform(X) def _get_hasher(self): return FeatureHasher( n_features=self.n_features, input_type="string", dtype=self.dtype, alternate_sign=self.alternate_sign, ) def _more_tags(self): return {"X_types": ["string"]} def _document_frequency(X): """Count the number of non-zero values for each feature in sparse X.""" if sp.issparse(X) and X.format == "csr": return np.bincount(X.indices, minlength=X.shape[1]) else: return np.diff(X.indptr) class CountVectorizer(_VectorizerMixin, BaseEstimator): r"""Convert a collection of text documents to a matrix of token counts. This implementation produces a sparse representation of the counts using scipy.sparse.csr_matrix. If you do not provide an a-priori dictionary and you do not use an analyzer that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analyzing the data. For an efficiency comparision of the different feature extractors, see :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. Read more in the :ref:`User Guide <text_feature_extraction>`. Parameters ---------- input : {'filename', 'file', 'content'}, default='content' - If `'filename'`, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. - If `'file'`, the sequence items must have a'read' method (file-like object) that is called to fetch the bytes in memory. - If `'content'`, the input is expected to be a sequence of items that can be of type string or byte. encoding : str, default='utf-8' If bytes or files are given to analyze, this encoding is used to decode. decode_error : {'strict', 'ignore','replace'}, default='strict' Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. By default, it is 'strict', meaning that a UnicodeDecodeError will be raised. Other values are 'ignore' and'replace'. strip_accents : {'ascii', 'unicode'} or callable, default=None Remove accents and perform other character normalization during the preprocessing step. 'ascii' is a fast method that only works on characters that have a direct ASCII mapping. 'unicode' is a slightly slower method that works on any characters. None (default) means no character normalization is performed. Both 'ascii' and 'unicode' use NFKD normalization from :func:`unicodedata.normalize`. lowercase : bool, default=True Convert all characters to lowercase before tokenizing. preprocessor : callable, default=None Override the preprocessing (strip_accents and lowercase) stage while preserving the tokenizing and n-grams generation steps. Only applies if ``analyzer`` is not callable. tokenizer : callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``. stop_words : {'english'}, list, default=None If 'english', a built-in stop word list for English is used. There are several known issues with 'english' and you should consider an alternative (see :ref:`stop_words`). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. If None, no stop words will be used. In this case, setting `max_df` to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms. token_pattern : str or None, default=r"(?u)\\b\\w\\w+\\b" Regular expression denoting what constitutes a "token", only used if ``analyzer == 'word'``. The default regexp select tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted. ngram_range : tuple (min_n, max_n), default=(1, 1) The lower and upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. All values of n such such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. Only applies if ``analyzer`` is not callable. analyzer : {'word', 'char', 'char_wb'} or callable, default='word' Whether the feature should be made of word n-gram or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. .. versionchanged:: 0.21 Since v0.21, if ``input`` is ``filename`` or ``file``, the data is first read from the file and then passed to the given callable analyzer. max_df : float in range [0.0, 1.0] or int, default=1.0 When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. min_df : float in range [0.0, 1.0] or int, default=1 When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. max_features : int, default=None If not None, build a vocabulary that only consider the top `max_features` ordered by term frequency across the corpus. Otherwise, all features are used. This parameter is ignored if vocabulary is not None. vocabulary : Mapping or iterable, default=None Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms. If not given, a vocabulary is determined from the input documents. Indices in the mapping should not be repeated and should not have any gap between 0 and the largest index. binary : bool, default=False If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. dtype : dtype, default=np.int64 Type of the matrix returned by fit_transform() or transform(). Attributes ---------- vocabulary_ : dict A mapping of terms to feature indices. fixed_vocabulary_ : bool True if a fixed vocabulary of term to indices mapping is provided by the user. stop_words_ : set Terms that were ignored because they either: - occurred in too many documents (`max_df`) - occurred in too few documents (`min_df`) - were cut off by feature selection (`max_features`). This is only available if no vocabulary was given. See Also -------- HashingVectorizer : Convert a collection of text documents to a matrix of token counts. TfidfVectorizer : Convert a collection of raw documents to a matrix of TF-IDF features. Notes ----- The ``stop_words_`` attribute can get large and increase the model size when pickling. This attribute is provided only for introspection and can be safely removed using delattr or set to None before pickling. Examples -------- >>> from sklearn.feature_extraction.text import CountVectorizer >>> corpus = [ ... 'This is the first document.', ... 'This document is the second document.', ... 'And this is the third one.', ... 'Is this the first document?', ... ] >>> vectorizer = CountVectorizer() >>> X = vectorizer.fit_transform(corpus) >>> vectorizer.get_feature_names_out() array(['and', 'document', 'first', 'is', 'one','second', 'the', 'third', 'this'],...) >>> print(X.toarray()) [[0 1 1 1 0 0 1 0 1] [0 2 0 1 0 1 1 0 1] [1 0 0 1 1 0 1 1 1] [0 1 1 1 0 0 1 0 1]] >>> vectorizer2 = CountVectorizer(analyzer='word', ngram_range=(2, 2)) >>> X2 = vectorizer2.fit_transform(corpus) >>> vectorizer2.get_feature_names_out() array(['and this', 'document is', 'first document', 'is the', 'is this', 'second document', 'the first', 'the second', 'the third', 'third one', 'this document', 'this is', 'this the'],...) >>> print(X2.toarray()) [[0 0 1 1 0 0 1 0 0 0 0 1 0] [0 1 0 1 0 1 0 1 0 0 1 0 0] [1 0 0 1 0 0 0 0 1 1 0 1 0] [0 0 1 0 1 0 1 0 0 0 0 0 1]] """ _parameter_constraints: dict = { "input": [StrOptions({"filename", "file", "content"})], "encoding": [str], "decode_error": [StrOptions({"strict", "ignore", "replace"})], "strip_accents": [StrOptions({"ascii", "unicode"}), None, callable], "lowercase": ["boolean"], "preprocessor": [callable, None], "tokenizer": [callable, None], "stop_words": [StrOptions({"english"}), list, None], "token_pattern": [str, None], "ngram_range": [tuple], "analyzer": [StrOptions({"word", "char", "char_wb"}), callable], "max_df": [ Interval(RealNotInt, 0, 1, closed="both"), Interval(Integral, 1, None, closed="left"), ], "min_df": [ Interval(RealNotInt, 0, 1, closed="both"), Interval(Integral, 1, None, closed="left"), ], "max_features": [Interval(Integral, 1, None, closed="left"), None], "vocabulary": [Mapping, HasMethods("__iter__"), None], "binary": ["boolean"], "dtype": "no_validation", # delegate to numpy } def __init__( self, *, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=r"(?u)\b\w\w+\b", ngram_range=(1, 1), analyzer="word", max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=np.int64, ): self.input = input self.encoding = encoding self.decode_error = decode_error self.strip_accents = strip_accents self.preprocessor = preprocessor self.tokenizer = tokenizer self.analyzer = analyzer self.lowercase = lowercase self.token_pattern = token_pattern self.stop_words = stop_words self.max_df = max_df self.min_df = min_df self.max_features = max_features self.ngram_range = ngram_range self.vocabulary = vocabulary self.binary = binary self.dtype = dtype def _sort_features(self, X, vocabulary): """Sort features by name Returns a reordered matrix and modifies the vocabulary in place """ sorted_features = sorted(vocabulary.items()) map_index = np.empty(len(sorted_features), dtype=X.indices.dtype) for new_val, (term, old_val) in enumerate(sorted_features): vocabulary[term] = new_val map_index[old_val] = new_val X.indices = map_index.take(X.indices, mode="clip") return X def _limit_features(self, X, vocabulary, high=None, low=None, limit=None): """Remove too rare or too common features. Prune features that are non zero in more samples than high or less documents than low, modifying the vocabulary, and restricting it to at most the limit most frequent. This does not prune samples with zero features. """ if high is None and low is None and limit is None: return X, set() # Calculate a mask based on document frequencies dfs = _document_frequency(X) mask = np.ones(len(dfs), dtype=bool) if high is not None: mask &= dfs <= high if low is not None: mask &= dfs >= low if limit is not None and mask.sum() > limit: tfs = np.asarray(X.sum(axis=0)).ravel() mask_inds = (-tfs[mask]).argsort()[:limit] new_mask = np.zeros(len(dfs), dtype=bool) new_mask[np.where(mask)[0][mask_inds]] = True mask = new_mask new_indices = np.cumsum(mask) - 1 # maps old indices to new removed_terms = set() for term, old_index in list(vocabulary.items()): if mask[old_index]: vocabulary[term] = new_indices[old_index] else: del vocabulary[term] removed_terms.add(term) kept_indices = np.where(mask)[0] if len(kept_indices) == 0: raise ValueError( "After pruning, no terms remain. Try a lower min_df or a higher max_df." ) return X[:, kept_indices], removed_terms def _count_vocab(self, raw_documents, fixed_vocab): """Create sparse feature matrix, and vocabulary where fixed_vocab=False""" if fixed_vocab: vocabulary = self.vocabulary_ else: # Add a new value when a new vocabulary item is seen vocabulary = defaultdict() vocabulary.default_factory = vocabulary.__len__ analyze = self.build_analyzer() j_indices = [] indptr = [] values = _make_int_array() indptr.append(0) for doc in raw_documents: feature_counter = {} for feature in analyze(doc): try: feature_idx = vocabulary[feature] if feature_idx not in feature_counter: feature_counter[feature_idx] = 1 else: feature_counter[feature_idx] += 1 except KeyError: # Ignore out-of-vocabulary items for fixed_vocab=True continue j_indices.extend(feature_counter.keys()) values.extend(feature_counter.values()) indptr.append(len(j_indices)) if not fixed_vocab: # disable defaultdict behaviour vocabulary = dict(vocabulary) if not vocabulary: raise ValueError( "empty vocabulary; perhaps the documents only contain stop words" ) if indptr[-1] > np.iinfo(np.int32).max: # = 2**31 - 1 if _IS_32BIT: raise ValueError( ( "sparse CSR array has {} non-zero " "elements and requires 64 bit indexing, " "which is unsupported with 32 bit Python." ).format(indptr[-1]) ) indices_dtype = np.int64 else: indices_dtype = np.int32 j_indices = np.asarray(j_indices, dtype=indices_dtype) indptr = np.asarray(indptr, dtype=indices_dtype) values = np.frombuffer(values, dtype=np.intc) X = sp.csr_matrix( (values, j_indices, indptr), shape=(len(indptr) - 1, len(vocabulary)), dtype=self.dtype, ) X.sort_indices() return vocabulary, X def fit(self, raw_documents, y=None): """Learn a vocabulary dictionary of all tokens in the raw documents. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is ignored. Returns ------- self : object Fitted vectorizer. """ self.fit_transform(raw_documents) return self @_fit_context(prefer_skip_nested_validation=True) def fit_transform(self, raw_documents, y=None): """Learn the vocabulary dictionary and return document-term matrix. This is equivalent to fit followed by transform, but more efficiently implemented. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is ignored. Returns ------- X : array of shape (n_samples, n_features) Document-term matrix. """ # We intentionally don't call the transform method to make # fit_transform overridable without unwanted side effects in # TfidfVectorizer. if isinstance(raw_documents, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._validate_ngram_range() self._warn_for_unused_params() self._validate_vocabulary() max_df = self.max_df min_df = self.min_df max_features = self.max_features if self.fixed_vocabulary_ and self.lowercase: for term in self.vocabulary: if any(map(str.isupper, term)): warnings.warn( "Upper case characters found in" " vocabulary while 'lowercase'" " is True. These entries will not" " be matched with any documents" ) break vocabulary, X = self._count_vocab(raw_documents, self.fixed_vocabulary_) if self.binary: X.data.fill(1) if not self.fixed_vocabulary_: n_doc = X.shape[0] max_doc_count = max_df if isinstance(max_df, Integral) else max_df * n_doc min_doc_count = min_df if isinstance(min_df, Integral) else min_df * n_doc if max_doc_count < min_doc_count: raise ValueError("max_df corresponds to < documents than min_df") if max_features is not None: X = self._sort_features(X, vocabulary) X, self.stop_words_ = self._limit_features( X, vocabulary, max_doc_count, min_doc_count, max_features ) if max_features is None: X = self._sort_features(X, vocabulary) self.vocabulary_ = vocabulary return X def transform(self, raw_documents): """Transform documents to document-term matrix. Extract token counts out of raw text documents using the vocabulary fitted with fit or the one provided to the constructor. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. Returns ------- X : sparse matrix of shape (n_samples, n_features) Document-term matrix. """ if isinstance(raw_documents, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._check_vocabulary() # use the same matrix-building strategy as fit_transform _, X = self._count_vocab(raw_documents, fixed_vocab=True) if self.binary: X.data.fill(1) return X def inverse_transform(self, X): """Return terms per document with nonzero entries in X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document-term matrix. Returns ------- X_inv : list of arrays of shape (n_samples,) List of arrays of terms. """ self._check_vocabulary() # We need CSR format for fast row manipulations. X = check_array(X, accept_sparse="csr") n_samples = X.shape[0] terms = np.array(list(self.vocabulary_.keys())) indices = np.array(list(self.vocabulary_.values())) inverse_vocabulary = terms[np.argsort(indices)] if sp.issparse(X): return [ inverse_vocabulary[X[i, :].nonzero()[1]].ravel() for i in range(n_samples) ] else: return [ inverse_vocabulary[np.flatnonzero(X[i, :])].ravel() for i in range(n_samples) ] def get_feature_names_out(self, input_features=None): """Get output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Not used, present here for API consistency by convention. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. """ self._check_vocabulary() return np.asarray( [t for t, i in sorted(self.vocabulary_.items(), key=itemgetter(1))], dtype=object, ) def _more_tags(self): return {"X_types": ["string"]} def _make_int_array(): """Construct an array.array of a type suitable for scipy.sparse indices.""" return array.array(str("i")) class TfidfTransformer( OneToOneFeatureMixin, TransformerMixin, BaseEstimator, auto_wrap_output_keys=None ): """Transform a count matrix to a normalized tf or tf-idf representation. Tf means term-frequency while tf-idf means term-frequency times inverse document-frequency. This is a common term weighting scheme in information retrieval, that has also found good use in document classification. The goal of using tf-idf instead of the raw frequencies of occurrence of a token in a given document is to scale down the impact of tokens that occur very frequently in a given corpus and that are hence empirically less informative than features that occur in a small fraction of the training corpus. The formula that is used to compute the tf-idf for a term t of a document d in a document set is tf-idf(t, d) = tf(t, d) * idf(t), and the idf is computed as idf(t) = log [ n / df(t) ] + 1 (if ``smooth_idf=False``), where n is the total number of documents in the document set and df(t) is the document frequency of t; the document frequency is the number of documents in the document set that contain the term t. The effect of adding "1" to the idf in the equation above is that terms with zero idf, i.e., terms that occur in all documents in a training set, will not be entirely ignored. (Note that the idf formula above differs from the standard textbook notation that defines the idf as idf(t) = log [ n / (df(t) + 1) ]). If ``smooth_idf=True`` (the default), the constant "1" is added to the numerator and denominator of the idf as if an extra document was seen containing every term in the collection exactly once, which prevents zero divisions: idf(t) = log [ (1 + n) / (1 + df(t)) ] + 1. Furthermore, the formulas used to compute tf and idf depend on parameter settings that correspond to the SMART notation used in IR as follows: Tf is "n" (natural) by default, "l" (logarithmic) when ``sublinear_tf=True``. Idf is "t" when use_idf is given, "n" (none) otherwise. Normalization is "c" (cosine) when ``norm='l2'``, "n" (none) when ``norm=None``. Read more in the :ref:`User Guide <text_feature_extraction>`. Parameters ---------- norm : {'l1', 'l2'} or None, default='l2' Each output row will have unit norm, either: - 'l2': Sum of squares of vector elements is 1. The cosine similarity between two vectors is their dot product when l2 norm has been applied. - 'l1': Sum of absolute values of vector elements is 1. See :func:`~sklearn.preprocessing.normalize`. - None: No normalization. use_idf : bool, default=True Enable inverse-document-frequency reweighting. If False, idf(t) = 1. smooth_idf : bool, default=True Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions. sublinear_tf : bool, default=False Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). Attributes ---------- idf_ : array of shape (n_features) The inverse document frequency (IDF) vector; only defined if ``use_idf`` is True. .. versionadded:: 0.20 n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 1.0 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- CountVectorizer : Transforms text into a sparse matrix of n-gram counts. TfidfVectorizer : Convert a collection of raw documents to a matrix of TF-IDF features. HashingVectorizer : Convert a collection of text documents to a matrix of token occurrences. References ---------- .. [Yates2011] R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 68-74. .. [MRS2008] C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 118-120. Examples -------- >>> from sklearn.feature_extraction.text import TfidfTransformer >>> from sklearn.feature_extraction.text import CountVectorizer >>> from sklearn.pipeline import Pipeline >>> corpus = ['this is the first document', ... 'this document is the second document', ... 'and this is the third one', ... 'is this the first document'] >>> vocabulary = ['this', 'document', 'first', 'is','second', 'the', ... 'and', 'one'] >>> pipe = Pipeline([('count', CountVectorizer(vocabulary=vocabulary)), ... ('tfid', TfidfTransformer())]).fit(corpus) >>> pipe['count'].transform(corpus).toarray() array([[1, 1, 1, 1, 0, 1, 0, 0], [1, 2, 0, 1, 1, 1, 0, 0], [1, 0, 0, 1, 0, 1, 1, 1], [1, 1, 1, 1, 0, 1, 0, 0]]) >>> pipe['tfid'].idf_ array([1. , 1.22314355, 1.51082562, 1. , 1.91629073, 1. , 1.91629073, 1.91629073]) >>> pipe.transform(corpus).shape (4, 8) """ _parameter_constraints: dict = { "norm": [StrOptions({"l1", "l2"}), None], "use_idf": ["boolean"], "smooth_idf": ["boolean"], "sublinear_tf": ["boolean"], } def __init__(self, *, norm="l2", use_idf=True, smooth_idf=True, sublinear_tf=False): self.norm = norm self.use_idf = use_idf self.smooth_idf = smooth_idf self.sublinear_tf = sublinear_tf @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Learn the idf vector (global term weights). Parameters ---------- X : sparse matrix of shape n_samples, n_features) A matrix of term/token counts. y : None This parameter is not needed to compute tf-idf. Returns ------- self : object Fitted transformer. """ # large sparse data is not supported for 32bit platforms because # _document_frequency uses np.bincount which works on arrays of # dtype NPY_INTP which is int32 for 32bit platforms. See #20923 X = self._validate_data( X, accept_sparse=("csr", "csc"), accept_large_sparse=not _IS_32BIT ) if not sp.issparse(X): X = sp.csr_matrix(X) dtype = X.dtype if X.dtype in FLOAT_DTYPES else np.float64 if self.use_idf: n_samples, n_features = X.shape df = _document_frequency(X) df = df.astype(dtype, copy=False) # perform idf smoothing if required df += int(self.smooth_idf) n_samples += int(self.smooth_idf) # log+1 instead of log makes sure terms with zero idf don't get # suppressed entirely. idf = np.log(n_samples / df) + 1 self._idf_diag = sp.diags( idf, offsets=0, shape=(n_features, n_features), format="csr", dtype=dtype, ) return self def transform(self, X, copy=True): """Transform a count matrix to a tf or tf-idf representation. Parameters ---------- X : sparse matrix of (n_samples, n_features) A matrix of term/token counts. copy : bool, default=True Whether to copy X and operate on the copy or perform in-place operations. Returns ------- vectors : sparse matrix of shape (n_samples, n_features) Tf-idf-weighted document-term matrix. """ X = self._validate_data( X, accept_sparse="csr", dtype=FLOAT_DTYPES, copy=copy, reset=False ) if not sp.issparse(X): X = sp.csr_matrix(X, dtype=np.float64) if self.sublinear_tf: np.log(X.data, X.data) X.data += 1 if self.use_idf: # idf_ being a property, the automatic attributes detection # does not work as usual and we need to specify the attribute # name: check_is_fitted(self, attributes=["idf_"], msg="idf vector is not fitted") # *= doesn't work X = X * self._idf_diag if self.norm is not None: X = normalize(X, norm=self.norm, copy=False) return X @property def idf_(self): """Inverse document frequency vector, only defined if `use_idf=True`. Returns ------- ndarray of shape (n_features,) """ # if _idf_diag is not set, this will raise an attribute error, # which means hasattr(self, "idf_") is False return np.ravel(self._idf_diag.sum(axis=0)) @idf_.setter def idf_(self, value): value = np.asarray(value, dtype=np.float64) n_features = value.shape[0] self._idf_diag = sp.spdiags( value, diags=0, m=n_features, n=n_features, format="csr" ) def _more_tags(self): return {"X_types": ["2darray", "sparse"]} class TfidfVectorizer(CountVectorizer): r"""Convert a collection of raw documents to a matrix of TF-IDF features. Equivalent to :class:`CountVectorizer` followed by :class:`TfidfTransformer`. For an example of usage, see :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`. For an efficiency comparision of the different feature extractors, see :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. Read more in the :ref:`User Guide <text_feature_extraction>`. Parameters ---------- input : {'filename', 'file', 'content'}, default='content' - If `'filename'`, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. - If `'file'`, the sequence items must have a'read' method (file-like object) that is called to fetch the bytes in memory. - If `'content'`, the input is expected to be a sequence of items that can be of type string or byte. encoding : str, default='utf-8' If bytes or files are given to analyze, this encoding is used to decode. decode_error : {'strict', 'ignore','replace'}, default='strict' Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. By default, it is 'strict', meaning that a UnicodeDecodeError will be raised. Other values are 'ignore' and'replace'. strip_accents : {'ascii', 'unicode'} or callable, default=None Remove accents and perform other character normalization during the preprocessing step. 'ascii' is a fast method that only works on characters that have a direct ASCII mapping. 'unicode' is a slightly slower method that works on any characters. None (default) means no character normalization is performed. Both 'ascii' and 'unicode' use NFKD normalization from :func:`unicodedata.normalize`. lowercase : bool, default=True Convert all characters to lowercase before tokenizing. preprocessor : callable, default=None Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. Only applies if ``analyzer`` is not callable. tokenizer : callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``. analyzer : {'word', 'char', 'char_wb'} or callable, default='word' Whether the feature should be made of word or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. .. versionchanged:: 0.21 Since v0.21, if ``input`` is ``'filename'`` or ``'file'``, the data is first read from the file and then passed to the given callable analyzer. stop_words : {'english'}, list, default=None If a string, it is passed to _check_stop_list and the appropriate stop list is returned. 'english' is currently the only supported string value. There are several known issues with 'english' and you should consider an alternative (see :ref:`stop_words`). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. If None, no stop words will be used. In this case, setting `max_df` to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms. token_pattern : str, default=r"(?u)\\b\\w\\w+\\b" Regular expression denoting what constitutes a "token", only used if ``analyzer == 'word'``. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted. ngram_range : tuple (min_n, max_n), default=(1, 1) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. Only applies if ``analyzer`` is not callable. max_df : float or int, default=1.0 When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float in range [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. min_df : float or int, default=1 When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float in range of [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. max_features : int, default=None If not None, build a vocabulary that only consider the top `max_features` ordered by term frequency across the corpus. Otherwise, all features are used. This parameter is ignored if vocabulary is not None. vocabulary : Mapping or iterable, default=None Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms. If not given, a vocabulary is determined from the input documents. binary : bool, default=False If True, all non-zero term counts are set to 1. This does not mean outputs will have only 0/1 values, only that the tf term in tf-idf is binary. (Set `binary` to True, `use_idf` to False and `norm` to None to get 0/1 outputs). dtype : dtype, default=float64 Type of the matrix returned by fit_transform() or transform(). norm : {'l1', 'l2'} or None, default='l2' Each output row will have unit norm, either: - 'l2': Sum of squares of vector elements is 1. The cosine similarity between two vectors is their dot product when l2 norm has been applied. - 'l1': Sum of absolute values of vector elements is 1. See :func:`~sklearn.preprocessing.normalize`. - None: No normalization. use_idf : bool, default=True Enable inverse-document-frequency reweighting. If False, idf(t) = 1. smooth_idf : bool, default=True Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions. sublinear_tf : bool, default=False Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). Attributes ---------- vocabulary_ : dict A mapping of terms to feature indices. fixed_vocabulary_ : bool True if a fixed vocabulary of term to indices mapping is provided by the user. idf_ : array of shape (n_features,) The inverse document frequency (IDF) vector; only defined if ``use_idf`` is True. stop_words_ : set Terms that were ignored because they either: - occurred in too many documents (`max_df`) - occurred in too few documents (`min_df`) - were cut off by feature selection (`max_features`). This is only available if no vocabulary was given. See Also -------- CountVectorizer : Transforms text into a sparse matrix of n-gram counts. TfidfTransformer : Performs the TF-IDF transformation from a provided matrix of counts. Notes ----- The ``stop_words_`` attribute can get large and increase the model size when pickling. This attribute is provided only for introspection and can be safely removed using delattr or set to None before pickling. Examples -------- >>> from sklearn.feature_extraction.text import TfidfVectorizer >>> corpus = [ ... 'This is the first document.', ... 'This document is the second document.', ... 'And this is the third one.', ... 'Is this the first document?', ... ] >>> vectorizer = TfidfVectorizer() >>> X = vectorizer.fit_transform(corpus) >>> vectorizer.get_feature_names_out() array(['and', 'document', 'first', 'is', 'one','second', 'the', 'third', 'this'],...) >>> print(X.shape) (4, 9) """ _parameter_constraints: dict = {**CountVectorizer._parameter_constraints} _parameter_constraints.update( { "norm": [StrOptions({"l1", "l2"}), None], "use_idf": ["boolean"], "smooth_idf": ["boolean"], "sublinear_tf": ["boolean"], } ) def __init__( self, *, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, analyzer="word", stop_words=None, token_pattern=r"(?u)\b\w\w+\b", ngram_range=(1, 1), max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=np.float64, norm="l2", use_idf=True, smooth_idf=True, sublinear_tf=False, ): super().__init__( input=input, encoding=encoding, decode_error=decode_error, strip_accents=strip_accents, lowercase=lowercase, preprocessor=preprocessor, tokenizer=tokenizer, analyzer=analyzer, stop_words=stop_words, token_pattern=token_pattern, ngram_range=ngram_range, max_df=max_df, min_df=min_df, max_features=max_features, vocabulary=vocabulary, binary=binary, dtype=dtype, ) self.norm = norm self.use_idf = use_idf self.smooth_idf = smooth_idf self.sublinear_tf = sublinear_tf # Broadcast the TF-IDF parameters to the underlying transformer instance # for easy grid search and repr @property def idf_(self): """Inverse document frequency vector, only defined if `use_idf=True`. Returns ------- ndarray of shape (n_features,) """ if not hasattr(self, "_tfidf"): raise NotFittedError( f"{self.__class__.__name__} is not fitted yet. Call 'fit' with " "appropriate arguments before using this attribute." ) return self._tfidf.idf_ @idf_.setter def idf_(self, value): if not self.use_idf: raise ValueError("`idf_` cannot be set when `user_idf=False`.") if not hasattr(self, "_tfidf"): # We should support transferring `idf_` from another `TfidfTransformer` # and therefore, we need to create the transformer instance it does not # exist yet. self._tfidf = TfidfTransformer( norm=self.norm, use_idf=self.use_idf, smooth_idf=self.smooth_idf, sublinear_tf=self.sublinear_tf, ) self._validate_vocabulary() if hasattr(self, "vocabulary_"): if len(self.vocabulary_)!= len(value): raise ValueError( "idf length = %d must be equal to vocabulary size = %d" % (len(value), len(self.vocabulary)) ) self._tfidf.idf_ = value def _check_params(self): if self.dtype not in FLOAT_DTYPES: warnings.warn( "Only {} 'dtype' should be used. {} 'dtype' will " "be converted to np.float64.".format(FLOAT_DTYPES, self.dtype), UserWarning, ) @_fit_context(prefer_skip_nested_validation=True) def fit(self, raw_documents, y=None): """Learn vocabulary and idf from training set. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is not needed to compute tfidf. Returns ------- self : object Fitted vectorizer. """ self._check_params() self._warn_for_unused_params() self._tfidf = TfidfTransformer( norm=self.norm, use_idf=self.use_idf, smooth_idf=self.smooth_idf, sublinear_tf=self.sublinear_tf, ) X = super().fit_transform(raw_documents) self._tfidf.fit(X) return self def fit_transform(self, raw_documents, y=None): """Learn vocabulary and idf, return document-term matrix. This is equivalent to fit followed by transform, but more efficiently implemented. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is ignored. Returns ------- X : sparse matrix of (n_samples, n_features) Tf-idf-weighted document-term matrix. """ self._check_params() self._tfidf = TfidfTransformer( norm=self.norm, use_idf=self.use_idf, smooth_idf=self.smooth_idf, sublinear_tf=self.sublinear_tf, ) X = super().fit_transform(raw_documents) self._tfidf.fit(X) # X is already a transformed view of raw_documents so # we set copy to False return self._tfidf.transform(X, copy=False) def transform(self, raw_documents): """Transform documents to document-term matrix. Uses the vocabulary and document frequencies (df) learned by fit (or fit_transform). Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. Returns ------- X : sparse matrix of (n_samples, n_features) Tf-idf-weighted document-term matrix. """ check_is_fitted(self, msg="The TF-IDF vectorizer is not fitted") X = super().transform(raw_documents) return self._tfidf.transform(X, copy=False) def _more_tags(self): return {"X_types": ["string"], "_skip_test": True}

pytorch__vision

feature_extraction.rst

Module doc / Tutorial

Generate documentation and example for this module

BSD 3-Clause New or Revised License

pytorch__vision/docs/source/feature_extraction.rst

Feature extraction for model inspection The torchvision.models.feature_extraction package contains feature extraction utilities that let us tap into our models to access intermediate transformations of our inputs. This could be useful for a variety of applications in computer vision. Just a few examples are: - Visualizing feature maps. - Extracting features to compute image descriptors for tasks like facial recognition, copy-detection, or image retrieval. - Passing selected features to downstream sub-networks for end-to-end training with a specific task in mind. For example, passing a hierarchy of features to a Feature Pyramid Network with object detection heads. Torchvision provides create_feature_extractor for this purpose. It works by following roughly these steps: 1. Symbolically tracing the model to get a graphical representation of how it transforms the input, step by step. 2. Setting the user-selected graph nodes as outputs. 3. Removing all redundant nodes (anything downstream of the output nodes). 4. Generating python code from the resulting graph and bundling that into a PyTorch module together with the graph itself. The torch.fx documentation provides a more general and detailed explanation of the above procedure and the inner workings of the symbolic tracing. About Node Names In order to specify which nodes should be output nodes for extracted features, one should be familiar with the node naming convention used here (which differs slightly from that used in torch.fx). A node name is specified as a . separated path walking the module hierarchy from top level module down to leaf operation or leaf module. For instance "layer4.2.relu" in ResNet-50 represents the output of the ReLU of the 2nd block of the 4th layer of the ResNet module. Here are some finer points to keep in mind: - When specifying node names for create_feature_extractor, you may provide a truncated version of a node name as a shortcut. To see how this works, try creating a ResNet-50 model and printing the node names with train_nodes, _ = get_graph_node_names(model) print(train_nodes) and observe that the last node pertaining to layer4 is "layer4.2.relu_2". One may specify "layer4.2.relu_2" as the return node, or just "layer4" as this, by convention, refers to the last node (in order of execution) of layer4. - If a certain module or operation is repeated more than once, node names get an additional _{int} postfix to disambiguate. For instance, maybe the addition (+) operation is used three times in the same forward method. Then there would be "path.to.module.add", "path.to.module.add_1", "path.to.module.add_2". The counter is maintained within the scope of the direct parent. So in ResNet-50 there is a "layer4.1.add" and a "layer4.2.add". Because the addition operations reside in different blocks, there is no need for a postfix to disambiguate. An Example Here is an example of how we might extract features for MaskRCNN: import torch from torchvision.models import resnet50 from torchvision.models.feature_extraction import get_graph_node_names from torchvision.models.feature_extraction import create_feature_extractor from torchvision.models.detection.mask_rcnn import MaskRCNN from torchvision.models.detection.backbone_utils import LastLevelMaxPool from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork # To assist you in designing the feature extractor you may want to print out # the available nodes for resnet50. m = resnet50() train_nodes, eval_nodes = get_graph_node_names(resnet50()) # The lists returned, are the names of all the graph nodes (in order of # execution) for the input model traced in train mode and in eval mode # respectively. You'll find that `train_nodes` and `eval_nodes` are the same # for this example. But if the model contains control flow that's dependent # on the training mode, they may be different. # To specify the nodes you want to extract, you could select the final node # that appears in each of the main layers: return_nodes = { # node_name: user-specified key for output dict 'layer1.2.relu_2': 'layer1', 'layer2.3.relu_2': 'layer2', 'layer3.5.relu_2': 'layer3', 'layer4.2.relu_2': 'layer4', } # But `create_feature_extractor` can also accept truncated node specifications # like "layer1", as it will just pick the last node that's a descendent of # of the specification. (Tip: be careful with this, especially when a layer # has multiple outputs. It's not always guaranteed that the last operation # performed is the one that corresponds to the output you desire. You should # consult the source code for the input model to confirm.) return_nodes = { 'layer1': 'layer1', 'layer2': 'layer2', 'layer3': 'layer3', 'layer4': 'layer4', } # Now you can build the feature extractor. This returns a module whose forward # method returns a dictionary like: # { # 'layer1': output of layer 1, # 'layer2': output of layer 2, # 'layer3': output of layer 3, # 'layer4': output of layer 4, # } create_feature_extractor(m, return_nodes=return_nodes) # Let's put all that together to wrap resnet50 with MaskRCNN # MaskRCNN requires a backbone with an attached FPN class Resnet50WithFPN(torch.nn.Module): def __init__(self): super(Resnet50WithFPN, self).__init__() # Get a resnet50 backbone m = resnet50() # Extract 4 main layers (note: MaskRCNN needs this particular name # mapping for return nodes) self.body = create_feature_extractor( m, return_nodes={f'layer{k}': str(v) for v, k in enumerate([1, 2, 3, 4])}) # Dry run to get number of channels for FPN inp = torch.randn(2, 3, 224, 224) with torch.no_grad(): out = self.body(inp) in_channels_list = [o.shape[1] for o in out.values()] # Build FPN self.out_channels = 256 self.fpn = FeaturePyramidNetwork( in_channels_list, out_channels=self.out_channels, extra_blocks=LastLevelMaxPool()) def forward(self, x): x = self.body(x) x = self.fpn(x) return x # Now we can build our model! model = MaskRCNN(Resnet50WithFPN(), num_classes=91).eval()

import inspect import math import re import warnings from collections import OrderedDict from copy import deepcopy from itertools import chain from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch import torchvision from torch import fx, nn from torch.fx.graph_module import _copy_attr __all__ = ["create_feature_extractor", "get_graph_node_names"] class LeafModuleAwareTracer(fx.Tracer): """ An fx.Tracer that allows the user to specify a set of leaf modules, i.e. modules that are not to be traced through. The resulting graph ends up having single nodes referencing calls to the leaf modules' forward methods. """ def __init__(self, *args, **kwargs): self.leaf_modules = {} if "leaf_modules" in kwargs: leaf_modules = kwargs.pop("leaf_modules") self.leaf_modules = leaf_modules super().__init__(*args, **kwargs) def is_leaf_module(self, m: nn.Module, module_qualname: str) -> bool: if isinstance(m, tuple(self.leaf_modules)): return True return super().is_leaf_module(m, module_qualname) class NodePathTracer(LeafModuleAwareTracer): """ NodePathTracer is an FX tracer that, for each operation, also records the name of the Node from which the operation originated. A node name here is a `.` separated path walking the hierarchy from top level module down to leaf operation or leaf module. The name of the top level module is not included as part of the node name. For example, if we trace a module whose forward method applies a ReLU module, the name for that node will simply be'relu'. Some notes on the specifics: - Nodes are recorded to `self.node_to_qualname` which is a dictionary mapping a given Node object to its node name. - Nodes are recorded in the order which they are executed during tracing. - When a duplicate node name is encountered, a suffix of the form _{int} is added. The counter starts from 1. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # Track the qualified name of the Node being traced self.current_module_qualname = "" # A map from FX Node to the qualified name\# # NOTE: This is loosely like the "qualified name" mentioned in the # torch.fx docs https://pytorch.org/docs/stable/fx.html but adapted # for the purposes of the torchvision feature extractor self.node_to_qualname = OrderedDict() def call_module(self, m: torch.nn.Module, forward: Callable, args, kwargs): """ Override of `fx.Tracer.call_module` This override: 1) Stores away the qualified name of the caller for restoration later 2) Adds the qualified name of the caller to `current_module_qualname` for retrieval by `create_proxy` 3) Once a leaf module is reached, calls `create_proxy` 4) Restores the caller's qualified name into current_module_qualname """ old_qualname = self.current_module_qualname try: module_qualname = self.path_of_module(m) self.current_module_qualname = module_qualname if not self.is_leaf_module(m, module_qualname): out = forward(*args, **kwargs) return out return self.create_proxy("call_module", module_qualname, args, kwargs) finally: self.current_module_qualname = old_qualname def create_proxy( self, kind: str, target: fx.node.Target, args, kwargs, name=None, type_expr=None, *_ ) -> fx.proxy.Proxy: """ Override of `Tracer.create_proxy`. This override intercepts the recording of every operation and stores away the current traced module's qualified name in `node_to_qualname` """ proxy = super().create_proxy(kind, target, args, kwargs, name, type_expr) self.node_to_qualname[proxy.node] = self._get_node_qualname(self.current_module_qualname, proxy.node) return proxy def _get_node_qualname(self, module_qualname: str, node: fx.node.Node) -> str: node_qualname = module_qualname if node.op!= "call_module": # In this case module_qualname from torch.fx doesn't go all the # way to the leaf function/op, so we need to append it if len(node_qualname) > 0: # Only append '.' if we are deeper than the top level module node_qualname += "." node_qualname += str(node) # Now we need to add an _{index} postfix on any repeated node names # For modules we do this from scratch # But for anything else, torch.fx already has a globally scoped # _{index} postfix. But we want it locally (relative to direct parent) # scoped. So first we need to undo the torch.fx postfix if re.match(r".+_[0-9]+$", node_qualname) is not None: node_qualname = node_qualname.rsplit("_", 1)[0] #... and now we add on our own postfix for existing_qualname in reversed(self.node_to_qualname.values()): # Check to see if existing_qualname is of the form # {node_qualname} or {node_qualname}_{int} if re.match(rf"{node_qualname}(_[0-9]+)?$", existing_qualname) is not None: postfix = existing_qualname.replace(node_qualname, "") if len(postfix): # existing_qualname is of the form {node_qualname}_{int} next_index = int(postfix[1:]) + 1 else: # existing_qualname is of the form {node_qualname} next_index = 1 node_qualname += f"_{next_index}" break return node_qualname def _is_subseq(x, y): """Check if y is a subsequence of x https://stackoverflow.com/a/24017747/4391249 """ iter_x = iter(x) return all(any(x_item == y_item for x_item in iter_x) for y_item in y) def _warn_graph_differences(train_tracer: NodePathTracer, eval_tracer: NodePathTracer): """ Utility function for warning the user if there are differences between the train graph nodes and the eval graph nodes. """ train_nodes = list(train_tracer.node_to_qualname.values()) eval_nodes = list(eval_tracer.node_to_qualname.values()) if len(train_nodes) == len(eval_nodes) and all(t == e for t, e in zip(train_nodes, eval_nodes)): return suggestion_msg = ( "When choosing nodes for feature extraction, you may need to specify " "output nodes for train and eval mode separately." ) if _is_subseq(train_nodes, eval_nodes): msg = ( "NOTE: The nodes obtained by tracing the model in eval mode " "are a subsequence of those obtained in train mode. " ) elif _is_subseq(eval_nodes, train_nodes): msg = ( "NOTE: The nodes obtained by tracing the model in train mode " "are a subsequence of those obtained in eval mode. " ) else: msg = "The nodes obtained by tracing the model in train mode are different to those obtained in eval mode. " warnings.warn(msg + suggestion_msg) def _get_leaf_modules_for_ops() -> List[type]: members = inspect.getmembers(torchvision.ops) result = [] for _, obj in members: if inspect.isclass(obj) and issubclass(obj, torch.nn.Module): result.append(obj) return result def _set_default_tracer_kwargs(original_tr_kwargs: Optional[Dict[str, Any]]) -> Dict[str, Any]: default_autowrap_modules = (math, torchvision.ops) default_leaf_modules = _get_leaf_modules_for_ops() result_tracer_kwargs = {} if original_tr_kwargs is None else original_tr_kwargs result_tracer_kwargs["autowrap_modules"] = ( tuple(set(result_tracer_kwargs["autowrap_modules"] + default_autowrap_modules)) if "autowrap_modules" in result_tracer_kwargs else default_autowrap_modules ) result_tracer_kwargs["leaf_modules"] = ( list(set(result_tracer_kwargs["leaf_modules"] + default_leaf_modules)) if "leaf_modules" in result_tracer_kwargs else default_leaf_modules ) return result_tracer_kwargs def get_graph_node_names( model: nn.Module, tracer_kwargs: Optional[Dict[str, Any]] = None, suppress_diff_warning: bool = False, ) -> Tuple[List[str], List[str]]: """ Dev utility to return node names in order of execution. See note on node names under :func:`create_feature_extractor`. Useful for seeing which node names are available for feature extraction. There are two reasons that node names can't easily be read directly from the code for a model: 1. Not all submodules are traced through. Modules from ``torch.nn`` all fall within this category. 2. Nodes representing the repeated application of the same operation or leaf module get a ``_{counter}`` postfix. The model is traced twice: once in train mode, and once in eval mode. Both sets of node names are returned. For more details on the node naming conventions used here, please see the :ref:`relevant subheading <about-node-names>` in the `documentation <https://pytorch.org/vision/stable/feature_extraction.html>`_. Args: model (nn.Module): model for which we'd like to print node names tracer_kwargs (dict, optional): a dictionary of keyword arguments for ``NodePathTracer`` (they are eventually passed onto `torch.fx.Tracer <https://pytorch.org/docs/stable/fx.html#torch.fx.Tracer>`_). By default, it will be set to wrap and make leaf nodes all torchvision ops: {"autowrap_modules": (math, torchvision.ops,),"leaf_modules": _get_leaf_modules_for_ops(),} WARNING: In case the user provides tracer_kwargs, above default arguments will be appended to the user provided dictionary. suppress_diff_warning (bool, optional): whether to suppress a warning when there are discrepancies between the train and eval version of the graph. Defaults to False. Returns: tuple(list, list): a list of node names from tracing the model in train mode, and another from tracing the model in eval mode. Examples:: >>> model = torchvision.models.resnet18() >>> train_nodes, eval_nodes = get_graph_node_names(model) """ tracer_kwargs = _set_default_tracer_kwargs(tracer_kwargs) is_training = model.training train_tracer = NodePathTracer(**tracer_kwargs) train_tracer.trace(model.train()) eval_tracer = NodePathTracer(**tracer_kwargs) eval_tracer.trace(model.eval()) train_nodes = list(train_tracer.node_to_qualname.values()) eval_nodes = list(eval_tracer.node_to_qualname.values()) if not suppress_diff_warning: _warn_graph_differences(train_tracer, eval_tracer) # Restore training state model.train(is_training) return train_nodes, eval_nodes class DualGraphModule(fx.GraphModule): """ A derivative of `fx.GraphModule`. Differs in the following ways: - Requires a train and eval version of the underlying graph - Copies submodules according to the nodes of both train and eval graphs. - Calling train(mode) switches between train graph and eval graph. """ def __init__( self, root: torch.nn.Module, train_graph: fx.Graph, eval_graph: fx.Graph, class_name: str = "GraphModule" ): """ Args: root (nn.Module): module from which the copied module hierarchy is built train_graph (fx.Graph): the graph that should be used in train mode eval_graph (fx.Graph): the graph that should be used in eval mode """ super(fx.GraphModule, self).__init__() self.__class__.__name__ = class_name self.train_graph = train_graph self.eval_graph = eval_graph # Copy all get_attr and call_module ops (indicated by BOTH train and # eval graphs) for node in chain(iter(train_graph.nodes), iter(eval_graph.nodes)): if node.op in ["get_attr", "call_module"]: if not isinstance(node.target, str): raise TypeError(f"node.target should be of type str instead of {type(node.target)}") _copy_attr(root, self, node.target) # train mode by default self.train() self.graph = train_graph # (borrowed from fx.GraphModule): # Store the Tracer class responsible for creating a Graph separately as part of the # GraphModule state, except when the Tracer is defined in a local namespace. # Locally defined Tracers are not pickleable. This is needed because torch.package will # serialize a GraphModule without retaining the Graph, and needs to use the correct Tracer # to re-create the Graph during deserialization. if self.eval_graph._tracer_cls!= self.train_graph._tracer_cls: raise TypeError( f"Train mode and eval mode should use the same tracer class. Instead got {self.eval_graph._tracer_cls} for eval vs {self.train_graph._tracer_cls} for train" ) self._tracer_cls = None if self.graph._tracer_cls and "<locals>" not in self.graph._tracer_cls.__qualname__: self._tracer_cls = self.graph._tracer_cls def train(self, mode=True): """ Swap out the graph depending on the selected training mode. NOTE this should be safe when calling model.eval() because that just calls this with mode == False. """ # NOTE: Only set self.graph if the current graph is not the desired # one. This saves us from recompiling the graph where not necessary. if mode and not self.training: self.graph = self.train_graph elif not mode and self.training: self.graph = self.eval_graph return super().train(mode=mode) def create_feature_extractor( model: nn.Module, return_nodes: Optional[Union[List[str], Dict[str, str]]] = None, train_return_nodes: Optional[Union[List[str], Dict[str, str]]] = None, eval_return_nodes: Optional[Union[List[str], Dict[str, str]]] = None, tracer_kwargs: Optional[Dict[str, Any]] = None, suppress_diff_warning: bool = False, ) -> fx.GraphModule: """ Creates a new graph module that returns intermediate nodes from a given model as dictionary with user specified keys as strings, and the requested outputs as values. This is achieved by re-writing the computation graph of the model via FX to return the desired nodes as outputs. All unused nodes are removed, together with their corresponding parameters. Desired output nodes must be specified as a ``.`` separated path walking the module hierarchy from top level module down to leaf operation or leaf module. For more details on the node naming conventions used here, please see the :ref:`relevant subheading <about-node-names>` in the `documentation <https://pytorch.org/vision/stable/feature_extraction.html>`_. Not all models will be FX traceable, although with some massaging they can be made to cooperate. Here's a (not exhaustive) list of tips: - If you don't need to trace through a particular, problematic sub-module, turn it into a "leaf module" by passing a list of ``leaf_modules`` as one of the ``tracer_kwargs`` (see example below). It will not be traced through, but rather, the resulting graph will hold a reference to that module's forward method. - Likewise, you may turn functions into leaf functions by passing a list of ``autowrap_functions`` as one of the ``tracer_kwargs`` (see example below). - Some inbuilt Python functions can be problematic. For instance, ``int`` will raise an error during tracing. You may wrap them in your own function and then pass that in ``autowrap_functions`` as one of the ``tracer_kwargs``. For further information on FX see the `torch.fx documentation <https://pytorch.org/docs/stable/fx.html>`_. Args: model (nn.Module): model on which we will extract the features return_nodes (list or dict, optional): either a ``List`` or a ``Dict`` containing the names (or partial names - see note above) of the nodes for which the activations will be returned. If it is a ``Dict``, the keys are the node names, and the values are the user-specified keys for the graph module's returned dictionary. If it is a ``List``, it is treated as a ``Dict`` mapping node specification strings directly to output names. In the case that ``train_return_nodes`` and ``eval_return_nodes`` are specified, this should not be specified. train_return_nodes (list or dict, optional): similar to ``return_nodes``. This can be used if the return nodes for train mode are different than those from eval mode. If this is specified, ``eval_return_nodes`` must also be specified, and ``return_nodes`` should not be specified. eval_return_nodes (list or dict, optional): similar to ``return_nodes``. This can be used if the return nodes for train mode are different than those from eval mode. If this is specified, ``train_return_nodes`` must also be specified, and `return_nodes` should not be specified. tracer_kwargs (dict, optional): a dictionary of keyword arguments for ``NodePathTracer`` (which passes them onto it's parent class `torch.fx.Tracer <https://pytorch.org/docs/stable/fx.html#torch.fx.Tracer>`_). By default, it will be set to wrap and make leaf nodes all torchvision ops: {"autowrap_modules": (math, torchvision.ops,),"leaf_modules": _get_leaf_modules_for_ops(),} WARNING: In case the user provides tracer_kwargs, above default arguments will be appended to the user provided dictionary. suppress_diff_warning (bool, optional): whether to suppress a warning when there are discrepancies between the train and eval version of the graph. Defaults to False. Examples:: >>> # Feature extraction with resnet >>> model = torchvision.models.resnet18() >>> # extract layer1 and layer3, giving as names `feat1` and feat2` >>> model = create_feature_extractor( >>> model, {'layer1': 'feat1', 'layer3': 'feat2'}) >>> out = model(torch.rand(1, 3, 224, 224)) >>> print([(k, v.shape) for k, v in out.items()]) >>> [('feat1', torch.Size([1, 64, 56, 56])), >>> ('feat2', torch.Size([1, 256, 14, 14]))] >>> # Specifying leaf modules and leaf functions >>> def leaf_function(x): >>> # This would raise a TypeError if traced through >>> return int(x) >>> >>> class LeafModule(torch.nn.Module): >>> def forward(self, x): >>> # This would raise a TypeError if traced through >>> int(x.shape[0]) >>> return torch.nn.functional.relu(x + 4) >>> >>> class MyModule(torch.nn.Module): >>> def __init__(self): >>> super().__init__() >>> self.conv = torch.nn.Conv2d(3, 1, 3) >>> self.leaf_module = LeafModule() >>> >>> def forward(self, x): >>> leaf_function(x.shape[0]) >>> x = self.conv(x) >>> return self.leaf_module(x) >>> >>> model = create_feature_extractor( >>> MyModule(), return_nodes=['leaf_module'], >>> tracer_kwargs={'leaf_modules': [LeafModule], >>> 'autowrap_functions': [leaf_function]}) """ tracer_kwargs = _set_default_tracer_kwargs(tracer_kwargs) is_training = model.training if all(arg is None for arg in [return_nodes, train_return_nodes, eval_return_nodes]): raise ValueError( "Either `return_nodes` or `train_return_nodes` and `eval_return_nodes` together, should be specified" ) if (train_return_nodes is None) ^ (eval_return_nodes is None): raise ValueError( "If any of `train_return_nodes` and `eval_return_nodes` are specified, then both should be specified" ) if not ((return_nodes is None) ^ (train_return_nodes is None)): raise ValueError("If `train_return_nodes` and `eval_return_nodes` are specified, then both should be specified") # Put *_return_nodes into Dict[str, str] format def to_strdict(n) -> Dict[str, str]: if isinstance(n, list): return {str(i): str(i) for i in n} return {str(k): str(v) for k, v in n.items()} if train_return_nodes is None: return_nodes = to_strdict(return_nodes) train_return_nodes = deepcopy(return_nodes) eval_return_nodes = deepcopy(return_nodes) else: train_return_nodes = to_strdict(train_return_nodes) eval_return_nodes = to_strdict(eval_return_nodes) # Repeat the tracing and graph rewriting for train and eval mode tracers = {} graphs = {} mode_return_nodes: Dict[str, Dict[str, str]] = {"train": train_return_nodes, "eval": eval_return_nodes} for mode in ["train", "eval"]: if mode == "train": model.train() elif mode == "eval": model.eval() # Instantiate our NodePathTracer and use that to trace the model tracer = NodePathTracer(**tracer_kwargs) graph = tracer.trace(model) name = model.__class__.__name__ if isinstance(model, nn.Module) else model.__name__ graph_module = fx.GraphModule(tracer.root, graph, name) available_nodes = list(tracer.node_to_qualname.values()) # FIXME We don't know if we should expect this to happen if len(set(available_nodes))!= len(available_nodes): raise ValueError( "There are duplicate nodes! Please raise an issue https://github.com/pytorch/vision/issues" ) # Check that all outputs in return_nodes are present in the model for query in mode_return_nodes[mode].keys(): # To check if a query is available we need to check that at least # one of the available names starts with it up to a. if not any([re.match(rf"^{query}(\.|$)", n) is not None for n in available_nodes]): raise ValueError( f"node: '{query}' is not present in model. Hint: use " "`get_graph_node_names` to make sure the " "`return_nodes` you specified are present. It may even " "be that you need to specify `train_return_nodes` and " "`eval_return_nodes` separately." ) # Remove existing output nodes (train mode) orig_output_nodes = [] for n in reversed(graph_module.graph.nodes): if n.op == "output": orig_output_nodes.append(n) if not orig_output_nodes: raise ValueError("No output nodes found in graph_module.graph.nodes") for n in orig_output_nodes: graph_module.graph.erase_node(n) # Find nodes corresponding to return_nodes and make them into output_nodes nodes = [n for n in graph_module.graph.nodes] output_nodes = OrderedDict() for n in reversed(nodes): module_qualname = tracer.node_to_qualname.get(n) if module_qualname is None: # NOTE - Know cases where this happens: # - Node representing creation of a tensor constant - probably # not interesting as a return node # - When packing outputs into a named tuple like in InceptionV3 continue for query in mode_return_nodes[mode]: depth = query.count(".") if ".".join(module_qualname.split(".")[: depth + 1]) == query: output_nodes[mode_return_nodes[mode][query]] = n mode_return_nodes[mode].pop(query) break output_nodes = OrderedDict(reversed(list(output_nodes.items()))) # And add them in the end of the graph with graph_module.graph.inserting_after(nodes[-1]): graph_module.graph.output(output_nodes) # Remove unused modules / parameters graph_module.graph.eliminate_dead_code() graph_module.recompile() # Keep track of the tracer and graph, so we can choose the main one tracers[mode] = tracer graphs[mode] = graph # Warn user if there are any discrepancies between the graphs of the # train and eval modes if not suppress_diff_warning: _warn_graph_differences(tracers["train"], tracers["eval"]) # Build the final graph module graph_module = DualGraphModule(model, graphs["train"], graphs["eval"], class_name=name) # Restore original training mode model.train(is_training) graph_module.train(is_training) return graph_module

pytorch__vision

transforms.rst

Module doc / Tutorial

Generate documentation and example for this module

BSD 3-Clause New or Revised License

pytorch__vision/docs/source/transforms.rst

pytorch__vision/torchvision/transforms

Transforming and augmenting images Torchvision supports common computer vision transformations in the torchvision.transforms and torchvision.transforms.v2 modules. Transforms can be used to transform or augment data for training or inference of different tasks (image classification, detection, segmentation, video classification). # Image Classification import torch from torchvision.transforms import v2 H, W = 32, 32 img = torch.randint(0, 256, size=(3, H, W), dtype=torch.uint8) transforms = v2.Compose([ v2.RandomResizedCrop(size=(224, 224), antialias=True), v2.RandomHorizontalFlip(p=0.5), v2.ToDtype(torch.float32, scale=True), v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) img = transforms(img) # Detection (re-using imports and transforms from above) from torchvision import tv_tensors img = torch.randint(0, 256, size=(3, H, W), dtype=torch.uint8) boxes = torch.randint(0, H // 2, size=(3, 4)) boxes[:, 2:] += boxes[:, :2] boxes = tv_tensors.BoundingBoxes(boxes, format="XYXY", canvas_size=(H, W)) # The same transforms can be used! img, boxes = transforms(img, boxes) # And you can pass arbitrary input structures output_dict = transforms({"image": img, "boxes": boxes}) Transforms are typically passed as the transform or transforms argument to the Datasets <datasets>. Start here Whether you're new to Torchvision transforms, or you're already experienced with them, we encourage you to start with sphx_glr_auto_examples_transforms_plot_transforms_getting_started.py in order to learn more about what can be done with the new v2 transforms. Then, browse the sections in below this page for general information and performance tips. The available transforms and functionals are listed in the API reference <v2_api_ref>. More information and tutorials can also be found in our example gallery <gallery>, e.g. sphx_glr_auto_examples_transforms_plot_transforms_e2e.py or sphx_glr_auto_examples_transforms_plot_custom_transforms.py. Supported input types and conventions Most transformations accept both PIL images and tensor inputs. Both CPU and CUDA tensors are supported. The result of both backends (PIL or Tensors) should be very close. In general, we recommend relying on the tensor backend for performance <transforms_perf>. The conversion transforms <conversion_transforms> may be used to convert to and from PIL images, or for converting dtypes and ranges. Tensor image are expected to be of shape (C, H, W), where C is the number of channels, and H and W refer to height and width. Most transforms support batched tensor input. A batch of Tensor images is a tensor of shape (N, C, H, W), where N is a number of images in the batch. The v2 <v1_or_v2> transforms generally accept an arbitrary number of leading dimensions (..., C, H, W) and can handle batched images or batched videos. Dtype and expected value range The expected range of the values of a tensor image is implicitly defined by the tensor dtype. Tensor images with a float dtype are expected to have values in [0, 1]. Tensor images with an integer dtype are expected to have values in [0, MAX_DTYPE] where MAX_DTYPE is the largest value that can be represented in that dtype. Typically, images of dtype torch.uint8 are expected to have values in [0, 255]. Use ~torchvision.transforms.v2.ToDtype to convert both the dtype and range of the inputs. V1 or V2? Which one should I use? TL;DR We recommending using the torchvision.transforms.v2 transforms instead of those in torchvision.transforms. They're faster and they can do more things. Just change the import and you should be good to go. In Torchvision 0.15 (March 2023), we released a new set of transforms available in the torchvision.transforms.v2 namespace. These transforms have a lot of advantages compared to the v1 ones (in torchvision.transforms): - They can transform images but also bounding boxes, masks, or videos. This provides support for tasks beyond image classification: detection, segmentation, video classification, etc. See sphx_glr_auto_examples_transforms_plot_transforms_getting_started.py and sphx_glr_auto_examples_transforms_plot_transforms_e2e.py. - They support more transforms like ~torchvision.transforms.v2.CutMix and ~torchvision.transforms.v2.MixUp. See sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py. - They're faster <transforms_perf>. - They support arbitrary input structures (dicts, lists, tuples, etc.). - Future improvements and features will be added to the v2 transforms only. These transforms are fully backward compatible with the v1 ones, so if you're already using tranforms from torchvision.transforms, all you need to do to is to update the import to torchvision.transforms.v2. In terms of output, there might be negligible differences due to implementation differences. Note The v2 transforms are still BETA, but at this point we do not expect disruptive changes to be made to their public APIs. We're planning to make them fully stable in version 0.17. Please submit any feedback you may have here. Performance considerations We recommend the following guidelines to get the best performance out of the transforms: - Rely on the v2 transforms from torchvision.transforms.v2 - Use tensors instead of PIL images - Use torch.uint8 dtype, especially for resizing - Resize with bilinear or bicubic mode This is what a typical transform pipeline could look like: from torchvision.transforms import v2 transforms = v2.Compose([ v2.ToImage(), # Convert to tensor, only needed if you had a PIL image v2.ToDtype(torch.uint8, scale=True), # optional, most input are already uint8 at this point # ... v2.RandomResizedCrop(size=(224, 224), antialias=True), # Or Resize(antialias=True) # ... v2.ToDtype(torch.float32, scale=True), # Normalize expects float input v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) The above should give you the best performance in a typical training environment that relies on the torch.utils.data.DataLoader with num_workers > 0. Transforms tend to be sensitive to the input strides / memory format. Some transforms will be faster with channels-first images while others prefer channels-last. Like torch operators, most transforms will preserve the memory format of the input, but this may not always be respected due to implementation details. You may want to experiment a bit if you're chasing the very best performance. Using torch.compile on individual transforms may also help factoring out the memory format variable (e.g. on ~torchvision.transforms.v2.Normalize). Note that we're talking about memory format, not tensor shape <conventions>. Note that resize transforms like ~torchvision.transforms.v2.Resize and ~torchvision.transforms.v2.RandomResizedCrop typically prefer channels-last input and tend not to benefit from torch.compile at this time. Transform classes, functionals, and kernels Transforms are available as classes like ~torchvision.transforms.v2.Resize, but also as functionals like ~torchvision.transforms.v2.functional.resize in the torchvision.transforms.v2.functional namespace. This is very much like the torch.nn package which defines both classes and functional equivalents in torch.nn.functional. The functionals support PIL images, pure tensors, or TVTensors <tv_tensors>, e.g. both resize(image_tensor) and resize(boxes) are valid. Note Random transforms like ~torchvision.transforms.v2.RandomCrop will randomly sample some parameter each time they're called. Their functional counterpart (~torchvision.transforms.v2.functional.crop) does not do any kind of random sampling and thus have a slighlty different parametrization. The get_params() class method of the transforms class can be used to perform parameter sampling when using the functional APIs. The torchvision.transforms.v2.functional namespace also contains what we call the "kernels". These are the low-level functions that implement the core functionalities for specific types, e.g. resize_bounding_boxes or `resized_crop_mask. They are public, although not documented. Check the code to see which ones are available (note that those starting with a leading underscore are not public!). Kernels are only really useful if you want torchscript support <transforms_torchscript> for types like bounding boxes or masks. Torchscript support Most transform classes and functionals support torchscript. For composing transforms, use torch.nn.Sequential instead of ~torchvision.transforms.v2.Compose: transforms = torch.nn.Sequential( CenterCrop(10), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ) scripted_transforms = torch.jit.script(transforms) Warning v2 transforms support torchscript, but if you call torch.jit.script() on a v2 class transform, you'll actually end up with its (scripted) v1 equivalent. This may lead to slightly different results between the scripted and eager executions due to implementation differences between v1 and v2. If you really need torchscript support for the v2 transforms, we recommend scripting the functionals from the torchvision.transforms.v2.functional namespace to avoid surprises. Also note that the functionals only support torchscript for pure tensors, which are always treated as images. If you need torchscript support for other types like bounding boxes or masks, you can rely on the low-level kernels <functional_transforms>. For any custom transformations to be used with torch.jit.script, they should be derived from torch.nn.Module.

import math import numbers import warnings from enum import Enum from typing import Any, List, Optional, Tuple, Union import numpy as np import torch from PIL import Image from torch import Tensor try: import accimage except ImportError: accimage = None from..utils import _log_api_usage_once from. import _functional_pil as F_pil, _functional_tensor as F_t class InterpolationMode(Enum): """Interpolation modes Available interpolation methods are ``nearest``, ``nearest-exact``, ``bilinear``, ``bicubic``, ``box``, ``hamming``, and ``lanczos``. """ NEAREST = "nearest" NEAREST_EXACT = "nearest-exact" BILINEAR = "bilinear" BICUBIC = "bicubic" # For PIL compatibility BOX = "box" HAMMING = "hamming" LANCZOS = "lanczos" # TODO: Once torchscript supports Enums with staticmethod # this can be put into InterpolationMode as staticmethod def _interpolation_modes_from_int(i: int) -> InterpolationMode: inverse_modes_mapping = { 0: InterpolationMode.NEAREST, 2: InterpolationMode.BILINEAR, 3: InterpolationMode.BICUBIC, 4: InterpolationMode.BOX, 5: InterpolationMode.HAMMING, 1: InterpolationMode.LANCZOS, } return inverse_modes_mapping[i] pil_modes_mapping = { InterpolationMode.NEAREST: 0, InterpolationMode.BILINEAR: 2, InterpolationMode.BICUBIC: 3, InterpolationMode.NEAREST_EXACT: 0, InterpolationMode.BOX: 4, InterpolationMode.HAMMING: 5, InterpolationMode.LANCZOS: 1, } _is_pil_image = F_pil._is_pil_image def get_dimensions(img: Tensor) -> List[int]: """Returns the dimensions of an image as [channels, height, width]. Args: img (PIL Image or Tensor): The image to be checked. Returns: List[int]: The image dimensions. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(get_dimensions) if isinstance(img, torch.Tensor): return F_t.get_dimensions(img) return F_pil.get_dimensions(img) def get_image_size(img: Tensor) -> List[int]: """Returns the size of an image as [width, height]. Args: img (PIL Image or Tensor): The image to be checked. Returns: List[int]: The image size. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(get_image_size) if isinstance(img, torch.Tensor): return F_t.get_image_size(img) return F_pil.get_image_size(img) def get_image_num_channels(img: Tensor) -> int: """Returns the number of channels of an image. Args: img (PIL Image or Tensor): The image to be checked. Returns: int: The number of channels. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(get_image_num_channels) if isinstance(img, torch.Tensor): return F_t.get_image_num_channels(img) return F_pil.get_image_num_channels(img) @torch.jit.unused def _is_numpy(img: Any) -> bool: return isinstance(img, np.ndarray) @torch.jit.unused def _is_numpy_image(img: Any) -> bool: return img.ndim in {2, 3} def to_tensor(pic) -> Tensor: """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor. This function does not support torchscript. See :class:`~torchvision.transforms.ToTensor` for more details. Args: pic (PIL Image or numpy.ndarray): Image to be converted to tensor. Returns: Tensor: Converted image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(to_tensor) if not (F_pil._is_pil_image(pic) or _is_numpy(pic)): raise TypeError(f"pic should be PIL Image or ndarray. Got {type(pic)}") if _is_numpy(pic) and not _is_numpy_image(pic): raise ValueError(f"pic should be 2/3 dimensional. Got {pic.ndim} dimensions.") default_float_dtype = torch.get_default_dtype() if isinstance(pic, np.ndarray): # handle numpy array if pic.ndim == 2: pic = pic[:, :, None] img = torch.from_numpy(pic.transpose((2, 0, 1))).contiguous() # backward compatibility if isinstance(img, torch.ByteTensor): return img.to(dtype=default_float_dtype).div(255) else: return img if accimage is not None and isinstance(pic, accimage.Image): nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32) pic.copyto(nppic) return torch.from_numpy(nppic).to(dtype=default_float_dtype) # handle PIL Image mode_to_nptype = {"I": np.int32, "I;16": np.int16, "F": np.float32} img = torch.from_numpy(np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True)) if pic.mode == "1": img = 255 * img img = img.view(pic.size[1], pic.size[0], F_pil.get_image_num_channels(pic)) # put it from HWC to CHW format img = img.permute((2, 0, 1)).contiguous() if isinstance(img, torch.ByteTensor): return img.to(dtype=default_float_dtype).div(255) else: return img def pil_to_tensor(pic: Any) -> Tensor: """Convert a ``PIL Image`` to a tensor of the same type. This function does not support torchscript. See :class:`~torchvision.transforms.PILToTensor` for more details. .. note:: A deep copy of the underlying array is performed. Args: pic (PIL Image): Image to be converted to tensor. Returns: Tensor: Converted image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(pil_to_tensor) if not F_pil._is_pil_image(pic): raise TypeError(f"pic should be PIL Image. Got {type(pic)}") if accimage is not None and isinstance(pic, accimage.Image): # accimage format is always uint8 internally, so always return uint8 here nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.uint8) pic.copyto(nppic) return torch.as_tensor(nppic) # handle PIL Image img = torch.as_tensor(np.array(pic, copy=True)) img = img.view(pic.size[1], pic.size[0], F_pil.get_image_num_channels(pic)) # put it from HWC to CHW format img = img.permute((2, 0, 1)) return img def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor: """Convert a tensor image to the given ``dtype`` and scale the values accordingly This function does not support PIL Image. Args: image (torch.Tensor): Image to be converted dtype (torch.dtype): Desired data type of the output Returns: Tensor: Converted image .. note:: When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly. If converted back and forth, this mismatch has no effect. Raises: RuntimeError: When trying to cast :class:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as well as for trying to cast :class:`torch.float64` to :class:`torch.int64`. These conversions might lead to overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range of the integer ``dtype``. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(convert_image_dtype) if not isinstance(image, torch.Tensor): raise TypeError("Input img should be Tensor Image") return F_t.convert_image_dtype(image, dtype) def to_pil_image(pic, mode=None): """Convert a tensor or an ndarray to PIL Image. This function does not support torchscript. See :class:`~torchvision.transforms.ToPILImage` for more details. Args: pic (Tensor or numpy.ndarray): Image to be converted to PIL Image. mode (`PIL.Image mode`_): color space and pixel depth of input data (optional). .. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes Returns: PIL Image: Image converted to PIL Image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(to_pil_image) if not (isinstance(pic, torch.Tensor) or isinstance(pic, np.ndarray)): raise TypeError(f"pic should be Tensor or ndarray. Got {type(pic)}.") elif isinstance(pic, torch.Tensor): if pic.ndimension() not in {2, 3}: raise ValueError(f"pic should be 2/3 dimensional. Got {pic.ndimension()} dimensions.") elif pic.ndimension() == 2: # if 2D image, add channel dimension (CHW) pic = pic.unsqueeze(0) # check number of channels if pic.shape[-3] > 4: raise ValueError(f"pic should not have > 4 channels. Got {pic.shape[-3]} channels.") elif isinstance(pic, np.ndarray): if pic.ndim not in {2, 3}: raise ValueError(f"pic should be 2/3 dimensional. Got {pic.ndim} dimensions.") elif pic.ndim == 2: # if 2D image, add channel dimension (HWC) pic = np.expand_dims(pic, 2) # check number of channels if pic.shape[-1] > 4: raise ValueError(f"pic should not have > 4 channels. Got {pic.shape[-1]} channels.") npimg = pic if isinstance(pic, torch.Tensor): if pic.is_floating_point() and mode!= "F": pic = pic.mul(255).byte() npimg = np.transpose(pic.cpu().numpy(), (1, 2, 0)) if not isinstance(npimg, np.ndarray): raise TypeError("Input pic must be a torch.Tensor or NumPy ndarray, not {type(npimg)}") if npimg.shape[2] == 1: expected_mode = None npimg = npimg[:, :, 0] if npimg.dtype == np.uint8: expected_mode = "L" elif npimg.dtype == np.int16: expected_mode = "I;16" elif npimg.dtype == np.int32: expected_mode = "I" elif npimg.dtype == np.float32: expected_mode = "F" if mode is not None and mode!= expected_mode: raise ValueError(f"Incorrect mode ({mode}) supplied for input type {np.dtype}. Should be {expected_mode}") mode = expected_mode elif npimg.shape[2] == 2: permitted_2_channel_modes = ["LA"] if mode is not None and mode not in permitted_2_channel_modes: raise ValueError(f"Only modes {permitted_2_channel_modes} are supported for 2D inputs") if mode is None and npimg.dtype == np.uint8: mode = "LA" elif npimg.shape[2] == 4: permitted_4_channel_modes = ["RGBA", "CMYK", "RGBX"] if mode is not None and mode not in permitted_4_channel_modes: raise ValueError(f"Only modes {permitted_4_channel_modes} are supported for 4D inputs") if mode is None and npimg.dtype == np.uint8: mode = "RGBA" else: permitted_3_channel_modes = ["RGB", "YCbCr", "HSV"] if mode is not None and mode not in permitted_3_channel_modes: raise ValueError(f"Only modes {permitted_3_channel_modes} are supported for 3D inputs") if mode is None and npimg.dtype == np.uint8: mode = "RGB" if mode is None: raise TypeError(f"Input type {npimg.dtype} is not supported") return Image.fromarray(npimg, mode=mode) def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool = False) -> Tensor: """Normalize a float tensor image with mean and standard deviation. This transform does not support PIL Image. .. note:: This transform acts out of place by default, i.e., it does not mutates the input tensor. See :class:`~torchvision.transforms.Normalize` for more details. Args: tensor (Tensor): Float tensor image of size (C, H, W) or (B, C, H, W) to be normalized. mean (sequence): Sequence of means for each channel. std (sequence): Sequence of standard deviations for each channel. inplace(bool,optional): Bool to make this operation inplace. Returns: Tensor: Normalized Tensor image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(normalize) if not isinstance(tensor, torch.Tensor): raise TypeError(f"img should be Tensor Image. Got {type(tensor)}") return F_t.normalize(tensor, mean=mean, std=std, inplace=inplace) def _compute_resized_output_size( image_size: Tuple[int, int], size: List[int], max_size: Optional[int] = None ) -> List[int]: if len(size) == 1: # specified size only for the smallest edge h, w = image_size short, long = (w, h) if w <= h else (h, w) requested_new_short = size if isinstance(size, int) else size[0] new_short, new_long = requested_new_short, int(requested_new_short * long / short) if max_size is not None: if max_size <= requested_new_short: raise ValueError( f"max_size = {max_size} must be strictly greater than the requested " f"size for the smaller edge size = {size}" ) if new_long > max_size: new_short, new_long = int(max_size * new_short / new_long), max_size new_w, new_h = (new_short, new_long) if w <= h else (new_long, new_short) else: # specified both h and w new_w, new_h = size[1], size[0] return [new_h, new_w] def resize( img: Tensor, size: List[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR, max_size: Optional[int] = None, antialias: Optional[Union[str, bool]] = "warn", ) -> Tensor: r"""Resize the input image to the given size. If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means an arbitrary number of leading dimensions .. warning:: The output image might be different depending on its type: when downsampling, the interpolation of PIL images and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences in the performance of a network. Therefore, it is preferable to train and serve a model with the same input types. See also below the ``antialias`` parameter, which can help making the output of PIL images and tensors closer. Args: img (PIL Image or Tensor): Image to be resized. size (sequence or int): Desired output size. If size is a sequence like (h, w), the output size will be matched to this. If size is an int, the smaller edge of the image will be matched to this number maintaining the aspect ratio. i.e, if height > width, then image will be rescaled to :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`. .. note:: In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``. interpolation (InterpolationMode): Desired interpolation enum defined by :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``, ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. max_size (int, optional): The maximum allowed for the longer edge of the resized image. If the longer edge of the image is greater than ``max_size`` after being resized according to ``size``, ``size`` will be overruled so that the longer edge is equal to ``max_size``. As a result, the smaller edge may be shorter than ``size``. This is only supported if ``size`` is an int (or a sequence of length 1 in torchscript mode). antialias (bool, optional): Whether to apply antialiasing. It only affects **tensors** with bilinear or bicubic modes and it is ignored otherwise: on PIL images, antialiasing is always applied on bilinear or bicubic modes; on other modes (for PIL images and tensors), antialiasing makes no sense and this parameter is ignored. Possible values are: - ``True``: will apply antialiasing for bilinear or bicubic modes. Other mode aren't affected. This is probably what you want to use. - ``False``: will not apply antialiasing for tensors on any mode. PIL images are still antialiased on bilinear or bicubic modes, because PIL doesn't support no antialias. - ``None``: equivalent to ``False`` for tensors and ``True`` for PIL images. This value exists for legacy reasons and you probably don't want to use it unless you really know what you are doing. The current default is ``None`` **but will change to** ``True`` **in v0.17** for the PIL and Tensor backends to be consistent. Returns: PIL Image or Tensor: Resized image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(resize) if isinstance(interpolation, int): interpolation = _interpolation_modes_from_int(interpolation) elif not isinstance(interpolation, InterpolationMode): raise TypeError( "Argument interpolation should be a InterpolationMode or a corresponding Pillow integer constant" ) if isinstance(size, (list, tuple)): if len(size) not in [1, 2]: raise ValueError( f"Size must be an int or a 1 or 2 element tuple/list, not a {len(size)} element tuple/list" ) if max_size is not None and len(size)!= 1: raise ValueError( "max_size should only be passed if size specifies the length of the smaller edge, " "i.e. size should be an int or a sequence of length 1 in torchscript mode." ) _, image_height, image_width = get_dimensions(img) if isinstance(size, int): size = [size] output_size = _compute_resized_output_size((image_height, image_width), size, max_size) if [image_height, image_width] == output_size: return img antialias = _check_antialias(img, antialias, interpolation) if not isinstance(img, torch.Tensor): if antialias is False: warnings.warn("Anti-alias option is always applied for PIL Image input. Argument antialias is ignored.") pil_interpolation = pil_modes_mapping[interpolation] return F_pil.resize(img, size=output_size, interpolation=pil_interpolation) return F_t.resize(img, size=output_size, interpolation=interpolation.value, antialias=antialias) def pad(img: Tensor, padding: List[int], fill: Union[int, float] = 0, padding_mode: str = "constant") -> Tensor: r"""Pad the given image on all sides with the given "pad" value. If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means at most 2 leading dimensions for mode reflect and symmetric, at most 3 leading dimensions for mode edge, and an arbitrary number of leading dimensions for mode constant Args: img (PIL Image or Tensor): Image to be padded. padding (int or sequence): Padding on each border. If a single int is provided this is used to pad all borders. If sequence of length 2 is provided this is the padding on left/right and top/bottom respectively. If a sequence of length 4 is provided this is the padding for the left, top, right and bottom borders respectively. .. note:: In torchscript mode padding as single int is not supported, use a sequence of length 1: ``[padding, ]``. fill (number or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant. Only number is supported for torch Tensor. Only int or tuple value is supported for PIL Image. padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant. - constant: pads with a constant value, this value is specified with fill - edge: pads with the last value at the edge of the image. If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2 - reflect: pads with reflection of image without repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2] - symmetric: pads with reflection of image repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3] Returns: PIL Image or Tensor: Padded image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(pad) if not isinstance(img, torch.Tensor): return F_pil.pad(img, padding=padding, fill=fill, padding_mode=padding_mode) return F_t.pad(img, padding=padding, fill=fill, padding_mode=padding_mode) def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor: """Crop the given image at specified location and output size. If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then cropped. Args: img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image. top (int): Vertical component of the top left corner of the crop box. left (int): Horizontal component of the top left corner of the crop box. height (int): Height of the crop box. width (int): Width of the crop box. Returns: PIL Image or Tensor: Cropped image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(crop) if not isinstance(img, torch.Tensor): return F_pil.crop(img, top, left, height, width) return F_t.crop(img, top, left, height, width) def center_crop(img: Tensor, output_size: List[int]) -> Tensor: """Crops the given image at the center. If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. Args: img (PIL Image or Tensor): Image to be cropped. output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int, it is used for both directions. Returns: PIL Image or Tensor: Cropped image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(center_crop) if isinstance(output_size, numbers.Number): output_size = (int(output_size), int(output_size)) elif isinstance(output_size, (tuple, list)) and len(output_size) == 1: output_size = (output_size[0], output_size[0]) _, image_height, image_width = get_dimensions(img) crop_height, crop_width = output_size if crop_width > image_width or crop_height > image_height: padding_ltrb = [ (crop_width - image_width) // 2 if crop_width > image_width else 0, (crop_height - image_height) // 2 if crop_height > image_height else 0, (crop_width - image_width + 1) // 2 if crop_width > image_width else 0, (crop_height - image_height + 1) // 2 if crop_height > image_height else 0, ] img = pad(img, padding_ltrb, fill=0) # PIL uses fill value 0 _, image_height, image_width = get_dimensions(img) if crop_width == image_width and crop_height == image_height: return img crop_top = int(round((image_height - crop_height) / 2.0)) crop_left = int(round((image_width - crop_width) / 2.0)) return crop(img, crop_top, crop_left, crop_height, crop_width) def resized_crop( img: Tensor, top: int, left: int, height: int, width: int, size: List[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR, antialias: Optional[Union[str, bool]] = "warn", ) -> Tensor: """Crop the given image and resize it to desired size. If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means an arbitrary number of leading dimensions Notably used in :class:`~torchvision.transforms.RandomResizedCrop`. Args: img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image. top (int): Vertical component of the top left corner of the crop box. left (int): Horizontal component of the top left corner of the crop box. height (int): Height of the crop box. width (int): Width of the crop box. size (sequence or int): Desired output size. Same semantics as ``resize``. interpolation (InterpolationMode): Desired interpolation enum defined by :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``, ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. antialias (bool, optional): Whether to apply antialiasing. It only affects **tensors** with bilinear or bicubic modes and it is ignored otherwise: on PIL images, antialiasing is always applied on bilinear or bicubic modes; on other modes (for PIL images and tensors), antialiasing makes no sense and this parameter is ignored. Possible values are: - ``True``: will apply antialiasing for bilinear or bicubic modes. Other mode aren't affected. This is probably what you want to use. - ``False``: will not apply antialiasing for tensors on any mode. PIL images are still antialiased on bilinear or bicubic modes, because PIL doesn't support no antialias. - ``None``: equivalent to ``False`` for tensors and ``True`` for PIL images. This value exists for legacy reasons and you probably don't want to use it unless you really know what you are doing. The current default is ``None`` **but will change to** ``True`` **in v0.17** for the PIL and Tensor backends to be consistent. Returns: PIL Image or Tensor: Cropped image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(resized_crop) img = crop(img, top, left, height, width) img = resize(img, size, interpolation, antialias=antialias) return img def hflip(img: Tensor) -> Tensor: """Horizontally flip the given image. Args: img (PIL Image or Tensor): Image to be flipped. If img is a Tensor, it is expected to be in [..., H, W] format, where... means it can have an arbitrary number of leading dimensions. Returns: PIL Image or Tensor: Horizontally flipped image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(hflip) if not isinstance(img, torch.Tensor): return F_pil.hflip(img) return F_t.hflip(img) def _get_perspective_coeffs(startpoints: List[List[int]], endpoints: List[List[int]]) -> List[float]: """Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms. In Perspective Transform each pixel (x, y) in the original image gets transformed as, (x, y) -> ( (ax + by + c) / (gx + hy + 1), (dx + ey + f) / (gx + hy + 1) ) Args: startpoints (list of list of ints): List containing four lists of two integers corresponding to four corners ``[top-left, top-right, bottom-right, bottom-left]`` of the original image. endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners ``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image. Returns: octuple (a, b, c, d, e, f, g, h) for transforming each pixel. """ a_matrix = torch.zeros(2 * len(startpoints), 8, dtype=torch.float) for i, (p1, p2) in enumerate(zip(endpoints, startpoints)): a_matrix[2 * i, :] = torch.tensor([p1[0], p1[1], 1, 0, 0, 0, -p2[0] * p1[0], -p2[0] * p1[1]]) a_matrix[2 * i + 1, :] = torch.tensor([0, 0, 0, p1[0], p1[1], 1, -p2[1] * p1[0], -p2[1] * p1[1]]) b_matrix = torch.tensor(startpoints, dtype=torch.float).view(8) res = torch.linalg.lstsq(a_matrix, b_matrix, driver="gels").solution output: List[float] = res.tolist() return output def perspective( img: Tensor, startpoints: List[List[int]], endpoints: List[List[int]], interpolation: InterpolationMode = InterpolationMode.BILINEAR, fill: Optional[List[float]] = None, ) -> Tensor: """Perform perspective transform of the given image. If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means an arbitrary number of leading dimensions. Args: img (PIL Image or Tensor): Image to be transformed. startpoints (list of list of ints): List containing four lists of two integers corresponding to four corners ``[top-left, top-right, bottom-right, bottom-left]`` of the original image. endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners ``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image. interpolation (InterpolationMode): Desired interpolation enum defined by :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. fill (sequence or number, optional): Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively. .. note:: In torchscript mode single int/float value is not supported, please use a sequence of length 1: ``[value, ]``. Returns: PIL Image or Tensor: transformed Image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(perspective) coeffs = _get_perspective_coeffs(startpoints, endpoints) if isinstance(interpolation, int): interpolation = _interpolation_modes_from_int(interpolation) elif not isinstance(interpolation, InterpolationMode): raise TypeError( "Argument interpolation should be a InterpolationMode or a corresponding Pillow integer constant" ) if not isinstance(img, torch.Tensor): pil_interpolation = pil_modes_mapping[interpolation] return F_pil.perspective(img, coeffs, interpolation=pil_interpolation, fill=fill) return F_t.perspective(img, coeffs, interpolation=interpolation.value, fill=fill) def vflip(img: Tensor) -> Tensor: """Vertically flip the given image. Args: img (PIL Image or Tensor): Image to be flipped. If img is a Tensor, it is expected to be in [..., H, W] format, where... means it can have an arbitrary number of leading dimensions. Returns: PIL Image or Tensor: Vertically flipped image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(vflip) if not isinstance(img, torch.Tensor): return F_pil.vflip(img) return F_t.vflip(img) def five_crop(img: Tensor, size: List[int]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: """Crop the given image into four corners and the central crop. If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means an arbitrary number of leading dimensions .. Note:: This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets your ``Dataset`` returns. Args: img (PIL Image or Tensor): Image to be cropped. size (sequence or int): Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). Returns: tuple: tuple (tl, tr, bl, br, center) Corresponding top left, top right, bottom left, bottom right and center crop. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(five_crop) if isinstance(size, numbers.Number): size = (int(size), int(size)) elif isinstance(size, (tuple, list)) and len(size) == 1: size = (size[0], size[0]) if len(size)!= 2: raise ValueError("Please provide only two dimensions (h, w) for size.") _, image_height, image_width = get_dimensions(img) crop_height, crop_width = size if crop_width > image_width or crop_height > image_height: msg = "Requested crop size {} is bigger than input size {}" raise ValueError(msg.format(size, (image_height, image_width))) tl = crop(img, 0, 0, crop_height, crop_width) tr = crop(img, 0, image_width - crop_width, crop_height, crop_width) bl = crop(img, image_height - crop_height, 0, crop_height, crop_width) br = crop(img, image_height - crop_height, image_width - crop_width, crop_height, crop_width) center = center_crop(img, [crop_height, crop_width]) return tl, tr, bl, br, center def ten_crop( img: Tensor, size: List[int], vertical_flip: bool = False ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]: """Generate ten cropped images from the given image. Crop the given image into four corners and the central crop plus the flipped version of these (horizontal flipping is used by default). If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means an arbitrary number of leading dimensions .. Note:: This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets your ``Dataset`` returns. Args: img (PIL Image or Tensor): Image to be cropped. size (sequence or int): Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). vertical_flip (bool): Use vertical flipping instead of horizontal Returns: tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip) Corresponding top left, top right, bottom left, bottom right and center crop and same for the flipped image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(ten_crop) if isinstance(size, numbers.Number): size = (int(size), int(size)) elif isinstance(size, (tuple, list)) and len(size) == 1: size = (size[0], size[0]) if len(size)!= 2: raise ValueError("Please provide only two dimensions (h, w) for size.") first_five = five_crop(img, size) if vertical_flip: img = vflip(img) else: img = hflip(img) second_five = five_crop(img, size) return first_five + second_five def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor: """Adjust brightness of an image. Args: img (PIL Image or Tensor): Image to be adjusted. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. brightness_factor (float): How much to adjust the brightness. Can be any non-negative number. 0 gives a black image, 1 gives the original image while 2 increases the brightness by a factor of 2. Returns: PIL Image or Tensor: Brightness adjusted image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(adjust_brightness) if not isinstance(img, torch.Tensor): return F_pil.adjust_brightness(img, brightness_factor) return F_t.adjust_brightness(img, brightness_factor) def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor: """Adjust contrast of an image. Args: img (PIL Image or Tensor): Image to be adjusted. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. contrast_factor (float): How much to adjust the contrast. Can be any non-negative number. 0 gives a solid gray image, 1 gives the original image while 2 increases the contrast by a factor of 2. Returns: PIL Image or Tensor: Contrast adjusted image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(adjust_contrast) if not isinstance(img, torch.Tensor): return F_pil.adjust_contrast(img, contrast_factor) return F_t.adjust_contrast(img, contrast_factor) def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor: """Adjust color saturation of an image. Args: img (PIL Image or Tensor): Image to be adjusted. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. saturation_factor (float): How much to adjust the saturation. 0 will give a black and white image, 1 will give the original image while 2 will enhance the saturation by a factor of 2. Returns: PIL Image or Tensor: Saturation adjusted image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(adjust_saturation) if not isinstance(img, torch.Tensor): return F_pil.adjust_saturation(img, saturation_factor) return F_t.adjust_saturation(img, saturation_factor) def adjust_hue(img: Tensor, hue_factor: float) -> Tensor: """Adjust hue of an image. The image hue is adjusted by converting the image to HSV and cyclically shifting the intensities in the hue channel (H). The image is then converted back to original image mode. `hue_factor` is the amount of shift in H channel and must be in the interval `[-0.5, 0.5]`. See `Hue`_ for more details. .. _Hue: https://en.wikipedia.org/wiki/Hue Args: img (PIL Image or Tensor): Image to be adjusted. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. If img is PIL Image mode "1", "I", "F" and modes with transparency (alpha channel) are not supported. Note: the pixel values of the input image has to be non-negative for conversion to HSV space; thus it does not work if you normalize your image to an interval with negative values, or use an interpolation that generates negative values before using this function. hue_factor (float): How much to shift the hue channel. Should be in [-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in HSV space in positive and negative direction respectively. 0 means no shift. Therefore, both -0.5 and 0.5 will give an image with complementary colors while 0 gives the original image. Returns: PIL Image or Tensor: Hue adjusted image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(adjust_hue) if not isinstance(img, torch.Tensor): return F_pil.adjust_hue(img, hue_factor) return F_t.adjust_hue(img, hue_factor) def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor: r"""Perform gamma correction on an image. Also known as Power Law Transform. Intensities in RGB mode are adjusted based on the following equation: .. math:: I_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma} See `Gamma Correction`_ for more details. .. _Gamma Correction: https://en.wikipedia.org/wiki/Gamma_correction Args: img (PIL Image or Tensor): PIL Image to be adjusted. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. If img is PIL Image, modes with transparency (alpha channel) are not supported. gamma (float): Non negative real number, same as :math:`\gamma` in the equation. gamma larger than 1 make the shadows darker, while gamma smaller than 1 make dark regions lighter. gain (float): The constant multiplier. Returns: PIL Image or Tensor: Gamma correction adjusted image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(adjust_gamma) if not isinstance(img, torch.Tensor): return F_pil.adjust_gamma(img, gamma, gain) return F_t.adjust_gamma(img, gamma, gain) def _get_inverse_affine_matrix( center: List[float], angle: float, translate: List[float], scale: float, shear: List[float], inverted: bool = True ) -> List[float]: # Helper method to compute inverse matrix for affine transformation # Pillow requires inverse affine transformation matrix: # Affine matrix is : M = T * C * RotateScaleShear * C^-1 # # where T is translation matrix: [1, 0, tx | 0, 1, ty | 0, 0, 1] # C is translation matrix to keep center: [1, 0, cx | 0, 1, cy | 0, 0, 1] # RotateScaleShear is rotation with scale and shear matrix # # RotateScaleShear(a, s, (sx, sy)) = # = R(a) * S(s) * SHy(sy) * SHx(sx) # = [ s*cos(a - sy)/cos(sy), s*(-cos(a - sy)*tan(sx)/cos(sy) - sin(a)), 0 ] # [ s*sin(a - sy)/cos(sy), s*(-sin(a - sy)*tan(sx)/cos(sy) + cos(a)), 0 ] # [ 0 , 0 , 1 ] # where R is a rotation matrix, S is a scaling matrix, and SHx and SHy are the shears: # SHx(s) = [1, -tan(s)] and SHy(s) = [1 , 0] # [0, 1 ] [-tan(s), 1] # # Thus, the inverse is M^-1 = C * RotateScaleShear^-1 * C^-1 * T^-1 rot = math.radians(angle) sx = math.radians(shear[0]) sy = math.radians(shear[1]) cx, cy = center tx, ty = translate # RSS without scaling a = math.cos(rot - sy) / math.cos(sy) b = -math.cos(rot - sy) * math.tan(sx) / math.cos(sy) - math.sin(rot) c = math.sin(rot - sy) / math.cos(sy) d = -math.sin(rot - sy) * math.tan(sx) / math.cos(sy) + math.cos(rot) if inverted: # Inverted rotation matrix with scale and shear # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1 matrix = [d, -b, 0.0, -c, a, 0.0] matrix = [x / scale for x in matrix] # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1 matrix[2] += matrix[0] * (-cx - tx) + matrix[1] * (-cy - ty) matrix[5] += matrix[3] * (-cx - tx) + matrix[4] * (-cy - ty) # Apply center translation: C * RSS^-1 * C^-1 * T^-1 matrix[2] += cx matrix[5] += cy else: matrix = [a, b, 0.0, c, d, 0.0] matrix = [x * scale for x in matrix] # Apply inverse of center translation: RSS * C^-1 matrix[2] += matrix[0] * (-cx) + matrix[1] * (-cy) matrix[5] += matrix[3] * (-cx) + matrix[4] * (-cy) # Apply translation and center : T * C * RSS * C^-1 matrix[2] += cx + tx matrix[5] += cy + ty return matrix def rotate( img: Tensor, angle: float, interpolation: InterpolationMode = InterpolationMode.NEAREST, expand: bool = False, center: Optional[List[int]] = None, fill: Optional[List[float]] = None, ) -> Tensor: """Rotate the image by angle. If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means an arbitrary number of leading dimensions. Args: img (PIL Image or Tensor): image to be rotated. angle (number): rotation angle value in degrees, counter-clockwise. interpolation (InterpolationMode): Desired interpolation enum defined by :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. expand (bool, optional): Optional expansion flag. If true, expands the output image to make it large enough to hold the entire rotated image. If false or omitted, make the output image the same size as the input image. Note that the expand flag assumes rotation around the center and no translation. center (sequence, optional): Optional center of rotation. Origin is the upper left corner. Default is the center of the image. fill (sequence or number, optional): Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively. .. note:: In torchscript mode single int/float value is not supported, please use a sequence of length 1: ``[value, ]``. Returns: PIL Image or Tensor: Rotated image. .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(rotate) if isinstance(interpolation, int): interpolation = _interpolation_modes_from_int(interpolation) elif not isinstance(interpolation, InterpolationMode): raise TypeError( "Argument interpolation should be a InterpolationMode or a corresponding Pillow integer constant" ) if not isinstance(angle, (int, float)): raise TypeError("Argument angle should be int or float") if center is not None and not isinstance(center, (list, tuple)): raise TypeError("Argument center should be a sequence") if not isinstance(img, torch.Tensor): pil_interpolation = pil_modes_mapping[interpolation] return F_pil.rotate(img, angle=angle, interpolation=pil_interpolation, expand=expand, center=center, fill=fill) center_f = [0.0, 0.0] if center is not None: _, height, width = get_dimensions(img) # Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center. center_f = [1.0 * (c - s * 0.5) for c, s in zip(center, [width, height])] # due to current incoherence of rotation angle direction between affine and rotate implementations # we need to set -angle. matrix = _get_inverse_affine_matrix(center_f, -angle, [0.0, 0.0], 1.0, [0.0, 0.0]) return F_t.rotate(img, matrix=matrix, interpolation=interpolation.value, expand=expand, fill=fill) def affine( img: Tensor, angle: float, translate: List[int], scale: float, shear: List[float], interpolation: InterpolationMode = InterpolationMode.NEAREST, fill: Optional[List[float]] = None, center: Optional[List[int]] = None, ) -> Tensor: """Apply affine transformation on the image keeping image center invariant. If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means an arbitrary number of leading dimensions. Args: img (PIL Image or Tensor): image to transform. angle (number): rotation angle in degrees between -180 and 180, clockwise direction. translate (sequence of integers): horizontal and vertical translations (post-rotation translation) scale (float): overall scale shear (float or sequence): shear angle value in degrees between -180 to 180, clockwise direction. If a sequence is specified, the first value corresponds to a shear parallel to the x-axis, while the second value corresponds to a shear parallel to the y-axis. interpolation (InterpolationMode): Desired interpolation enum defined by :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. fill (sequence or number, optional): Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively. .. note:: In torchscript mode single int/float value is not supported, please use a sequence of length 1: ``[value, ]``. center (sequence, optional): Optional center of rotation. Origin is the upper left corner. Default is the center of the image. Returns: PIL Image or Tensor: Transformed image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(affine) if isinstance(interpolation, int): interpolation = _interpolation_modes_from_int(interpolation) elif not isinstance(interpolation, InterpolationMode): raise TypeError( "Argument interpolation should be a InterpolationMode or a corresponding Pillow integer constant" ) if not isinstance(angle, (int, float)): raise TypeError("Argument angle should be int or float") if not isinstance(translate, (list, tuple)): raise TypeError("Argument translate should be a sequence") if len(translate)!= 2: raise ValueError("Argument translate should be a sequence of length 2") if scale <= 0.0: raise ValueError("Argument scale should be positive") if not isinstance(shear, (numbers.Number, (list, tuple))): raise TypeError("Shear should be either a single value or a sequence of two values") if isinstance(angle, int): angle = float(angle) if isinstance(translate, tuple): translate = list(translate) if isinstance(shear, numbers.Number): shear = [shear, 0.0] if isinstance(shear, tuple): shear = list(shear) if len(shear) == 1: shear = [shear[0], shear[0]] if len(shear)!= 2: raise ValueError(f"Shear should be a sequence containing two values. Got {shear}") if center is not None and not isinstance(center, (list, tuple)): raise TypeError("Argument center should be a sequence") _, height, width = get_dimensions(img) if not isinstance(img, torch.Tensor): # center = (width * 0.5 + 0.5, height * 0.5 + 0.5) # it is visually better to estimate the center without 0.5 offset # otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine if center is None: center = [width * 0.5, height * 0.5] matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear) pil_interpolation = pil_modes_mapping[interpolation] return F_pil.affine(img, matrix=matrix, interpolation=pil_interpolation, fill=fill) center_f = [0.0, 0.0] if center is not None: _, height, width = get_dimensions(img) # Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center. center_f = [1.0 * (c - s * 0.5) for c, s in zip(center, [width, height])] translate_f = [1.0 * t for t in translate] matrix = _get_inverse_affine_matrix(center_f, angle, translate_f, scale, shear) return F_t.affine(img, matrix=matrix, interpolation=interpolation.value, fill=fill) # Looks like to_grayscale() is a stand-alone functional that is never called # from the transform classes. Perhaps it's still here for BC? I can't be # bothered to dig. @torch.jit.unused def to_grayscale(img, num_output_channels=1): """Convert PIL image of any mode (RGB, HSV, LAB, etc) to grayscale version of image. This transform does not support torch Tensor. Args: img (PIL Image): PIL Image to be converted to grayscale. num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default is 1. Returns: PIL Image: Grayscale version of the image. - if num_output_channels = 1 : returned image is single channel - if num_output_channels = 3 : returned image is 3 channel with r = g = b """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(to_grayscale) if isinstance(img, Image.Image): return F_pil.to_grayscale(img, num_output_channels) raise TypeError("Input should be PIL Image") def rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor: """Convert RGB image to grayscale version of image. If the image is torch Tensor, it is expected to have [..., 3, H, W] shape, where... means an arbitrary number of leading dimensions Note: Please, note that this method supports only RGB images as input. For inputs in other color spaces, please, consider using meth:`~torchvision.transforms.functional.to_grayscale` with PIL Image. Args: img (PIL Image or Tensor): RGB Image to be converted to grayscale. num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default, 1. Returns: PIL Image or Tensor: Grayscale version of the image. - if num_output_channels = 1 : returned image is single channel - if num_output_channels = 3 : returned image is 3 channel with r = g = b """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(rgb_to_grayscale) if not isinstance(img, torch.Tensor): return F_pil.to_grayscale(img, num_output_channels) return F_t.rgb_to_grayscale(img, num_output_channels) def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool = False) -> Tensor: """Erase the input Tensor Image with given value. This transform does not support PIL Image. Args: img (Tensor Image): Tensor image of size (C, H, W) to be erased i (int): i in (i,j) i.e coordinates of the upper left corner. j (int): j in (i,j) i.e coordinates of the upper left corner. h (int): Height of the erased region. w (int): Width of the erased region. v: Erasing value. inplace(bool, optional): For in-place operations. By default, is set False. Returns: Tensor Image: Erased image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(erase) if not isinstance(img, torch.Tensor): raise TypeError(f"img should be Tensor Image. Got {type(img)}") return F_t.erase(img, i, j, h, w, v, inplace=inplace) def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Tensor: """Performs Gaussian blurring on the image by given kernel. If the image is torch Tensor, it is expected to have [..., H, W] shape, where... means an arbitrary number of leading dimensions. Args: img (PIL Image or Tensor): Image to be blurred kernel_size (sequence of ints or int): Gaussian kernel size. Can be a sequence of integers like ``(kx, ky)`` or a single integer for square kernels. .. note:: In torchscript mode kernel_size as single int is not supported, use a sequence of length 1: ``[ksize, ]``. sigma (sequence of floats or float, optional): Gaussian kernel standard deviation. Can be a sequence of floats like ``(sigma_x, sigma_y)`` or a single float to define the same sigma in both X/Y directions. If None, then it is computed using ``kernel_size`` as ``sigma = 0.3 * ((kernel_size - 1) * 0.5 - 1) + 0.8``. Default, None. .. note:: In torchscript mode sigma as single float is not supported, use a sequence of length 1: ``[sigma, ]``. Returns: PIL Image or Tensor: Gaussian Blurred version of the image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(gaussian_blur) if not isinstance(kernel_size, (int, list, tuple)): raise TypeError(f"kernel_size should be int or a sequence of integers. Got {type(kernel_size)}") if isinstance(kernel_size, int): kernel_size = [kernel_size, kernel_size] if len(kernel_size)!= 2: raise ValueError(f"If kernel_size is a sequence its length should be 2. Got {len(kernel_size)}") for ksize in kernel_size: if ksize % 2 == 0 or ksize < 0: raise ValueError(f"kernel_size should have odd and positive integers. Got {kernel_size}") if sigma is None: sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size] if sigma is not None and not isinstance(sigma, (int, float, list, tuple)): raise TypeError(f"sigma should be either float or sequence of floats. Got {type(sigma)}") if isinstance(sigma, (int, float)): sigma = [float(sigma), float(sigma)] if isinstance(sigma, (list, tuple)) and len(sigma) == 1: sigma = [sigma[0], sigma[0]] if len(sigma)!= 2: raise ValueError(f"If sigma is a sequence, its length should be 2. Got {len(sigma)}") for s in sigma: if s <= 0.0: raise ValueError(f"sigma should have positive values. Got {sigma}") t_img = img if not isinstance(img, torch.Tensor): if not F_pil._is_pil_image(img): raise TypeError(f"img should be PIL Image or Tensor. Got {type(img)}") t_img = pil_to_tensor(img) output = F_t.gaussian_blur(t_img, kernel_size, sigma) if not isinstance(img, torch.Tensor): output = to_pil_image(output, mode=img.mode) return output def invert(img: Tensor) -> Tensor: """Invert the colors of an RGB/grayscale image. Args: img (PIL Image or Tensor): Image to have its colors inverted. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode "L" or "RGB". Returns: PIL Image or Tensor: Color inverted image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(invert) if not isinstance(img, torch.Tensor): return F_pil.invert(img) return F_t.invert(img) def posterize(img: Tensor, bits: int) -> Tensor: """Posterize an image by reducing the number of bits for each color channel. Args: img (PIL Image or Tensor): Image to have its colors posterized. If img is torch Tensor, it should be of type torch.uint8, and it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode "L" or "RGB". bits (int): The number of bits to keep for each channel (0-8). Returns: PIL Image or Tensor: Posterized image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(posterize) if not (0 <= bits <= 8): raise ValueError(f"The number if bits should be between 0 and 8. Got {bits}") if not isinstance(img, torch.Tensor): return F_pil.posterize(img, bits) return F_t.posterize(img, bits) def solarize(img: Tensor, threshold: float) -> Tensor: """Solarize an RGB/grayscale image by inverting all pixel values above a threshold. Args: img (PIL Image or Tensor): Image to have its colors inverted. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode "L" or "RGB". threshold (float): All pixels equal or above this value are inverted. Returns: PIL Image or Tensor: Solarized image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(solarize) if not isinstance(img, torch.Tensor): return F_pil.solarize(img, threshold) return F_t.solarize(img, threshold) def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor: """Adjust the sharpness of an image. Args: img (PIL Image or Tensor): Image to be adjusted. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. sharpness_factor (float): How much to adjust the sharpness. Can be any non-negative number. 0 gives a blurred image, 1 gives the original image while 2 increases the sharpness by a factor of 2. Returns: PIL Image or Tensor: Sharpness adjusted image. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(adjust_sharpness) if not isinstance(img, torch.Tensor): return F_pil.adjust_sharpness(img, sharpness_factor) return F_t.adjust_sharpness(img, sharpness_factor) def autocontrast(img: Tensor) -> Tensor: """Maximize contrast of an image by remapping its pixels per channel so that the lowest becomes black and the lightest becomes white. Args: img (PIL Image or Tensor): Image on which autocontrast is applied. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode "L" or "RGB". Returns: PIL Image or Tensor: An image that was autocontrasted. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(autocontrast) if not isinstance(img, torch.Tensor): return F_pil.autocontrast(img) return F_t.autocontrast(img) def equalize(img: Tensor) -> Tensor: """Equalize the histogram of an image by applying a non-linear mapping to the input in order to create a uniform distribution of grayscale values in the output. Args: img (PIL Image or Tensor): Image on which equalize is applied. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. The tensor dtype must be ``torch.uint8`` and values are expected to be in ``[0, 255]``. If img is PIL Image, it is expected to be in mode "P", "L" or "RGB". Returns: PIL Image or Tensor: An image that was equalized. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(equalize) if not isinstance(img, torch.Tensor): return F_pil.equalize(img) return F_t.equalize(img) def elastic_transform( img: Tensor, displacement: Tensor, interpolation: InterpolationMode = InterpolationMode.BILINEAR, fill: Optional[List[float]] = None, ) -> Tensor: """Transform a tensor image with elastic transformations. Given alpha and sigma, it will generate displacement vectors for all pixels based on random offsets. Alpha controls the strength and sigma controls the smoothness of the displacements. The displacements are added to an identity grid and the resulting grid is used to grid_sample from the image. Applications: Randomly transforms the morphology of objects in images and produces a see-through-water-like effect. Args: img (PIL Image or Tensor): Image on which elastic_transform is applied. If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, where... means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode "P", "L" or "RGB". displacement (Tensor): The displacement field. Expected shape is [1, H, W, 2]. interpolation (InterpolationMode): Desired interpolation enum defined by :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. fill (number or str or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant. """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(elastic_transform) # Backward compatibility with integer value if isinstance(interpolation, int): warnings.warn( "Argument interpolation should be of type InterpolationMode instead of int. " "Please, use InterpolationMode enum." ) interpolation = _interpolation_modes_from_int(interpolation) if not isinstance(displacement, torch.Tensor): raise TypeError("Argument displacement should be a Tensor") t_img = img if not isinstance(img, torch.Tensor): if not F_pil._is_pil_image(img): raise TypeError(f"img should be PIL Image or Tensor. Got {type(img)}") t_img = pil_to_tensor(img) shape = t_img.shape shape = (1,) + shape[-2:] + (2,) if shape!= displacement.shape: raise ValueError(f"Argument displacement shape should be {shape}, but given {displacement.shape}") # TODO: if image shape is [N1, N2,..., C, H, W] and # displacement is [1, H, W, 2] we need to reshape input image # such grid_sampler takes internal code for 4D input output = F_t.elastic_transform( t_img, displacement, interpolation=interpolation.value, fill=fill, ) if not isinstance(img, torch.Tensor): output = to_pil_image(output, mode=img.mode) return output # TODO in v0.17: remove this helper and change default of antialias to True everywhere def _check_antialias( img: Tensor, antialias: Optional[Union[str, bool]], interpolation: InterpolationMode ) -> Optional[bool]: if isinstance(antialias, str): # it should be "warn", but we don't bother checking against that if isinstance(img, Tensor) and ( interpolation == InterpolationMode.BILINEAR or interpolation == InterpolationMode.BICUBIC ): warnings.warn( "The default value of the antialias parameter of all the resizing transforms " "(Resize(), RandomResizedCrop(), etc.) " "will change from None to True in v0.17, " "in order to be consistent across the PIL and Tensor backends. " "To suppress this warning, directly pass " "antialias=True (recommended, future default), antialias=None (current default, " "which means False for Tensors and True for PIL), " "or antialias=False (only works on Tensors - PIL will still use antialiasing). " "This also applies if you are using the inference transforms from the models weights: " "update the call to weights.transforms(antialias=True)." ) antialias = None return antialias

pytables__pytables

filenode.rst

Module doc / Tutorial

Generate documentation for this module

BSD 3-Clause New or Revised License

pytables__pytables/doc/source/usersguide/filenode.rst

filenode - simulating a filesystem with PyTables What is filenode? filenode is a module which enables you to create a PyTables database of nodes which can be used like regular opened files in Python. In other words, you can store a file in a PyTables database, and read and write it as you would do with any other file in Python. Used in conjunction with PyTables hierarchical database organization, you can have your database turned into an open, extensible, efficient, high capacity, portable and metadata-rich filesystem for data exchange with other systems (including backup purposes). Between the main features of filenode, one can list: - Open: Since it relies on PyTables, which in turn, sits over HDF5 (see [HDGG1] <HDFG1>), a standard hierarchical data format from NCSA. - Extensible: You can define new types of nodes, and their instances will be safely preserved (as are normal groups, leafs and attributes) by PyTables applications having no knowledge of their types. Moreover, the set of possible attributes for a node is not fixed, so you can define your own node attributes. - Efficient: Thanks to PyTables' proven extreme efficiency on handling huge amounts of data. filenode can make use of PyTables' on-the-fly compression and decompression of data. - High capacity: Since PyTables and HDF5 are designed for massive data storage (they use 64-bit addressing even where the platform does not support it natively). - Portable: Since the HDF5 format has an architecture-neutral design, and the HDF5 libraries and PyTables are known to run under a variety of platforms. Besides that, a PyTables database fits into a single file, which poses no trouble for transportation. - Metadata-rich: Since PyTables can store arbitrary key-value pairs (even Python objects!) for every database node. Metadata may include authorship, keywords, MIME types and encodings, ownership information, access control lists (ACL), decoding functions and anything you can imagine! Finding a filenode node filenode nodes can be recognized because they have a NODE_TYPE system attribute with a 'file' value. It is recommended that you use the File.get_node_attr method of tables.File class to get the NODE_TYPE attribute independently of the nature (group or leaf) of the node, so you do not need to care about. filenode - simulating files inside PyTables The filenode module is part of the nodes sub-package of PyTables. The recommended way to import the module is: >>> from tables.nodes import filenode However, filenode exports very few symbols, so you can import * for interactive usage. In fact, you will most probably only use the NodeType constant and the new_node() and open_node() calls. The NodeType constant contains the value that the NODE_TYPE system attribute of a node file is expected to contain ('file', as we have seen). Although this is not expected to change, you should use filenode.NodeType instead of the literal 'file' when possible. new_node() and open_node() are the equivalent to the Python file() call (alias open()) for ordinary files. Their arguments differ from that of file(), but this is the only point where you will note the difference between working with a node file and working with an ordinary file. For this little tutorial, we will assume that we have a PyTables database opened for writing. Also, if you are somewhat lazy at typing sentences, the code that we are going to explain is included in the examples/filenodes1.py file. You can create a brand new file with these sentences: >>> import tables >>> h5file = tables.open_file('fnode.h5', 'w') Creating a new file node Creation of a new file node is achieved with the new_node() call. You must tell it in which PyTables file you want to create it, where in the PyTables hierarchy you want to create the node and which will be its name. The PyTables file is the first argument to new_node(); it will be also called the 'host PyTables file'. The other two arguments must be given as keyword arguments where and name, respectively. As a result of the call, a brand new appendable and readable file node object is returned. So let us create a new node file in the previously opened h5file PyTables file, named 'fnode_test' and placed right under the root of the database hierarchy. This is that command: >>> fnode = filenode.new_node(h5file, where='/', name='fnode_test') That is basically all you need to create a file node. Simple, isn't it? From that point on, you can use fnode as any opened Python file (i.e. you can write data, read data, lines of text and so on). new_node() accepts some more keyword arguments. You can give a title to your file with the title argument. You can use PyTables' compression features with the filters argument. If you know beforehand the size that your file will have, you can give its final file size in bytes to the expectedsize argument so that the PyTables library would be able to optimize the data access. new_node() creates a PyTables node where it is told to. To prove it, we will try to get the NODE_TYPE attribute from the newly created node: >>> print(h5file.get_node_attr('/fnode_test', 'NODE_TYPE')) file Using a file node As stated above, you can use the new node file as any other opened file. Let us try to write some text in and read it: >>> print("This is a test text line.", file=fnode) >>> print("And this is another one.", file=fnode) >>> print(file=fnode) >>> fnode.write("Of course, file methods can also be used.") >>> >>> fnode.seek(0) # Go back to the beginning of file. >>> >>> for line in fnode: ... print(repr(line)) 'This is a test text line.\\n' 'And this is another one.\\n' '\\n' 'Of course, file methods can also be used.' This was run on a Unix system, so newlines are expressed as 'n'. In fact, you can override the line separator for a file by setting its line_separator property to any string you want. While using a file node, you should take care of closing it before you close the PyTables host file. Because of the way PyTables works, your data it will not be at a risk, but every operation you execute after closing the host file will fail with a ValueError. To close a file node, simply delete it or call its close() method: >>> fnode.close() >>> print(fnode.closed) True Opening an existing file node If you have a file node that you created using new_node(), you can open it later by calling open_node(). Its arguments are similar to that of file() or open(): the first argument is the PyTables node that you want to open (i.e. a node with a NODE_TYPE attribute having a 'file' value), and the second argument is a mode string indicating how to open the file. Contrary to file(), open_node() can not be used to create a new file node. File nodes can be opened in read-only mode ('r') or in read-and-append mode ('a+'). Reading from a file node is allowed in both modes, but appending is only allowed in the second one. Just like Python files do, writing data to an appendable file places it after the file pointer if it is on or beyond the end of the file, or otherwise after the existing data. Let us see an example: >>> node = h5file.root.fnode_test >>> fnode = filenode.open_node(node, 'a+') >>> print(repr(fnode.readline())) 'This is a test text line.\\n' >>> print(fnode.tell()) 26 >>> print("This is a new line.", file=fnode) >>> print(repr(fnode.readline())) '' Of course, the data append process places the pointer at the end of the file, so the last readline() call hit EOF. Let us seek to the beginning of the file to see the whole contents of our file: >>> fnode.seek(0) >>> for line in fnode: ... print(repr(line)) 'This is a test text line.\\n' 'And this is another one.\\n' '\\n' 'Of course, file methods can also be used.This is a new line.\\n' As you can check, the last string we wrote was correctly appended at the end of the file, instead of overwriting the second line, where the file pointer was positioned by the time of the appending. Adding metadata to a file node You can associate arbitrary metadata to any open node file, regardless of its mode, as long as the host PyTables file is writable. Of course, you could use the set_node_attr() method of tables.File to do it directly on the proper node, but filenode offers a much more comfortable way to do it. filenode objects have an attrs property which gives you direct access to their corresponding AttributeSet object. For instance, let us see how to associate MIME type metadata to our file node: >>> fnode.attrs.content_type = 'text/plain; charset=us-ascii' As simple as A-B-C. You can put nearly anything in an attribute, which opens the way to authorship, keywords, permissions and more. Moreover, there is not a fixed list of attributes. However, you should avoid names in all caps or starting with '_', since PyTables and filenode may use them internally. Some valid examples: >>> fnode.attrs.author = "Ivan Vilata i Balaguer" >>> fnode.attrs.creation_date = '2004-10-20T13:25:25+0200' >>> fnode.attrs.keywords_en = ["FileNode", "test", "metadata"] >>> fnode.attrs.keywords_ca = ["FileNode", "prova", "metadades"] >>> fnode.attrs.owner = 'ivan' >>> fnode.attrs.acl = {'ivan': 'rw', '@users': 'r'} You can check that these attributes get stored by running the ptdump command on the host PyTables file. $ ptdump -a fnode.h5:/fnode_test /fnode_test (EArray(113,)) '' /fnode_test.attrs (AttributeSet), 14 attributes: [CLASS := 'EARRAY', EXTDIM := 0, FLAVOR := 'numpy', NODE_TYPE := 'file', NODE_TYPE_VERSION := 2, TITLE := '', VERSION := '1.2', acl := {'ivan': 'rw', '@users': 'r'}, author := 'Ivan Vilata i Balaguer', content_type := 'text/plain; charset=us-ascii', creation_date := '2004-10-20T13:25:25+0200', keywords_ca := ['FileNode', 'prova', 'metadades'], keywords_en := ['FileNode', 'test', 'metadata'], owner := 'ivan'] Note that filenode makes no assumptions about the meaning of your metadata, so its handling is entirely left to your needs and imagination. Complementary notes You can use file nodes and PyTables groups to mimic a filesystem with files and directories. Since you can store nearly anything you want as file metadata, this enables you to use a PyTables file as a portable compressed backup, even between radically different platforms. Take this with a grain of salt, since node files are restricted in their naming (only valid Python identifiers are valid); however, remember that you can use node titles and metadata to overcome this limitation. Also, you may need to devise some strategy to represent special files such as devices, sockets and such (not necessarily using filenode). We are eager to hear your opinion about filenode and its potential uses. Suggestions to improve filenode and create other node types are also welcome. Do not hesitate to contact us! Current limitations filenode is still a young piece of software, so it lacks some functionality. This is a list of known current limitations: 1. Node files can only be opened for read-only or read and append mode. This should be enhanced in the future. 2. Near future? 3. Only binary I/O is supported currently (read/write strings of bytes) 4. There is no universal newline support yet. The only new-line character used at the moment is \n. This is likely to be improved in a near future. 5. Sparse files (files with lots of zeros) are not treated specially; if you want them to take less space, you should be better off using compression. These limitations still make filenode entirely adequate to work with most binary and text files. Of course, suggestions and patches are welcome. See filenode_classes for detailed documentation on the filenode interface.

"""A file interface to nodes for PyTables databases. The FileNode module provides a file interface for using inside of PyTables database files. Use the new_node() function to create a brand new file node which can be read and written as any ordinary Python file. Use the open_node() function to open an existing (i.e. created with new_node()) node for read-only or read-write access. Read acces is always available. Write access (enabled on new files and files opened with mode 'a+') only allows appending data to a file node. Currently only binary I/O is supported. See :ref:`filenode_usersguide` for instructions on use. .. versionchanged:: 3.0 In version 3.0 the module as been completely rewritten to be fully compliant with the interfaces defined in the :mod:`io` module. """ import io import os import re import warnings from pathlib import Path import numpy as np import tables as tb NodeType = 'file' """Value for NODE_TYPE node system attribute.""" NodeTypeVersions = [1, 2] """Supported values for NODE_TYPE_VERSION node system attribute.""" class RawPyTablesIO(io.RawIOBase): """Base class for raw binary I/O on HDF5 files using PyTables.""" # A lambda to turn a size into a shape, for each version. _size_to_shape = [ None, lambda l: (l, 1), lambda l: (l, ), ] def __init__(self, node, mode=None): super().__init__() self._check_node(node) self._check_attributes(node) if mode is None: mode = node._v_file.mode else: self._check_mode(mode) self._cross_check_mode(mode, node._v_file.mode) self._node = node self._mode = mode self._pos = 0 self._version = int(node.attrs.NODE_TYPE_VERSION) self._vshape = self._size_to_shape[self._version] self._vtype = node.atom.dtype.base.type # read only attribute @property def mode(self): """File mode.""" return self._mode # def tell(self) -> int: def tell(self): """Return current stream position.""" self._checkClosed() return self._pos # def seek(self, pos: int, whence: int = 0) -> int: def seek(self, pos, whence=0): """Change stream position. Change the stream position to byte offset offset. offset is interpreted relative to the position indicated by whence. Values for whence are: * 0 -- start of stream (the default); offset should be zero or positive * 1 -- current stream position; offset may be negative * 2 -- end of stream; offset is usually negative Return the new absolute position. """ self._checkClosed() try: pos = pos.__index__() # except AttributeError as err: # raise TypeError("an integer is required") from err except AttributeError: raise TypeError("an integer is required") if whence == 0: if pos < 0: raise ValueError(f"negative seek position {pos!r}") self._pos = pos elif whence == 1: self._pos = max(0, self._pos + pos) elif whence == 2: self._pos = max(0, self._node.nrows + pos) else: raise ValueError("invalid whence value") return self._pos # def seekable(self) -> bool: def seekable(self): """Return whether object supports random access. If False, seek(), tell() and truncate() will raise IOError. This method may need to do a test seek(). """ return True # def fileno(self) -> int: def fileno(self): """Returns underlying file descriptor if one exists. An IOError is raised if the IO object does not use a file descriptor. """ self._checkClosed() return self._node._v_file.fileno() # def close(self) -> None: def close(self): """Flush and close the IO object. This method has no effect if the file is already closed. """ if not self.closed: if getattr(self._node, '_v_file', None) is None: warnings.warn("host PyTables file is already closed!") try: super().close() finally: # Release node object to allow closing the file. self._node = None def flush(self): """Flush write buffers, if applicable. This is not implemented for read-only and non-blocking streams. """ self._checkClosed() self._node.flush() # def truncate(self, pos: int = None) -> int: def truncate(self, pos=None): """Truncate file to size bytes. Size defaults to the current IO position as reported by tell(). Return the new size. Currently, this method only makes sense to grow the file node, since data can not be rewritten nor deleted. """ self._checkClosed() self._checkWritable() if pos is None: pos = self._pos elif pos < 0: raise ValueError(f"negative truncate position {pos!r}") if pos < self._node.nrows: raise OSError("truncating is only allowed for growing a file") self._append_zeros(pos - self._node.nrows) return self.seek(pos) # def readable(self) -> bool: def readable(self): """Return whether object was opened for reading. If False, read() will raise IOError. """ mode = self._mode return 'r' in mode or '+' in mode # def writable(self) -> bool: def writable(self): """Return whether object was opened for writing. If False, write() and truncate() will raise IOError. """ mode = self._mode return 'w' in mode or 'a' in mode or '+' in mode # def readinto(self, b: bytearray) -> int: def readinto(self, b): """Read up to len(b) bytes into b. Returns number of bytes read (0 for EOF), or None if the object is set not to block as has no data to read. """ self._checkClosed() self._checkReadable() if self._pos >= self._node.nrows: return 0 n = len(b) start = self._pos stop = self._pos + n # XXX optimized path # if stop <= self._node.nrows and isinstance(b, np.ndarray): # self._node.read(start, stop, out=b) # self._pos += n # return n if stop > self._node.nrows: stop = self._node.nrows n = stop - start # XXX This ought to work with anything that supports the buffer API b[:n] = self._node.read(start, stop).tobytes() self._pos += n return n # def readline(self, limit: int = -1) -> bytes: def readline(self, limit=-1): """Read and return a line from the stream. If limit is specified, at most limit bytes will be read. The line terminator is always ``\\n`` for binary files; for text files, the newlines argument to open can be used to select the line terminator(s) recognized. """ self._checkClosed() self._checkReadable() chunksize = self._node.chunkshape[0] if self._node.chunkshape else -1 # XXX: check lsep = b'\n' lseplen = len(lsep) # Set the remaining bytes to read to the specified size. remsize = limit partial = [] finished = False while not finished: # Read a string limited by the remaining number of bytes. if limit <= 0: ibuff = self.read(chunksize) else: ibuff = self.read(min(remsize, chunksize)) ibufflen = len(ibuff) remsize -= ibufflen if ibufflen >= lseplen: # Separator fits, look for EOL string. eolindex = ibuff.find(lsep) elif ibufflen == 0: # EOF was immediately reached. finished = True continue else: # ibufflen < lseplen # EOF was hit and separator does not fit. ;) partial.append(ibuff) finished = True continue if eolindex >= 0: # Found an EOL. If there are trailing characters, # cut the input buffer and seek back; # else add the whole input buffer. trailing = ibufflen - lseplen - eolindex # Bytes beyond EOL. if trailing > 0: obuff = ibuff[:-trailing] self.seek(-trailing, 1) remsize += trailing else: obuff = ibuff finished = True elif lseplen > 1 and (limit <= 0 or remsize > 0): # Seek back a little since the end of the read string # may have fallen in the middle of the line separator. obuff = ibuff[:-lseplen + 1] self.seek(-lseplen + 1, 1) remsize += lseplen - 1 else: # eolindex<0 and (lseplen<=1 or (limit>0 and remsize<=0)) # Did not find an EOL, add the whole input buffer. obuff = ibuff # Append (maybe cut) buffer. partial.append(obuff) # If a limit has been specified and the remaining count # reaches zero, the reading is finished. if limit > 0 and remsize <= 0: finished = True return b''.join(partial) # def write(self, b: bytes) -> int: def write(self, b): """Write the given buffer to the IO stream. Returns the number of bytes written, which may be less than len(b). """ self._checkClosed() self._checkWritable() if isinstance(b, str): raise TypeError("can't write str to binary stream") n = len(b) if n == 0: return 0 pos = self._pos # Is the pointer beyond the real end of data? end2off = pos - self._node.nrows if end2off > 0: # Zero-fill the gap between the end of data and the pointer. self._append_zeros(end2off) # Append data. self._node.append( np.ndarray(buffer=b, dtype=self._vtype, shape=self._vshape(n))) self._pos += n return n def _checkClosed(self): """Checks if file node is open. Checks whether the file node is open or has been closed. In the second case, a ValueError is raised. If the host PyTables has been closed, ValueError is also raised. """ super()._checkClosed() if getattr(self._node, '_v_file', None) is None: raise ValueError("host PyTables file is already closed!") def _check_node(self, node): if not isinstance(node, tb.EArray): raise TypeError('the "node" parameter should be a tables.EArray') if not isinstance(node.atom, tb.UInt8Atom): raise TypeError('only nodes with atom "UInt8Atom" are allowed') def _check_mode(self, mode): if not isinstance(mode, str): raise TypeError("invalid mode: %r" % mode) modes = set(mode) if modes - set("arwb+tU") or len(mode) > len(modes): raise ValueError("invalid mode: %r" % mode) reading = "r" in modes writing = "w" in modes appending = "a" in modes # updating = "+" in modes text = "t" in modes binary = "b" in modes if "U" in modes: if writing or appending: raise ValueError("can't use U and writing mode at once") reading = True if text and binary: raise ValueError("can't have text and binary mode at once") if reading + writing + appending > 1: raise ValueError("can't have read/write/append mode at once") if not (reading or writing or appending): raise ValueError("must have exactly one of read/write/append mode") def _cross_check_mode(self, mode, h5filemode): # XXX: check # readable = bool('r' in mode or '+' in mode) # h5readable = bool('r' in h5filemode or '+' in h5filemode) # # if readable and not h5readable: # raise ValueError("RawPyTablesIO can't be open in read mode if " # "the underlying hdf5 file is not readable") writable = bool('w' in mode or 'a' in mode or '+' in mode) h5writable = bool('w' in h5filemode or 'a' in h5filemode or '+' in h5filemode) if writable and not h5writable: raise ValueError("RawPyTablesIO can't be open in write mode if " "the underlying hdf5 file is not writable") def _check_attributes(self, node): """Checks file node-specific attributes. Checks for the presence and validity of the system attributes 'NODE_TYPE' and 'NODE_TYPE_VERSION' in the specified PyTables node (leaf). ValueError is raised if an attribute is missing or incorrect. """ attrs = node.attrs ltype = getattr(attrs, 'NODE_TYPE', None) ltypever = getattr(attrs, 'NODE_TYPE_VERSION', None) if ltype!= NodeType: raise ValueError(f"invalid type of node object: {ltype}") if ltypever not in NodeTypeVersions: raise ValueError( f"unsupported type version of node object: {ltypever}") def _append_zeros(self, size): """_append_zeros(size) -> None. Appends a string of zeros. Appends a string of'size' zeros to the array, without moving the file pointer. """ # Appending an empty array would raise an error. if size == 0: return # XXX This may be redone to avoid a potentially large in-memory array. self._node.append( np.zeros(dtype=self._vtype, shape=self._vshape(size))) class FileNodeMixin: """Mixin class for FileNode objects. It provides access to the attribute set of the node that becomes available via the attrs property. You can add attributes there, but try to avoid attribute names in all caps or starting with '_', since they may clash with internal attributes. """ # The attribute set property methods. def _get_attrs(self): """Returns the attribute set of the file node.""" # sefl._checkClosed() return self._node.attrs def _set_attrs(self, value): """set_attrs(string) -> None. Raises ValueError.""" raise ValueError("changing the whole attribute set is not allowed") def _del_attrs(self): """del_attrs() -> None. Raises ValueError.""" raise ValueError("deleting the whole attribute set is not allowed") # The attribute set property. attrs = property( _get_attrs, _set_attrs, _del_attrs, "A property pointing to the attribute set of the file node.") class ROFileNode(FileNodeMixin, RawPyTablesIO): """Creates a new read-only file node. Creates a new read-only file node associated with the specified PyTables node, providing a standard Python file interface to it. The node has to have been created on a previous occasion using the new_node() function. The node used as storage is also made available via the read-only attribute node. Please do not tamper with this object if it's avoidable, since you may break the operation of the file node object. The constructor is not intended to be used directly. Use the open_node() function in read-only mode ('r') instead. :Version 1: implements the file storage as a UInt8 uni-dimensional EArray. :Version 2: uses an UInt8 N vector EArray. .. versionchanged:: 3.0 The offset attribute is no more available, please use seek/tell methods instead. .. versionchanged:: 3.0 The line_separator property is no more available. The only line separator used for binary I/O is ``\\n``. """ def __init__(self, node): RawPyTablesIO.__init__(self, node, 'r') self._checkReadable() @property def node(self): return self._node class RAFileNode(FileNodeMixin, RawPyTablesIO): """Creates a new read-write file node. The first syntax opens the specified PyTables node, while the second one creates a new node in the specified PyTables file. In the second case, additional named arguments 'where' and 'name' must be passed to specify where the file node is to be created. Other named arguments such as 'title' and 'filters' may also be passed. The special named argument 'expectedsize', indicating an estimate of the file size in bytes, may also be passed. Write access means reading as well as appending data is allowed. The node used as storage is also made available via the read-only attribute node. Please do not tamper with this object if it's avoidable, since you may break the operation of the file node object. The constructor is not intended to be used directly. Use the new_node() or open_node() functions instead. :Version 1: implements the file storage as a UInt8 uni-dimensional EArray. :Version 2: uses an UInt8 N vector EArray. .. versionchanged:: 3.0 The offset attribute is no more available, please use seek/tell methods instead. .. versionchanged:: 3.0 The line_separator property is no more available. The only line separator used for binary I/O is ``\\n``. """ # The atom representing a byte in the array, for each version. _byte_shape = [ None, (0, 1), (0,), ] __allowed_init_kwargs = [ 'where', 'name', 'title', 'filters', 'expectedsize'] def __init__(self, node, h5file, **kwargs): if node is not None: # Open an existing node and get its version. self._check_attributes(node) self._version = node.attrs.NODE_TYPE_VERSION elif h5file is not None: # Check for allowed keyword arguments, # to avoid unwanted arguments falling through to array constructor. for kwarg in kwargs: if kwarg not in self.__allowed_init_kwargs: raise TypeError( "%s keyword argument is not allowed" % repr(kwarg)) # Turn 'expectedsize' into 'expectedrows'. if 'expectedsize' in kwargs: # These match since one byte is stored per row. expectedrows = kwargs['expectedsize'] kwargs = kwargs.copy() del kwargs['expectedsize'] kwargs['expectedrows'] = expectedrows # Create a new array in the specified PyTables file. self._version = NodeTypeVersions[-1] shape = self._byte_shape[self._version] node = h5file.create_earray( atom=tb.UInt8Atom(), shape=shape, **kwargs) # Set the node attributes, else remove the array itself. try: self._set_attributes(node) except RuntimeError: h5file.remove_node(kwargs['where'], kwargs['name']) raise RawPyTablesIO.__init__(self, node, 'a+') self._checkReadable() self._checkWritable() @property def node(self): return self._node def _set_attributes(self, node): """_set_attributes(node) -> None. Adds file node-specific attributes. Sets the system attributes 'NODE_TYPE' and 'NODE_TYPE_VERSION' in the specified PyTables node (leaf). """ attrs = node.attrs attrs.NODE_TYPE = NodeType attrs.NODE_TYPE_VERSION = NodeTypeVersions[-1] def new_node(h5file, **kwargs): """Creates a new file node object in the specified PyTables file object. Additional named arguments where and name must be passed to specify where the file node is to be created. Other named arguments such as title and filters may also be passed. The special named argument expectedsize, indicating an estimate of the file size in bytes, may also be passed. It returns the file node object. """ return RAFileNode(None, h5file, **kwargs) def open_node(node, mode='r'): """Opens an existing file node. Returns a file node object from the existing specified PyTables node. If mode is not specified or it is 'r', the file can only be read, and the pointer is positioned at the beginning of the file. If mode is 'a+', the file can be read and appended, and the pointer is positioned at the end of the file. """ if mode == 'r': return ROFileNode(node) elif mode == 'a+': return RAFileNode(node, None) else: raise OSError(f"invalid mode: {mode}") def save_to_filenode(h5file, filename, where, name=None, overwrite=False, title="", filters=None): """Save a file's contents to a filenode inside a PyTables file. .. versionadded:: 3.2 Parameters ---------- h5file The PyTables file to be written to; can be either a string giving the file's location or a :class:`File` object. If a file with name *h5file* already exists, it will be opened in mode ``a``. filename Path of the file which shall be stored within the PyTables file. where, name Location of the filenode where the data shall be stored. If *name* is not given, and *where* is either a :class:`Group` object or a string ending on ``/``, the leaf name will be set to the file name of *filename*. The *name* will be modified to adhere to Python's natural naming convention; the original filename will be preserved in the filenode's *_filename* attribute. overwrite Whether or not a possibly existing filenode of the specified name shall be overwritten. title A description for this node (it sets the ``TITLE`` HDF5 attribute on disk). filters An instance of the :class:`Filters` class that provides information about the desired I/O filters to be applied during the life of this object. """ path = Path(filename).resolve() # sanity checks if not os.access(path, os.R_OK): raise OSError(f"The file '{path}' could not be read") if isinstance(h5file, tb.file.File) and h5file.mode == "r": raise OSError(f"The file '{h5file.filename}' is opened read-only") # guess filenode's name if necessary if name is None: if isinstance(where, tb.group.Group): name = os.path.split(filename)[1] if isinstance(where, str): if where.endswith("/"): name = os.path.split(filename)[1] else: nodepath = where.split("/") where = "/" + "/".join(nodepath[:-1]) name = nodepath[-1] # sanitize name if necessary if not tb.path._python_id_re.match(name): name = re.sub('(?![a-zA-Z0-9_]).', "_", re.sub('^(?![a-zA-Z_]).', "_", name)) new_h5file = not isinstance(h5file, tb.file.File) f = tb.File(h5file, "a") if new_h5file else h5file # check for already existing filenode try: f.get_node(where=where, name=name) if not overwrite: if new_h5file: f.close() raise OSError( f"Specified node already exists in file '{f.filename}'" ) except tb.NoSuchNodeError: pass # read data from disk data = path.read_bytes() # remove existing filenode if present try: f.remove_node(where=where, name=name) except tb.NoSuchNodeError: pass # write file's contents to filenode fnode = new_node(f, where=where, name=name, title=title, filters=filters) fnode.write(data) fnode.attrs._filename = path.name fnode.close() # cleanup if new_h5file: f.close() def read_from_filenode(h5file, filename, where, name=None, overwrite=False, create_target=False): r"""Read a filenode from a PyTables file and write its contents to a file. .. versionadded:: 3.2 Parameters ---------- h5file The PyTables file to be read from; can be either a string giving the file's location or a :class:`File` object. filename Path of the file where the contents of the filenode shall be written to. If *filename* points to a directory or ends with ``/`` (``\`` on Windows), the filename will be set to the *_filename* (if present; otherwise the *name*) attribute of the read filenode. where, name Location of the filenode where the data shall be read from. If no node *name* can be found at *where*, the first node at *where* whose *_filename* attribute matches *name* will be read. overwrite Whether or not a possibly existing file of the specified *filename* shall be overwritten. create_target Whether or not the folder hierarchy needed to accomodate the given target ``filename`` will be created. """ path = Path(filename).resolve() new_h5file = not isinstance(h5file, tb.file.File) f = tb.File(h5file, "r") if new_h5file else h5file try: fnode = open_node(f.get_node(where=where, name=name)) except tb.NoSuchNodeError: fnode = None for n in f.walk_nodes(where=where, classname="EArray"): if n.attrs._filename == name: fnode = open_node(n) break if fnode is None: f.close() raise tb.NoSuchNodeError("A filenode '%s' cannot be found at " "'%s'" % (name, where)) # guess output filename if necessary # TODO: pathlib.Path strips trailing slash automatically :-( if path.is_dir() or filename.endswith(os.path.sep): try: path = path / fnode.node.attrs._filename except Exception: path = path / fnode.node.name if os.access(path, os.R_OK) and not overwrite: if new_h5file: f.close() raise OSError(f"The file '{path}' already exists") # create folder hierarchy if necessary if create_target: path.parent.mkdir(parents=True, exist_ok=True) if not os.access(path.parent, os.W_OK): if new_h5file: f.close() raise OSError("The file '%s' cannot be written to" % filename) # read data from filenode data = fnode.read() fnode.close() # store data to file path.write_bytes(data) # cleanup del data if new_h5file: f.close()

pyspeckit__pyspeckit

classfiles.rst

Module doc / Tutorial

Generate documentation for this module

MIT License

pyspeckit__pyspeckit/docs/classfiles.rst

Gildas CLASS files Pyspeckit is capable of reading files from some versions of CLASS. The CLASS developers have stated that the GILDAS file format is private and will remain so, and therefore there are no guarantees that the CLASS reader will work for your file. Nonetheless, if you want to develop in python instead of SIC, the ~pyspeckit.spectrum.readers.read_class module is probably the best way to access CLASS data. The CLASS file specification is incomplete, so much of the data reading is hacked together. The code style is based off of Tom Robitaille's idlsave package. An example usage. Note that telescope and line are NOT optional keyword arguments, they are just specified as such for clarity : n2hp = class_to_obsblocks(fn1, telescope=['SMT-F1M-HU','SMT-F1M-VU'], line=['N2HP(3-2)','N2H+(3-2)']) This will generate a ~pyspeckit.spectrum.ObsBlock from all data tagged with the 'telescope' flags listed and lines matching either of those above. The data selection is equivalent to a combination of : find /telescope SMT-F1M-HU find /telescope SMT-F1M-VU find /line N2HP(3-2) find /line N2H+(3-2) ALL of the data matching those criteria will be included in an ObsBlock. They will then be accessible through the ObsBlock's speclist attribute, or just by indexing the ObsBlock directly.

""" ------------------------ GILDAS CLASS file reader ------------------------ Read a CLASS file into an :class:`pyspeckit.spectrum.ObsBlock` """ from __future__ import print_function from six.moves import xrange from six import iteritems import six import astropy.io.fits as pyfits import numpy import numpy as np from numpy import pi from astropy import log # from astropy.time import Time from astropy import units as u import pyspeckit import sys import re try: from astropy.utils.console import ProgressBar except ImportError: ProgressBar = lambda x: None ProgressBar.update = lambda x: None import struct import time # 'range' is needed as a keyword irange = range def print_timing(func): """ Prints execution time of decorated function. Included here because CLASS files can take a little while to read; this should probably be replaced with a progressbar """ def wrapper(*arg,**kwargs): t1 = time.time() res = func(*arg,**kwargs) t2 = time.time() log.info('%s took %0.5g s' % (func.__name__, (t2-t1))) return res wrapper.__doc__ = func.__doc__ return wrapper def ensure_bytes(string): """ Ensure a given string is in byte form """ if six.PY3: return bytes(string, 'utf-8') else: return str(string) """ Specification: http://iram.fr/IRAMFR/GILDAS/doc/html/class-html/node58.html """ filetype_dict = {'1A ':'Multiple_IEEE', '1 ':'Multiple_Vax', '1B ':'Multiple_EEEI', '2A ':'v2', '2 ':'v2', '2B ':'v2', '9A ':'Single_IEEE', '9 ':'Single_Vax', '9B ':'Single_EEEI'} for key in list(filetype_dict.keys()): filetype_dict[ensure_bytes(key)] = filetype_dict[key] fileversion_dict = {'1A ':'v1', '2A ':'v2', '9A ':'v1', # untested } for key in list(fileversion_dict.keys()): fileversion_dict[ensure_bytes(key)] = fileversion_dict[key] record_lengths = {'1A': 512, '2A': 1024*4} header_id_numbers = {0: 'USER CODE', -1: 'COMMENT', -2: 'GENERAL', -3: 'POSITION', -4: 'SPECTRO', -5: 'BASELINE', -6: 'HISTORY', -7: 'UNKNOWN-APEX', # -8: 'SWITCH', -9: 'GAUSSFIT', # "private"; see class-interfaces-private.f90 -10: 'DRIFT', -11: 'BEAMSWITCH', # "private"; see class-interfaces-private.f90 -12: 'SHELLFIT', # "private"; see class-interfaces-private.f90 -13: 'NH3FIT', # "private"; see class-interfaces-private.f90 -14: 'CALIBRATION', -18: 'ABSFIT', # "private"; see class-interfaces-private.f90 } header_id_lengths = {-2: 9, # may really be 10? -3: 17, -4: 17, -5: None, # variable length -6: 3, # variable length -14: 25, } # from packages/classic/lib/classic_mod.f90 filedescv2_nw1=14 """ GENERAL integer(kind=obsnum_length) :: num ! [ ] Observation number integer(kind=4) :: ver ! [ ] Version number integer(kind=4) :: teles(3)! [ ] Telescope name integer(kind=4) :: dobs ! [MJD-60549] Date of observation integer(kind=4) :: dred ! [MJD-60549] Date of reduction integer(kind=4) :: typec ! [ code] Type of coordinates integer(kind=4) :: kind ! [ code] Type of data integer(kind=4) :: qual ! [ code] Quality of data integer(kind=4) :: subscan ! [ ] Subscan number integer(kind=obsnum_length) :: scan ! [ ] Scan number ! Written in the entry real(kind=8) :: ut ! 1-2 [ rad] UT of observation real(kind=8) :: st ! 3-4 [ rad] LST of observation real(kind=4) :: az ! 5 [ rad] Azimuth real(kind=4) :: el ! 6 [ rad] Elevation real(kind=4) :: tau ! 7 [neper] Opacity real(kind=4) :: tsys ! 8 [ K] System temperature real(kind=4) :: time ! 9 [ s] Integration time ! Not in this section in file integer(kind=4) :: xunit ! [ code] X unit (if X coordinates section is present) ! NOT in data --- character(len=12) :: cdobs ! [string] Duplicate of dobs character(len=12) :: cdred ! [string] Duplicate of dred """ keys_lengths = { 'unknown': [ ('NUM' ,1,'int32'), # Observation number ('VER' ,1,'int32'), # Version number ('TELES' ,3,'|S12'), # Telescope name ('DOBS' ,1,'int32'), # Date of observation ('DRED' ,1,'int32'), # Date of reduction ('TYPEC' ,1,'int32'), # Type of coordinates ('KIND' ,1,'int32'), # Type of data ('QUAL' ,1,'int32'), # Quality of data ('SCAN' ,1,'int32'), # Scan number ('SUBSCAN',1,'int32'), # Subscan number ], 'COMMENT': [ # -1 ('LTEXT',1,'int32'), # integer(kind=4) :: ltext ! Length of comment ('CTEXT',1024//4,'|S1024'), # character ctext*1024 ! Comment string ], 'GENERAL': [ # -2 ('UT' ,2,'float64'), # rad UT of observation ('ST' ,2,'float64'), # rad LST of observation ('AZ' ,1,'float32'), # rad Azimuth ('EL' ,1,'float32'), # rad Elevation ('TAU' ,1,'float32'), # neper Opacity ('TSYS' ,1,'float32'), # K System temperature ('TIME' ,1,'float32'), # s Integration time # XUNIT should not be there? #( 'XUNIT' ,1,'int32'), # code X unit (if xcoord_sec is present) ], 'POSITION': [ # -3 ('SOURC',3,'|S12') , # [ ] Source name ('EPOCH',1,'float32'), # [ ] Epoch of coordinates ('LAM' ,2,'float64'), #[rad] Lambda ('BET' ,2,'float64'), #[rad] Beta ('LAMOF',1,'float32'), # [rad] Offset in Lambda ('BETOF',1,'float32'), # [rad] Offset in Beta ('PROJ',1,'int32') , # [rad] Projection system ('SL0P',1,'float64'), # lambda of descriptive system # MAY NOT EXIST IN OLD CLASS ('SB0P',1,'float64'), # beta of descriptive system # MAY NOT EXIST IN OLD CLASS ('SK0P',1,'float64'), # angle of descriptive system # MAY NOT EXIST IN OLD CLASS ], 'SPECTRO': [ # -4 #('align' ,1,'int32'), # [ ] Alignment padding ('LINE' ,3,'|S12'), # [ ] Line name ('RESTF' ,2,'float64'), # [ MHz] Rest frequency ('NCHAN' ,1,'int32'), # [ ] Number of channels ('RCHAN' ,1,'float32'), # [ ] Reference channels ('FRES' ,1,'float32'), # [ MHz] Frequency resolution ('FOFF' ,1,'float32'), # [ MHz] Frequency offset ('VRES' ,1,'float32'), # [km/s] Velocity resolution ('VOFF' ,1,'float32'), # [km/s] Velocity at reference channel ('BAD' ,1,'float32'), # [ ] Blanking value #('ALIGN_1',1,'int32'), # [ ] Alignment padding ('IMAGE' ,2,'float64'), # [ MHz] Image frequency #('ALIGN_2',1,'int32'), # [ ] Alignment padding ('VTYPE' ,1,'int32'), # [code] Type of velocity ('DOPPLER',2,'float64'), # [ ] Doppler factor = -V/c (CLASS convention) ], 'CALIBRATION': [ # -14 ('ALIGN',1,'int32'), # BUFFER (it's a zero - it is not declared in the docs!!!!) ('BEEFF',1,'float32'), # [ ] Beam efficiency ('FOEFF',1,'float32'), # [ ] Forward efficiency ('GAINI',1,'float32'), # [ ] Image/Signal gain ratio ('H2OMM',1,'float32'), # [ mm] Water vapor content ('PAMB',1,'float32'), # [ hPa] Ambient pressure ('TAMB',1,'float32'), # [ K] Ambient temperature ('TATMS',1,'float32'), # [ K] Atmosphere temp. in signal band ('TCHOP',1,'float32'), # [ K] Chopper temperature ('TCOLD',1,'float32'), # [ K] Cold load temperature ('TAUS',1,'float32'), # [neper] Opacity in signal band ('TAUI',1,'float32'), # [neper] Opacity in image band ('TATMI',1,'float32'), # [ K] Atmosphere temp. in image band ('TREC',1,'float32'), # [ K] Receiver temperature ('CMODE',1,'int32'), # [ code] Calibration mode ('ATFAC',1,'float32'), # [ ] Applied calibration factor ('ALTI',1,'float32'), # [ m] Site elevation ('COUNT',3,'3float32'), # [count] Power of Atm., Chopp., Cold ('LCALOF',1,'float32'), # [ rad] Longitude offset for sky measurement ('BCALOF',1,'float32'), # [ rad] Latitude offset for sky measurement ('GEOLONG',1,'float64'), # [ rad] Geographic longitude of observatory # MAY NOT EXIST IN OLD CLASS ('GEOLAT',1,'float64'), # [ rad] Geographic latitude of observatory # MAY NOT EXIST IN OLD CLASS ], 'BASELINE':[ ('DEG',1,'int32'), #! [ ] Degree of last baseline ('SIGFI',1,'float32'), #! [Int. unit] Sigma ('AIRE',1,'float32'), #! [Int. unit] Area under windows ('NWIND',1,'int32'), #! [ ] Number of line windows # WARNING: These should probably have 'n', the second digit, = NWIND # The docs are really unclear about this, they say "W1(MWIND)" ('W1MWIND',1,'float32'), #! [km/s] Lower limits of windows ('W2MWIND',1,'float32'), #! [km/s] Upper limits of windows ('SINUS',3,'float32'), #![] Sinus baseline results ], 'DRIFT':[ # 16? ('FREQ',1,'float64'), #! [ MHz] Rest frequency real(kind=8) :: ('WIDTH',1,'float32'), #! [ MHz] Bandwidth real(kind=4) :: ('NPOIN',1,'int32') , #! [ ] Number of data points integer(kind=4) :: ('RPOIN',1,'float32'), #! [ ] Reference point real(kind=4) :: ('TREF',1,'float32'), #! [ ?] Time at reference real(kind=4) :: ('AREF',1,'float32'), #! [ rad] Angular offset at ref. real(kind=4) :: ('APOS',1,'float32'), #! [ rad] Position angle of drift real(kind=4) :: ('TRES',1,'float32'), #! [ ?] Time resolution real(kind=4) :: ('ARES',1,'float32'), #! [ rad] Angular resolution real(kind=4) :: ('BAD',1,'float32') , #! [ ] Blanking value real(kind=4) :: ('CTYPE',1,'int32') , #! [code] Type of offsets integer(kind=4) :: ('CIMAG',1,'float64'), #! [ MHz] Image frequency real(kind=8) :: ('COLLA',1,'float32'), #! [ ?] Collimation error Az real(kind=4) :: ('COLLE',1,'float32'), #! [ ?] Collimation error El real(kind=4) :: ], } def _read_bytes(f, n): '''Read the next `n` bytes (from idlsave)''' return f.read(n) """ Warning: UNCLEAR what endianness should be! Numpy seemed to get it right, and I think numpy assumes NATIVE endianness """ def _read_byte(f): '''Read a single byte (from idlsave)''' return numpy.uint8(struct.unpack('=B', f.read(4)[:1])[0]) def _read_int16(f): '''Read a signed 16-bit integer (from idlsave)''' return numpy.int16(struct.unpack('=h', f.read(4)[2:4])[0]) def _read_int32(f): '''Read a signed 32-bit integer (from idlsave)''' return numpy.int32(struct.unpack('=i', f.read(4))[0]) def _read_int64(f): '''Read a signed 64-bit integer ''' return numpy.int64(struct.unpack('=q', f.read(8))[0]) def _read_float32(f): '''Read a 32-bit float (from idlsave)''' return numpy.float32(struct.unpack('=f', f.read(4))[0]) def _align_32(f): '''Align to the next 32-bit position in a file (from idlsave)''' pos = f.tell() if pos % 4!= 0: f.seek(pos + 4 - pos % 4) return def _read_word(f,length): if length > 0: chars = _read_bytes(f, length) _align_32(f) else: chars = None return chars def _read_int(f): return struct.unpack('i',f.read(4)) def is_ascii(s): """Check if there are non-ascii characters in Unicode string Parameters ---------- s : str The string to be checked Returns ------- is_ascii : bool Returns True if all characters in the string are ascii. False otherwise. """ return len(s) == len(s.decode('ascii').encode('utf-8')) def is_all_null(s): return all(x=='\x00' for x in s) or all(x==b'\x00' for x in s) """ from clic_file.f90: v1, v2 integer(kind=4) :: bloc ! 1 : observation address [records] integer(kind=8) :: bloc ! 1- 2: observation address [records] integer(kind=4) :: bloc ! 1 : block read from index integer(kind=4) :: num ! 2 : observation number integer(kind=4) :: word ! 3 : address offset [4-bytes] integer(kind=4) :: num ! 2 : number read integer(kind=4) :: ver ! 3 : observation version integer(kind=4) :: ver ! 4 : observation version integer(kind=4) :: ver ! 3 : version read from index integer(kind=4) :: sourc(3) ! 4- 6: source name integer(kind=8) :: num ! 5- 6: observation number character(len=12) :: csour ! 4- 6: source read from index integer(kind=4) :: line(3) ! 7- 9: line name integer(kind=4) :: sourc(3) ! 7- 9: source name character(len=12) :: cline ! 7- 9: line read from index integer(kind=4) :: teles(3) ! 10-12: telescope name integer(kind=4) :: line(3) ! 10-12: line name character(len=12) :: ctele ! 10-12: telescope read from index integer(kind=4) :: dobs ! 13 : observation date [class_date] integer(kind=4) :: teles(3) ! 13-15: telescope name integer(kind=4) :: dobs ! 13 : date obs. read from index integer(kind=4) :: dred ! 14 : reduction date [class_date] integer(kind=4) :: dobs ! 16 : observation date [class_date] integer(kind=4) :: dred ! 14 : date red. read from index real(kind=4) :: off1 ! 15 : lambda offset [radian] integer(kind=4) :: dred ! 17 : reduction date [class_date] real(kind=4) :: off1 ! 15 : read offset 1 real(kind=4) :: off2 ! 16 : beta offset [radian] real(kind=4) :: off1 ! 18 : lambda offset [radian] real(kind=4) :: off2 ! 16 : read offset 2 integer(kind=4) :: typec ! 17 : coordinates types real(kind=4) :: off2 ! 19 : beta offset [radian] integer(kind=4) :: type ! 17 : type of read offsets integer(kind=4) :: kind ! 18 : data kind integer(kind=4) :: typec ! 20 : coordinates types integer(kind=4) :: kind ! 18 : type of observation integer(kind=4) :: qual ! 19 : data quality integer(kind=4) :: kind ! 21 : data kind integer(kind=4) :: qual ! 19 : Quality read from index integer(kind=4) :: scan ! 20 : scan number integer(kind=4) :: qual ! 22 : data quality integer(kind=4) :: scan ! 20 : Scan number read from index integer(kind=4) :: proc ! 21 : procedure type integer(kind=4) :: scan ! 23 : scan number real(kind=4) :: posa ! 21 : Position angle integer(kind=4) :: itype ! 22 : observation type integer(kind=4) :: proc ! 24 : procedure type integer(kind=4) :: subscan ! 22 : Subscan number real(kind=4) :: houra ! 23 : hour angle [radian] integer(kind=4) :: itype ! 25 : observation type integer(kind=4) :: pad(10) ! 23-32: Pad to 32 words integer(kind=4) :: project ! 24 : project name real(kind=4) :: houra ! 26 : hour angle [radian] integer(kind=4) :: pad1 ! 25 : unused word integer(kind=4) :: project(2)! 27 : project name integer(kind=4) :: bpc ! 26 : baseline bandpass cal status integer(kind=4) :: bpc ! 29 : baseline bandpass cal status integer(kind=4) :: ic ! 27 : instrumental cal status integer(kind=4) :: ic ! 30 : instrumental cal status integer(kind=4) :: recei ! 28 : receiver number integer(kind=4) :: recei ! 31 : receiver number real(kind=4) :: ut ! 29 : UT [s] real(kind=4) :: ut ! 32 : UT [s] integer(kind=4) :: pad2(3) ! 30-32: padding to 32 4-bytes word equivalently integer(kind=obsnum_length) :: num ! [ ] Observation number integer(kind=4) :: ver ! [ ] Version number integer(kind=4) :: teles(3)! [ ] Telescope name integer(kind=4) :: dobs ! [MJD-60549] Date of observation integer(kind=4) :: dred ! [MJD-60549] Date of reduction integer(kind=4) :: typec ! [ code] Type of coordinates integer(kind=4) :: kind ! [ code] Type of data integer(kind=4) :: qual ! [ code] Quality of data integer(kind=4) :: subscan ! [ ] Subscan number integer(kind=obsnum_length) :: scan ! [ ] Scan number """ """ index.f90: call conv%read%i8(data(1), indl%bloc, 1) ! bloc call conv%read%i4(data(3), indl%word, 1) ! word call conv%read%i8(data(4), indl%num, 1) ! num call conv%read%i4(data(6), indl%ver, 1) ! ver call conv%read%cc(data(7), indl%csour, 3) ! csour call conv%read%cc(data(10),indl%cline, 3) ! cline call conv%read%cc(data(13),indl%ctele, 3) ! ctele call conv%read%i4(data(16),indl%dobs, 1) ! dobs call conv%read%i4(data(17),indl%dred, 1) ! dred call conv%read%r4(data(18),indl%off1, 1) ! off1 call conv%read%r4(data(19),indl%off2, 1) ! off2 call conv%read%i4(data(20),indl%type, 1) ! type call conv%read%i4(data(21),indl%kind, 1) ! kind call conv%read%i4(data(22),indl%qual, 1) ! qual call conv%read%r4(data(23),indl%posa, 1) ! posa call conv%read%i8(data(24),indl%scan, 1) ! scan call conv%read%i4(data(26),indl%subscan,1) ! subscan if (isv3) then call conv%read%r8(data(27),indl%ut, 1) ! ut else """ def _read_indices(f, file_description): #if file_description['version'] in (1,2): # extension_positions = (file_description['aex']-1)*file_description['reclen']*4 # all_indices = {extension: # [_read_index(f, # filetype=file_description['version'], # entry=ii, # #position=position, # ) # for ii in range(file_description['lex1'])] # for extension,position in enumerate(extension_positions) # if position > 0 # } #elif file_description['version'] == 1: extension_positions = ((file_description['aex'].astype('int64')-1) *file_description['reclen']*4) all_indices = [_read_index(f, filetype=file_description['version'], # 1-indexed files entry_number=ii+1, file_description=file_description, ) for ii in range(file_description['xnext']-1)] #else: # raise ValueError("Invalid file version {0}".format(file_description['version'])) return all_indices def _find_index(entry_number, file_description, return_position=False): if file_description['gex'] == 10: kex=(entry_number-1)//file_description['lex1'] + 1 else: # exponential growth: #kex = gi8_dicho(file_description['nex'], file_description['lexn'], entry_number) - 1 kex = len([xx for xx in file_description['lexn'] if xx<entry_number]) ken = entry_number - file_description['lexn'][kex-1] #! Find ken (relative entry number in the extension, starts from 1) #ken = entry_num - file%desc%lexn(kex-1) kb = ((ken-1)*file_description['lind'])//file_description['reclen'] #kb = ((ken-1)*file%desc%lind)/file%desc%reclen ! In the extension, the # ! relative record position (as an offset, starts from 0) where the # ! Entry Index starts. NB: there can be a non-integer number of Entry # ! Indexes per record # Subtract 1: 'aex' is 1-indexed kbl = (file_description['aex'][kex-1]+kb)-1 # kbl = file%desc%aex(kex)+kb ! The absolute record number where the Entry Index goes k = ((ken-1)*file_description['lind']) % file_description['reclen'] #k = mod((ken-1)*file%desc%lind,file%desc%reclen)+1 ! = in the record, the # ! first word of the Entry Index of the entry number 'entry_num' if return_position: return (kbl*file_description['reclen']+k)*4 else: return kbl,k def _read_index(f, filetype='v1', DEBUG=False, clic=False, position=None, entry_number=None, file_description=None): if position is not None: f.seek(position) if entry_number is not None: indpos = _find_index(entry_number, file_description, return_position=True) f.seek(indpos) x0 = f.tell() if filetype in ('1A ','v1', 1): log.debug('Index filetype 1A') index = { "XBLOC":_read_int32(f), "XNUM":_read_int32(f), "XVER":_read_int32(f), "XSOURC":_read_word(f,12), "XLINE":_read_word(f,12), "XTEL":_read_word(f,12), "XDOBS":_read_int32(f), "XDRED":_read_int32(f), "XOFF1":_read_float32(f),# first offset (real, radians) "XOFF2":_read_float32(f),# second offset (real, radians) "XTYPE":_read_int32(f),# coordinate system ('EQ'', 'GA', 'HO') "XKIND":_read_int32(f),# Kind of observation (0: spectral, 1: continuum, ) "XQUAL":_read_int32(f),# Quality (0-9) "XSCAN":_read_int32(f),# Scan number } index['BLOC'] = index['XBLOC'] # v2 compatibility index['WORD'] = 1 # v2 compatibility index['SOURC'] = index['CSOUR'] = index['XSOURC'] index['DOBS'] = index['CDOBS'] = index['XDOBS'] index['CTELE'] = index['XTEL'] index['LINE'] = index['XLINE'] index['OFF1'] = index['XOFF1'] index['OFF2'] = index['XOFF2'] index['QUAL'] = index['XQUAL'] index['SCAN'] = index['XSCAN'] index['KIND'] = index['XKIND'] if clic: # use header set up in clic nextchunk = { "XPROC":_read_int32(f),# "procedure type" "XITYPE":_read_int32(f),# "XHOURANG":_read_float32(f),# "XPROJNAME":_read_int32(f),# "XPAD1":_read_int32(f), "XBPC" :_read_int32(f), "XIC" :_read_int32(f), "XRECEI" :_read_int32(f), "XUT":_read_float32(f), "XPAD2":numpy.fromfile(f,count=3,dtype='int32') # BLANK is NOT ALLOWED!!! It is a special KW } else: nextchunk = {"XPOSA":_read_float32(f), "XSUBSCAN":_read_int32(f), 'XPAD2': numpy.fromfile(f,count=10,dtype='int32'), } nextchunk['SUBSCAN'] = nextchunk['XSUBSCAN'] nextchunk['POSA'] = nextchunk['XPOSA'] index.update(nextchunk) if (f.tell() - x0!= 128): missed_bits = (f.tell()-x0) X = f.read(128-missed_bits) if DEBUG: print("read_index missed %i bits: %s" % (128-missed_bits,X)) #raise IndexError("read_index did not successfully read 128 bytes at %i. Read %i bytes." % (x0,f.tell()-x0)) if any(not is_ascii(index[x]) for x in ('XSOURC','XLINE','XTEL')): raise ValueError("Invalid index read from {0}.".format(x0)) elif filetype in ('2A ','v2', 2): log.debug('Index filetype 2A') index = { "BLOC" : _read_int64(f) , #(data(1), 1) ! bloc "WORD" : _read_int32(f) , #(data(3), 1) ! word "NUM" : _read_int64(f) , #(data(4), 1) ! num "VER" : _read_int32(f) , #(data(6), 1) ! ver "CSOUR" : _read_word(f,12), #(data(7), 3) ! csour "CLINE" : _read_word(f,12), #(data(10), 3) ! cline "CTELE" : _read_word(f,12), #(data(13), 3) ! ctele "DOBS" : _read_int32(f) , #(data(16), 1) ! dobs "DRED" : _read_int32(f) , #(data(17), 1) ! dred "OFF1" : _read_float32(f), #(data(18), 1) ! off1 "OFF2" : _read_float32(f), #(data(19), 1) ! off2 "TYPE" : _read_int32(f) , #(data(20), 1) ! type "KIND" : _read_int32(f) , #(data(21), 1) ! kind "QUAL" : _read_int32(f) , #(data(22), 1) ! qual "POSA" : _read_float32(f), #(data(23), 1) ! posa "SCAN" : _read_int64(f) , #(data(24), 1) ! scan "SUBSCAN": _read_int32(f) , #(data(26), 1) ! subscan } #last24bits = f.read(24) #log.debug("Read 24 bits: '{0}'".format(last24bits)) if any((is_all_null(index[x]) or not is_ascii(index[x])) for x in ('CSOUR','CLINE','CTELE')): raise ValueError("Invalid index read from {0}.".format(x0)) index['SOURC'] = index['XSOURC'] = index['CSOUR'] index['LINE'] = index['XLINE'] = index['CLINE'] index['XKIND'] = index['KIND'] try: index['DOBS'] = index['XDOBS'] = index['CDOBS'] except KeyError: index['CDOBS'] = index['XDOBS'] = index['DOBS'] else: raise NotImplementedError("Filetype {0} not implemented.".format(filetype)) # from kernel/lib/gsys/date.f90: gag_julda index['MJD'] = index['DOBS'] + 60549 class_dobs = index['DOBS'] index['DOBS'] = ((class_dobs + 365*2025)/365.2425 + 1) # SLOW #index['DATEOBS'] = Time(index['DOBS'], format='jyear') #index['DATEOBSS'] = index['DATEOBS'].iso log.debug("Indexing finished at {0}".format(f.tell())) return index def _read_header(f, type=0, position=None): """ Read a header entry from a CLASS file (helper function) """ if position is not None: f.seek(position) if type in keys_lengths: hdrsec = [(x[0],numpy.fromfile(f,count=1,dtype=x[2])[0]) for x in keys_lengths[type]] return dict(hdrsec) else: return {} raise ValueError("Unrecognized type {0}".format(type)) def _read_first_record(f): f.seek(0) filetype = f.read(4) if fileversion_dict[filetype] == 'v1': return _read_first_record_v1(f) elif fileversion_dict[filetype] == 'v2': return _read_first_record_v2(f) else: raise ValueError("Unrecognized filetype {0}".format(filetype)) def _read_first_record_v1(f, record_length_words=128): r""" Position & Parameter & Fortran Kind & Purpose \\ \hline 1 & {\tt code} & Character*4 & File code \\ 2 & {\tt next} & Integer*4 & Next free record \\ 3 & {\tt lex} & Integer*4 & Length of first extension (number of entries) \\ 4 & {\tt nex} & Integer*4 & Number of extensions \\ 5 & {\tt xnext} & Integer*4 & Next available entry number \\ 6:2*{\tt reclen} & {\tt ex(:)} & Integer*4 & Array of extension addresses from classic_mod.f90: integer(kind=4) :: code ! 1 File code integer(kind=4) :: next ! 2 Next free record integer(kind=4) :: lex ! 3 Extension length (number of entries) integer(kind=4) :: nex ! 4 Number of extensions integer(kind=4) :: xnext ! 5 Next available entry number integer(kind=4) :: aex(mex_v1) ! 6:256 Extension addresses from old (<dec2013) class, file.f90: read(ilun,rec=1,err=11,iostat=ier) ibx%code,ibx%next, & & ibx%ilex,ibx%imex,ibx%xnext also uses filedesc_v1tov2 from classic/lib/file.f90 """ # OLD NOTES # hdr = header # hdr.update(obshead) # re-overwrite things # hdr.update({'OBSNUM':obsnum,'RECNUM':spcount}) # hdr.update({'RA':hdr['LAM']/pi*180,'DEC':hdr['BET']/pi*180}) # hdr.update({'RAoff':hdr['LAMOF']/pi*180,'DECoff':hdr['BETOF']/pi*180}) # hdr.update({'OBJECT':hdr['SOURC'].strip()}) # hdr.update({'BUNIT':'Tastar'}) # hdr.update({'EXPOSURE':hdr['TIME']}) f.seek(0) file_description = { 'code': f.read(4), 'next': _read_int32(f), 'lex': _read_int32(f), 'nex': _read_int32(f), 'xnext': _read_int32(f), 'gex': 10., 'vind': 1, # classic_vind_v1 packages/classic/lib/classic_mod.f90 'version': 1, 'nextrec': 3, 'nextword': 1, 'lind': 32, #classic_lind_v1 packages/classic/lib/classic_mod.f90 'kind': 'unknown', 'flags': 0, } file_description['reclen'] = record_length_words # should be 128w = 512 bytes ex = np.fromfile(f, count=(record_length_words*2-5), dtype='int32') file_description['ex'] = ex[ex!=0] file_description['nextrec'] = file_description['next'] # this can't be... file_description['lex1'] = file_description['lex'] # number of entries file_description['lexn'] = (np.arange(file_description['nex']+1) * file_description['lex1']) file_description['nentries'] = np.sum(file_description['lexn']) file_description['aex'] = file_description['ex'][:file_description['nex']] #file_description['version'] = fileversion_dict[file_description['code']] assert f.tell() == 1024 # Something is not quite right with the 'ex' parsing #assert len(file_description['ex']) == file_description['nex'] return file_description def _read_first_record_v2(f): r""" packages/classic/lib/file.f90 Position & Parameter & Fortran Kind & Purpose & Unit \\ \hline 1 & {\tt code} & Character*4 & File code & - \\ 2 & {\tt reclen} & Integer*4 & Record length & words \\ 3 & {\tt kind} & Integer*4 & File kind & - \\ 4 & {\tt vind} & Integer*4 & Index version & - \\ 5 & {\tt lind} & Integer*4 & Index length & words \\ 6 & {\tt flags} & Integer*4 & Bit flags. \#1: single or multiple, & - \\ & & & \#2-32: provision (0-filled) & \\ \hline 7:8 & {\tt xnext} & Integer*8 & Next available entry number & - \\ 9:10 & {\tt nextrec} & Integer*8 & Next record which contains free space & record \\ 11 & {\tt nextword} & Integer*4 & Next free word in this record & word \\ \hline 12 & {\tt lex1} & Integer*4 & Length of first extension index & entries \\ 13 & {\tt nex} & Integer*4 & Number of extensions & - \\ 14 & {\tt gex} & Integer*4 & Extension growth rule & - \\ 15:{\tt reclen} & {\tt aex(:)} & Integer*8 & Array of extension addresses & record """ f.seek(0) file_description = { 'code': f.read(4), 'reclen': _read_int32(f), 'kind': _read_int32(f), 'vind': _read_int32(f), 'lind': _read_int32(f), 'flags': _read_int32(f), 'xnext': _read_int64(f), 'nextrec': _read_int64(f), 'nextword': _read_int32(f), 'lex1': _read_int32(f), 'nex': _read_int32(f), 'gex': _read_int32(f), } file_description['lexn'] = [0] if file_description['gex'] == 10: for ii in range(1, file_description['nex']+1): file_description['lexn'].append(file_description['lexn'][-1]+file_description['lex1']) else: #! Exponential growth. Only growth with mantissa 2.0 is supported for ii in range(1, file_description['nex']): # I don't know what the fortran does here!!! # ahh, maybe 2_8 means int(2, dtype='int64') nent = int(file_description['lex1'] * 2**(ii-1)) #nent = int(file%desc%lex1,kind=8) * 2_8**(iex-1) file_description['lexn'].append(file_description['lexn'][-1]+nent) #file%desc%lexn(iex) = file%desc%lexn(iex-1) + nent file_description['nentries'] = np.sum(file_description['lexn']) record_length_words = file_description['reclen'] aex = numpy.fromfile(f, count=(record_length_words-15)//2, dtype='int64') file_description['aex'] = aex[aex!=0] assert len(file_description['aex']) == file_description['nex'] file_description['version'] = 2 return file_description def gi8_dicho(ninp,lexn,xval,ceil=True): """ ! @ public ! Find ival such as ! X(ival-1) < xval <= X(ival) (ceiling mode) ! or ! X(ival) <= xval < X(ival+1) (floor mode) ! for input data ordered. Use a dichotomic search for that. call gi8_dicho(nex,file%desc%lexn,entry_num,.true.,kex,error) """ #integer(kind=size_length), intent(in) :: np ! Number of input points #integer(kind=8), intent(in) :: x(np) ! Input ordered Values #integer(kind=8), intent(in) :: xval ! The value we search for #logical, intent(in) :: ceil ! Ceiling or floor mode? #integer(kind=size_length), intent(out) :: ival ! Position in the array #logical, intent(inout) :: error ! Logical error flag iinf = 1 isup = ninp #! Ceiling mode while isup > (iinf+1): imid = int(np.floor((isup + iinf)/2.)) if (lexn[imid-1] < xval): iinf = imid else: isup = imid ival = isup return ival def _read_obshead(f, file_description, position=None, verbose=False): if file_description['version'] == 1: return _read_obshead_v1(f, position=position, verbose=verbose) if file_description['version'] == 2: return _read_obshead_v2(f, position=position) else: raise ValueError("Invalid file version {0}.". format(file_description['version'])) def _read_obshead_v2(f, position=None): """ ! Version 2 (public) integer(kind=4), parameter :: entrydescv2_nw1=11 ! Number of words, in 1st part integer(kind=4), parameter :: entrydescv2_nw2=5 ! Number of words for 1 section in 2nd part type classic_entrydesc_t sequence integer(kind=4) :: code ! 1 : code observation icode integer(kind=4) :: version ! 2 : observation version integer(kind=4) :: nsec ! 3 : number of sections integer(kind=4) :: pad1 ! - : memory padding (not in data) integer(kind=8) :: nword ! 4- 5: number of words integer(kind=8) :: adata ! 6- 7: data address integer(kind=8) :: ldata ! 8- 9: data length integer(kind=8) :: xnum ! 10-11: entry number ! Out of the'sequence' block: integer(kind=4) :: msec ! Not in data: maximum number of sections the ! Observation Index can hold integer(kind=4) :: pad2 ! Memory padding for 8 bytes alignment integer(kind=4) :: seciden(classic_maxsec) ! Section Numbers (on disk: 1 to ed%nsec) integer(kind=8) :: secleng(classic_maxsec) ! Section Lengths (on disk: 1 to ed%nsec) integer(kind=8) :: secaddr(classic_maxsec) ! Section Addresses (on disk: 1 to ed%nsec) end type classic_entrydesc_t """ if position is not None: f.seek(position) else: position = f.tell() IDcode = f.read(4) if IDcode.strip()!= b'2': raise IndexError("Observation Header reading failure at {0}. " "Record does not appear to be an observation header.". format(position)) f.seek(position) entrydescv2_nw1 = 11 entrydescv2_nw2 = 5 obshead = { 'CODE': f.read(4), 'VERSION': _read_int32(f), 'NSEC': _read_int32(f), #'_blank': _read_int32(f), 'NWORD': _read_int64(f), 'ADATA': _read_int64(f), 'LDATA': _read_int64(f), 'XNUM': _read_int64(f), #'MSEC': _read_int32(f), #'_blank2': _read_int32(f), } section_numbers = np.fromfile(f, count=obshead['NSEC'], dtype='int32') section_lengths = np.fromfile(f, count=obshead['NSEC'], dtype='int64') section_addresses = np.fromfile(f, count=obshead['NSEC'], dtype='int64') return obshead['XNUM'],obshead,dict(zip(section_numbers,section_addresses)) def _read_obshead_v1(f, position=None, verbose=False): """ Read the observation header of a CLASS file (helper function for read_class; should not be used independently) """ if position is not None: f.seek(position) IDcode = f.read(4) if IDcode.strip()!= b'2': raise IndexError("Observation Header reading failure at {0}. " "Record does not appear to be an observation header.". format(f.tell() - 4)) (nblocks, nbyteob, data_address, nheaders, data_length, obindex, nsec, obsnum) = numpy.fromfile(f, count=8, dtype='int32') if verbose: print("nblocks,nbyteob,data_address,data_length,nheaders,obindex,nsec,obsnum",nblocks,nbyteob,data_address,data_length,nheaders,obindex,nsec,obsnum) print("DATA_LENGTH: ",data_length) seccodes = numpy.fromfile(f,count=nsec,dtype='int32') # Documentation says addresses then length: It is apparently wrong seclen = numpy.fromfile(f,count=nsec,dtype='int32') secaddr = numpy.fromfile(f,count=nsec,dtype='int32') if verbose: print("Section codes, addresses, lengths: ",seccodes,secaddr,seclen) hdr = {'NBLOCKS':nblocks, 'NBYTEOB':nbyteob, 'DATAADDR':data_address, 'DATALEN':data_length, 'NHEADERS':nheaders, 'OBINDEX':obindex, 'NSEC':nsec, 'OBSNUM':obsnum} #return obsnum,seccodes return obsnum,hdr,dict(zip(seccodes,secaddr)) # THIS IS IN READ_OBSHEAD!!! # def _read_preheader(f): # """ # Not entirely clear what this is, but it is stuff that precedes the actual data # # Looks something like this: # array([ 1, -2, -3, -4, -14, # 9, 17, 18, 25, 55, # 64, 81, 99, -1179344801, 979657591, # # -2, -3, -4, -14 indicate the 4 header types # 9,17,18,25 *MAY* indicate the number of bytes in each # # # HOW is it indicated how many entries there are? # """ # # 13 comes from counting 1, -2,....99 above # numbers = np.fromfile(f, count=13, dtype='int32') # sections = [n for n in numbers if n in header_id_numbers] # return sections def downsample_1d(myarr,factor,estimator=np.mean, weight=None): """ Downsample a 1D array by averaging over *factor* pixels. Crops right side if the shape is not a multiple of factor. This code is pure numpy and should be fast. keywords: estimator - default to mean. You can downsample by summing or something else if you want a different estimator (e.g., downsampling error: you want to sum & divide by sqrt(n)) weight: np.ndarray An array of weights to use for the downsampling. If None, assumes uniform 1 """ if myarr.ndim!= 1: raise ValueError("Only works on 1d data. Says so in the title.") xs = myarr.size crarr = myarr[:xs-(xs % int(factor))] if weight is None: dsarr = estimator(np.concatenate([[crarr[i::factor] for i in range(factor)]]),axis=0) else: dsarr = estimator(np.concatenate([[crarr[i::factor]*weight[i::factor] for i in range(factor)]]),axis=0) warr = estimator(np.concatenate([[weight[i::factor] for i in range(factor)]]),axis=0) dsarr = dsarr/warr return dsarr # unit test def test_downsample1d(): data = np.arange(10) weight = np.ones(10) weight[5]=0 assert np.all(downsample_1d(data, 2, weight=weight, estimator=np.mean) == np.array([0.5, 2.5, 4.0, 6.5, 8.5])) def read_observation(f, obsid, file_description=None, indices=None, my_memmap=None, memmap=True, verbose=False): if isinstance(f, str): f = open(f,'rb') opened = True if memmap: my_memmap = numpy.memmap(f, offset=0, dtype='float32', mode='r') else: my_memmap = None elif my_memmap is None and memmap: raise ValueError("Must pass in a memmap object if passing in a file object.") else: opened = False if file_description is None: file_description = _read_first_record(f) if indices is None: indices = _read_indices(f, file_description) index = indices[obsid] obs_position = (index['BLOC']-1)*file_description['reclen']*4 + (index['WORD']-1)*4 log.debug("Reading observation at position {0}".format(obs_position)) obsnum,obshead,sections = _read_obshead(f, file_description, position=obs_position, verbose=verbose) header = obshead datastart = 0 for section_id,section_address in iteritems(sections): # Section addresses are 1-indexed byte addresses # in the current "block" sec_position = obs_position + (section_address-1)*4 temp_hdr = _read_header(f, type=header_id_numbers[section_id], position=sec_position) header.update(temp_hdr) datastart = max(datastart,f.tell()) hdr = header hdr.update(obshead) # re-overwrite things hdr.update({'OBSNUM':obsnum,'RECNUM':obsid}) hdr.update({'RA':hdr['LAM']/pi*180,'DEC':hdr['BET']/pi*180}) hdr.update({'RAoff':hdr['LAMOF']/pi*180,'DECoff':hdr['BETOF']/pi*180}) hdr.update({'OBJECT':hdr['SOURC'].strip()}) hdr.update({'BUNIT':'Tastar'}) hdr.update({'EXPOSURE':float(hdr['TIME'])}) hdr['HDRSTART'] = obs_position hdr['DATASTART'] = datastart hdr.update(indices[obsid]) # Define MJD as mid-exposure time in MJD hdr.update({'OBSDATE': hdr['MJD'] + hdr['UT']/2./pi}) # Apparently the data are still valid in this case? #if hdr['XNUM']!= obsid+1: # log.error("The spectrum read was {0} but {1} was requested.". # format(hdr['XNUM']-1, obsid)) if hdr['KIND'] == 1: # continuum nchan = hdr['NPOIN'] elif 'NCHAN' in hdr: nchan = hdr['NCHAN'] else: log.error("No NCHAN in header. This is not a spectrum.") import ipdb; ipdb.set_trace() # There may be a 1-channel offset? CHECK!!! # (changed by 1 pixel - October 14, 2014) # (changed back - October 21, 2014 - I think the ends are just bad, but not # zero.) f.seek(datastart-1) spec = _read_spectrum(f, position=datastart-1, nchan=nchan, memmap=memmap, my_memmap=my_memmap) if opened: f.close() return spec, hdr def _read_spectrum(f, position, nchan, my_memmap=None, memmap=True): if position!= f.tell(): log.warning("Reading data from {0}, but the file is wound " "to {1}.".format(position, f.tell())) if memmap: here = position #spectrum = numpy.memmap(filename, offset=here, dtype='float32', # mode='r', shape=(nchan,)) spectrum = my_memmap[here//4:here//4+nchan] f.seek(here+nchan*4) else: f.seek(position) spectrum = numpy.fromfile(f,count=nchan,dtype='float32') return spectrum def _spectrum_from_header(fileobj, header, memmap=None): return _read_spectrum(fileobj, position=header['DATASTART'], nchan=header['NCHAN'] if 'NCHAN' in hdr else hdr['NPOIN'], my_memmap=memmap) def clean_header(header): newheader = {} for k in header: if not isinstance(header[k], (int, float, str)): if isinstance(header[k], np.ndarray) and header[k].size > 1: if header[k].size > 10: raise ValueError("Large array being put in header. That's no good. key={0}".format(k)) for ii,val in enumerate(header[k]): newheader[k[:7]+str(ii)] = val else: newheader[k[:8]] = str(header[k]) else: newheader[k[:8]] = header[k] return newheader class ClassObject(object): def __init__(self, filename, verbose=False): t0 = time.time() self._file = open(filename, 'rb') self.file_description = _read_first_record(self._file) self.allind = _read_indices(self._file, self.file_description) self._data = np.memmap(self._file, dtype='float32', mode='r') if verbose: log.info("Setting _spectra") self._spectra = LazyItem(self) t1 = time.time() if verbose: log.info("Setting posang. t={0}".format(t1-t0)) self.set_posang() t2 = time.time() if verbose: log.info("Identifying otf scans. t={0}".format(t2-t1)) self._identify_otf_scans(verbose=verbose) t3 = time.time() #self._load_all_spectra() if verbose: log.info("Loaded CLASS object with {3} indices. Time breakdown:" " {0}s for indices, " "{1}s for posang, and {2}s for OTF scan identification" .format(t1-t0, t2-t1, t3-t2, len(self.allind))) def __repr__(self): s = "\n".join(["{k}: {v}".format(k=k,v=v) for k,v in iteritems(self.getinfo())]) return "ClassObject({id}) with {nspec} entries\n".format(id=id(self), nspec=len(self.allind)) + s def getinfo(self, allsources=False): info = dict( tels = self.tels, lines = self.lines, scans = self.scans, sources = self.sources if allsources else self.sci_sources, ) return info def set_posang(self): h0 = self.headers[0] for h in self.headers: dx = h['OFF1'] - h0['OFF1'] dy = h['OFF2'] - h0['OFF2'] h['COMPPOSA'] = np.arctan2(dy,dx)*180/np.pi h0 = h def _identify_otf_scans(self, verbose=False): h0 = self.allind[0] st = 0 otfscan = 0 posangs = [h['COMPPOSA'] for h in self.allind] if verbose: pb = ProgressBar(len(self.allind)) for ii,h in enumerate(self.allind): if (h['SCAN']!= h0['SCAN'] or h['SOURC']!= h0['SOURC']): h0['FIRSTSCAN'] = st cpa = np.median(posangs[st:ii]) for hh in self.allind[st:ii]: hh['SCANPOSA'] = cpa % 180 st = ii if h['SCAN'] == h0['SCAN']: h0['OTFSCAN'] = otfscan otfscan += 1 h['OTFSCAN'] = otfscan else: otfscan = 0 h['OTFSCAN'] = otfscan else: h['OTFSCAN'] = otfscan if verbose: pb.update(ii) def listscans(self, source=None, telescope=None, out=sys.stdout): minid=0 scan = -1 sourc = "" #tel = '' minoff1,maxoff1 = np.inf,-np.inf minoff2,maxoff2 = np.inf,-np.inf ttlangle,nangle = 0.0,0 print("{entries:15s} {SOURC:12s} {XTEL:12s} {SCAN:>8s} {SUBSCAN:>8s} " "[ {RAmin:>12s}, {RAmax:>12s} ] " "[ {DECmin:>12s}, {DECmax:>12s} ] " "{angle:>12s} {SCANPOSA:>12s} {OTFSCAN:>8s} {TSYS:>8s} {UTD:>12s}" .format(entries='Scans', SOURC='Source', XTEL='Telescope', SCAN='Scan', SUBSCAN='Subscan', RAmin='min(RA)', RAmax='max(RA)', DECmin='min(DEC)', DECmax='max(DEC)', SCANPOSA='Scan PA', angle='Angle', OTFSCAN='OTFscan', TSYS='TSYS', UTD='UTD'), file=out) data_rows = [] for ii,row in enumerate(self.headers): if (row['SCAN'] == scan and row['SOURC'] == sourc #and row['XTEL'] == tel ): minoff1 = min(minoff1, row['OFF1']) maxoff1 = max(maxoff1, row['OFF1']) minoff2 = min(minoff2, row['OFF2']) maxoff2 = max(maxoff2, row['OFF2']) ttlangle += np.arctan2(row['OFF2'] - prevrow['OFF2'], row['OFF1'] - prevrow['OFF1'])%np.pi nangle += 1 prevrow = row else: if scan == -1: scan = row['SCAN'] sourc = row['SOURC'] #tel = row['XTEL'] prevrow = row continue ok = True if source is not None: if isinstance(source, (list,tuple)): ok = ok and any(re.search((s), prevrow['SOURC']) for s in source) else: ok = ok and re.search((source), prevrow['SOURC']) if telescope is not None: ok = ok and re.search((telescope), prevrow['XTEL']) if ok: data = dict(RAmin=minoff1*180/np.pi*3600, RAmax=maxoff1*180/np.pi*3600, DECmin=minoff2*180/np.pi*3600, DECmax=maxoff2*180/np.pi*3600, angle=(ttlangle/nangle)*180/np.pi if nangle>0 else 0, e0=minid, e1=ii-1, #TSYS=row['TSYS'] if 'TSYS' in row else '--', UTD=row['DOBS']+row['UT'] if 'UT' in row else -99, **prevrow) print("{e0:7d}-{e1:7d} {SOURC:12s} {XTEL:12s} {SCAN:8d} {SUBSCAN:8d} " "[ {RAmin:12f}, {RAmax:12f} ] " "[ {DECmin:12f}, {DECmax:12f} ] " "{angle:12.1f} {SCANPOSA:12.1f} {OTFSCAN:8d}" " {TSYS:>8.1f} {UTD:12f}". format(**data), file=out) data_rows.append(data) minoff1,maxoff1 = np.inf,-np.inf minoff2,maxoff2 = np.inf,-np.inf ttlangle,nangle = 0.0,0 scan = row['SCAN'] sourc = row['SOURC'] #tel = row['XTEL'] minid = ii return data @property def tels(self): if hasattr(self,'_tels'): return self._tels else: self._tels = set([h['XTEL'] for h in self.allind]) return self._tels @property def sources(self): if hasattr(self,'_source'): return self._source else: self._source = set([h['SOURC'] for h in self.allind]) return self._source @property def scans(self): if hasattr(self,'_scan'): return self._scan else: self._scan = set([h['SCAN'] for h in self.allind]) return self._scan @property def sci_sources(self): return set([s for s in self.sources if s[:4] not in ('SKY-', 'TSYS', 'TCAL', 'TREC', 'HOT-', 'COLD')]) @property def lines(self): if hasattr(self,'_lines'): return self._lines else: self._lines = set([h['LINE'] for h in self.allind]) return self._lines def _load_all_spectra(self, indices=None): if indices is None: indices = range(self.file_description['xnext']-1) if hasattr(self, '_loaded_indices'): indices_set = set(indices) indices_to_load = (indices_set.difference(self._loaded_indices)) self._loaded_indices = self._loaded_indices.union(indices_set) if any(indices_to_load): pb = ProgressBar(len(indices_to_load)) for ii,k in enumerate(xrange(indices_to_load)): self._spectra[k] pb.update(ii) else: self._loaded_indices = set(indices) self._spectra.load_all() @property def spectra(self): return [x[0] for x in self._spectra] @property def headers(self): return [self._spectra[ii][1] if ii in self._spectra else x for ii,x in enumerate(self.allind)] def select_spectra(self, all=None, line=None, linere=None, linereflags=re.IGNORECASE, number=None, scan=None, offset=None, source=None, sourcere=None, sourcereflags=re.IGNORECASE, range=None, quality=None, telescope=None, telescopere=None, telescopereflags=re.IGNORECASE, subscan=None, entry=None, posang=None, #observed=None, #reduced=None, frequency=None, section=None, user=None, include_old_versions=False, ): """ Parameters ---------- include_old_versions: bool Include spectra with XVER numbers <0? These are CLASS spectra that have been "overwritten" (re-reduced?) """ if entry is not None and len(entry)==2: return irange(entry[0], entry[1]) if frequency is not None: self._load_all_spectra() sel = [(re.search(re.escape(ensure_bytes(line)), h['LINE'], re.IGNORECASE) if line is not None else True) and (re.search(ensure_bytes(linere), h['LINE'], linereflags) if linere is not None else True) and (h['SCAN'] == scan if scan is not None else True) and ((h['OFF1'] == offset or h['OFF2'] == offset) if offset is not None else True) and (re.search(re.escape(ensure_bytes(source)), h['CSOUR'], re.IGNORECASE) if source is not None else True) and (re.search(ensure_bytes(sourcere), h['CSOUR'], sourcereflags) if sourcere is not None else True) and (h['OFF1']>range[0] and h['OFF1'] < range[1] and h['OFF2']>range[2] and h['OFF2'] < range[3] if range is not None and len(range)==4 else True) and (h['QUAL'] == quality if quality is not None else True) and (re.search(re.escape(ensure_bytes(telescope)), h['CTELE'], re.IGNORECASE) if telescope is not None else True) and (re.search(ensure_bytes(telescopere), h['CTELE'], telescopereflags) if telescopere is not None else True) and (h['SUBSCAN']==subscan if subscan is not None else True) and (h['NUM'] >= number[0] and h['NUM'] < number[1] if number is not None else True) and ('RESTF' in h and # Need to check that it IS a spectrum: continuum data can't be accessed this way h['RESTF'] > frequency[0] and h['RESTF'] < frequency[1] if frequency is not None and len(frequency)==2 else True) and (h['COMPPOSA']%180 > posang[0] and h['COMPPOSA']%180 < posang[1] if posang is not None and len(posang)==2 else True) and # 1A uses XVER, 2A uses VER. If neither are present, it's # probably not a valid spectrum? (h.get('XVER', h.get('VER', -999)) > 0 if not include_old_versions else True) for h in self.headers ] return [ii for ii,k in enumerate(sel) if k] def get_spectra(self, progressbar=True, **kwargs): selected_indices = self.select_spectra(**kwargs) if not any(selected_indices): raise ValueError("Selection yielded empty.") self._spectra.load(selected_indices, progressbar=progressbar) return [self._spectra[ii] for ii in selected_indices] def get_pyspeckit_spectra(self, progressbar=True, **kwargs): spdata = self.get_spectra(progressbar=progressbar, **kwargs) spectra = [pyspeckit.Spectrum(data=data, xarr=make_axis(header), header=clean_header(header)) for data,header in spdata] return spectra def read_observations(self, observation_indices, progressbar=True): self._spectra.load(observation_indices, progressbar=progressbar) return [self._spectra[ii] for ii in observation_indices] @print_timing def read_class(filename, downsample_factor=None, sourcename=None, telescope=None, line=None, posang=None, verbose=False, flag_array=None): """ Read a binary class file. Based on the `GILDAS CLASS file type Specification <http://iram.fr/IRAMFR/GILDAS/doc/html/class-html/node58.html>`_ Parameters ---------- filename: str downsample_factor: None or int Factor by which to downsample data by averaging. Useful for overresolved data. sourcename: str or list of str Source names to match to the data (uses regex) telescope: str or list of str 'XTEL' or 'TELE' parameters: the telescope & instrument line: str or list of str The line name posang: tuple of 2 floats The first float is the minimum value for the position angle. The second float is the maximum value for the position angle. verbose: bool Log messages with severity INFO flag_array: np.ndarray An array with the same shape as the data used to flag out (remove) data when downsampling. True = flag out """ classobj = ClassObject(filename) if not isinstance(sourcename, (list,tuple)): sourcename = [sourcename] if not isinstance(telescope, (list,tuple)): telescope = [telescope] if not isinstance(line, (list,tuple)): line = [line] spectra,headers = [],[] if verbose: log.info("Reading...") selection = [ii for source in sourcename for tel in telescope for li in line for ii in classobj.select_spectra(sourcere=source, telescope=tel, line=li, posang=posang)] sphdr = classobj.read_observations(selection) if len(sphdr) == 0: return None spec,hdr = zip(*sphdr) spectra += spec headers += hdr indexes = headers weight = ~flag_array if flag_array is not None else None if downsample_factor is not None: if verbose: log.info("Downsampling...") spectra = [downsample_1d(spec, downsample_factor, weight=weight) for spec in ProgressBar(spectra)] headers = [downsample_header(h, downsample_factor) for h in ProgressBar(headers)] for hdr in headers: stringify_header(hdr) return spectra,headers,indexes def stringify_header(header): from six import string_types, integer_types import string FITS_allowed_types = (string_types + integer_types + (float, complex, bool, np.floating, np.integer, np.complexfloating, np.bool_)) bad_chars = string.printable[96:] badcharre = re.compile("[{0}]".format(bad_chars)) for key, value in header.items(): if isinstance(value, bytes): header[key] = value.decode() elif not isinstance(value, FITS_allowed_types): header[key] = badcharre.sub("", str(header[key])) def downsample_header(hdr, downsample_factor): for k in ('NCHAN','NPOIN','DATALEN'): if k in hdr: hdr[k] = int((hdr[k] / downsample_factor)) # maybe wrong? h['RCHAN'] = (h['RCHAN']-1) / downsample_factor + 1 scalefactor = 1./downsample_factor hdr['RCHAN'] = (hdr['RCHAN']-1)*scalefactor + 0.5 + scalefactor/2. for kw in ['FRES','VRES']: if kw in hdr: hdr[kw] *= downsample_factor return hdr def make_axis(header,imagfreq=False): """ Create a :class:`pyspeckit.spectrum.units.SpectroscopicAxis` from the CLASS "header" """ from.. import units rest_frequency = header.get('RESTF') xunits = 'MHz' nchan = header.get('NCHAN') voff = header.get('VOFF') foff = header.get('FOFF') doppler = header.get('DOPPLER') fres = header.get('FRES') refchan = header.get('RCHAN') imfreq = header.get('IMAGE') if foff in (None, 0.0) and voff not in (None, 0.0): # Radio convention foff = -voff/2.997924580e5 * rest_frequency if not imagfreq: xarr = rest_frequency + foff + (numpy.arange(1, nchan+1) - refchan) * fres XAxis = units.SpectroscopicAxis(xarr,unit='MHz',refX=rest_frequency*u.MHz) else: xarr = imfreq - (numpy.arange(1, nchan+1) - refchan) * fres XAxis = units.SpectroscopicAxis(xarr,unit='MHz',refX=imfreq*u.MHz) return XAxis @print_timing def class_to_obsblocks(filename, telescope, line, datatuple=None, source=None, imagfreq=False, DEBUG=False, **kwargs): """ Load an entire CLASS observing session into a list of ObsBlocks based on matches to the 'telescope', 'line' and'source' names Parameters ---------- filename : string The Gildas CLASS data file to read the spectra from. telescope : list List of telescope names to be matched. line : list List of line names to be matched. source : list (optional) List of source names to be matched. Defaults to None. imagfreq : bool Create a SpectroscopicAxis with the image frequency. """ if datatuple is None: spectra,header,indexes = read_class(filename, **kwargs) else: spectra,header,indexes = datatuple obslist = [] lastscannum = -1 spectrumlist = None for sp,hdr,ind in zip(spectra,header,indexes): hdr.update(ind) # this is slow but necessary... H = pyfits.Header() for k,v in iteritems(hdr): if hasattr(v,"__len__") and not isinstance(v,str): # make an array of header entries, but this # supports only up to 10 of them... if len(v) > 1: if len(v) < 10: for ii,vv in enumerate(v): newkey = k[:7]+str(ii) H[newkey] = vv elif len(v) < 100: for ii,vv in enumerate(v): newkey = k[:6]+str(ii) H[newkey] = vv else: raise ValueError("Too many entries for {0}".format(k)) else: H[k] = v[0] #elif not any(x in str(v).lower() for x in ('comment', 'end', 'history')): # # do not try to add comments... # This commented out block used to attempt to reject comments # using a private regex in the old pyfits which no longer exists. # I don't know if it was necessary. else: H[k] = v scannum = hdr['SCAN'] if 'XTEL' in hdr and hdr['XTEL'].strip() not in telescope: continue if hdr['LINE'].strip() not in line: continue if (source is not None) and (hdr['SOURC'].strip() not in source): continue hdr['RESTFREQ'] = hdr.get('RESTF') H['RESTFREQ'] = hdr.get('RESTF') #print "Did not skip %s,%s. Scannum, last: %i,%i" % (hdr['XTEL'],hdr['LINE'],scannum,lastscannum) if scannum!= lastscannum: lastscannum = scannum if spectrumlist is not None: obslist.append(pyspeckit.ObsBlock(spectrumlist)) xarr = make_axis(hdr,imagfreq=imagfreq) spectrumlist = [( pyspeckit.Spectrum(xarr=xarr, header=H, data=sp))] else: spectrumlist.append( pyspeckit.Spectrum(xarr=xarr, header=H, data=sp)) return obslist class LazyItem(object): """ Simple lazy spectrum-retriever wrapper """ def __init__(self, parent): self.parent = parent self.sphdr = {} self.nind = len(self.parent.allind) self.nloaded = 0 def __repr__(self): return ("Set of {0} spectra & headers, {1} loaded" " ({2:0.2f}%)".format(self.nind, self.nloaded, (float(self.nloaded)/self.nind)*100)) def load_all(self, progressbar=True): self.load(range(self.nind)) def load(self, indices, progressbar=True): pb = ProgressBar(len(indices)) counter = 0 for k in indices: self[k] counter += 1 pb.update(counter) def __getitem__(self, key): if key in self.sphdr: return self.sphdr[key] elif isinstance(key, slice): return [self[k] for k in xrange(key.start or 0, key.end or len(self.parent.allind), key.step or 1)] else: sphd = read_observation(self.parent._file, key, file_description=self.parent.file_description, indices=self.parent.allind, my_memmap=self.parent._data) # Update the header with OTFSCAN and POSANG info sphd[1].update(self.parent.allind[key]) self.sphdr[key] = sphd self.nloaded += 1 return sphd def __iter__(self): return self.next() def __next__(self): for k in self.spheader: yield self.spheader[k] def __contains__(self, key): return key in self.sphdr @print_timing def class_to_spectra(filename, datatuple=None, **kwargs): """ Load each individual spectrum within a CLASS file into a list of Spectrum objects """ if datatuple is None: spectra,header,indexes = read_class(filename, **kwargs) else: spectra,header,indexes = datatuple spectrumlist = [] for sp,hdr,ind in zip(spectra,header,indexes): hdr.update(ind) xarr = make_axis(hdr) spectrumlist.append( pyspeckit.Spectrum(xarr=xarr, header=hdr, data=sp)) return pyspeckit.Spectra(spectrumlist) def tests(): """ Tests are specific to the machine on which this code was developed. """ fn1 = '/Users/adam/work/bolocam/hht/class_003.smt' #fn1 = '/Users/adam/work/bolocam/hht/class_001.smt' #fn1 = '/Users/adam/work/bolocam/hht/test_SMT-F1M-VU-20824-073.cls' #fn2 = '/Users/adam/work/bolocam/hht/test_SMT-F1M-VU-79472+203.cls' #F1 = read_class(fn1)#,DEBUG=True) #F2 = read_class(fn2) n2hp = class_to_obsblocks(fn1,telescope=['SMT-F1M-HU','SMT-F1M-VU'],line=['N2HP(3-2)','N2H+(3-2)']) hcop = class_to_obsblocks(fn1,telescope=['SMT-F1M-HL','SMT-F1M-VL'],line=['HCOP(3-2)','HCO+(3-2)'])

pyspeckit__pyspeckit

cubes.rst

Module doc / Tutorial

Generate documentation for this module

MIT License

pyspeckit__pyspeckit/docs/cubes.rst

Cubes Pyspeckit can do a few things with spectral cubes. The most interesting is the spectral line fitting. ~pyspeckit.cubes.SpectralCube.Cube objects have a ~pyspeckit.cubes.SpectralCube.Cube.fiteach method that will fit each spectral line within a cube. It can be made to do this in parallel with the multicore option. As of version 0.16, pyspeckit cubes can be read from SpectralCube objects: >>> pcube = pyspeckit.Cube(cube=mySpectralCube) Otherwise, they can be created from FITS cubes on disk: >>> pcube = pyspeckit.Cube(filename="mycube.fits") or from arrays: >>> mycube = np.random.randn(250,50,50) >>> myxaxis = np.linspace(-100,100,250) >>> pcube = pyspeckit.Cube(cube=mycube, xarr=myxaxis, xunit='km/s') The most interesting features of the ~pyspeckit.cubes.SpectralCube.Cube object are the ~pyspeckit.cubes.SpectralCube.Cube.fiteach method, which fits a model spectrum to each element of the cube, and mapplot <pyspeckit.cubes.mapplot.MapPlotter>, which plots up various projections of the cube. Cube.mapplot <pyspeckit.cubes.mapplot.MapPlotter> will create an interactive plot window. You can click on any pixel shown in that window and pull up a second window showing the spectrum at that pixel. If you've fitted the cube, the associated best-fit model will also be shown. This interactive setup can be a bit fragile, though, so please report bugs aggressively so we can weed them out! The interactive viewer has a few button interactions described here <pyspeckit.cubes.mapplot.MapPlotter.mapplot>.

""" MapPlot ------- Make plots of the cube and interactively connect them to spectrum plotting. This is really an interactive component of the package; nothing in here is meant for publication-quality plots, but more for user interactive analysis. That said, the plotter makes use of `APLpy <https://github.com/aplpy/aplpy>`_, so it is possible to make publication-quality plots. :author: Adam Ginsburg :date: 03/17/2011 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """ from __future__ import print_function import matplotlib import matplotlib.figure import numpy as np import copy import itertools import six try: import astropy.wcs as pywcs import astropy.io.fits as pyfits pywcsOK = True except ImportError: try: import pyfits import pywcs pywcsOK = True except ImportError: pywcsOK = False try: import aplpy icanhasaplpy = True except: # aplpy fails with generic exceptions instead of ImportError icanhasaplpy = False from. import cubes class MapPlotter(object): """ Class to plot a spectrum See `mapplot` for use documentation; this docstring is only for initialization. """ def __init__(self, Cube=None, figure=None, doplot=False, **kwargs): """ Create a map figure for future plotting """ import matplotlib.pyplot self._pyplot = matplotlib.pyplot # figure out where to put the plot if isinstance(figure,matplotlib.figure.Figure): self.figure = figure elif type(figure) is int: self.figure = self._pyplot.figure(figure) else: self.figure = None self.axis = None self.FITSFigure = None self._click_marks = [] self._circles = [] self._clickX = None self._clickY = None self.overplot_colorcycle = itertools.cycle(['b', 'g', 'r', 'c','m', 'y']) self.overplot_linestyle = '-' self.Cube = Cube if self.Cube is not None: self.header = cubes.flatten_header(self.Cube.header, delete=True) if pywcsOK: self.wcs = pywcs.WCS(self.header) if doplot: self.mapplot(**kwargs) def __call__(self, **kwargs): """ see mapplot """ return self.mapplot(**kwargs) def mapplot(self, convention='calabretta', colorbar=True, useaplpy=True, vmin=None, vmax=None, cmap=None, plotkwargs={}, **kwargs): """ Plot up a map based on an input data cube. The map to be plotted is selected using `makeplane`. The `estimator` keyword argument is passed to that function. The plotted map, once shown, is interactive. You can click on it with any of the three mouse buttons. Button 1 or keyboard '1': Plot the selected pixel's spectrum in another window. Mark the clicked pixel with an 'x' Button 2 or keyboard 'o': Overplot a second (or third, fourth, fifth...) spectrum in the external plot window Button 3: Disconnect the interactive viewer You can also click-and-drag with button 1 to average over a circular region. This same effect can be achieved by using the 'c' key to set the /c/enter of a circle and the 'r' key to set its /r/adius (i.e., hover over the center and press 'c', then hover some distance away and press 'r'). Parameters ---------- convention : 'calabretta' or 'griesen' The default projection to assume for Galactic data when plotting with aplpy. colorbar : bool Whether to show a colorbar plotkwargs : dict, optional A dictionary of keyword arguments to pass to aplpy.show_colorscale or matplotlib.pyplot.imshow useaplpy : bool Use aplpy if a FITS header is available vmin, vmax: float or None Override values for the vmin/vmax values. Will be automatically determined if left as None .. todo: Allow mapplot in subfigure """ if (self.figure is None): self.figure = self._pyplot.figure() elif (not self._pyplot.fignum_exists(self.figure.number)): self.figure = self._pyplot.figure() else: self._disconnect() self.figure.clf() # this is where the map is created; everything below this is just plotting self.makeplane(**kwargs) # have tot pop out estimator so that kwargs can be passed to imshow if 'estimator' in kwargs: kwargs.pop('estimator') # Below here is all plotting stuff if vmin is None: vmin = self.plane[self.plane==self.plane].min() if vmax is None: vmax = self.plane[self.plane==self.plane].max() if icanhasaplpy and useaplpy: self.fitsfile = pyfits.PrimaryHDU(data=self.plane,header=self.header) self.FITSFigure = aplpy.FITSFigure(self.fitsfile,figure=self.figure,convention=convention) self.FITSFigure.show_colorscale(vmin=vmin, vmax=vmax, cmap=cmap, **plotkwargs) if hasattr(self.FITSFigure, '_ax1'): self.axis = self.FITSFigure._ax1 else: self.axis = self.FITSFigure.ax if colorbar: try: self.FITSFigure.add_colorbar() except Exception as ex: print("ERROR: Could not create colorbar! Error was %s" % str(ex)) self._origin = 0 # FITS convention # TODO: set _origin to 1 if using PIXEL units, not real wcs else: self.axis = self.figure.add_subplot(111) if hasattr(self,'colorbar') and self.colorbar is not None: if self.colorbar.ax in self.axis.figure.axes: self.axis.figure.delaxes(self.colorbar.ax) self.axis.imshow(self.plane, vmin=vmin, vmax=vmax, cmap=cmap, **plotkwargs) if colorbar: try: self.colorbar = self._pyplot.colorbar(self.axis.images[0]) except Exception as ex: print("ERROR: Could not create colorbar! Error was %s" % str(ex)) self._origin = 0 # normal convention self.canvas = self.axis.figure.canvas self._connect() def _connect(self): """ Connect click, click up (release click), and key press to events """ self.clickid = self.canvas.callbacks.connect('button_press_event',self.click) self.clickupid = self.canvas.callbacks.connect('button_release_event',self.plot_spectrum) self.keyid = self.canvas.callbacks.connect('key_press_event',self.plot_spectrum) def _disconnect(self): """ Disconnect click, click up (release click), and key press from events """ if hasattr(self,'canvas'): self.canvas.mpl_disconnect(self.clickid) self.canvas.mpl_disconnect(self.clickupid) self.canvas.mpl_disconnect(self.keyid) def makeplane(self, estimator=np.nanmean): """ Create a "plane" view of the cube, either by slicing or projecting it or by showing a slice from the best-fit model parameter cube. Parameters ---------- estimator : [ function |'max' | 'int' | FITS filename | integer | slice ] A non-pythonic, non-duck-typed variable. If it's a function, apply that function along the cube's spectral axis to obtain an estimate (e.g., mean, min, max, etc.). 'max' will do the same thing as passing np.max 'int' will attempt to integrate the image (which is why I didn't duck-type) (integrate means sum and multiply by dx) a.fits filename will be read using pyfits (so you can make your own cover figure) an integer will get then'th slice in the parcube if it exists If it's a slice, slice the input data cube along the Z-axis with this slice """ # THIS IS A HACK!!! isinstance(a function, function) must be a thing... FUNCTION = type(np.max) # estimator is NOT duck-typed if type(estimator) is FUNCTION: self.plane = estimator(self.Cube.cube,axis=0) elif isinstance(estimator, six.string_types): if estimator =='max': self.plane = self.Cube.cube.max(axis=0) elif estimator == 'int': dx = np.abs(self.Cube.xarr[1:] - self.Cube.xarr[:-1]) dx = np.concatenate([dx,[dx[-1]]]) self.plane = (self.Cube.cube * dx[:,np.newaxis,np.newaxis]).sum(axis=0) elif estimator[-5:] == ".fits": self.plane = pyfits.getdata(estimator) elif type(estimator) is slice: self.plane = self.Cube.cube[estimator,:,:] elif type(estimator) is int: if hasattr(self.Cube,'parcube'): self.plane = self.Cube.parcube[estimator,:,:] if self.plane is None: raise ValueError("Invalid estimator %s" % (str(estimator))) if np.sum(np.isfinite(self.plane)) == 0: raise ValueError("Map is all NaNs or infs. Check your estimator or your input cube.") def click(self,event): """ Record location of downclick """ if event.inaxes: self._clickX = np.round(event.xdata) - self._origin self._clickY = np.round(event.ydata) - self._origin def plot_spectrum(self, event, plot_fit=True): """ Connects map cube to Spectrum... """ self.event = event if event.inaxes: clickX = np.round(event.xdata) - self._origin clickY = np.round(event.ydata) - self._origin # grab toolbar info so that we don't do anything if a tool is selected tb = self.canvas.toolbar if tb.mode!= '': return elif event.key is not None: if event.key == 'c': self._center = (clickX-1,clickY-1) self._remove_circle() self._add_click_mark(clickX,clickY,clear=True) elif event.key == 'r': x,y = self._center self._add_circle(x,y,clickX,clickY) self.circle(x,y,clickX-1,clickY-1) elif event.key == 'o': clickX,clickY = round(clickX),round(clickY) print("OverPlotting spectrum from point %i,%i" % (clickX-1,clickY-1)) color = next(self.overplot_colorcycle) self._add_click_mark(clickX,clickY,clear=False, color=color) self.Cube.plot_spectrum(clickX-1,clickY-1,clear=False, color=color, linestyle=self.overplot_linestyle) elif event.key in ('1','2'): event.button = int(event.key) event.key = None self.plot_spectrum(event) elif (hasattr(event,'button') and event.button in (1,2) and not (self._clickX == clickX and self._clickY == clickY)): if event.button == 1: self._remove_circle() clear=True color = 'k' linestyle ='steps-mid' else: color = next(self.overplot_colorcycle) linestyle = self.overplot_linestyle clear=False rad = ( (self._clickX-clickX)**2 + (self._clickY-clickY)**2 )**0.5 print("Plotting circle from point %i,%i to %i,%i (r=%f)" % (self._clickX,self._clickY,clickX,clickY,rad)) self._add_circle(self._clickX,self._clickY,clickX,clickY) self.circle(self._clickX,self._clickY,clickX,clickY,clear=clear,linestyle=linestyle,color=color) elif hasattr(event,'button') and event.button is not None: if event.button==1: clickX,clickY = round(clickX),round(clickY) print("Plotting spectrum from point %i,%i" % (clickX,clickY)) self._remove_circle() self._add_click_mark(clickX,clickY,clear=True) self.Cube.plot_spectrum(clickX,clickY,clear=True) if plot_fit: self.Cube.plot_fit(clickX, clickY, silent=True) elif event.button==2: clickX,clickY = round(clickX),round(clickY) print("OverPlotting spectrum from point %i,%i" % (clickX,clickY)) color = next(self.overplot_colorcycle) self._add_click_mark(clickX,clickY,clear=False, color=color) self.Cube.plot_spectrum(clickX,clickY,clear=False, color=color, linestyle=self.overplot_linestyle) elif event.button==3: print("Disconnecting GAIA-like tool") self._disconnect() else: print("Call failed for some reason: ") print("event: ",event) else: pass # never really needed... warn("Click outside of axes") def _add_click_mark(self,x,y,clear=False,color='k'): """ Add an X at some position """ if clear: self._clear_click_marks() if self.FITSFigure is not None: label = 'xmark%i' % (len(self._click_marks)+1) x,y = self.FITSFigure.pixel2world(x,y) self.FITSFigure.show_markers(x,y,marker='x',c=color,layer=label) self._click_marks.append( label ) else: self._click_marks.append( self.axis.plot(x,y,'kx') ) self.refresh() def _clear_click_marks(self): """ Remove all marks added by previous clicks """ if self.FITSFigure is not None: for mark in self._click_marks: if mark in self.FITSFigure._layers: self.FITSFigure.remove_layer(mark) else: for mark in self._click_marks: self._click_marks.remove(mark) if mark in self.axis.lines: self.axis.lines.remove(mark) self.refresh() def _add_circle(self,x,y,x2,y2,**kwargs): """ """ if self.FITSFigure is not None: x,y = self.FITSFigure.pixel2world(x,y) x2,y2 = self.FITSFigure.pixel2world(x2,y2) r = (np.linalg.norm(np.array([x,y])-np.array([x2,y2]))) #self.FITSFigure.show_markers(x,y,s=r,marker='o',facecolor='none',edgecolor='black',layer='circle') layername = "circle%02i" % len(self._circles) self.FITSFigure.show_circles(x,y,r,edgecolor='black',facecolor='none',layer=layername,**kwargs) self._circles.append(layername) else: r = np.linalg.norm(np.array([x,y])-np.array([x2,y2])) circle = matplotlib.patches.Circle([x,y],radius=r,**kwargs) self._circles.append( circle ) self.axis.patches.append(circle) self.refresh() def _remove_circle(self): """ """ if self.FITSFigure is not None: for layername in self._circles: if layername in self.FITSFigure._layers: self.FITSFigure.remove_layer(layername) else: for circle in self._circles: if circle in self.axis.patches: self.axis.patches.remove(circle) self._circles.remove(circle) self.refresh() def refresh(self): if self.axis is not None: self.axis.figure.canvas.draw() def circle(self,x1,y1,x2,y2,**kwargs): """ Plot the spectrum of a circular aperture """ r = (np.linalg.norm(np.array([x1,y1])-np.array([x2,y2]))) self.Cube.plot_apspec([x1,y1,r],**kwargs) #self.Cube.data = cubes.extract_aperture( self.Cube.cube, [x1,y1,r], coordsys=None ) #self.Cube.plotter() def copy(self, parent=None): """ Create a copy of the map plotter with blank (uninitialized) axis & figure [ parent ] A spectroscopic axis instance that is the parent of the specfit instance. This needs to be specified at some point, but defaults to None to prevent overwriting a previous plot. """ newmapplot = copy.copy(self) newmapplot.Cube = parent newmapplot.axis = None newmapplot.figure = None return newmapplot """ ~~~~~~~~ cubes.py ~~~~~~~~ From `agpy <http://code.google.com/p/agpy/source/browse/trunk/agpy/cubes.py>`_, contains functions to perform various transformations on data cubes and their headers. """ from __future__ import print_function from six.moves import xrange from numpy import sqrt,repeat,indices,newaxis,pi,cos,sin,array,mean,nansum from math import acos,atan2,tan import numpy import numpy as np import copy import os import astropy.io.fits as fits import astropy.wcs as pywcs import tempfile import warnings import astropy from astropy import coordinates from astropy import log try: from AG_fft_tools import smooth smoothOK = True except ImportError: smoothOK = False try: from scipy.interpolate import UnivariateSpline scipyOK = True except ImportError: scipyOK = False from. import posang # agpy code from..parallel_map import parallel_map dtor = pi/180.0 def blfunc_generator(x=None, polyorder=None, splineorder=None, sampling=1): """ Generate a function that will fit a baseline (polynomial or spline) to a data set. Either ``splineorder`` or ``polyorder`` must be set Parameters ---------- x : np.ndarray or None The X-axis of the fitted array. Will be set to ``np.arange(len(data))`` if not specified polyorder : None or int The polynomial order. splineorder : None or int sampling : int The sampling rate to use for the data. Can set to higher numbers to effectively downsample the data before fitting """ def blfunc(args, x=x): yfit,yreal = args if hasattr(yfit,'mask'): mask = ~yfit.mask else: mask = np.isfinite(yfit) if x is None: x = np.arange(yfit.size, dtype=yfit.dtype) ngood = np.count_nonzero(mask) if polyorder is not None: if ngood < polyorder: return yreal else: endpoint = ngood - (ngood % sampling) y = np.mean([yfit[mask][ii:endpoint:sampling] for ii in range(sampling)], axis=0) polypars = np.polyfit(x[mask][sampling/2:endpoint:sampling], y, polyorder) return yreal-np.polyval(polypars, x).astype(yreal.dtype) elif splineorder is not None and scipyOK: if splineorder < 1 or splineorder > 4: raise ValueError("Spline order must be in {1,2,3,4}") elif ngood <= splineorder: return yreal else: log.debug("splinesampling: {0} " "splineorder: {1}".format(sampling, splineorder)) endpoint = ngood - (ngood % sampling) y = np.mean([yfit[mask][ii:endpoint:sampling] for ii in range(sampling)], axis=0) if len(y) <= splineorder: raise ValueError("Sampling is too sparse. Use finer sampling or " "decrease the spline order.") spl = UnivariateSpline(x[mask][sampling/2:endpoint:sampling], y, k=splineorder, s=0) return yreal-spl(x) else: raise ValueError("Must provide polyorder or splineorder") return blfunc def baseline_cube(cube, polyorder=None, cubemask=None, splineorder=None, numcores=None, sampling=1): """ Given a cube, fit a polynomial to each spectrum Parameters ---------- cube: np.ndarray An ndarray with ndim = 3, and the first dimension is the spectral axis polyorder: int Order of the polynomial to fit and subtract cubemask: boolean ndarray Mask to apply to cube. Values that are True will be ignored when fitting. numcores : None or int Number of cores to use for parallelization. If None, will be set to the number of available cores. """ x = np.arange(cube.shape[0], dtype=cube.dtype) #polyfitfunc = lambda y: np.polyfit(x, y, polyorder) blfunc = blfunc_generator(x=x, splineorder=splineorder, polyorder=polyorder, sampling=sampling) reshaped_cube = cube.reshape(cube.shape[0], cube.shape[1]*cube.shape[2]).T if cubemask is None: log.debug("No mask defined.") fit_cube = reshaped_cube else: if cubemask.dtype!= 'bool': raise TypeError("Cube mask *must* be a boolean array.") if cubemask.shape!= cube.shape: raise ValueError("Mask shape does not match cube shape") log.debug("Masking cube with shape {0} " "with mask of shape {1}".format(cube.shape, cubemask.shape)) masked_cube = cube.copy() masked_cube[cubemask] = np.nan fit_cube = masked_cube.reshape(cube.shape[0], cube.shape[1]*cube.shape[2]).T baselined = np.array(parallel_map(blfunc, zip(fit_cube,reshaped_cube), numcores=numcores)) blcube = baselined.T.reshape(cube.shape) return blcube def flatten_header(header,delete=False): """ Attempt to turn an N-dimensional fits header into a 2-dimensional header Turns all CRPIX[>2] etc. into new keywords with suffix 'A' header must be a fits.Header instance """ if not isinstance(header,fits.Header): raise Exception("flatten_header requires a fits.Header instance") newheader = header.copy() for key in newheader.keys(): try: if delete and int(key[-1]) >= 3 and key[:2] in ['CD','CR','CT','CU','NA']: newheader.pop(key) elif (int(key[-1]) >= 3 or int(key[2])>=3) and key[:2] in ['CD','CR','CT','CU','NA','PC']: newheader.rename_keyword(key,'A'+key,force=True) if delete and (int(key[4]) >= 3 or int(key[7]) >= 3) and key[:2]=='PC' and key in newheader: newheader.pop(key) except ValueError: # if key[-1] is not an int pass except IndexError: # if len(key) < 2 pass newheader['NAXIS'] = 2 if header.get('WCSAXES'): newheader['WCSAXES'] = 2 return newheader def speccen_header(header, lon=None, lat=None, proj='TAN', system='celestial', spectral_axis=3, celestial_axes=[1,2]): """ Turn a cube header into a spectrum header, retaining RA/Dec vals where possible (speccen is like flatten; spec-ify would be better but, specify? nah) Assumes 3rd axis is velocity """ newheader = header.copy() new_spectral_axis = 1 newheader['CRVAL{0}'.format(new_spectral_axis)] = header.get('CRVAL{0}'.format(spectral_axis)) newheader['CRPIX{0}'.format(new_spectral_axis)] = header.get('CRPIX{0}'.format(spectral_axis)) if 'CD{0}_{0}'.format(new_spectral_axis) in header: newheader.rename_keyword('CD{0}_{0}'.format(new_spectral_axis), 'OLDCD{0}_{0}'.format(new_spectral_axis)) elif 'CDELT{0}'.format(new_spectral_axis) in header: newheader.rename_keyword('CDELT{0}'.format(new_spectral_axis),'OLDCDEL{0}'.format(new_spectral_axis)) if 'CD{0}_{0}'.format(spectral_axis) in header: newheader['CDELT{0}'.format(new_spectral_axis)] = header.get('CD{0}_{0}'.format(spectral_axis)) elif 'CDELT{0}'.format(spectral_axis) in header: newheader['CDELT{0}'.format(new_spectral_axis)] = header.get('CDELT{0}'.format(spectral_axis)) newheader['CTYPE{0}'.format(new_spectral_axis)] = 'VRAD' if header.get('CUNIT{0}'.format(spectral_axis)): newheader['CUNIT{0}'.format(new_spectral_axis)] = header.get('CUNIT{0}'.format(spectral_axis)) else: print("Assuming CUNIT3 is km/s in speccen_header") newheader['CUNIT{0}'.format(new_spectral_axis)] = 'km/s' newheader['CRPIX2'] = 1 newheader['CRPIX{0}'.format(spectral_axis)] = 1 if system == 'celestial': c2 = 'RA---' c3 = 'DEC--' elif system == 'galactic': c2 = 'GLON-' c3 = 'GLAT-' elif system == 'PIXEL': c2 = 'PIX--' c3 = 'PIX--' newheader['CTYPE2'] = c2+proj newheader['CTYPE{0}'.format(spectral_axis)] = c3+proj if lon is not None: newheader['CRVAL2'] = lon if lat is not None: newheader['CRVAL{0}'.format(spectral_axis)] = lat if 'CD2_2' in header: newheader.rename_keyword('CD2_2','OLDCD2_2') if 'CD{0}_{0}'.format(spectral_axis) in header: newheader.rename_keyword('CD{0}_{0}'.format(spectral_axis), 'OLDCD{0}_{0}'.format(spectral_axis)) if 'CROTA2' in header: newheader.rename_keyword('CROTA2','OLDCROT2') return newheader def extract_aperture(cube, ap, r_mask=False, wcs=None, coordsys='galactic', wunit='arcsec', debug=False, method='mean'): """ Extract an aperture from a data cube. E.g. to acquire a spectrum of an outflow that is extended. Cube should have shape [z,y,x], e.g. cube = fits.getdata('datacube.fits') Apertures are specified in PIXEL units with an origin of 0,0 (NOT the 1,1 fits standard!) unless wcs and coordsys are specified Parameters ---------- ap : list For a circular aperture, len(ap)=3: ap = [xcen,ycen,radius] For an elliptical aperture, len(ap)=5: ap = [xcen,ycen,height,width,PA] wcs : wcs a pywcs.WCS instance associated with the data cube coordsys : str the coordinate system the aperture is specified in. Options are 'celestial' and 'galactic'. Default is 'galactic' wunit : str units of width/height. default 'arcsec', options 'arcmin' and 'degree' method : str 'mean' or'sum' (average over spectra, or sum them) or 'error' for sqrt(sum-of-squares / n) Other Parameters ---------------- r_mask : bool return mask in addition to spectrum (for error checking?) """ warnings.warn("SpectralCube can do what subimage_integ does much more easily!", DeprecationWarning) if wcs is not None and coordsys is not None: if debug: print("Converting aperture ",ap,) ap = aper_world2pix(ap,wcs,coordsys=coordsys,wunit=wunit) if debug: print(" to ",ap) if len(ap) == 3: sh = cube.shape yind,xind = indices(sh[1:3]) # recall that python indices are backwards dis = sqrt((xind-ap[0])**2+(yind-ap[1])**2) mask = dis < ap[2] elif len(ap) == 5: yinds,xinds = indices(cube.shape[1:3]) th = (ap[4])*dtor xindr = (xinds-ap[0])*cos(th) + (yinds-ap[1])*sin(th) yindr = (xinds-ap[0])*-sin(th) + (yinds-ap[1])*cos(th) ratio = max(ap[2:4])/min(ap[2:4]) mask = ((xindr*ratio)**2 + yindr**2)**0.5 < max(ap[2:4]) else: raise Exception("Wrong number of parameters. Need either 3 parameters " "for a circular aperture or 5 parameters for an " "elliptical aperture.") npixinmask = mask.sum() if method =='mean': specsum = nansum(cube[:, mask], axis=1) spec = specsum / npixinmask elif method == 'error': specsum = nansum(cube[:, mask]**2, axis=1) spec = (specsum)**0.5 / npixinmask else: specsum = nansum(cube[:, mask], axis=1) if r_mask: return spec,mask else: return spec def integ(file,vrange,xcen=None,xwidth=None,ycen=None,ywidth=None,**kwargs): """ wrapper of subimage_integ that defaults to using the full image """ if isinstance(file,fits.PrimaryHDU): header = file.header cube = file.data elif isinstance(file,fits.HDUList): header = file[0].header cube = file[0].data else: file = fits.open(file) header = file[0].header cube = file[0].data if None in [xcen,xwidth,ycen,ywidth]: xcen = header['NAXIS1'] / 2 xwidth = xcen + header['NAXIS1'] % 2 ycen = header['NAXIS2'] / 2 ywidth = ycen + header['NAXIS2'] % 2 return subimage_integ(cube,xcen,xwidth,ycen,ywidth,vrange,header=header,**kwargs) def subimage_integ(cube, xcen, xwidth, ycen, ywidth, vrange, header=None, average=mean, dvmult=False, return_HDU=False, units="pixels", zunits=None): """ Returns a sub-image from a data cube integrated over the specified velocity range NOTE: With `spectral_cube <spectral-cube.rtfd.org>`_, subcube features can be easily applied with the `.subcube` method, and integration is handled separately. Parameters ---------- cube : np.ndarray A 3-dimensional numpy array with dimensions (velocity, y, x) xcen,ycen : float The center in the X,Y-dimension. See `units` below for unit information xwidth,ywidth : float The width in the X,Y-dimension. See `units` below for unit information xwidth and ywidth are "radius" values, i.e. half the length that will be extracted vrange : (float,float) The velocity range to integrate over. See `zunits` below for unit information header : `astropy.io.fits.Header` or None If specified, will allow the use of WCS units average : function The function to apply when 'integrating' over the subcube dvmult : bool If dvmult is set, multiply the average by DV (this is useful if you set average=sum and dvmul=True to get an integrated value, e.g. K km/s or Jy km/s) return_hdu : bool If specified, will return an HDU object, otherwise will return the array and header units : 'pixels' or 'wcs' If 'pixels', all units (xcen, ycen, xwidth, ywidth) will be in pixels. If 'wcs', the values will be converted from WCS units to pixel units using the WCS specified by the `header` zunits : 'pixels' or 'wcs' or None If None, will be set to be the same as `units` Returns ------- subim, hdu : tuple A tuple (integrated array, header) if ``return_hdu`` is ``False``, or an HDU if it is True """ if header: flathead = flatten_header(header.copy()) wcs = pywcs.WCS(header=flathead) if header.get('CD3_3'): CD3 = header.get('CD3_3') else: CD3 = header.get('CDELT3') if units=="pixels": xlo = int( max([xcen-xwidth,0]) ) ylo = int( max([ycen-ywidth,0]) ) xhi = int( min([xcen+xwidth,cube.shape[2]]) ) yhi = int( min([ycen+ywidth,cube.shape[1]]) ) elif units=="wcs" and header: newxcen,newycen = wcs.wcs_world2pix(xcen,ycen,0) try: newxwid,newywid = xwidth / abs(wcs.wcs.cd[0,0]), ywidth / abs(wcs.wcs.cd[1,1]) except AttributeError: newxwid,newywid = xwidth / abs(wcs.wcs.cdelt[0]), ywidth / abs(wcs.wcs.cdelt[1]) xlo = int( max([newxcen-newxwid,0]) ) ylo = int( max([newycen-newywid,0]) ) xhi = int( min([newxcen+newxwid,cube.shape[2]]) ) yhi = int( min([newycen+newywid,cube.shape[1]]) ) else: print("Can only use wcs if you pass a header.") if zunits is None: zunits = units if zunits == 'pixels': zrange = vrange if zunits == 'wcs': zrange = ( array(vrange)-header.get('CRVAL3') ) / CD3 - 1 + header.get('CRPIX3') subim = average(cube[zrange[0]:zrange[1],ylo:yhi,xlo:xhi],axis=0) if dvmult and CD3: subim *= CD3 elif dvmult: print("Error: could not multiply by dv; CD3=",CD3) if header is None: return subim else: # Cannot set crval2!= 0 for Galactic coordinates: therefore, probably # wrong approach in general #crv1,crv2 = wcs.wcs_pix2world(xlo,ylo,0) #try: # flathead['CRVAL1'] = crv1[0] # flathead['CRVAL2'] = crv2[0] #except IndexError: # flathead['CRVAL1'] = crv1.item() # np 0-d arrays are not scalar # flathead['CRVAL2'] = crv2.item() # np 0-d arrays are not scalar # xlo, ylo have been forced to integers already above flathead['CRPIX1'] = flathead['CRPIX1'] - xlo flathead['CRPIX2'] = flathead['CRPIX2'] - ylo if return_HDU: return fits.PrimaryHDU(data=subim,header=flathead) else: return subim,flathead def subcube(cube, xcen, xwidth, ycen, ywidth, header=None, dvmult=False, return_HDU=False, units="pixels", widthunits="pixels"): """ Crops a data cube All units assumed to be pixel units cube has dimensions (velocity, y, x) xwidth and ywidth are "radius" values, i.e. half the length that will be extracted if dvmult is set, multiple the average by DV (this is useful if you set average=sum and dvmul=True to get an integrated value) """ if header: newheader = header.copy() flathead = flatten_header(header.copy()) wcs = pywcs.WCS(header=flathead) if widthunits == "pixels": newxwid, newywid = xwidth, ywidth elif widthunits == "wcs": try: newxwid,newywid = xwidth / abs(wcs.wcs.cd[0,0]), ywidth / abs(wcs.wcs.cd[1,1]) except AttributeError: newxwid,newywid = xwidth / abs(wcs.wcs.cdelt[0]), ywidth / abs(wcs.wcs.cdelt[1]) else: raise Exception("widthunits must be either 'wcs' or 'pixels'") if units=="pixels": newxcen,newycen = xcen,ycen elif units=="wcs" and header: newxcen,newycen = wcs.wcs_world2pix(xcen,ycen,0) else: raise Exception("units must be either 'wcs' or 'pixels'") x1 = int( numpy.floor( max([newxcen-newxwid,0]) ) ) y1 = int( numpy.floor( max([newycen-newywid,0]) ) ) x2 = int( numpy.ceil( min([newxcen+newxwid,cube.shape[2]]) ) ) y2 = int( numpy.ceil( min([newycen+newywid,cube.shape[1]]) ) ) xhi = max(x1,x2) xlo = min(x1,x2) yhi = max(y1,y2) ylo = min(y1,y2) subim = cube[:,ylo:yhi,xlo:xhi] if return_HDU: xmid_sky,ymid_sky = wcs.wcs_pix2world(xlo+xwidth,ylo+ywidth,0) try: newheader['CRVAL1'] = xmid_sky[0] newheader['CRVAL2'] = ymid_sky[0] except IndexError: newheader['CRVAL1'] = float(xmid_sky) newheader['CRVAL2'] = float(ymid_sky) newheader['CRPIX1'] = 1+xwidth newheader['CRPIX2'] = 1+ywidth newHDU = fits.PrimaryHDU(data=subim,header=newheader) if newHDU.header.get('NAXIS1') == 0 or newHDU.header.get('NAXIS2') == 0: raise Exception("Cube has been cropped to 0 in one dimension") return newHDU else: return subim def aper_world2pix(ap,wcs,coordsys='galactic',wunit='arcsec'): """ Converts an elliptical aperture (x,y,width,height,PA) from WCS to pixel coordinates given an input wcs (an instance of the pywcs.WCS class). Must be a 2D WCS header. """ convopt = {'arcsec':3600.0,'arcmin':60.0,'degree':1.0} try: conv = convopt[wunit] except: raise Exception("Must specify wunit='arcsec','arcmin', or 'degree'") if len(wcs.wcs.cdelt)!= 2: raise Exception("WCS header is not strictly 2-dimensional. Look for 3D keywords.") if '' in wcs.wcs.ctype: raise Exception("WCS header has no CTYPE.") if coordsys.lower() == 'galactic': pos = coordinates.SkyCoord(ap[0],ap[1],unit=('deg','deg'), frame='galactic') elif coordsys.lower() in ('radec','fk5','icrs','celestial'): pos = coordinates.SkyCoord(ap[0],ap[1],unit=('deg','deg'), frame='fk5') if wcs.wcs.ctype[0][:2] == 'RA': ra,dec = pos.icrs.ra.deg,pos.icrs.dec.deg elif wcs.wcs.ctype[0][:4] == 'GLON': ra,dec = pos.galactic.l.deg,pos.galactic.b.deg else: raise Exception("WCS CTYPE has no match.") # workaround for a broken wcs.wcs_sky2pix try: radif = (wcs.wcs.crval[0]-ra)*dtor gamma = acos(cos(dec*dtor)*cos(wcs.wcs.crval[1]*dtor)*cos(radif)+sin(dec*dtor)*sin(wcs.wcs.crval[1]*dtor)) / dtor theta = atan2( sin(radif), ( tan(dec*dtor)*cos(wcs.wcs.crval[1]*dtor)-sin(wcs.wcs.crval[1]*dtor)*cos(radif) ) ) x = -gamma * sin(theta) / wcs.wcs.cd[0,0] + wcs.wcs.crpix[0] y = gamma * cos(theta) / wcs.wcs.cd[1,1] + wcs.wcs.crpix[1] except: radif = (wcs.wcs.crval[0]-ra)*dtor gamma = acos(cos(dec*dtor)*cos(wcs.wcs.crval[1]*dtor)*cos(radif)+sin(dec*dtor)*sin(wcs.wcs.crval[1]*dtor)) / dtor theta = atan2( sin(radif), ( tan(dec*dtor)*cos(wcs.wcs.crval[1]*dtor)-sin(wcs.wcs.crval[1]*dtor)*cos(radif) ) ) x = -gamma * sin(theta) / wcs.wcs.cdelt[0] + wcs.wcs.crpix[0] y = gamma * cos(theta) / wcs.wcs.cdelt[1] + wcs.wcs.crpix[1] #print "DEBUG: x,y from math (vectors): ",x,y #x,y = wcs.wcs_world2pix(ra,dec,0) # convert WCS coordinate to pixel coordinate (0 is origin, do not use fits convention) #print "DEBUG: x,y from wcs: ",x,y try: x=x[0] - 1 # change from FITS to python convention y=y[0] - 1 # change from FITS to python convention #print "DEBUG: x,y from math: ",x,y except: pass # cd is default, cdelt is backup if len(ap) > 3: try: width = ap[2] / conv / abs(wcs.wcs.cd[0,0]) # first is width, second is height in DS9 PA convention height = ap[3] / conv / abs(wcs.wcs.cd[0,0]) except: width = ap[2] / conv / abs(wcs.wcs.cdelt[0]) # first is width, second is height in DS9 PA convention height = ap[3] / conv / abs(wcs.wcs.cdelt[0]) apold = copy.copy(ap) if len(ap) == 5: PA = ap[4] ap = [x,y,width,height,PA] else: ap = [x,y,width,height] elif len(ap) == 3: try: width = ap[2] / conv / abs(wcs.wcs.cd[0,0]) # first is width, second is height in DS9 PA convention except: width = ap[2] / conv / abs(wcs.wcs.cdelt[0]) # first is width, second is height in DS9 PA convention apold = copy.copy(ap) ap = [x,y,width] else: raise TypeError("Aperture length is incorrect.") return ap def getspec(lon,lat,rad,cube,header,r_fits=True,inherit=True,wunit='arcsec'): """ Given a longitude, latitude, aperture radius (arcsec), and a cube file, return a.fits file or a spectrum. Parameters ---------- lon: float lat: float longitude and latitude center of a circular aperture in WCS coordinates must be in coordinate system of the file rad: float radius (default degrees) of aperture """ convopt = {'arcsec':1.0,'arcmin':60.0,'degree':3600.0} flathead = flatten_header(header) wcs = pywcs.WCS(flathead) if wcs.wcs.ctype[0][:2] == 'RA': coordsys='celestial' elif wcs.wcs.ctype[0][:4] == 'GLON': coordsys='galactic' spec = extract_aperture(cube,[lon,lat,rad],wcs=wcs, coordsys=coordsys,wunit=wunit) if nansum(spec) == 0: print("Total of extracted spectrum was zero. lon,lat,rad: ",lon,lat,rad) #import pdb; pdb.set_trace() if r_fits: if inherit: newhead = header.copy() else: newhead = fits.Header() try: newhead['CD1_1'] = header['CD3_3'] except KeyError: newhead['CD1_1'] = header['CDELT3'] newhead['CRPIX1'] = header['CRPIX3'] newhead['CRVAL1'] = header['CRVAL3'] try: newhead['CTYPE1'] = header['CTYPE3'] except KeyError: newhead['CTYPE1'] = "VRAD" try: newhead['CUNIT1'] = header['CUNIT3'] except KeyError: print("Header did not contain CUNIT3 keyword. Defaulting to km/s") newhead['CUNIT1'] = "km/s" newhead['BUNIT'] = header['BUNIT'] newhead['APGLON'] = lon newhead['APGLAT'] = lat newhead['APRAD'] = (rad*convopt[wunit],'arcseconds') # radius in arcsec newfile = fits.PrimaryHDU(data=spec,header=newhead) return newfile else: return spec def getspec_reg(cubefilename,region,**kwargs): """ Aperture extraction from a cube using a pyregion circle region The region must be in the same coordinate system as the cube header .. warning:: The second argument of getspec_reg requires a pyregion region list, and therefore this code depends on `pyregion`_. """ ds9tocoords = {'fk5':'celestial','galactic':'galactic','icrs':'celestial'} if region.name!= 'circle': raise Exception("Only circular apertures are implemented so far") l,b,r = region.coord_list #pos = coords.Position([l,b],system=ds9tocoords[region.coord_format]) if isinstance(cubefilename,fits.HDUList): cubefile = cubefilename else: cubefile = fits.open(cubefilename) header = cubefile[0].header cube = cubefile[0].data if len(cube.shape) == 4: cube = cube[0,:,:,:] sp = getspec(l,b,r,cube,header,wunit='degree',**kwargs) return sp def coords_in_image(fitsfile,lon,lat,system='galactic'): """ Determine whether the coordinates are inside the image """ if not isinstance(fitsfile,fits.HDUList): fitsfile = fits.open(fitsfile) wcs = pywcs.WCS(flatten_header(fitsfile[0].header)) if 'RA' in wcs.wcs.ctype[0]: pos = coordinates.Position((lon,lat),system=system) lon,lat = pos.j2000() if 'GLON' in wcs.wcs.ctype[0]: pos = coordinates.Position((lon,lat),system=system) lon,lat = pos.galactic() x,y = wcs.wcs_world2pix(lon,lat,0) #DEBUG print x,y,wcs.naxis1,wcs.naxis2 if (0 < x < wcs.naxis1) and (0 < y < wcs.naxis2): return True else: return False def spectral_smooth(cube, smooth_factor, downsample=True, parallel=True, numcores=None, **kwargs): """ Smooth the cube along the spectral direction """ yy,xx = numpy.indices(cube.shape[1:]) if downsample: newshape = cube[::smooth_factor,:,:].shape else: newshape = cube.shape # need to make the cube "flat" along dims 1&2 for iteration in the "map" flatshape = (cube.shape[0],cube.shape[1]*cube.shape[2]) Ssmooth = lambda x: smooth.smooth(x, smooth_factor, downsample=downsample, **kwargs) if parallel: newcube = numpy.array(parallel_map(Ssmooth, cube.reshape(flatshape).T, numcores=numcores)).T.reshape(newshape) else: newcube = numpy.array(map(Ssmooth, cube.reshape(flatshape).T)).T.reshape(newshape) #naive, non-optimal version # for (x,y) in zip(xx.flat,yy.flat): # newcube[:,y,x] = smooth.smooth(cube[:,y,x], smooth_factor, # downsample=downsample, **kwargs) return newcube def plane_smooth(cube,cubedim=0,parallel=True,numcores=None,**kwargs): """ parallel-map the smooth function Parameters ---------- parallel: bool defaults True. Set to false if you want serial (for debug purposes?) numcores: int pass to parallel_map (None = use all available) """ if not smoothOK: return if cubedim!= 0: cube = cube.swapaxes(0,cubedim) cubelist = [cube[ii,:,:] for ii in xrange(cube.shape[0])] Psmooth = lambda C: smooth.smooth(C,**kwargs) if parallel: smoothcube = array(parallel_map(Psmooth,cubelist,numcores=numcores)) else: smoothcube = array(map(Psmooth,cubelist)) if cubedim!= 0: smoothcube = smoothcube.swapaxes(0,cubedim) return smoothcube try: import montage def rotcrop_cube(x1, y1, x2, y2, cubename, outname, xwidth=25, ywidth=25, in_system='galactic', out_system='equatorial', overwrite=True, newheader=None, xcen=None, ycen=None): """ Crop a data cube and then rotate it with montage """ cubefile = fits.open(cubename) if xcen is None and ycen is None: pos1 = coordinates.Position([x1,y1],system=in_system) pos2 = coordinates.Position([x2,y2],system=in_system) if cubefile[0].header.get('CTYPE1')[:2] == 'RA': x1,y1 = pos1.j2000() x2,y2 = pos2.j2000() coord_system = 'celestial' elif cubefile[0].header.get('CTYPE1')[:4] == 'GLON': x1,y1 = pos1.galactic() x2,y2 = pos2.galactic() coord_system = 'galactic' xcen = (x1+x2)/2.0 ycen = (y1+y2)/2.0 print(xcen,ycen,xwidth,ywidth,coord_system) else: coord_system = in_system sc = subcube(cubefile[0].data, xcen, xwidth, ycen, ywidth, widthunits='pixels', units="wcs", header=cubefile[0].header, return_HDU=True) # note: there should be no security risk here because fits' writeto # will not overwrite by default tempcube = tempfile.mktemp(suffix='.fits') sc.writeto(tempcube) pa = posang.posang(x1,y1,x2,y2,system=coord_system) - 90 if newheader is None: newheader = sc.header.copy() cd11 = newheader.get('CDELT1') if newheader.get('CDELT1') else newheader.get('CD1_1') cd22 = newheader.get('CDELT2') if newheader.get('CDELT2') else newheader.get('CD2_2') cd12 = newheader.get('CD1_2') if newheader.get('CD1_2') else 0.0 cd21 = newheader.get('CD2_1') if newheader.get('CD2_1') else 0.0 cdelt = numpy.sqrt(cd11**2+cd12**2) tempheader = tempfile.mktemp(suffix='.hdr') ycensign = "+" if numpy.sign(ycen) >= 0 else "-" montage.mHdr("%s %1s%s" % (xcen, ycensign, numpy.abs(ycen)), xwidth*cdelt, tempheader, system=out_system, height=ywidth*cdelt, pix_size=cdelt*3600.0, rotation=pa) os.system("sed -i bck '/END/d' %s" % (tempheader)) newheader2 = fits.Header() newheader2.fromTxtFile(tempheader) #newheader2.fromtextfile(tempheader) for key in ('CRPIX3','CRVAL3','CDELT3','CD3_3','CUNIT3','WCSTYPE3','CTYPE3'): if newheader.get(key): newheader2[key] = newheader.get(key) if newheader.get('CD3_3') and newheader2.get('CDELT3') is None: newheader2['CDELT3'] = newheader.get('CD3_3') if astropy.version.major >= 2 or (astropy.version.major==1 and astropy.version.minor>=3): newheader2.toTxtFile(tempheader,overwrite=True) else: newheader2.toTxtFile(tempheader,clobber=True) #if newheader2.get('CDELT3') is None: # raise Exception("No CD3_3 or CDELT3 in header.") else: if isinstance(newheader,str): newheader2 = fits.Header() newheader2.fromTxtFile(newheader) tempheader = tempfile.mktemp(suffix='.hdr') if astropy.version.major >= 2 or (astropy.version.major==1 and astropy.version.minor>=3): newheader2.toTxtFile(tempheader,overwrite=True) else: newheader2.toTxtFile(tempheader,clobber=True) montage.wrappers.reproject_cube(tempcube,outname,header=tempheader,clobber=overwrite) #print "\n",outname #os.system('imhead %s | grep CDELT' % outname) # AWFUL hack because montage removes CDELT3 tempcube = fits.open(outname) tempcube.header = newheader2 #if tempcube.header.get('CDELT3') is None: # raise Exception("No CD3_3 or CDELT3 in header.") #print tempcube.header.get('CDELT3') if astropy.version.major >= 2 or (astropy.version.major==1 and astropy.version.minor>=3): tempcube.writeto(outname,overwrite=True) else: tempcube.writeto(outname,clobber=True) #print tempcube.get('CDELT3') #print "\n",outname #os.system('imhead %s | grep CDELT' % outname) return def resample_cube(cubefilename, header): inhdr = fits.getheader(cubefilename) except: pass

pyspeckit__pyspeckit

models.rst

Module doc / Tutorial

Generate documentation for this module

MIT License

pyspeckit__pyspeckit/docs/models.rst

Models See parameters for information on how to restrict/modify model parameters. The generic SpectralModel class is a wrapper for model functions. A model should take in an X-axis and some number of parameters. In order to declare a SpectralModel, you give SpectralModel the function name and the number of parameters it requires. The rest of the options are optional, though parnames & shortvarnames are strongly recommended. If you do not specify fitunits, your fitting code must deal with units internally. Here are some examples of how to make your own fitters: hill5_fitter = model.SpectralModel(hill5_model, 5, parnames=['tau', 'v_lsr', 'v_infall', 'sigma', 'tpeak'], parlimited=[(True,False),(False,False),(True,False),(True,False), (True,False)], parlimits=[(0,0), (0,0), (0,0), (0,0), (0,0)], # specify the parameter names (TeX is OK) shortvarnames=("\\tau","v_{lsr}","v_{infall}","\\sigma","T_{peak}"), fitunits='Hz' ) gaussfitter = model.SpectralModel(gaussian, 3, parnames=['amplitude','shift','width'], parlimited=[(False,False),(False,False),(True,False)], parlimits=[(0,0), (0,0), (0,0)], shortvarnames=('A',r'\Delta x',r'\sigma')) Then you can register these fitters. Fitting Once you have a model defined, you can fit it using the pyspeckit.Spectrum.specfit module. Documents on fitting have not been prepared yet, but you can learn most of the tricks by looking at the various fitting examples and the parameters documentation. See also fitting. Implement the gaussian-hermite profile described here: http://pipelinesandarchives.blogspot.com/2012/09/fit1d-new-smurf-command-for-acsis-data.html Specific Models Ammonia Temperature and Hyperfine model <ammonia_model> Formaldehyde model <formaldehyde_model> HCN model <hcn_model> hill5infall_model n2hp_model hydrogen_model API Documentation for Models We include the API documentation for the generic model and fitter wrappers here.

""" ==================== SimpleFitter wrapper ==================== Adds a variable height (background) component to any model Module API ^^^^^^^^^^ """ import numpy from pyspeckit.mpfit import mpfit from numpy.ma import median from pyspeckit.spectrum.moments import moments class SimpleFitter(object): def __init__(): pass def moments(self, *args, **kwargs): """ Get the spectral moments from the moments package """ return moments(*args,**kwargs) def vheightmodel(zeroheightmodel): def vhm(xax, *pars,**kwargs): """ Wrapper function vhm to set variable height. Parameter order: height, amplitude, shift, width """ vheight=True if 'vheight' in kwargs: vheight = kwargs.pop('vheight') if vheight: return zeroheightmodel(xax, *pars[1:],**kwargs) + pars[0] else: return zeroheightmodel(xax, *pars[1:],**kwargs) vhm.__doc__ += zeroheightmodel.__doc__ return vhm """ ============================= Generic SpectralModel wrapper ============================= .. moduleauthor:: Adam Ginsburg <adam.g.ginsburg@gmail.com> Module API ^^^^^^^^^^ """ import numpy as np from pyspeckit.mpfit import mpfit,mpfitException from pyspeckit.spectrum.parinfo import ParinfoList,Parinfo import copy from astropy import log import matplotlib.cbook as mpcb from. import fitter from. import mpfit_messages from pyspeckit.specwarnings import warn from pyspeckit.spectrum.units import SpectroscopicAxis import itertools import operator import six try: from collections import OrderedDict except ImportError: from ordereddict import OrderedDict except ImportError: warn("OrderedDict is required for modeling. " "If you have python <2.7, install the ordereddict module.") # define the allowed guess types and the order in which they are received valid_guess_types = ('amplitude', 'center', 'width') class SpectralModel(fitter.SimpleFitter): """ A wrapper class for a spectra model. Includes internal functions to generate multi-component models, annotations, integrals, and individual components. The declaration can be complex, since you should name individual variables, set limits on them, set the units the fit will be performed in, and set the annotations to be used. Check out some of the hyperfine codes (hcn, n2hp) for examples. """ def __init__(self, modelfunc, npars, shortvarnames=("A","\\Delta x","\\sigma"), fitunit=None, centroid_par=None, fwhm_func=None, fwhm_pars=None, integral_func=None, use_lmfit=False, guess_types=('amplitude', 'center', 'width'), **kwargs): """ Spectral Model Initialization Create a Spectral Model class for data fitting Parameters ---------- modelfunc : function the model function to be fitted. Should take an X-axis (spectroscopic axis) as an input followed by input parameters. Returns an array with the same shape as the input X-axis npars : int number of parameters required by the model use_lmfit: bool Use lmfit instead of mpfit to do the fitting parnames : list (optional) a list or tuple of the parameter names parvalues : list (optional) the initial guesses for the input parameters (defaults to ZEROS) parlimits : list (optional) the upper/lower limits for each variable (defaults to ZEROS) parfixed : list (optional) Can declare any variables to be fixed (defaults to ZEROS) parerror : list (optional) technically an output parameter. Specifying it here will have no effect. (defaults to ZEROS) partied : list (optional) not the past tense of party. Can declare, via text, that some parameters are tied to each other. Defaults to zeros like the others, but it's not clear if that's a sensible default fitunit : str (optional) convert X-axis to these units before passing to model parsteps : list (optional) minimum step size for each paremeter (defaults to ZEROS) npeaks : list (optional) default number of peaks to assume when fitting (can be overridden) shortvarnames : list (optional) TeX names of the variables to use when annotating amplitude_types : tuple A tuple listing the types of the different parameters when guessing. The valid values are 'amplitude', 'width', and 'center'. These are handled by parse_3par_guesses, which translate these into input guess lists for the fitter. For a "standard" 3-parameter Gaussian fitter, nothing changes, but for other models that have more than 3 parameters, some translation is needed. Returns ------- A tuple containing (model best-fit parameters, the model, parameter errors, chi^2 value) """ self.modelfunc = modelfunc if self.__doc__ is None: self.__doc__ = modelfunc.__doc__ elif modelfunc.__doc__ is not None: self.__doc__ += modelfunc.__doc__ self.npars = npars self.default_npars = npars self.fitunit = fitunit # this needs to be set once only self.shortvarnames = shortvarnames self.default_parinfo = None self.default_parinfo, kwargs = self._make_parinfo(**kwargs) self.parinfo = copy.copy(self.default_parinfo) self.modelfunc_kwargs = kwargs self.use_lmfit = use_lmfit # default name of parameter that represents the profile centroid self.centroid_par = centroid_par # FWHM function and parameters self.fwhm_func = fwhm_func self.fwhm_pars = fwhm_pars # analytic integral function self.integral_func = integral_func for gt in guess_types: if not isinstance(gt, float) and not any(g in gt for g in valid_guess_types): raise ValueError("Guess type must be one of {0} or a float" .format(valid_guess_types)) self.guess_types = guess_types def __copy__(self): # http://stackoverflow.com/questions/1500718/what-is-the-right-way-to-override-the-copy-deepcopy-operations-on-an-object-in-p cls = self.__class__ result = cls.__new__(cls) result.__dict__.update(self.__dict__) return result def __deepcopy__(self, memo): cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result for k, v in self.__dict__.items(): setattr(result, k, copy.deepcopy(v, memo)) return result def __call__(self, *args, **kwargs): log.debug("Fitter called with args={0} and kwargs={1}".format(args, kwargs)) use_lmfit = kwargs.pop('use_lmfit') if 'use_lmfit' in kwargs else self.use_lmfit if use_lmfit: return self.lmfitter(*args,**kwargs) return self.fitter(*args,**kwargs) @property def npeaks(self): return int(self._npeaks) @npeaks.setter def npeaks(self, value): if int(value)!= value: raise ValueError("npeaks must be an integer") self._npeaks = int(value) def make_parinfo(self, **kwargs): return self._make_parinfo(**kwargs)[0] def _make_parinfo(self, params=None, parnames=None, parvalues=None, parlimits=None, parlimited=None, parfixed=None, parerror=None, partied=None, fitunit=None, parsteps=None, npeaks=1, parinfo=None, names=None, values=None, limits=None, limited=None, fixed=None, error=None, tied=None, steps=None, negamp=None, limitedmin=None, limitedmax=None, minpars=None, maxpars=None, vheight=False, debug=False, **kwargs): """ Generate a `ParinfoList` that matches the inputs This code is complicated - it can take inputs in a variety of different forms with different priority. It will return a `ParinfoList` (and therefore must have values within parameter ranges) """ log.debug("BEGIN _make_parinfo") # for backwards compatibility - partied = tied, etc. locals_dict = locals() for varname in str.split("parnames,parvalues,parsteps,parlimits,parlimited,parfixed,parerror,partied",","): shortvarname = varname.replace("par","") if locals_dict.get(shortvarname) is not None and locals_dict.get(varname) is not None: raise ValueError("Cannot specify both {0} and {1}".format(varname, shortvarname)) input_pardict = {k: locals_dict.get(k) for k in str.split("parnames,parvalues,parsteps,parlimits,parlimited,parfixed,parerror,partied",",")} _tip = {'par'+k: locals_dict.get(k) for k in str.split("names,values,steps,limits,limited,fixed,error,tied",",") if locals_dict.get(k) } input_pardict.update(_tip) if params is not None and parvalues is not None: raise ValueError("parvalues and params both specified; they're redundant so that's not allowed.") elif params is not None and parvalues is None: input_pardict['parvalues'] = params log.debug("Parvalues = {0}, npeaks = {1}".format(input_pardict['parvalues'], npeaks)) # this is used too many damned times to keep referencing a dict. parnames = input_pardict['parnames'] parlimited = input_pardict['parlimited'] parlimits = input_pardict['parlimits'] parvalues = input_pardict['parvalues'] if parnames is not None: self.parnames = parnames elif parnames is None and hasattr(self,'parnames') and self.parnames is not None: parnames = self.parnames elif self.default_parinfo is not None and parnames is None: parnames = [p['parname'] for p in self.default_parinfo] input_pardict['parnames'] = parnames assert input_pardict['parnames'] is not None if limitedmin is not None: if limitedmax is not None: parlimited = list(zip(limitedmin,limitedmax)) else: parlimited = list(zip(limitedmin,(False,)*len(parnames))) elif limitedmax is not None: parlimited = list(zip((False,)*len(parnames),limitedmax)) elif self.default_parinfo is not None and parlimited is None: parlimited = [p['limited'] for p in self.default_parinfo] input_pardict['parlimited'] = parlimited if minpars is not None: if maxpars is not None: parlimits = list(zip(minpars,maxpars)) else: parlimits = list(zip(minpars,(False,)*len(parnames))) elif maxpars is not None: parlimits = list(zip((False,)*len(parnames),maxpars)) elif limits is not None: parlimits = limits elif self.default_parinfo is not None and parlimits is None: parlimits = [p['limits'] for p in self.default_parinfo] input_pardict['parlimits'] = parlimits self.npeaks = int(npeaks) # the height / parvalue popping needs to be done before the temp_pardict is set in order to make sure # that the height guess isn't assigned to the amplitude self.vheight = vheight if ((vheight and len(self.parinfo) == self.default_npars and len(parvalues) == self.default_npars + 1)): # if the right number of parameters are passed, the first is the height self.parinfo = [{'n':0, 'value':parvalues.pop(0), 'limits':(0,0), 'limited': (False,False), 'fixed':False, 'parname':'HEIGHT', 'error': 0, 'tied':""}] elif vheight and len(self.parinfo) == self.default_npars and len(parvalues) == self.default_npars: # if you're one par short, guess zero self.parinfo = [{ 'n':0, 'value': 0, 'limits':(0,0), 'limited': (False,False), 'fixed':False, 'parname':'HEIGHT', 'error': 0, 'tied':"" }] elif vheight and len(self.parinfo) == self.default_npars+1 and len(parvalues) == self.default_npars+1: # the right numbers are passed *AND* there is already a height param self.parinfo = [{ 'n':0, 'value':parvalues.pop(0), 'limits':(0,0), 'limited': (False,False), 'fixed': False, 'parname':'HEIGHT', 'error': 0, 'tied':"" }] #heightparnum = (i for i,s in self.parinfo if 'HEIGHT' in s['parname']) #for hpn in heightparnum: # self.parinfo[hpn]['value'] = parvalues[0] elif vheight: raise ValueError('VHEIGHT is specified but a case was found that did not allow it to be included.') else: self.parinfo = [] log.debug("After VHEIGHT parse len(parinfo): %i vheight: %s" % (len(self.parinfo), vheight)) # this is a clever way to turn the parameter lists into a dict of lists # clever = hard to read temp_pardict = OrderedDict([(varname, np.zeros(self.npars*self.npeaks, dtype='bool')) if input_pardict.get(varname) is None else (varname, list(input_pardict.get(varname))) for varname in str.split("parnames,parvalues,parsteps,parlimits,parlimited,parfixed,parerror,partied",",")]) temp_pardict['parlimits'] = parlimits if parlimits is not None else [(0,0)] * (self.npars*self.npeaks) temp_pardict['parlimited'] = parlimited if parlimited is not None else [(False,False)] * (self.npars*self.npeaks) for k,v in temp_pardict.items(): if (self.npars*self.npeaks) / len(v) > 1: n_components = ((self.npars*self.npeaks) / len(v)) if n_components!= int(n_components): raise ValueError("The number of parameter values is not a " "multiple of the number of allowed " "parameters.") temp_pardict[k] = list(v) * int(n_components) # generate the parinfo dict # note that 'tied' must be a blank string (i.e. ""), not False, if it is not set # parlimited, parfixed, and parlimits are all two-element items (tuples or lists) self.parinfo += [{'n':ii+self.npars*jj+vheight, 'value':float(temp_pardict['parvalues'][ii+self.npars*jj]), 'step':temp_pardict['parsteps'][ii+self.npars*jj], 'limits':temp_pardict['parlimits'][ii+self.npars*jj], 'limited':temp_pardict['parlimited'][ii+self.npars*jj], 'fixed':temp_pardict['parfixed'][ii+self.npars*jj], 'parname':temp_pardict['parnames'][ii].upper()+"%0i" % int(jj), 'error':float(temp_pardict['parerror'][ii+self.npars*jj]), 'tied':temp_pardict['partied'][ii+self.npars*jj] if temp_pardict['partied'][ii+self.npars*jj] else ""} for jj in range(self.npeaks) for ii in range(self.npars) ] # order matters! log.debug("After Generation step len(parinfo): %i vheight: %s " "parinfo: %s" % (len(self.parinfo), vheight, self.parinfo)) if debug > True: import pdb; pdb.set_trace() # special keyword to specify emission/absorption lines if negamp is not None: if negamp: for p in self.parinfo: if 'AMP' in p['parname']: p['limited'] = (p['limited'][0], True) p['limits'] = (p['limits'][0], 0) else: for p in self.parinfo: if 'AMP' in p['parname']: p['limited'] = (True, p['limited'][1]) p['limits'] = (0, p['limits'][1]) # This is effectively an override of all that junk above (3/11/2012) # Much of it is probably unnecessary, but it was easier to do this than # rewrite the above self.parinfo = ParinfoList([Parinfo(p) for p in self.parinfo]) # New feature: scaleability for par in self.parinfo: if par.parname.lower().strip('0123456789') in ('amplitude','amp'): par.scaleable = True log.debug("Parinfo has been set: {0}".format(self.parinfo)) log.debug("kwargs {0} were passed.".format(kwargs)) assert self.parinfo!= [] return self.parinfo, kwargs def n_modelfunc(self, pars=None, debug=False, **kwargs): """ Simple wrapper to deal with N independent peaks for a given spectral model """ if pars is None: pars = self.parinfo elif not isinstance(pars, ParinfoList): try: partemp = copy.copy(self.parinfo) partemp._from_Parameters(pars) pars = partemp except AttributeError: log.log(5, "Reading pars {0} as LMPar failed.".format(pars)) if debug > 1: import pdb; pdb.set_trace() if hasattr(pars,'values'): # important to treat as Dictionary, since lmfit params & parinfo both have.items parnames,parvals = list(zip(*list(pars.items()))) parnames = [p.lower() for p in parnames] parvals = [p.value for p in parvals] else: parvals = list(pars) if np.any(np.isnan(parvals)): raise ValueError("A parameter is NaN. Unless you gave a NaN " "value directly, this is a bug and should be " "reported. If you specified a NaN parameter, " "don't do that.") log.debug("pars to n_modelfunc: {0}, parvals:{1}".format(pars, parvals)) def L(x): if hasattr(x, 'value') and not hasattr(x, 'x_to_coord'): x = SpectroscopicAxis(x) v = np.zeros(len(x)) if self.vheight: v += parvals[0] # use len(pars) instead of self.npeaks because we want this to work # independent of the current best fit for jj in range(int((len(parvals)-self.vheight)/self.npars)): lower_parind = jj*self.npars+self.vheight upper_parind = (jj+1)*self.npars+self.vheight v += self.modelfunc(x, *parvals[lower_parind:upper_parind], **kwargs) return v return L def mpfitfun(self,x,y,err=None): """ Wrapper function to compute the fit residuals in an mpfit-friendly format """ if err is None: def f(p,fjac=None): residuals = (y-self.n_modelfunc(p, **self.modelfunc_kwargs)(x)) return [0,residuals] else: def f(p,fjac=None): residuals = (y-self.n_modelfunc(p, **self.modelfunc_kwargs)(x))/err return [0,residuals] return f def lmfitfun(self,x,y,err=None,debug=False): """ Wrapper function to compute the fit residuals in an lmfit-friendly format """ def f(p): #pars = [par.value for par in p.values()] kwargs = {} kwargs.update(self.modelfunc_kwargs) log.debug("Pars, kwarg keys: {0},{1}".format(p,list(kwargs.keys()))) if err is None: return (y-self.n_modelfunc(p,**kwargs)(x)) else: return (y-self.n_modelfunc(p,**kwargs)(x))/err return f def lmfitter(self, xax, data, err=None, parinfo=None, quiet=True, debug=False, **kwargs): """ Use lmfit instead of mpfit to do the fitting Parameters ---------- xax : SpectroscopicAxis The X-axis of the spectrum data : ndarray The data to fit err : ndarray (optional) The error on the data. If unspecified, will be uniform unity parinfo : ParinfoList The guesses, parameter limits, etc. See `pyspeckit.spectrum.parinfo` for details quiet : bool If false, print out some messages about the fitting """ try: import lmfit except ImportError as e: raise ImportError("Could not import lmfit, try using mpfit instead.") log.debug("lmfit called with parinfo=\n{0}".format(parinfo)) self.xax = xax # the'stored' xax is just a link to the original if hasattr(xax,'convert_to_unit') and self.fitunit is not None: # some models will depend on the input units. For these, pass in an X-axis in those units # (gaussian, voigt, lorentz profiles should not depend on units. Ammonia, formaldehyde, # H-alpha, etc. should) xax = copy.copy(xax) xax.convert_to_unit(self.fitunit, quiet=quiet) elif self.fitunit is not None: raise TypeError("X axis does not have a convert method") if np.any(np.isnan(data)) or np.any(np.isinf(data)): err[np.isnan(data) + np.isinf(data)] = np.inf data[np.isnan(data) + np.isinf(data)] = 0 if np.any(np.isnan(err)): raise ValueError("One or more of the error values is NaN." " This is not allowed. Errors can be infinite " "(which is equivalent to giving zero weight to " "a data point), but otherwise they must be positive " "floats.") elif np.any(err<0): raise ValueError("At least one error value is negative, which is " "not allowed as negative errors are not " "meaningful in the optimization process.") if parinfo is None: parinfo, kwargs = self._make_parinfo(debug=debug, **kwargs) log.debug("Parinfo created from _make_parinfo: {0}".format(parinfo)) LMParams = parinfo.as_Parameters() log.debug("LMParams: "+"\n".join([repr(p) for p in list(LMParams.values())])) log.debug("parinfo: {0}".format(parinfo)) log.debug("BEGIN MINIMIZER") minimizer = lmfit.minimize(self.lmfitfun(xax,np.array(data),err,debug=debug),LMParams,**kwargs) log.debug("END MINIMIZER") if not quiet: log.info("There were %i function evaluations" % (minimizer.nfev)) #modelpars = [p.value for p in parinfo.values()] #modelerrs = [p.stderr for p in parinfo.values() if p.stderr is not None else 0] self.LMParams = minimizer.params # Force consistency w/earlier versions of lmfit: if error == 0 exactly, # change it to None for par in self.LMParams: if hasattr(par,'stderr') and par.stderr == 0: #assert minimizer.ier == 4 par.stderr = None self.parinfo._from_Parameters(self.LMParams) log.debug("LMParams: {0}".format(self.LMParams)) log.debug("parinfo: {0}".format(parinfo)) self.mp = minimizer self.mpp = self.parinfo.values self.mpperr = self.parinfo.errors self.mppnames = self.parinfo.names modelkwargs = {} modelkwargs.update(self.modelfunc_kwargs) self.model = self.n_modelfunc(self.parinfo, **modelkwargs)(xax) if hasattr(minimizer,'chisqr'): chi2 = minimizer.chisqr else: try: chi2 = (((data-self.model)/err)**2).sum() except TypeError: chi2 = ((data-self.model)**2).sum() if np.isnan(chi2): warn("Warning: chi^2 is nan") if hasattr(self.mp,'ier') and self.mp.ier not in [1,2,3,4]: log.warning("Fitter failed: %s, %s" % (self.mp.message, self.mp.lmdif_message)) return self.mpp,self.model,self.mpperr,chi2 def fitter(self, xax, data, err=None, quiet=True, veryverbose=False, debug=False, parinfo=None, **kwargs): """ Run the fitter using mpfit. kwargs will be passed to _make_parinfo and mpfit. Parameters ---------- xax : SpectroscopicAxis The X-axis of the spectrum data : ndarray The data to fit err : ndarray (optional) The error on the data. If unspecified, will be uniform unity parinfo : ParinfoList The guesses, parameter limits, etc. See `pyspeckit.spectrum.parinfo` for details quiet : bool pass to mpfit. If False, will print out the parameter values for each iteration of the fitter veryverbose : bool print out a variety of mpfit output parameters debug : bool raise an exception (rather than a warning) if chi^2 is nan """ if parinfo is None: parinfo, kwargs = self._make_parinfo(debug=debug, **kwargs) else: log.debug("Using user-specified parinfo dict") # clean out disallowed kwargs (don't want to pass them to mpfit) #throwaway, kwargs = self._make_parinfo(debug=debug, **kwargs) self.xax = xax # the'stored' xax is just a link to the original if hasattr(xax,'as_unit') and self.fitunit is not None: # some models will depend on the input units. For these, pass in an X-axis in those units # (gaussian, voigt, lorentz profiles should not depend on units. Ammonia, formaldehyde, # H-alpha, etc. should) xax = copy.copy(xax) # xax.convert_to_unit(self.fitunit, quiet=quiet) xax = xax.as_unit(self.fitunit, quiet=quiet, **kwargs) elif self.fitunit is not None: raise TypeError("X axis does not have a convert method") if np.any(np.isnan(data)) or np.any(np.isinf(data)): err[np.isnan(data) + np.isinf(data)] = np.inf data[np.isnan(data) + np.isinf(data)] = 0 if np.any(np.isnan(err)): raise ValueError("One or more of the error values is NaN." " This is not allowed. Errors can be infinite " "(which is equivalent to giving zero weight to " "a data point), but otherwise they must be positive " "floats.") elif np.any(err<0): raise ValueError("At least one error value is negative, which is " "not allowed as negative errors are not " "meaningful in the optimization process.") for p in parinfo: log.debug( p ) log.debug( "\n".join(["%s %i: tied: %s value: %s" % (p['parname'],p['n'],p['tied'],p['value']) for p in parinfo]) ) mp = mpfit(self.mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet,debug=debug,**kwargs) mpp = mp.params if mp.perror is not None: mpperr = mp.perror else: mpperr = mpp*0 chi2 = mp.fnorm if mp.status == 0: if "parameters are not within PARINFO limits" in mp.errmsg: log.warning(parinfo) raise mpfitException(mp.errmsg) for i,(p,e) in enumerate(zip(mpp,mpperr)): self.parinfo[i]['value'] = p # for consistency w/lmfit, and because it makes more sense, errors # of 0 will instead be None self.parinfo[i]['error'] = e if (e!= 0 or mp.status!= 4) else None # sanity check: if status==4, errors could not be computed # Apparently some parameters can have errors estimated even if all can't? #if mp.status == 4: # assert all([self.parinfo[ii]['error'] is None # for ii in range(len(mpp))]) if veryverbose: log.info("Fit status: {0}".format(mp.status)) log.info("Fit error message: {0}".format(mp.errmsg)) log.info("Fit message: {0}".format(mpfit_messages[mp.status])) for i,p in enumerate(mpp): log.info("{0}: {1} +/- {2}".format(self.parinfo[i]['parname'], p,mpperr[i])) log.info("Chi2: {0} Reduced Chi2: {1} DOF:{2}".format(mp.fnorm, mp.fnorm/(len(data)-len(mpp)), len(data)-len(mpp))) self.mp = mp self.mpp = self.parinfo.values self.mpperr = self.parinfo.errors self.mppnames = self.parinfo.names self.model = self.n_modelfunc(self.parinfo,**self.modelfunc_kwargs)(xax) log.debug("Modelpars: {0}".format(self.mpp)) if np.isnan(chi2): if debug: raise ValueError("Error: chi^2 is nan") else: log.warning("Warning: chi^2 is nan") return mpp,self.model,mpperr,chi2 def slope(self, xinp): """ Find the local slope of the model at location x (x must be in xax's units) """ if hasattr(self,'model'): dm = np.diff(self.model) # convert requested x to pixels xpix = self.xax.x_to_pix(xinp) dmx = np.average(dm[xpix-1:xpix+1]) if np.isfinite(dmx): return dmx else: return 0 def annotations(self, shortvarnames=None, debug=False): """ Return a list of TeX-formatted labels The values and errors are formatted so that only the significant digits are displayed. Rounding is performed using the decimal package. Parameters ---------- shortvarnames : list A list of variable names (tex is allowed) to include in the annotations. Defaults to self.shortvarnames Examples -------- >>> # Annotate a Gaussian >>> sp.specfit.annotate(shortvarnames=['A','\\Delta x','\\sigma']) """ from decimal import Decimal # for formatting svn = self.shortvarnames if shortvarnames is None else shortvarnames # if pars need to be replicated.... if len(svn) < self.npeaks*self.npars: svn = svn * self.npeaks parvals = self.parinfo.values parerrs = self.parinfo.errors loop_list = [(parvals[ii+jj*self.npars+self.vheight], parerrs[ii+jj*self.npars+self.vheight], svn[ii+jj*self.npars], self.parinfo.fixed[ii+jj*self.npars+self.vheight], jj) for jj in range(self.npeaks) for ii in range(self.npars)] label_list = [] for (value, error, varname, fixed, varnumber) in loop_list: log.debug(", ".join([str(x) for x in (value, error, varname, fixed, varnumber)])) if None in (value, error): label = "{0}({1})=None".format(varname, varnumber) elif fixed or error==0: label = ("$%s(%i)$=%8s" % (varname, varnumber, Decimal("%g" % value).quantize(Decimal("%0.6g" % (value))))) else: label = ("$%s(%i)$=%8s $\\pm$ %8s" % (varname, varnumber, Decimal("%g" % value).quantize(Decimal("%0.2g" % (min(np.abs([value,error]))))), Decimal("%g" % error).quantize(Decimal("%0.2g" % (error))),)) label_list.append(label) labels = tuple(mpcb.flatten(label_list)) return labels def components(self, xarr, pars, **kwargs): """ Return a numpy ndarray of shape [npeaks x modelshape] of the independent components of the fits """ modelcomponents = np.array( [self.modelfunc(xarr, *pars[i*self.npars:(i+1)*self.npars], **dict(list(self.modelfunc_kwargs.items())+list(kwargs.items()))) for i in range(self.npeaks)]) if len(modelcomponents.shape) == 3: newshape = [modelcomponents.shape[0]*modelcomponents.shape[1], modelcomponents.shape[2]] modelcomponents = np.reshape(modelcomponents, newshape) return modelcomponents def integral(self, modelpars, dx=None, **kwargs): """ Extremely simple integrator: IGNORES modelpars; just sums self.model """ if not hasattr(self,'model'): raise ValueError("Must fit (or compute) a model before computing" " its integral.") if dx is not None: return (self.model*dx).sum() else: return self.model.sum() def analytic_integral(self, modelpars=None, npeaks=None, npars=None): """ Placeholder for analyic integrals; these must be defined for individual models """ if self.integral_func is None: raise NotImplementedError("Analytic integrals must be implemented independently for each model type") # all of these parameters are allowed to be overwritten if modelpars is None: modelpars = self.parinfo.values if npeaks is None: npeaks = self.npeaks if npars is None: npars = self.npars return np.sum([ self.integral_func(modelpars[npars*ii:npars*(1+ii)]) for ii in range(npeaks)]) def component_integrals(self, xarr, dx=None): """ Compute the integrals of each component """ components = self.components(xarr, self.parinfo.values) if dx is None: dx = 1 integrals = [com.sum()*dx for com in components] return integrals def analytic_fwhm(self, parinfo=None): """ Return the FWHMa of the model components *if* a fwhm_func has been defined Done with incomprehensible list comprehensions instead of nested for loops... readability sacrificed for speed and simplicity. This is unpythonic. """ if self.fwhm_func is None and self.fwhm_pars is None: raise TypeError("fwhm_func not implemented for model %s" % self.__name__) if parinfo is None: parinfo = self.parinfo fwhm = [self.fwhm_func( *[self.parinfo[str.upper(p+'%i' % n)] for p in self.fwhm_pars] ) for n in range(self.npeaks)] return fwhm def analytic_centroids(self, centroidpar=None): """ Return the *analytic* centroids of the model components Parameters ---------- centroidpar : None or string The name of the parameter in the fit that represents the centroid *some models have default centroid parameters - these will be used if centroidpar is unspecified* Returns ------- List of the centroid values (even if there's only 1) """ if centroidpar is None: centroidpar = self.centroid_par centr = [par.value for par in self.parinfo if str.upper(centroidpar) in par.parname] return centr def computed_centroid(self, xarr=None): """ Return the *computed* centroid of the model Parameters ---------- xarr : None or np.ndarray The X coordinates of the model over which the centroid should be computed. If unspecified, the centroid will be in pixel units """ if not hasattr(self,'model'): raise ValueError("Must fit (or compute) a model before measuring " "its centroid") if xarr is None: xarr = np.arange(self.model.size) centr = (self.model*xarr).sum() / self.model.sum() return centr def logp(self, xarr, data, error, pars=None): """ Return the log probability of the model. If the parameter is out of range, return -inf """ if pars is None: pars = self.parinfo else: parinfo = copy.copy(self.parinfo) for value,parameter in zip(pars,parinfo): try: parameter.value = value except ValueError: return -np.inf model = self.n_modelfunc(pars, **self.modelfunc_kwargs)(xarr) difference = np.abs(data-model) # prob = 1/(2*np.pi)**0.5/error * exp(-difference**2/(2.*error**2)) #logprob = np.log(1./(2.*np.pi)**0.5/error) * (-difference**2/(2.*error**2)) logprob = (-difference**2/(2.*error**2)) totallogprob = np.sum(logprob) return totallogprob def get_emcee_sampler(self, xarr, data, error, **kwargs): """ Get an emcee walker for the data & model Parameters ---------- xarr : pyspeckit.units.SpectroscopicAxis data : np.ndarray error : np.ndarray Examples -------- >>> import pyspeckit >>> x = pyspeckit.units.SpectroscopicAxis(np.linspace(-10,10,50), unit='km/s') >>> e = np.random.randn(50) >>> d = np.exp(-np.asarray(x)**2/2.)*5 + e >>> sp = pyspeckit.Spectrum(data=d, xarr=x, error=np.ones(50)*e.std()) >>> sp.specfit(fittype='gaussian') >>> emcee_sampler = sp.specfit.fitter.get_emcee_sampler(sp.xarr, sp.data, sp.error) >>> p0 = sp.specfit.parinfo >>> emcee_sampler.run_mcmc(p0,100) """ try: import emcee except ImportError: return def probfunc(pars): return self.logp(xarr, data, error, pars=pars) raise NotImplementedError("emcee's metropolis-hastings sampler is not implemented; use pymc") sampler = emcee.MHSampler(self.npars*self.npeaks+self.vheight, probfunc, **kwargs) return sampler def get_emcee_ensemblesampler(self, xarr, data, error, nwalkers, **kwargs): """ Get an emcee walker ensemble for the data & model Parameters ---------- data : np.ndarray error : np.ndarray nwalkers : int Number of walkers to use Examples -------- >>> import pyspeckit >>> x = pyspeckit.units.SpectroscopicAxis(np.linspace(-10,10,50), unit='km/s') >>> e = np.random.randn(50) >>> d = np.exp(-np.asarray(x)**2/2.)*5 + e >>> sp = pyspeckit.Spectrum(data=d, xarr=x, error=np.ones(50)*e.std()) >>> sp.specfit(fittype='gaussian') >>> nwalkers = sp.specfit.fitter.npars * 2 >>> emcee_ensemble = sp.specfit.fitter.get_emcee_ensemblesampler(sp.xarr, sp.data, sp.error, nwalkers) >>> p0 = np.array([sp.specfit.parinfo.values] * nwalkers) >>> p0 *= np.random.randn(*p0.shape) / 10. + 1.0 >>> pos,logprob,state = emcee_ensemble.run_mcmc(p0,100) """ try: import emcee except ImportError: return def probfunc(pars): return self.logp(xarr, data, error, pars=pars) sampler = emcee.EnsembleSampler(nwalkers, self.npars*self.npeaks+self.vheight, probfunc, **kwargs) return sampler def get_pymc(self, xarr, data, error, use_fitted_values=False, inf=np.inf, use_adaptive=False, return_dict=False, **kwargs): """ Create a pymc MCMC sampler. Defaults to 'uninformative' priors Parameters ---------- data : np.ndarray error : np.ndarray use_fitted_values : bool Each parameter with a measured error will have a prior defined by the Normal distribution with sigma = par.error and mean = par.value use_adaptive : bool Use the Adaptive Metropolis-Hastings sampler? Examples -------- >>> x = pyspeckit.units.SpectroscopicAxis(np.linspace(-10,10,50), unit='km/s') >>> e = np.random.randn(50) >>> d = np.exp(-np.asarray(x)**2/2.)*5 + e >>> sp = pyspeckit.Spectrum(data=d, xarr=x, error=np.ones(50)*e.std()) >>> sp.specfit(fittype='gaussian') >>> MCuninformed = sp.specfit.fitter.get_pymc(sp.xarr, sp.data, sp.error) >>> MCwithpriors = sp.specfit.fitter.get_pymc(sp.xarr, sp.data, sp.error, use_fitted_values=True) >>> MCuninformed.sample(1000) >>> MCuninformed.stats()['AMPLITUDE0'] >>> # WARNING: This will fail because width cannot be set <0, but it may randomly reach that... >>> # How do you define a likelihood distribution with a lower limit?! >>> MCwithpriors.sample(1000) >>> MCwithpriors.stats()['AMPLITUDE0'] """ old_errsettings = np.geterr() try: import pymc finally: # pymc breaks error settings np.seterr(**old_errsettings) #def lowerlimit_like(x,lolim): # "lower limit (log likelihood - set very positive for unacceptable values)" # return (x>=lolim) / 1e10 #def upperlimit_like(x,uplim): # "upper limit" # return (x<=uplim) / 1e10 #LoLim = pymc.distributions.stochastic_from_dist('lolim', logp=lowerlimit_like, dtype=np.float, mv=False) #UpLim = pymc.distributions.stochastic_from_dist('uplim', logp=upperlimit_like, dtype=np.float, mv=False) funcdict = {} # very, very worrisome: pymc changes the values of parinfo parcopy = copy.deepcopy(self.parinfo) for par in parcopy: lolim = par.limits[0] if par.limited[0] else -inf uplim = par.limits[1] if par.limited[1] else inf if par.fixed: funcdict[par.parname] = pymc.distributions.Uniform(par.parname, par.value, par.value, value=par.value) elif use_fitted_values: if par.error > 0: if any(par.limited): try: funcdict[par.parname] = pymc.distributions.TruncatedNormal(par.parname, par.value, 1./par.error**2, lolim, uplim) except AttributeError: # old versions used this? funcdict[par.parname] = pymc.distributions.TruncNorm(par.parname, par.value, 1./par.error**2, lolim, uplim) else: funcdict[par.parname] = pymc.distributions.Normal(par.parname, par.value, 1./par.error**2) else: if any(par.limited): funcdict[par.parname] = pymc.distributions.Uniform(par.parname, lolim, uplim, value=par.value) else: funcdict[par.parname] = pymc.distributions.Uninformative(par.parname, value=par.value) elif any(par.limited): lolim = par.limits[0] if par.limited[0] else -1e10 uplim = par.limits[1] if par.limited[1] else 1e10 funcdict[par.parname] = pymc.distributions.Uniform(par.parname, lower=lolim, upper=uplim, value=par.value) else: funcdict[par.parname] = pymc.distributions.Uninformative(par.parname, value=par.value) d = dict(funcdict) def modelfunc(xarr, pars=parcopy, **kwargs): for k,v in kwargs.items(): if k in list(pars.keys()): pars[k].value = v return self.n_modelfunc(pars, **self.modelfunc_kwargs)(xarr) funcdict['xarr'] = xarr funcdet=pymc.Deterministic(name='f',eval=modelfunc,parents=funcdict,doc="The model function") d['f'] = funcdet datamodel = pymc.distributions.Normal('data', mu=funcdet, tau=1/np.asarray(error)**2, observed=True, value=np.asarray(data)) d['data']=datamodel if return_dict: return d mc = pymc.MCMC(d) if use_adaptive: mc.use_step_method(pymc.AdaptiveMetropolis,[d[p] for p in self.parinfo.names]) return mc def parse_3par_guesses(self, guesses): """ Try to convert a set of interactive guesses (peak, center, width) into guesses appropriate to the model. """ if len(guesses) % 3!= 0: raise ValueError("Guesses passed to parse_3par_guesses must have " "length % 3 == 0") npeaks_guessed = len(guesses) // 3 gtypes = [parse_offset_guess(gtype, gval)[0] for gtype, gval in zip(itertools.cycle(self.guess_types), [0]*len(self.guess_types))] guess_dict = {(valid_guess_types[ii % 3], ii // 3): gval for ii, gval in enumerate(guesses)} new_guesses = [guess_dict[(gtype, ii)] if isinstance(gtype, str) else gtype for ii in range(npeaks_guessed) for gtype in gtypes ] new_guesses = [parse_offset_guess(gtype, gval)[1] for gtype, gval in zip(itertools.cycle(self.guess_types), new_guesses)] assert len(new_guesses) % len(self.guess_types) == 0 return new_guesses class AstropyModel(SpectralModel): def __init__(self, model, shortvarnames=None, **kwargs): """ Override the SpectralModel initialization """ if hasattr(self,__doc__): # how do you extend a docstring really? self.__doc__ += SpectralModel.__doc__ if shortvarnames is None: shortvarnames = model.param_names super(AstropyModel,self).__init__(model, len(model.parameters), shortvarnames=shortvarnames, model=model, **kwargs) self.mp = None self.vheight = False self.npeaks = 1 def _make_parinfo(self, model=None): self.parinfo = ParinfoList([ Parinfo(parname=name,value=value) for name,value in zip(model.param_names,model.parameters)]) return self.parinfo, {} def _parse_parinfo(self, parinfo): """ Parse a ParinfoList into astropy.models parameters """ if len(parinfo) > self.npars: if len(parinfo) % self.npars!= 0: raise ValueError("Need to have an integer number of models") else: self.modelfunc.param_names = parinfo.names self.modelfunc.parameters = parinfo.values else: self.modelfunc.param_names = parinfo.names self.modelfunc.parameters = parinfo.values def fitter(self, xax, data, err=None, quiet=True, veryverbose=False, debug=False, parinfo=None, params=None, npeaks=None, **kwargs): import astropy.models as models if npeaks is not None and npeaks > 1: raise NotImplementedError("Astropy models cannot be used to fit multiple peaks yet") if parinfo is not None: self._parse_parinfo(parinfo) if params is not None: self.modelfunc.parameters = params self.astropy_fitter = models.fitting.NonLinearLSQFitter(self.modelfunc) if err is None: self.astropy_fitter(xax, data, **kwargs) else: self.astropy_fitter(xax, data, weights=1./err**2, **kwargs) mpp = self.astropy_fitter.fitpars cov = self.astropy_fitter.covar if cov is None: mpperr = np.zeros(len(mpp)) else: mpperr = cov.diagonal() self.model = self.astropy_fitter.model(xax) if err is None: chi2 = ((data-self.model)**2).sum() else: chi2 = ((data-self.model)**2/err**2).sum() # update object paramters self.modelfunc.parameters = mpp self._make_parinfo(self.modelfunc) return mpp,self.model,mpperr,chi2 def n_modelfunc(self, pars=None, debug=False, **kwargs): """ Only deals with single-peak functions """ try: self._parse_parinfo(pars) except AttributeError: self.modelfunc.parameters = pars return self.modelfunc def parse_offset_guess(gname, gval): """ Utility function for handling guesses. Allows guess types to be specified as 'amplitude*2' or 'width+3'. """ operators = '+-*/' if not isinstance(gname, six.string_types): return gname, gval ops = [x for x in operators if x in gname] if len(ops)>1: raise ValueError("Invalid offset guess") elif len(ops) == 0: return gname,gval else: opmap = {"+": operator.add, "-": operator.sub, "*": operator.mul, "/": operator.truediv, } op = ops[0] pars = gname.split(op) gname = [p for p in gname.split(op) if p in valid_guess_types][0] pars = [gval if p in valid_guess_types else float(p) for p in pars] gval = opmap[op](*pars) return gname, gval

piccolo-orm__piccolo

baseuser.rst

Module doc

Generate documentation for this module

MIT License

piccolo-orm__piccolo/docs/src/piccolo/authentication/baseuser.rst

BaseUser BaseUser is a Table you can use to store and authenticate your users. ------------------------------------------------------------------------ Creating the Table Run the migrations: piccolo migrations forwards user ------------------------------------------------------------------------ Commands The app comes with some useful commands. create Creates a new user. It presents an interactive prompt, asking for the username, password etc. piccolo user create If you'd prefer to create a user without the interactive prompt (perhaps in a script), you can pass all of the arguments in as follows: piccolo user create --username=bob --password=bob123 --email=foo@bar.com --is_admin=t --is_superuser=t --is_active=t Warning If you choose this approach then be careful, as the password will be in the shell's history. change_password Change a user's password. piccolo user change_password change_permissions Change a user's permissions. The options are --admin, --superuser and --active, which change the corresponding attributes on BaseUser. For example: piccolo user change_permissions some_user --active=true The Piccolo Admin<PiccoloAdmin> uses these attributes to control who can login and what they can do. - active and admin - must be true for a user to be able to login. - superuser - must be true for a user to be able to change other user's passwords. ------------------------------------------------------------------------ Within your code create_user / create_user_sync To create a new user: # From within a coroutine: await BaseUser.create_user(username="bob", password="abc123", active=True) # When not in an event loop: BaseUser.create_user_sync(username="bob", password="abc123", active=True) It saves the user in the database, and returns the created BaseUser instance. Note It is preferable to use this rather than instantiating and saving BaseUser directly, as we add additional validation. login / login_sync To check a user's credentials, do the following: from piccolo.apps.user.tables import BaseUser # From within a coroutine: >>> await BaseUser.login(username="bob", password="abc123") 1 # When not in an event loop: >>> BaseUser.login_sync(username="bob", password="abc123") 1 If the login is successful, the user's id is returned, otherwise None is returned. update_password / update_password_sync To change a user's password: # From within a coroutine: await BaseUser.update_password(username="bob", password="abc123") # When not in an event loop: BaseUser.update_password_sync(username="bob", password="abc123") Warning Don't use bulk updates for passwords - use update_password / update_password_sync, and they'll correctly hash the password. ------------------------------------------------------------------------ Limits The maximum password length allowed is 128 characters. This should be sufficiently long for most use cases. ------------------------------------------------------------------------ Extending BaseUser If you want to extend BaseUser with additional fields, we recommend creating a Profile table with a ForeignKey to BaseUser, which can include any custom fields. from piccolo.apps.user.tables import BaseUser from piccolo.columns import ForeignKey, Text, Varchar from piccolo.table import Table class Profile(Table): custom_user = ForeignKey(BaseUser) phone_number = Varchar() bio = Text() Alternatively, you can copy the entire user app into your project, and customise it to fit your needs. ------------------------------------------------------------------------

""" A User model, used for authentication. """ from __future__ import annotations import datetime import hashlib import logging import secrets import typing as t from piccolo.columns import Boolean, Secret, Timestamp, Varchar from piccolo.columns.column_types import Serial from piccolo.columns.readable import Readable from piccolo.table import Table from piccolo.utils.sync import run_sync logger = logging.getLogger(__name__) class BaseUser(Table, tablename="piccolo_user"): """ Provides a basic user, with authentication support. """ id: Serial username = Varchar(length=100, unique=True) password = Secret(length=255) first_name = Varchar(null=True) last_name = Varchar(null=True) email = Varchar(length=255, unique=True) active = Boolean(default=False) admin = Boolean( default=False, help_text="An admin can log into the Piccolo admin GUI." ) superuser = Boolean( default=False, help_text=( "If True, this user can manage other users's passwords in the " "Piccolo admin GUI." ), ) last_login = Timestamp( null=True, default=None, required=False, help_text="When this user last logged in.", ) _min_password_length = 6 _max_password_length = 128 # The number of hash iterations recommended by OWASP: # https://cheatsheetseries.owasp.org/cheatsheets/Password_Storage_Cheat_Sheet.html#pbkdf2 _pbkdf2_iteration_count = 600_000 def __init__(self, **kwargs): # Generating passwords upfront is expensive, so might need reworking. password = kwargs.get("password", None) if password: if not password.startswith("pbkdf2_sha256"): kwargs["password"] = self.__class__.hash_password(password) super().__init__(**kwargs) @classmethod def get_salt(cls): return secrets.token_hex(16) @classmethod def get_readable(cls) -> Readable: """ Used to get a readable string, representing a table row. """ return Readable(template="%s", columns=[cls.username]) ########################################################################### @classmethod def _validate_password(cls, password: str): """ Validate the raw password. Used by :meth:`update_password` and :meth:`create_user`. :param password: The raw password e.g. ``'hello123'``. :raises ValueError: If the password fails any of the criteria. """ if not password: raise ValueError("A password must be provided.") if len(password) < cls._min_password_length: raise ValueError("The password is too short.") if len(password) > cls._max_password_length: raise ValueError("The password is too long.") if password.startswith("pbkdf2_sha256"): logger.warning( "Tried to create a user with an already hashed password." ) raise ValueError("Do not pass a hashed password.") ########################################################################### @classmethod def update_password_sync(cls, user: t.Union[str, int], password: str): """ A sync equivalent of :meth:`update_password`. """ return run_sync(cls.update_password(user, password)) @classmethod async def update_password(cls, user: t.Union[str, int], password: str): """ The password is the raw password string e.g. ``'password123'``. The user can be a user ID, or a username. """ if isinstance(user, str): clause = cls.username == user elif isinstance(user, int): clause = cls.id == user else: raise ValueError( "The `user` arg must be a user id, or a username." ) cls._validate_password(password=password) password = cls.hash_password(password) await cls.update({cls.password: password}).where(clause).run() ########################################################################### @classmethod def hash_password( cls, password: str, salt: str = "", iterations: t.Optional[int] = None ) -> str: """ Hashes the password, ready for storage, and for comparing during login. :raises ValueError: If an excessively long password is provided. """ if len(password) > cls._max_password_length: logger.warning("Excessively long password provided.") raise ValueError("The password is too long.") if not salt: salt = cls.get_salt() if iterations is None: iterations = cls._pbkdf2_iteration_count hashed = hashlib.pbkdf2_hmac( "sha256", bytes(password, encoding="utf-8"), bytes(salt, encoding="utf-8"), iterations, ).hex() return f"pbkdf2_sha256${iterations}${salt}${hashed}" def __setattr__(self, name: str, value: t.Any): """ Make sure that if the password is set, it's stored in a hashed form. """ if name == "password" and not value.startswith("pbkdf2_sha256"): value = self.__class__.hash_password(value) super().__setattr__(name, value) @classmethod def split_stored_password(cls, password: str) -> t.List[str]: elements = password.split("$") if len(elements)!= 4: raise ValueError("Unable to split hashed password") return elements ########################################################################### @classmethod def login_sync(cls, username: str, password: str) -> t.Optional[int]: """ A sync equivalent of :meth:`login`. """ return run_sync(cls.login(username, password)) @classmethod async def login(cls, username: str, password: str) -> t.Optional[int]: """ Make sure the user exists and the password is valid. If so, the ``last_login`` value is updated in the database. :returns: The id of the user if a match is found, otherwise ``None``. """ if len(username) > cls.username.length: logger.warning("Excessively long username provided.") return None if len(password) > cls._max_password_length: logger.warning("Excessively long password provided.") return None response = ( await cls.select(cls._meta.primary_key, cls.password) .where(cls.username == username) .first() .run() ) if not response: # No match found return None stored_password = response["password"] algorithm, iterations_, salt, hashed = cls.split_stored_password( stored_password ) iterations = int(iterations_) if cls.hash_password(password, salt, iterations) == stored_password: # If the password was hashed in an earlier Piccolo version, update # it so it's hashed with the currently recommended number of # iterations: if iterations!= cls._pbkdf2_iteration_count: await cls.update_password(username, password) await cls.update({cls.last_login: datetime.datetime.now()}).where( cls.username == username ) return response["id"] else: return None ########################################################################### @classmethod def create_user_sync( cls, username: str, password: str, **extra_params ) -> BaseUser: """ A sync equivalent of :meth:`create_user`. """ return run_sync( cls.create_user( username=username, password=password, **extra_params ) ) @classmethod async def create_user( cls, username: str, password: str, **extra_params ) -> BaseUser: """ Creates a new user, and saves it in the database. It is recommended to use this rather than instantiating and saving ``BaseUser`` directly, as we add extra validation. :raises ValueError: If the username or password is invalid. :returns: The created ``BaseUser`` instance. """ if not username: raise ValueError("A username must be provided.") cls._validate_password(password=password) user = cls(username=username, password=password, **extra_params) await user.save() return user

piccolo-orm__piccolo

cockroach_engine.rst

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piccolo-orm__piccolo/docs/src/piccolo/engines/cockroach_engine.rst

CockroachEngine Configuration # piccolo_conf.py from piccolo.engine.cockroach import CockroachEngine DB = CockroachEngine(config={ 'host': 'localhost', 'database': 'piccolo', 'user': 'root', 'password': '', 'port': '26257', }) config The config dictionary is passed directly to the underlying database adapter, asyncpg. See the asyncpg docs to learn more. ------------------------------------------------------------------------ Connection pool To use a connection pool, you need to first initialise it. The best place to do this is in the startup event handler of whichever web framework you are using. Here's an example using Starlette. Notice that we also close the connection pool in the shutdown event handler. from piccolo.engine import engine_finder from starlette.applications import Starlette app = Starlette() @app.on_event('startup') async def open_database_connection_pool(): engine = engine_finder() await engine.start_connection_pool() @app.on_event('shutdown') async def close_database_connection_pool(): engine = engine_finder() await engine.close_connection_pool() Hint Using a connection pool helps with performance, since connections are reused instead of being created for each query. Once a connection pool has been started, the engine will use it for making queries. Hint If you're running several instances of an app on the same server, you may prefer an external connection pooler - like pgbouncer. Configuration The connection pool uses the same configuration as your engine. You can also pass in additional parameters, which are passed to the underlying database adapter. Here's an example: # To increase the number of connections available: await engine.start_connection_pool(max_size=20) ------------------------------------------------------------------------

from __future__ import annotations import typing as t from piccolo.utils.lazy_loader import LazyLoader from piccolo.utils.warnings import Level, colored_warning from.postgres import PostgresEngine asyncpg = LazyLoader("asyncpg", globals(), "asyncpg") class CockroachEngine(PostgresEngine): """ An extension of :class:`PostgresEngine <piccolo.engine.postgres.PostgresEngine>`. """ engine_type = "cockroach" min_version_number = 0 # Doesn't seem to work with cockroach versioning. def __init__( self, config: t.Dict[str, t.Any], extensions: t.Sequence[str] = (), log_queries: bool = False, log_responses: bool = False, extra_nodes: t.Dict[str, CockroachEngine] = None, ) -> None: super().__init__( config=config, extensions=extensions, log_queries=log_queries, log_responses=log_responses, extra_nodes=extra_nodes, ) async def prep_database(self): try: await self._run_in_new_connection( "SET CLUSTER SETTING sql.defaults.experimental_alter_column_type.enabled = true;" # noqa: E501 ) except asyncpg.exceptions.InsufficientPrivilegeError: colored_warning( "=> Unable to set up Cockroach DB " "functionality may not behave as expected. Make sure " "your database user has permission to set cluster options.", level=Level.medium, )

piccolo-orm__piccolo

piccolo_apps.rst

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piccolo-orm__piccolo/docs/src/piccolo/projects_and_apps/piccolo_apps.rst

Piccolo Apps By leveraging Piccolo apps you can: - Modularise your code. - Share your apps with other Piccolo users. - Unlock some useful functionality like auto migrations. ------------------------------------------------------------------------ Creating an app Run the following command within your project: piccolo app new my_app Where my_app is your new app's name. This will create a folder like this: my_app/ __init__.py piccolo_app.py piccolo_migrations/ __init__.py tables.py It's important to register your new app with the APP_REGISTRY in piccolo_conf.py. # piccolo_conf.py APP_REGISTRY = AppRegistry(apps=['my_app.piccolo_app']) Anytime you invoke the piccolo command, you will now be able to perform operations on your app, such as Migrations. ------------------------------------------------------------------------ AppConfig Inside your app's piccolo_app.py file is an AppConfig instance. This is how you customise your app's settings. # piccolo_app.py import os from piccolo.conf.apps import AppConfig from .tables import ( Author, Post, Category, CategoryToPost, ) CURRENT_DIRECTORY = os.path.dirname(os.path.abspath(__file__)) APP_CONFIG = AppConfig( app_name='blog', migrations_folder_path=os.path.join( CURRENT_DIRECTORY, 'piccolo_migrations' ), table_classes=[Author, Post, Category, CategoryToPost], migration_dependencies=[], commands=[] ) app_name This is used to identify your app, when using the piccolo CLI, for example: piccolo migrations forwards blog migrations_folder_path Specifies where your app's migrations are stored. By default, a folder called piccolo_migrations is used. table_classes Use this to register your app's Table subclasses. This is important for auto migrations <Migrations>. You can register them manually (see the example above), or can use table_finder <TableFinder>. migration_dependencies Used to specify other Piccolo apps whose migrations need to be run before the current app's migrations. commands You can register functions and coroutines, which are automatically added to the piccolo CLI. The targ library is used under the hood. It makes it really easy to write command lines tools - just use type annotations and docstrings. Here's an example: def say_hello(name: str): """ Say hello. :param name: The person to greet. """ print("hello,", name) We then register it with the AppConfig. # piccolo_app.py APP_CONFIG = AppConfig( # ... commands=[say_hello] ) And from the command line: >>> piccolo my_app say_hello bob hello, bob If the code contains an error to see more details in the output add a --trace flag to the command line. >>> piccolo my_app say_hello bob --trace By convention, store the command definitions in a commands folder in your app. my_app/ __init__.py piccolo_app.py commands/ __init__.py say_hello.py Piccolo itself is bundled with several apps - have a look at the source code for inspiration. ------------------------------------------------------------------------ table_finder Instead of manually registering Table subclasses, you can use table_finder to automatically import any Table subclasses from a given list of modules. from piccolo.conf.apps import table_finder APP_CONFIG = AppConfig( app_name='blog', migrations_folder_path=os.path.join( CURRENT_DIRECTORY, 'piccolo_migrations' ), table_classes=table_finder(modules=['blog.tables']), migration_dependencies=[], commands=[] ) The module path should be from the root of the project (the same directory as your piccolo_conf.py file, rather than a relative path). You can filter the Table subclasses returned using tags <TableTags>. ------------------------------------------------------------------------ Sharing Apps By breaking up your project into apps, the project becomes more maintainable. You can also share these apps between projects, and they can even be installed using pip.

from __future__ import annotations import inspect import itertools import os import pathlib import traceback import typing as t from dataclasses import dataclass, field from importlib import import_module from types import ModuleType from piccolo.engine.base import Engine from piccolo.table import Table from piccolo.utils.graphlib import TopologicalSorter from piccolo.utils.warnings import Level, colored_warning class MigrationModule(ModuleType): ID: str VERSION: str DESCRIPTION: str @staticmethod async def forwards() -> None: pass class PiccoloAppModule(ModuleType): APP_CONFIG: AppConfig def table_finder( modules: t.Sequence[str], include_tags: t.Sequence[str] = None, exclude_tags: t.Sequence[str] = None, exclude_imported: bool = False, ) -> t.List[t.Type[Table]]: """ Rather than explicitly importing and registering table classes with the ``AppConfig``, ``table_finder`` can be used instead. It imports any ``Table`` subclasses in the given modules. Tags can be used to limit which ``Table`` subclasses are imported. :param modules: The module paths to check for ``Table`` subclasses. For example, ``['blog.tables']``. The path should be from the root of your project, not a relative path. :param include_tags: If the ``Table`` subclass has one of these tags, it will be imported. The special tag ``'__all__'`` will import all ``Table`` subclasses found. :param exclude_tags: If the ``Table`` subclass has any of these tags, it won't be imported. ``exclude_tags`` overrides ``include_tags``. :param exclude_imported: If ``True``, only ``Table`` subclasses defined within the module are used. Any ``Table`` subclasses imported by that module from other modules are ignored. For example: .. code-block:: python from piccolo.table import Table from piccolo.column import Varchar, ForeignKey from piccolo.apps.user.tables import BaseUser # excluded class Task(Table): # included title = Varchar() creator = ForeignKey(BaseUser) """ # noqa: E501 if include_tags is None: include_tags = ["__all__"] if exclude_tags is None: exclude_tags = [] if isinstance(modules, str): # Guard against the user just entering a string, for example # 'blog.tables', instead of ['blog.tables']. modules = [modules] table_subclasses: t.List[t.Type[Table]] = [] for module_path in modules: try: module = import_module(module_path) except ImportError as exception: print(f"Unable to import {module_path}") raise exception from exception object_names = [i for i in dir(module) if not i.startswith("_")] for object_name in object_names: _object = getattr(module, object_name) if ( inspect.isclass(_object) and issubclass(_object, Table) and _object is not Table ): table: Table = _object # type: ignore if exclude_imported and table.__module__!= module_path: continue if exclude_tags and set(table._meta.tags).intersection( set(exclude_tags) ): continue elif "__all__" in include_tags: table_subclasses.append(_object) elif set(table._meta.tags).intersection(set(include_tags)): table_subclasses.append(_object) return table_subclasses @dataclass class Command: callable: t.Callable aliases: t.List[str] = field(default_factory=list) @dataclass class AppConfig: """ Each app needs an AppConfig, which is defined in piccolo_app.py. :param app_name: The name of the app, for example ``'article'``. :param migrations_folder_path: The path of the folder containing this app's migration files. :param table_classes: By registering table classes, Piccolo's auto migrations can detect changes to tables. :param migration_dependencies: A list of Piccolo apps whose migrations this app depends on. For example: ``['piccolo.apps.user.piccolo_conf']``. The migrations for those apps will be run before the migrations for this app. :param commands: A list of functions and coroutines, which are then registered with the Piccolo CLI. For example, with a Piccolo app called ``'article'``, and a command called ``new``, it can be called on the command line using ``piccolo article new``. """ app_name: str migrations_folder_path: str table_classes: t.List[t.Type[Table]] = field(default_factory=list) migration_dependencies: t.List[str] = field(default_factory=list) commands: t.List[t.Union[t.Callable, Command]] = field( default_factory=list ) def __post_init__(self): self.commands = [ i if isinstance(i, Command) else Command(i) for i in self.commands ] if isinstance(self.migrations_folder_path, pathlib.Path): self.migrations_folder_path = str(self.migrations_folder_path) self._migration_dependency_app_configs: t.Optional[ t.List[AppConfig] ] = None def register_table(self, table_class: t.Type[Table]): self.table_classes.append(table_class) return table_class @property def migration_dependency_app_configs(self) -> t.List[AppConfig]: """ Get all of the ``AppConfig`` instances from this app's migration dependencies. """ # We cache the value so it's more efficient, and also so we can set the # underlying value in unit tests for easier mocking. if self._migration_dependency_app_configs is None: modules: t.List[PiccoloAppModule] = [ t.cast(PiccoloAppModule, import_module(module_path)) for module_path in self.migration_dependencies ] self._migration_dependency_app_configs = [ i.APP_CONFIG for i in modules ] return self._migration_dependency_app_configs def get_table_with_name(self, table_class_name: str) -> t.Type[Table]: """ Returns a ``Table`` subclass with the given name from this app, if it exists. Otherwise raises a ``ValueError``. """ filtered = [ table_class for table_class in self.table_classes if table_class.__name__ == table_class_name ] if not filtered: raise ValueError( f"No table with class name {table_class_name} exists." ) return filtered[0] class AppRegistry: """ Records all of the Piccolo apps in your project. Kept in ``piccolo_conf.py``. :param apps: A list of paths to Piccolo apps, e.g. ``['blog.piccolo_app']``. """ def __init__(self, apps: t.List[str] = None): self.apps = apps or [] self.app_configs: t.Dict[str, AppConfig] = {} app_names = [] for app in self.apps: try: app_conf_module = import_module(app) app_config: AppConfig = getattr(app_conf_module, "APP_CONFIG") except (ImportError, AttributeError) as e: if app.endswith(".piccolo_app"): raise e from e app += ".piccolo_app" app_conf_module = import_module(app) app_config = getattr(app_conf_module, "APP_CONFIG") colored_warning( f"App {app[:-12]} should end with `.piccolo_app`", level=Level.medium, ) self.app_configs[app_config.app_name] = app_config app_names.append(app_config.app_name) self._validate_app_names(app_names) @staticmethod def _validate_app_names(app_names: t.List[str]): """ Raise a ValueError if an app_name is repeated. """ app_names.sort() grouped = itertools.groupby(app_names) for key, value in grouped: count = len(list(value)) if count > 1: raise ValueError( f"There are {count} apps with the name `{key}`. This can " "cause unexpected behavior. Make sure each app has a " "unique name, and you haven't registered the same app " "multiple times." ) def get_app_config(self, app_name: str) -> t.Optional[AppConfig]: return self.app_configs.get(app_name) def get_table_classes(self, app_name: str) -> t.List[t.Type[Table]]: """ Returns each Table subclass defined in the given app if it exists. Otherwise raises a ValueError. :raises ValueError: If an AppConfig can't be found for the given app_name. """ app_config = self.get_app_config(app_name=app_name) if not app_config: raise ValueError(f"Unrecognised app_name: {app_name}") return app_config.table_classes def get_table_with_name( self, app_name: str, table_class_name: str ) -> t.Optional[t.Type[Table]]: """ Returns a Table subclass registered with the given app if it exists. Otherwise raises a ValueError. """ app_config = self.get_app_config(app_name=app_name) if app_config is None: raise ValueError(f"Can't find an app_config for {app_name}") else: return app_config.get_table_with_name( table_class_name=table_class_name ) class PiccoloConfModule(ModuleType): DB: Engine APP_REGISTRY: AppRegistry DEFAULT_MODULE_NAME = "piccolo_conf" ENVIRONMENT_VARIABLE = "PICCOLO_CONF" ENGINE_VAR = "DB" class Finder: """ Contains useful methods for locating and loading apps within your project, and tables within apps. """ def __init__(self, diagnose: bool = False): """ :param diagnose: If True, when trying to import piccolo_conf, a traceback will be printed out if an error occurs. """ self.diagnose = diagnose def _deduplicate( self, config_modules: t.List[PiccoloAppModule] ) -> t.List[PiccoloAppModule]: """ Remove all duplicates - just leaving the first instance. """ # Deduplicate, but preserve order - which is why set() isn't used. return list({c: None for c in config_modules}.keys()) def _import_app_modules( self, config_module_paths: t.List[str] ) -> t.List[PiccoloAppModule]: """ Import all piccolo_app.py modules within your apps, and all dependencies. """ config_modules = [] for config_module_path in config_module_paths: try: config_module = t.cast( PiccoloAppModule, import_module(config_module_path) ) except ImportError as e: raise Exception( f"Unable to import {config_module_path}" ) from e app_config: AppConfig = getattr(config_module, "APP_CONFIG") dependency_config_modules = self._import_app_modules( app_config.migration_dependencies ) config_modules.extend(dependency_config_modules + [config_module]) return config_modules def get_piccolo_conf_module( self, module_name: t.Optional[str] = None ) -> t.Optional[PiccoloConfModule]: """ Searches the path for a 'piccolo_conf.py' module to import. The location searched can be overriden by: * Explicitly passing a module name into this method. * Setting the PICCOLO_CONF environment variable. An example override is'my_folder.piccolo_conf'. """ env_module_name = os.environ.get(ENVIRONMENT_VARIABLE, None) if not module_name and env_module_name: module_name = env_module_name if not module_name: module_name = DEFAULT_MODULE_NAME try: module = t.cast(PiccoloConfModule, import_module(module_name)) except ModuleNotFoundError as exc: if self.diagnose: colored_warning( ( f"{module_name} either doesn't exist or the import " "failed. Traceback:" ), level=Level.high, ) print(traceback.format_exc()) if str(exc) == "No module named 'asyncpg'": raise ModuleNotFoundError( "PostgreSQL driver not found. " "Try running `pip install 'piccolo[postgres]'`" ) from exc elif str(exc) == "No module named 'aiosqlite'": raise ModuleNotFoundError( "SQLite driver not found. " "Try running `pip install 'piccolo[sqlite]'`" ) from exc else: raise exc from exc else: return module def get_app_registry(self) -> AppRegistry: """ Returns the ``AppRegistry`` instance within piccolo_conf. """ piccolo_conf_module = self.get_piccolo_conf_module() return getattr(piccolo_conf_module, "APP_REGISTRY") def get_engine( self, module_name: t.Optional[str] = None ) -> t.Optional[Engine]: piccolo_conf = self.get_piccolo_conf_module(module_name=module_name) engine: t.Optional[Engine] = getattr(piccolo_conf, ENGINE_VAR, None) if not engine: colored_warning( f"{module_name} doesn't define a {ENGINE_VAR} variable.", level=Level.high, ) elif not isinstance(engine, Engine): colored_warning( f"{module_name} contains a {ENGINE_VAR} variable of the " "wrong type - it should be an Engine subclass.", level=Level.high, ) return engine def get_app_modules(self) -> t.List[PiccoloAppModule]: """ Returns the ``piccolo_app.py`` modules for each registered Piccolo app in your project. """ app_registry = self.get_app_registry() app_modules = self._import_app_modules(app_registry.apps) # Now deduplicate any dependencies app_modules = self._deduplicate(app_modules) return app_modules def get_app_names( self, sort_by_migration_dependencies: bool = True ) -> t.List[str]: """ Return all of the app names. :param sort_by_migration_dependencies: If True, sorts the app names using the migration dependencies, so dependencies are before dependents in the list. """ return [ i.app_name for i in self.get_app_configs( sort_by_migration_dependencies=sort_by_migration_dependencies ) ] def get_sorted_app_names(self) -> t.List[str]: """ Just here for backwards compatibility - use ``get_app_names`` directly. """ return self.get_app_names(sort_by_migration_dependencies=True) def sort_app_configs( self, app_configs: t.List[AppConfig] ) -> t.List[AppConfig]: app_config_map = { app_config.app_name: app_config for app_config in app_configs } sorted_app_names = TopologicalSorter( { app_config.app_name: [ i.app_name for i in app_config.migration_dependency_app_configs ] for app_config in app_config_map.values() } ).static_order() return [app_config_map[i] for i in sorted_app_names] def get_app_configs( self, sort_by_migration_dependencies: bool = True ) -> t.List[AppConfig]: """ Returns a list of ``AppConfig``, optionally sorted by migration dependencies. """ app_configs = [i.APP_CONFIG for i in self.get_app_modules()] return ( self.sort_app_configs(app_configs=app_configs) if sort_by_migration_dependencies else app_configs ) def get_app_config(self, app_name: str) -> AppConfig: """ Returns an ``AppConfig`` for the given app name. """ for app_config in self.get_app_configs(): if app_config.app_name == app_name: return app_config raise ValueError(f"No app found with name {app_name}") def get_table_with_name( self, app_name: str, table_class_name: str ) -> t.Type[Table]: """ Returns a ``Table`` class registered with the given app if it exists. Otherwise it raises an ``ValueError``. """ app_config = self.get_app_config(app_name=app_name) return app_config.get_table_with_name( table_class_name=table_class_name ) def get_table_classes( self, include_apps: t.Optional[t.List[str]] = None, exclude_apps: t.Optional[t.List[str]] = None, ) -> t.List[t.Type[Table]]: """ Returns all ``Table`` classes registered with the given apps. If ``include_apps`` is ``None``, then ``Table`` classes will be returned for all apps. """ if include_apps and exclude_apps: raise ValueError("Only specify `include_apps` or `exclude_apps`.") if include_apps: app_names = include_apps else: app_names = self.get_app_names() if exclude_apps: app_names = [i for i in app_names if i not in exclude_apps] tables: t.List[t.Type[Table]] = [] for app_name in app_names: app_config = self.get_app_config(app_name=app_name) tables.extend(app_config.table_classes) return tables

piccolo-orm__piccolo

piccolo_projects.rst

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piccolo-orm__piccolo/docs/src/piccolo/projects_and_apps/piccolo_projects.rst

Piccolo Projects A Piccolo project is a collection of apps. ------------------------------------------------------------------------ piccolo_conf.py A project requires a piccolo_conf.py file. To create this, use the following command: piccolo project new The file serves two important purposes: - Contains your database settings. - Is used for registering PiccoloApps. Location By convention, the piccolo_conf.py file should be at the root of your project: my_project/ piccolo_conf.py my_app/ piccolo_app.py This means that when you use the piccolo CLI from the my_project folder it can import piccolo_conf.py. If you prefer to keep piccolo_conf.py in a different location, or to give it a different name, you can do so using the PICCOLO_CONF environment variable (see PICCOLO_CONF<PICCOLO_CONF>). For example: my_project/ conf/ piccolo_conf_local.py my_app/ piccolo_app.py export PICCOLO_CONF=conf.piccolo_conf_local ------------------------------------------------------------------------ Example Here's an example: from piccolo.engine.postgres import PostgresEngine from piccolo.conf.apps import AppRegistry DB = PostgresEngine( config={ "database": "piccolo_project", "user": "postgres", "password": "", "host": "localhost", "port": 5432, } ) APP_REGISTRY = AppRegistry( apps=["home.piccolo_app", "piccolo_admin.piccolo_app"] ) ------------------------------------------------------------------------ DB The DB setting is an Engine instance (see the Engine docs <Engines>). ------------------------------------------------------------------------ APP_REGISTRY The APP_REGISTRY setting is an AppRegistry instance. piccolo.conf.apps AppRegistry

from __future__ import annotations import inspect import itertools import os import pathlib import traceback import typing as t from dataclasses import dataclass, field from importlib import import_module from types import ModuleType from piccolo.engine.base import Engine from piccolo.table import Table from piccolo.utils.graphlib import TopologicalSorter from piccolo.utils.warnings import Level, colored_warning class MigrationModule(ModuleType): ID: str VERSION: str DESCRIPTION: str @staticmethod async def forwards() -> None: pass class PiccoloAppModule(ModuleType): APP_CONFIG: AppConfig def table_finder( modules: t.Sequence[str], include_tags: t.Sequence[str] = None, exclude_tags: t.Sequence[str] = None, exclude_imported: bool = False, ) -> t.List[t.Type[Table]]: """ Rather than explicitly importing and registering table classes with the ``AppConfig``, ``table_finder`` can be used instead. It imports any ``Table`` subclasses in the given modules. Tags can be used to limit which ``Table`` subclasses are imported. :param modules: The module paths to check for ``Table`` subclasses. For example, ``['blog.tables']``. The path should be from the root of your project, not a relative path. :param include_tags: If the ``Table`` subclass has one of these tags, it will be imported. The special tag ``'__all__'`` will import all ``Table`` subclasses found. :param exclude_tags: If the ``Table`` subclass has any of these tags, it won't be imported. ``exclude_tags`` overrides ``include_tags``. :param exclude_imported: If ``True``, only ``Table`` subclasses defined within the module are used. Any ``Table`` subclasses imported by that module from other modules are ignored. For example: .. code-block:: python from piccolo.table import Table from piccolo.column import Varchar, ForeignKey from piccolo.apps.user.tables import BaseUser # excluded class Task(Table): # included title = Varchar() creator = ForeignKey(BaseUser) """ # noqa: E501 if include_tags is None: include_tags = ["__all__"] if exclude_tags is None: exclude_tags = [] if isinstance(modules, str): # Guard against the user just entering a string, for example # 'blog.tables', instead of ['blog.tables']. modules = [modules] table_subclasses: t.List[t.Type[Table]] = [] for module_path in modules: try: module = import_module(module_path) except ImportError as exception: print(f"Unable to import {module_path}") raise exception from exception object_names = [i for i in dir(module) if not i.startswith("_")] for object_name in object_names: _object = getattr(module, object_name) if ( inspect.isclass(_object) and issubclass(_object, Table) and _object is not Table ): table: Table = _object # type: ignore if exclude_imported and table.__module__!= module_path: continue if exclude_tags and set(table._meta.tags).intersection( set(exclude_tags) ): continue elif "__all__" in include_tags: table_subclasses.append(_object) elif set(table._meta.tags).intersection(set(include_tags)): table_subclasses.append(_object) return table_subclasses @dataclass class Command: callable: t.Callable aliases: t.List[str] = field(default_factory=list) @dataclass class AppConfig: """ Each app needs an AppConfig, which is defined in piccolo_app.py. :param app_name: The name of the app, for example ``'article'``. :param migrations_folder_path: The path of the folder containing this app's migration files. :param table_classes: By registering table classes, Piccolo's auto migrations can detect changes to tables. :param migration_dependencies: A list of Piccolo apps whose migrations this app depends on. For example: ``['piccolo.apps.user.piccolo_conf']``. The migrations for those apps will be run before the migrations for this app. :param commands: A list of functions and coroutines, which are then registered with the Piccolo CLI. For example, with a Piccolo app called ``'article'``, and a command called ``new``, it can be called on the command line using ``piccolo article new``. """ app_name: str migrations_folder_path: str table_classes: t.List[t.Type[Table]] = field(default_factory=list) migration_dependencies: t.List[str] = field(default_factory=list) commands: t.List[t.Union[t.Callable, Command]] = field( default_factory=list ) def __post_init__(self): self.commands = [ i if isinstance(i, Command) else Command(i) for i in self.commands ] if isinstance(self.migrations_folder_path, pathlib.Path): self.migrations_folder_path = str(self.migrations_folder_path) self._migration_dependency_app_configs: t.Optional[ t.List[AppConfig] ] = None def register_table(self, table_class: t.Type[Table]): self.table_classes.append(table_class) return table_class @property def migration_dependency_app_configs(self) -> t.List[AppConfig]: """ Get all of the ``AppConfig`` instances from this app's migration dependencies. """ # We cache the value so it's more efficient, and also so we can set the # underlying value in unit tests for easier mocking. if self._migration_dependency_app_configs is None: modules: t.List[PiccoloAppModule] = [ t.cast(PiccoloAppModule, import_module(module_path)) for module_path in self.migration_dependencies ] self._migration_dependency_app_configs = [ i.APP_CONFIG for i in modules ] return self._migration_dependency_app_configs def get_table_with_name(self, table_class_name: str) -> t.Type[Table]: """ Returns a ``Table`` subclass with the given name from this app, if it exists. Otherwise raises a ``ValueError``. """ filtered = [ table_class for table_class in self.table_classes if table_class.__name__ == table_class_name ] if not filtered: raise ValueError( f"No table with class name {table_class_name} exists." ) return filtered[0] class AppRegistry: """ Records all of the Piccolo apps in your project. Kept in ``piccolo_conf.py``. :param apps: A list of paths to Piccolo apps, e.g. ``['blog.piccolo_app']``. """ def __init__(self, apps: t.List[str] = None): self.apps = apps or [] self.app_configs: t.Dict[str, AppConfig] = {} app_names = [] for app in self.apps: try: app_conf_module = import_module(app) app_config: AppConfig = getattr(app_conf_module, "APP_CONFIG") except (ImportError, AttributeError) as e: if app.endswith(".piccolo_app"): raise e from e app += ".piccolo_app" app_conf_module = import_module(app) app_config = getattr(app_conf_module, "APP_CONFIG") colored_warning( f"App {app[:-12]} should end with `.piccolo_app`", level=Level.medium, ) self.app_configs[app_config.app_name] = app_config app_names.append(app_config.app_name) self._validate_app_names(app_names) @staticmethod def _validate_app_names(app_names: t.List[str]): """ Raise a ValueError if an app_name is repeated. """ app_names.sort() grouped = itertools.groupby(app_names) for key, value in grouped: count = len(list(value)) if count > 1: raise ValueError( f"There are {count} apps with the name `{key}`. This can " "cause unexpected behavior. Make sure each app has a " "unique name, and you haven't registered the same app " "multiple times." ) def get_app_config(self, app_name: str) -> t.Optional[AppConfig]: return self.app_configs.get(app_name) def get_table_classes(self, app_name: str) -> t.List[t.Type[Table]]: """ Returns each Table subclass defined in the given app if it exists. Otherwise raises a ValueError. :raises ValueError: If an AppConfig can't be found for the given app_name. """ app_config = self.get_app_config(app_name=app_name) if not app_config: raise ValueError(f"Unrecognised app_name: {app_name}") return app_config.table_classes def get_table_with_name( self, app_name: str, table_class_name: str ) -> t.Optional[t.Type[Table]]: """ Returns a Table subclass registered with the given app if it exists. Otherwise raises a ValueError. """ app_config = self.get_app_config(app_name=app_name) if app_config is None: raise ValueError(f"Can't find an app_config for {app_name}") else: return app_config.get_table_with_name( table_class_name=table_class_name ) class PiccoloConfModule(ModuleType): DB: Engine APP_REGISTRY: AppRegistry DEFAULT_MODULE_NAME = "piccolo_conf" ENVIRONMENT_VARIABLE = "PICCOLO_CONF" ENGINE_VAR = "DB" class Finder: """ Contains useful methods for locating and loading apps within your project, and tables within apps. """ def __init__(self, diagnose: bool = False): """ :param diagnose: If True, when trying to import piccolo_conf, a traceback will be printed out if an error occurs. """ self.diagnose = diagnose def _deduplicate( self, config_modules: t.List[PiccoloAppModule] ) -> t.List[PiccoloAppModule]: """ Remove all duplicates - just leaving the first instance. """ # Deduplicate, but preserve order - which is why set() isn't used. return list({c: None for c in config_modules}.keys()) def _import_app_modules( self, config_module_paths: t.List[str] ) -> t.List[PiccoloAppModule]: """ Import all piccolo_app.py modules within your apps, and all dependencies. """ config_modules = [] for config_module_path in config_module_paths: try: config_module = t.cast( PiccoloAppModule, import_module(config_module_path) ) except ImportError as e: raise Exception( f"Unable to import {config_module_path}" ) from e app_config: AppConfig = getattr(config_module, "APP_CONFIG") dependency_config_modules = self._import_app_modules( app_config.migration_dependencies ) config_modules.extend(dependency_config_modules + [config_module]) return config_modules def get_piccolo_conf_module( self, module_name: t.Optional[str] = None ) -> t.Optional[PiccoloConfModule]: """ Searches the path for a 'piccolo_conf.py' module to import. The location searched can be overriden by: * Explicitly passing a module name into this method. * Setting the PICCOLO_CONF environment variable. An example override is'my_folder.piccolo_conf'. """ env_module_name = os.environ.get(ENVIRONMENT_VARIABLE, None) if not module_name and env_module_name: module_name = env_module_name if not module_name: module_name = DEFAULT_MODULE_NAME try: module = t.cast(PiccoloConfModule, import_module(module_name)) except ModuleNotFoundError as exc: if self.diagnose: colored_warning( ( f"{module_name} either doesn't exist or the import " "failed. Traceback:" ), level=Level.high, ) print(traceback.format_exc()) if str(exc) == "No module named 'asyncpg'": raise ModuleNotFoundError( "PostgreSQL driver not found. " "Try running `pip install 'piccolo[postgres]'`" ) from exc elif str(exc) == "No module named 'aiosqlite'": raise ModuleNotFoundError( "SQLite driver not found. " "Try running `pip install 'piccolo[sqlite]'`" ) from exc else: raise exc from exc else: return module def get_app_registry(self) -> AppRegistry: """ Returns the ``AppRegistry`` instance within piccolo_conf. """ piccolo_conf_module = self.get_piccolo_conf_module() return getattr(piccolo_conf_module, "APP_REGISTRY") def get_engine( self, module_name: t.Optional[str] = None ) -> t.Optional[Engine]: piccolo_conf = self.get_piccolo_conf_module(module_name=module_name) engine: t.Optional[Engine] = getattr(piccolo_conf, ENGINE_VAR, None) if not engine: colored_warning( f"{module_name} doesn't define a {ENGINE_VAR} variable.", level=Level.high, ) elif not isinstance(engine, Engine): colored_warning( f"{module_name} contains a {ENGINE_VAR} variable of the " "wrong type - it should be an Engine subclass.", level=Level.high, ) return engine def get_app_modules(self) -> t.List[PiccoloAppModule]: """ Returns the ``piccolo_app.py`` modules for each registered Piccolo app in your project. """ app_registry = self.get_app_registry() app_modules = self._import_app_modules(app_registry.apps) # Now deduplicate any dependencies app_modules = self._deduplicate(app_modules) return app_modules def get_app_names( self, sort_by_migration_dependencies: bool = True ) -> t.List[str]: """ Return all of the app names. :param sort_by_migration_dependencies: If True, sorts the app names using the migration dependencies, so dependencies are before dependents in the list. """ return [ i.app_name for i in self.get_app_configs( sort_by_migration_dependencies=sort_by_migration_dependencies ) ] def get_sorted_app_names(self) -> t.List[str]: """ Just here for backwards compatibility - use ``get_app_names`` directly. """ return self.get_app_names(sort_by_migration_dependencies=True) def sort_app_configs( self, app_configs: t.List[AppConfig] ) -> t.List[AppConfig]: app_config_map = { app_config.app_name: app_config for app_config in app_configs } sorted_app_names = TopologicalSorter( { app_config.app_name: [ i.app_name for i in app_config.migration_dependency_app_configs ] for app_config in app_config_map.values() } ).static_order() return [app_config_map[i] for i in sorted_app_names] def get_app_configs( self, sort_by_migration_dependencies: bool = True ) -> t.List[AppConfig]: """ Returns a list of ``AppConfig``, optionally sorted by migration dependencies. """ app_configs = [i.APP_CONFIG for i in self.get_app_modules()] return ( self.sort_app_configs(app_configs=app_configs) if sort_by_migration_dependencies else app_configs ) def get_app_config(self, app_name: str) -> AppConfig: """ Returns an ``AppConfig`` for the given app name. """ for app_config in self.get_app_configs(): if app_config.app_name == app_name: return app_config raise ValueError(f"No app found with name {app_name}") def get_table_with_name( self, app_name: str, table_class_name: str ) -> t.Type[Table]: """ Returns a ``Table`` class registered with the given app if it exists. Otherwise it raises an ``ValueError``. """ app_config = self.get_app_config(app_name=app_name) return app_config.get_table_with_name( table_class_name=table_class_name ) def get_table_classes( self, include_apps: t.Optional[t.List[str]] = None, exclude_apps: t.Optional[t.List[str]] = None, ) -> t.List[t.Type[Table]]: """ Returns all ``Table`` classes registered with the given apps. If ``include_apps`` is ``None``, then ``Table`` classes will be returned for all apps. """ if include_apps and exclude_apps: raise ValueError("Only specify `include_apps` or `exclude_apps`.") if include_apps: app_names = include_apps else: app_names = self.get_app_names() if exclude_apps: app_names = [i for i in app_names if i not in exclude_apps] tables: t.List[t.Type[Table]] = [] for app_name in app_names: app_config = self.get_app_config(app_name=app_name) tables.extend(app_config.table_classes) return tables

piccolo-orm__piccolo

postgres_engine.rst

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Generate documentation for this module

MIT License

piccolo-orm__piccolo/docs/src/piccolo/engines/postgres_engine.rst

PostgresEngine Configuration # piccolo_conf.py from piccolo.engine.postgres import PostgresEngine DB = PostgresEngine(config={ 'host': 'localhost', 'database': 'my_app', 'user': 'postgres', 'password': '' }) config The config dictionary is passed directly to the underlying database adapter, asyncpg. See the asyncpg docs to learn more. ------------------------------------------------------------------------ Connection pool To use a connection pool, you need to first initialise it. The best place to do this is in the startup event handler of whichever web framework you are using. Here's an example using Starlette. Notice that we also close the connection pool in the shutdown event handler. from piccolo.engine import engine_finder from starlette.applications import Starlette app = Starlette() @app.on_event('startup') async def open_database_connection_pool(): engine = engine_finder() await engine.start_connection_pool() @app.on_event('shutdown') async def close_database_connection_pool(): engine = engine_finder() await engine.close_connection_pool() Hint Using a connection pool helps with performance, since connections are reused instead of being created for each query. Once a connection pool has been started, the engine will use it for making queries. Hint If you're running several instances of an app on the same server, you may prefer an external connection pooler - like pgbouncer. Configuration The connection pool uses the same configuration as your engine. You can also pass in additional parameters, which are passed to the underlying database adapter. Here's an example: # To increase the number of connections available: await engine.start_connection_pool(max_size=20) ------------------------------------------------------------------------

from __future__ import annotations import contextvars import typing as t from dataclasses import dataclass from piccolo.engine.base import Batch, Engine from piccolo.engine.exceptions import TransactionError from piccolo.query.base import DDL, Query from piccolo.querystring import QueryString from piccolo.utils.lazy_loader import LazyLoader from piccolo.utils.sync import run_sync from piccolo.utils.warnings import Level, colored_warning asyncpg = LazyLoader("asyncpg", globals(), "asyncpg") if t.TYPE_CHECKING: # pragma: no cover from asyncpg.connection import Connection from asyncpg.cursor import Cursor from asyncpg.pool import Pool @dataclass class AsyncBatch(Batch): connection: Connection query: Query batch_size: int # Set internally _transaction = None _cursor: t.Optional[Cursor] = None @property def cursor(self) -> Cursor: if not self._cursor: raise ValueError("_cursor not set") return self._cursor async def next(self) -> t.List[t.Dict]: data = await self.cursor.fetch(self.batch_size) return await self.query._process_results(data) def __aiter__(self): return self async def __anext__(self): response = await self.next() if response == []: raise StopAsyncIteration() return response async def __aenter__(self): self._transaction = self.connection.transaction() await self._transaction.start() querystring = self.query.querystrings[0] template, template_args = querystring.compile_string() self._cursor = await self.connection.cursor(template, *template_args) return self async def __aexit__(self, exception_type, exception, traceback): if exception: await self._transaction.rollback() else: await self._transaction.commit() await self.connection.close() return exception is not None ############################################################################### class Atomic: """ This is useful if you want to build up a transaction programatically, by adding queries to it. Usage:: transaction = engine.atomic() transaction.add(Foo.create_table()) # Either: transaction.run_sync() await transaction.run() """ __slots__ = ("engine", "queries") def __init__(self, engine: PostgresEngine): self.engine = engine self.queries: t.List[Query] = [] def add(self, *query: Query): self.queries += list(query) async def run(self): from piccolo.query.methods.objects import Create, GetOrCreate try: async with self.engine.transaction(): for query in self.queries: if isinstance(query, (Query, DDL, Create, GetOrCreate)): await query.run() else: raise ValueError("Unrecognised query") self.queries = [] except Exception as exception: self.queries = [] raise exception from exception def run_sync(self): return run_sync(self.run()) def __await__(self): return self.run().__await__() ############################################################################### class Savepoint: def __init__(self, name: str, transaction: PostgresTransaction): self.name = name self.transaction = transaction async def rollback_to(self): await self.transaction.connection.execute( f"ROLLBACK TO SAVEPOINT {self.name}" ) async def release(self): await self.transaction.connection.execute( f"RELEASE SAVEPOINT {self.name}" ) class PostgresTransaction: """ Used for wrapping queries in a transaction, using a context manager. Currently it's async only. Usage:: async with engine.transaction(): # Run some queries: await Band.select().run() """ __slots__ = ( "engine", "transaction", "context", "connection", "_savepoint_id", "_parent", "_committed", "_rolled_back", ) def __init__(self, engine: PostgresEngine, allow_nested: bool = True): """ :param allow_nested: If ``True`` then if we try creating a new transaction when another is already active, we treat this as a no-op:: async with DB.transaction(): async with DB.transaction(): pass If we want to disallow this behaviour, then setting ``allow_nested=False`` will cause a ``TransactionError`` to be raised. """ self.engine = engine current_transaction = self.engine.current_transaction.get() self._savepoint_id = 0 self._parent = None self._committed = False self._rolled_back = False if current_transaction: if allow_nested: self._parent = current_transaction else: raise TransactionError( "A transaction is already active - nested transactions " "aren't allowed." ) async def __aenter__(self) -> PostgresTransaction: if self._parent is not None: return self._parent self.connection = await self.get_connection() self.transaction = self.connection.transaction() await self.begin() self.context = self.engine.current_transaction.set(self) return self async def get_connection(self): if self.engine.pool: return await self.engine.pool.acquire() else: return await self.engine.get_new_connection() async def begin(self): await self.transaction.start() async def commit(self): await self.transaction.commit() self._committed = True async def rollback(self): await self.transaction.rollback() self._rolled_back = True async def rollback_to(self, savepoint_name: str): """ Used to rollback to a savepoint just using the name. """ await Savepoint(name=savepoint_name, transaction=self).rollback_to() ########################################################################### def get_savepoint_id(self) -> int: self._savepoint_id += 1 return self._savepoint_id async def savepoint(self, name: t.Optional[str] = None) -> Savepoint: name = name or f"savepoint_{self.get_savepoint_id()}" await self.connection.execute(f"SAVEPOINT {name}") return Savepoint(name=name, transaction=self) ########################################################################### async def __aexit__(self, exception_type, exception, traceback): if self._parent: return exception is None if exception: # The user may have manually rolled it back. if not self._rolled_back: await self.rollback() else: # The user may have manually committed it. if not self._committed and not self._rolled_back: await self.commit() if self.engine.pool: await self.engine.pool.release(self.connection) else: await self.connection.close() self.engine.current_transaction.reset(self.context) return exception is None ############################################################################### class PostgresEngine(Engine[t.Optional[PostgresTransaction]]): """ Used to connect to PostgreSQL. :param config: The config dictionary is passed to the underlying database adapter, asyncpg. Common arguments you're likely to need are: * host * port * user * password * database For example, ``{'host': 'localhost', 'port': 5432}``. See the `asyncpg docs <https://magicstack.github.io/asyncpg/current/api/index.html#connection>`_ for all available options. :param extensions: When the engine starts, it will try and create these extensions in Postgres. If you're using a read only database, set this value to an empty tuple ``()``. :param log_queries: If ``True``, all SQL and DDL statements are printed out before being run. Useful for debugging. :param log_responses: If ``True``, the raw response from each query is printed out. Useful for debugging. :param extra_nodes: If you have additional database nodes (e.g. read replicas) for the server, you can specify them here. It's a mapping of a memorable name to a ``PostgresEngine`` instance. For example:: DB = PostgresEngine( config={'database':'main_db'}, extra_nodes={ 'read_replica_1': PostgresEngine( config={ 'database':'main_db', host:'read_replicate.my_db.com' }, extensions=() ) } ) Note how we set ``extensions=()``, because it's a read only database. When executing a query, you can specify one of these nodes instead of the main database. For example:: >>> await MyTable.select().run(node="read_replica_1") """ # noqa: E501 __slots__ = ( "config", "extensions", "log_queries", "log_responses", "extra_nodes", "pool", "current_transaction", ) engine_type = "postgres" min_version_number = 10 def __init__( self, config: t.Dict[str, t.Any], extensions: t.Sequence[str] = ("uuid-ossp",), log_queries: bool = False, log_responses: bool = False, extra_nodes: t.Mapping[str, PostgresEngine] = None, ) -> None: if extra_nodes is None: extra_nodes = {} self.config = config self.extensions = extensions self.log_queries = log_queries self.log_responses = log_responses self.extra_nodes = extra_nodes self.pool: t.Optional[Pool] = None database_name = config.get("database", "Unknown") self.current_transaction = contextvars.ContextVar( f"pg_current_transaction_{database_name}", default=None ) super().__init__() @staticmethod def _parse_raw_version_string(version_string: str) -> float: """ The format of the version string isn't always consistent. Sometimes it's just the version number e.g. '9.6.18', and sometimes it contains specific build information e.g. '12.4 (Ubuntu 12.4-0ubuntu0.20.04.1)'. Just extract the major and minor version numbers. """ version_segment = version_string.split(" ")[0] major, minor = version_segment.split(".")[:2] return float(f"{major}.{minor}") async def get_version(self) -> float: """ Returns the version of Postgres being run. """ try: response: t.Sequence[t.Dict] = await self._run_in_new_connection( "SHOW server_version" ) except ConnectionRefusedError as exception: # Suppressing the exception, otherwise importing piccolo_conf.py # containing an engine will raise an ImportError. colored_warning(f"Unable to connect to database - {exception}") return 0.0 else: version_string = response[0]["server_version"] return self._parse_raw_version_string( version_string=version_string ) def get_version_sync(self) -> float: return run_sync(self.get_version()) async def prep_database(self): for extension in self.extensions: try: await self._run_in_new_connection( f'CREATE EXTENSION IF NOT EXISTS "{extension}"', ) except asyncpg.exceptions.InsufficientPrivilegeError: colored_warning( f"=> Unable to create {extension} extension - some " "functionality may not behave as expected. Make sure " "your database user has permission to create " "extensions, or add it manually using " f'`CREATE EXTENSION "{extension}";`', level=Level.medium, ) ########################################################################### # These typos existed in the codebase for a while, so leaving these proxy # methods for now to ensure backwards compatibility. async def start_connnection_pool(self, **kwargs) -> None: colored_warning( "`start_connnection_pool` is a typo - please change it to " "`start_connection_pool`.", category=DeprecationWarning, ) return await self.start_connection_pool() async def close_connnection_pool(self, **kwargs) -> None: colored_warning( "`close_connnection_pool` is a typo - please change it to " "`close_connection_pool`.", category=DeprecationWarning, ) return await self.close_connection_pool() ########################################################################### async def start_connection_pool(self, **kwargs) -> None: if self.pool: colored_warning( "A pool already exists - close it first if you want to create " "a new pool.", ) else: config = dict(self.config) config.update(**kwargs) self.pool = await asyncpg.create_pool(**config) async def close_connection_pool(self) -> None: if self.pool: await self.pool.close() self.pool = None else: colored_warning("No pool is running.") ########################################################################### async def get_new_connection(self) -> Connection: """ Returns a new connection - doesn't retrieve it from the pool. """ return await asyncpg.connect(**self.config) ########################################################################### async def batch( self, query: Query, batch_size: int = 100, node: t.Optional[str] = None, ) -> AsyncBatch: """ :param query: The database query to run. :param batch_size: How many rows to fetch on each iteration. :param node: Which node to run the query on (see ``extra_nodes``). If not specified, it runs on the main Postgres node. """ engine: t.Any = self.extra_nodes.get(node) if node else self connection = await engine.get_new_connection() return AsyncBatch( connection=connection, query=query, batch_size=batch_size ) ########################################################################### async def _run_in_pool(self, query: str, args: t.Sequence[t.Any] = None): if args is None: args = [] if not self.pool: raise ValueError("A pool isn't currently running.") async with self.pool.acquire() as connection: response = await connection.fetch(query, *args) return response async def _run_in_new_connection( self, query: str, args: t.Sequence[t.Any] = None ): if args is None: args = [] connection = await self.get_new_connection() try: results = await connection.fetch(query, *args) except asyncpg.exceptions.PostgresError as exception: await connection.close() raise exception await connection.close() return results async def run_querystring( self, querystring: QueryString, in_pool: bool = True ): query, query_args = querystring.compile_string( engine_type=self.engine_type ) query_id = self.get_query_id() if self.log_queries: self.print_query(query_id=query_id, query=querystring.__str__()) # If running inside a transaction: current_transaction = self.current_transaction.get() if current_transaction: response = await current_transaction.connection.fetch( query, *query_args ) elif in_pool and self.pool: response = await self._run_in_pool(query, query_args) else: response = await self._run_in_new_connection(query, query_args) if self.log_responses: self.print_response(query_id=query_id, response=response) return response async def run_ddl(self, ddl: str, in_pool: bool = True): query_id = self.get_query_id() if self.log_queries: self.print_query(query_id=query_id, query=ddl) # If running inside a transaction: current_transaction = self.current_transaction.get() if current_transaction: response = await current_transaction.connection.fetch(ddl) elif in_pool and self.pool: response = await self._run_in_pool(ddl) else: response = await self._run_in_new_connection(ddl) if self.log_responses: self.print_response(query_id=query_id, response=response) return response def atomic(self) -> Atomic: return Atomic(engine=self) def transaction(self, allow_nested: bool = True) -> PostgresTransaction: return PostgresTransaction(engine=self, allow_nested=allow_nested)

piccolo-orm__piccolo

running.rst

Module doc

Generate documentation for this module

MIT License

piccolo-orm__piccolo/docs/src/piccolo/migrations/running.rst

Running migrations Hint To see all available options for these commands, use the --help flag, for example piccolo migrations forwards --help. Forwards When the migration is run, the forwards function is executed. To do this: piccolo migrations forwards my_app Multiple apps If you have multiple apps you can run them all using: piccolo migrations forwards all Fake We can 'fake' running a migration - we record that it ran in the database without actually running it. piccolo migrations forwards my_app 2022-09-04T19:44:09 --fake This is useful if we started from an existing database using piccolo schema generate, and the initial migration we generated is for tables which already exist, hence we fake run it. ------------------------------------------------------------------------ Reversing migrations To reverse the migration, run the following command, specifying the ID of a migration: piccolo migrations backwards my_app 2022-09-04T19:44:09 Piccolo will then reverse the migrations for the given app, starting with the most recent migration, up to and including the migration with the specified ID. You can try going forwards and backwards a few times to make sure it works as expected. ------------------------------------------------------------------------ Preview To see the SQL queries of a migration without actually running them, use the --preview flag. This works when running migrations forwards: piccolo migrations forwards my_app --preview Or backwards: piccolo migrations backwards 2022-09-04T19:44:09 --preview ------------------------------------------------------------------------ Checking migrations You can easily check which migrations have and haven't ran using the following: piccolo migrations check

from __future__ import annotations import os import sys import typing as t from piccolo.apps.migrations.auto.migration_manager import MigrationManager from piccolo.apps.migrations.commands.base import ( BaseMigrationManager, MigrationResult, ) from piccolo.apps.migrations.tables import Migration from piccolo.conf.apps import AppConfig, MigrationModule from piccolo.utils.printing import print_heading class BackwardsMigrationManager(BaseMigrationManager): def __init__( self, app_name: str, migration_id: str, auto_agree: bool = False, clean: bool = False, preview: bool = False, ): self.migration_id = migration_id self.app_name = app_name self.auto_agree = auto_agree self.clean = clean self.preview = preview super().__init__() async def run_migrations_backwards(self, app_config: AppConfig): migration_modules: t.Dict[ str, MigrationModule ] = self.get_migration_modules(app_config.migrations_folder_path) ran_migration_ids = await Migration.get_migrations_which_ran( app_name=self.app_name ) if len(ran_migration_ids) == 0: # Make sure a success is returned, as we don't want this # to appear as an error in automated scripts. message = "🏁 No migrations to reverse!" print(message) return MigrationResult(success=True, message=message) ####################################################################### if self.migration_id == "all": earliest_migration_id = ran_migration_ids[0] elif self.migration_id == "1": earliest_migration_id = ran_migration_ids[-1] else: earliest_migration_id = self.migration_id if earliest_migration_id not in ran_migration_ids: message = ( "Unrecognized migration name - must be one of " f"{ran_migration_ids}" ) print(message, file=sys.stderr) return MigrationResult(success=False, message=message) ####################################################################### latest_migration_id = ran_migration_ids[-1] start_index = ran_migration_ids.index(earliest_migration_id) end_index = ran_migration_ids.index(latest_migration_id) + 1 subset = ran_migration_ids[start_index:end_index] reversed_migration_ids = list(reversed(subset)) ####################################################################### n = len(reversed_migration_ids) _continue = ( "y" if self.auto_agree else input(f"Reverse {n} migration{'s' if n!= 1 else ''}? [y/N] ") ) if _continue in "yY": for migration_id in reversed_migration_ids: migration_module = migration_modules[migration_id] response = await migration_module.forwards() if isinstance(response, MigrationManager): if self.preview: response.preview = True await response.run(backwards=True) if not self.preview: await Migration.delete().where( Migration.name == migration_id ).run() if self.clean and migration_module.__file__: os.unlink(migration_module.__file__) print("ok! ✔️") return MigrationResult(success=True) else: # pragma: no cover message = "Not proceeding." print(message, file=sys.stderr) return MigrationResult(success=False, message=message) async def run(self) -> MigrationResult: await self.create_migration_table() app_config = self.get_app_config(self.app_name) return await self.run_migrations_backwards(app_config=app_config) async def run_backwards( app_name: str, migration_id: str = "1", auto_agree: bool = False, clean: bool = False, preview: bool = False, ) -> MigrationResult: if app_name == "all": sorted_app_names = BaseMigrationManager().get_sorted_app_names() sorted_app_names.reverse() names = [f"'{name}'" for name in sorted_app_names] _continue = ( "y" if auto_agree else input( "You are about to undo the migrations for the following " "apps:\n" f"{', '.join(names)}\n" "Are you sure you want to continue? [y/N] " ) ) if _continue not in "yY": return MigrationResult(success=False, message="user cancelled") for _app_name in sorted_app_names: print_heading(_app_name) manager = BackwardsMigrationManager( app_name=_app_name, migration_id="all", auto_agree=auto_agree, preview=preview, ) await manager.run() return MigrationResult(success=True) else: manager = BackwardsMigrationManager( app_name=app_name, migration_id=migration_id, auto_agree=auto_agree, clean=clean, preview=preview, ) return await manager.run() async def backwards( app_name: str, migration_id: str = "1", auto_agree: bool = False, clean: bool = False, preview: bool = False, ): """ Undo migrations up to a specific migration. :param app_name: The app to reverse migrations for. Specify a value of 'all' to reverse migrations for all apps. :param migration_id: Migrations will be reversed up to and including this migration_id. Specify a value of 'all' to undo all of the migrations. Specify a value of '1' to undo the most recent migration. :param auto_agree: If true, automatically agree to any input prompts. :param clean: If true, the migration files which have been run backwards are deleted from the disk after completing. :param preview: If true, don't actually run the migration, just print the SQL queries. """ response = await run_backwards( app_name=app_name, migration_id=migration_id, auto_agree=auto_agree, clean=clean, preview=preview, ) if not response.success: sys.exit(1) import dataclasses import typing as t from piccolo.apps.migrations.commands.base import BaseMigrationManager from piccolo.apps.migrations.tables import Migration from piccolo.utils.printing import get_fixed_length_string @dataclasses.dataclass class MigrationStatus: app_name: str migration_id: str description: str has_ran: bool class CheckMigrationManager(BaseMigrationManager): def __init__(self, app_name: str): self.app_name = app_name super().__init__() async def get_migration_statuses(self) -> t.List[MigrationStatus]: # Make sure the migration table exists, otherwise we'll get an error. await self.create_migration_table() migration_statuses: t.List[MigrationStatus] = [] app_modules = self.get_app_modules() for app_module in app_modules: app_config = app_module.APP_CONFIG app_name = app_config.app_name if self.app_name not in ["all", app_name]: continue migration_modules = self.get_migration_modules( app_config.migrations_folder_path ) ids = self.get_migration_ids(migration_modules) for _id in ids: has_ran = ( await Migration.exists() .where( (Migration.name == _id) & (Migration.app_name == app_name) ) .run() ) description = getattr( migration_modules[_id], "DESCRIPTION", "-" ) migration_statuses.append( MigrationStatus( app_name=app_name, migration_id=_id, description=description, has_ran=has_ran, ) ) return migration_statuses async def have_ran_count(self) -> int: """ :returns: The number of migrations which have been ran. """ migration_statuses = await self.get_migration_statuses() return len([i for i in migration_statuses if i.has_ran]) async def havent_ran_count(self) -> int: """ :returns: The number of migrations which haven't been ran. """ migration_statuses = await self.get_migration_statuses() return len([i for i in migration_statuses if not i.has_ran]) async def run(self): """ Prints out the migrations which have and haven't ran. """ print("Listing migrations...") desc_length = 40 id_length = 26 print( f'{get_fixed_length_string("APP NAME")} | ' f'{get_fixed_length_string("MIGRATION_ID", id_length)} | ' f'{get_fixed_length_string("DESCRIPTION", desc_length)} | RAN' ) migration_statuses = await self.get_migration_statuses() for migration_status in migration_statuses: fixed_length_app_name = get_fixed_length_string( migration_status.app_name ) fixed_length_id = get_fixed_length_string( migration_status.migration_id, length=id_length ) fixed_length_description = get_fixed_length_string( migration_status.description, desc_length ) has_ran = migration_status.has_ran print( f"{fixed_length_app_name} | " f"{fixed_length_id} | " f"{fixed_length_description} | " f"{has_ran}" ) async def check(app_name: str = "all"): """ Lists all migrations which have and haven't ran. :param app_name: The name of the app to check. Specify a value of 'all' to check the migrations for all apps. """ await CheckMigrationManager(app_name=app_name).run() from __future__ import annotations import sys import typing as t from piccolo.apps.migrations.auto.migration_manager import MigrationManager from piccolo.apps.migrations.commands.base import ( BaseMigrationManager, MigrationResult, ) from piccolo.apps.migrations.tables import Migration from piccolo.conf.apps import AppConfig, MigrationModule from piccolo.utils.printing import print_heading class ForwardsMigrationManager(BaseMigrationManager): def __init__( self, app_name: str, migration_id: str = "all", fake: bool = False, preview: bool = False, ): self.app_name = app_name self.migration_id = migration_id self.fake = fake self.preview = preview super().__init__() async def run_migrations(self, app_config: AppConfig) -> MigrationResult: already_ran = await Migration.get_migrations_which_ran( app_name=app_config.app_name ) migration_modules: t.Dict[ str, MigrationModule ] = self.get_migration_modules(app_config.migrations_folder_path) ids = self.get_migration_ids(migration_modules) n = len(ids) print(f"👍 {n} migration{'s' if n!= 1 else ''} already complete") havent_run = sorted(set(ids) - set(already_ran)) if len(havent_run) == 0: # Make sure this still appears successful, as we don't want this # to appear as an error in automated scripts. message = "🏁 No migrations need to be run" print(message) return MigrationResult(success=True, message=message) else: n = len(havent_run) print(f"⏩ {n} migration{'s' if n!= 1 else ''} not yet run") if self.migration_id == "all": subset = havent_run elif self.migration_id == "1": subset = havent_run[:1] else: try: index = havent_run.index(self.migration_id) except ValueError: message = f"{self.migration_id} is unrecognised" print(message, file=sys.stderr) return MigrationResult(success=False, message=message) else: subset = havent_run[: index + 1] if subset: n = len(subset) print(f"🚀 Running {n} migration{'s' if n!= 1 else ''}:") for _id in subset: if self.fake: print(f"- {_id}: faked! ⏭️") else: migration_module = migration_modules[_id] response = await migration_module.forwards() if isinstance(response, MigrationManager): if self.preview: response.preview = True await response.run() print("ok! ✔️") if not self.preview: await Migration.insert().add( Migration(name=_id, app_name=app_config.app_name) ).run() return MigrationResult(success=True, message="migration succeeded") async def run(self) -> MigrationResult: await self.create_migration_table() app_config = self.get_app_config(app_name=self.app_name) return await self.run_migrations(app_config) async def run_forwards( app_name: str, migration_id: str = "all", fake: bool = False, preview: bool = False, ) -> MigrationResult: """ Run the migrations. This function can be used to programatically run migrations - for example, in a unit test. """ if app_name == "all": sorted_app_names = BaseMigrationManager().get_sorted_app_names() for _app_name in sorted_app_names: print_heading(_app_name) manager = ForwardsMigrationManager( app_name=_app_name, migration_id="all", fake=fake, preview=preview, ) response = await manager.run() if not response.success: return response return MigrationResult(success=True) else: manager = ForwardsMigrationManager( app_name=app_name, migration_id=migration_id, fake=fake, preview=preview, ) return await manager.run() async def forwards( app_name: str, migration_id: str = "all", fake: bool = False, preview: bool = False, ): """ Runs any migrations which haven't been run yet. :param app_name: The name of the app to migrate. Specify a value of 'all' to run migrations for all apps. :param migration_id: Migrations will be ran up to and including this migration_id. Specify a value of 'all' to run all of the migrations. Specify a value of '1' to just run the next migration. :param fake: If set, will record the migrations as being run without actually running them. :param preview: If true, don't actually run the migration, just print the SQL queries """ response = await run_forwards( app_name=app_name, migration_id=migration_id, fake=fake, preview=preview, ) if not response.success: sys.exit(1)

piccolo-orm__piccolo

using_sqlite_and_asyncio_effectively.rst

Module doc

Generate documentation for this module

MIT License

piccolo-orm__piccolo/docs/src/piccolo/tutorials/using_sqlite_and_asyncio_effectively.rst

Using SQLite and asyncio effectively When using Piccolo with SQLite, there are some best practices to follow. asyncio => lots of connections With asyncio, we can potentially open lots of database connections, and attempt to perform concurrent database writes. SQLite doesn't support such concurrent behavior as effectively as Postgres, so we need to be careful. One write at a time SQLite can easily support lots of transactions concurrently if they are reading, but only one write can be performed at a time. ------------------------------------------------------------------------ Transactions SQLite has several transaction types, as specified by Piccolo's TransactionType enum: piccolo.engine.sqlite TransactionType Which to use? When creating a transaction, Piccolo uses DEFERRED by default (to be consistent with SQLite). This means that the first SQL query executed within the transaction determines whether it's a READ or WRITE: - READ - if the first query is a SELECT - WRITE - if the first query is something like an INSERT / UPDATE / DELETE If a transaction starts off with a SELECT, but then tries to perform an INSERT / UPDATE / DELETE, SQLite tries to 'promote' the transaction so it can write. The problem is, if multiple concurrent connections try doing this at the same time, SQLite will return a database locked error. So if you're creating a transaction which you know will perform writes, then create an IMMEDIATE transaction: from piccolo.engine.sqlite import TransactionType async with Band._meta.db.transaction( transaction_type=TransactionType.immediate ): # We perform a SELECT first, but as it's an IMMEDIATE transaction, # we can later perform writes without getting a database locked # error. if not await Band.exists().where(Band.name == 'Pythonistas'): await Band.objects().create(name="Pythonistas") Multiple IMMEDIATE transactions can exist concurrently - SQLite uses a lock to make sure only one of them writes at a time. If your transaction will just be performing SELECT queries, then just use the default DEFERRED transactions - you will get improved performance, as no locking is involved: async with Band._meta.db.transaction(): bands = await Band.select() managers = await Manager.select() ------------------------------------------------------------------------ timeout It's recommended to specify the timeout argument in SQLiteEngine <piccolo.engine.sqlite.SQLiteEngine>. DB = SQLiteEngine(timeout=60) Imagine you have a web app, and each endpoint creates a transaction which runs multiple queries. With SQLite, only a single write operation can happen at a time, so if several connections are open, they may be queued for a while. By increasing timeout it means that queries are less likely to timeout. To find out more about timeout see the Python sqlite3 docs <sqlite3.connect>.

from __future__ import annotations import contextvars import datetime import enum import os import sqlite3 import typing as t import uuid from dataclasses import dataclass from decimal import Decimal from piccolo.engine.base import Batch, Engine from piccolo.engine.exceptions import TransactionError from piccolo.query.base import DDL, Query from piccolo.querystring import QueryString from piccolo.utils.encoding import dump_json, load_json from piccolo.utils.lazy_loader import LazyLoader from piccolo.utils.sync import run_sync aiosqlite = LazyLoader("aiosqlite", globals(), "aiosqlite") if t.TYPE_CHECKING: # pragma: no cover from aiosqlite import Connection, Cursor # type: ignore from piccolo.table import Table ############################################################################### # We need to register some adapters so sqlite returns types which are more # consistent with the Postgres engine. # In def convert_numeric_in(value): """ Convert any Decimal values into floats. """ return float(value) def convert_uuid_in(value) -> str: """ Converts the UUID value being passed into sqlite. """ return str(value) def convert_time_in(value: datetime.time) -> str: """ Converts the time value being passed into sqlite. """ return value.isoformat() def convert_date_in(value: datetime.date): """ Converts the date value being passed into sqlite. """ return value.isoformat() def convert_datetime_in(value: datetime.datetime) -> str: """ Converts the datetime into a string. If it's timezone aware, we want to convert it to UTC first. This is to replicate Postgres, which stores timezone aware datetimes in UTC. """ if value.tzinfo is not None: value = value.astimezone(datetime.timezone.utc) return str(value) def convert_timedelta_in(value: datetime.timedelta): """ Converts the timedelta value being passed into sqlite. """ return value.total_seconds() def convert_array_in(value: list): """ Converts a list value into a string. """ if value and type(value[0]) not in [str, int, float]: raise ValueError("Can only serialise str, int and float.") return dump_json(value) # Out def convert_numeric_out(value: bytes) -> Decimal: """ Convert float values into Decimals. """ return Decimal(value.decode("ascii")) def convert_int_out(value: bytes) -> int: """ Make sure Integer values are actually of type int. """ return int(float(value)) def convert_uuid_out(value: bytes) -> uuid.UUID: """ If the value is a uuid, convert it to a UUID instance. """ return uuid.UUID(value.decode("utf8")) def convert_date_out(value: bytes) -> datetime.date: return datetime.date.fromisoformat(value.decode("utf8")) def convert_time_out(value: bytes) -> datetime.time: """ If the value is a time, convert it to a UUID instance. """ return datetime.time.fromisoformat(value.decode("utf8")) def convert_seconds_out(value: bytes) -> datetime.timedelta: """ If the value is from a seconds column, convert it to a timedelta instance. """ return datetime.timedelta(seconds=float(value.decode("utf8"))) def convert_boolean_out(value: bytes) -> bool: """ If the value is from a boolean column, convert it to a bool value. """ _value = value.decode("utf8") return _value == "1" def convert_timestamp_out(value: bytes) -> datetime.datetime: """ If the value is from a timestamp column, convert it to a datetime value. """ return datetime.datetime.fromisoformat(value.decode("utf8")) def convert_timestamptz_out(value: bytes) -> datetime.datetime: """ If the value is from a timestamptz column, convert it to a datetime value, with a timezone of UTC. """ _value = datetime.datetime.fromisoformat(value.decode("utf8")) _value = _value.replace(tzinfo=datetime.timezone.utc) return _value def convert_array_out(value: bytes) -> t.List: """ If the value if from an array column, deserialise the string back into a list. """ return load_json(value.decode("utf8")) def convert_M2M_out(value: bytes) -> t.List: _value = value.decode("utf8") return _value.split(",") sqlite3.register_converter("Numeric", convert_numeric_out) sqlite3.register_converter("Integer", convert_int_out) sqlite3.register_converter("UUID", convert_uuid_out) sqlite3.register_converter("Date", convert_date_out) sqlite3.register_converter("Time", convert_time_out) sqlite3.register_converter("Seconds", convert_seconds_out) sqlite3.register_converter("Boolean", convert_boolean_out) sqlite3.register_converter("Timestamp", convert_timestamp_out) sqlite3.register_converter("Timestamptz", convert_timestamptz_out) sqlite3.register_converter("Array", convert_array_out) sqlite3.register_converter("M2M", convert_M2M_out) sqlite3.register_adapter(Decimal, convert_numeric_in) sqlite3.register_adapter(uuid.UUID, convert_uuid_in) sqlite3.register_adapter(datetime.time, convert_time_in) sqlite3.register_adapter(datetime.date, convert_date_in) sqlite3.register_adapter(datetime.datetime, convert_datetime_in) sqlite3.register_adapter(datetime.timedelta, convert_timedelta_in) sqlite3.register_adapter(list, convert_array_in) ############################################################################### @dataclass class AsyncBatch(Batch): connection: Connection query: Query batch_size: int # Set internally _cursor: t.Optional[Cursor] = None @property def cursor(self) -> Cursor: if not self._cursor: raise ValueError("_cursor not set") return self._cursor async def next(self) -> t.List[t.Dict]: data = await self.cursor.fetchmany(self.batch_size) return await self.query._process_results(data) def __aiter__(self): return self async def __anext__(self): response = await self.next() if response == []: raise StopAsyncIteration() return response async def __aenter__(self): querystring = self.query.querystrings[0] template, template_args = querystring.compile_string() self._cursor = await self.connection.execute(template, *template_args) return self async def __aexit__(self, exception_type, exception, traceback): await self._cursor.close() await self.connection.close() return exception is not None ############################################################################### class TransactionType(enum.Enum): """ See the `SQLite <https://www.sqlite.org/lang_transaction.html>`_ docs for more info. """ deferred = "DEFERRED" immediate = "IMMEDIATE" exclusive = "EXCLUSIVE" class Atomic: """ Usage: transaction = engine.atomic() transaction.add(Foo.create_table()) # Either: transaction.run_sync() await transaction.run() """ __slots__ = ("engine", "queries", "transaction_type") def __init__( self, engine: SQLiteEngine, transaction_type: TransactionType = TransactionType.deferred, ): self.engine = engine self.transaction_type = transaction_type self.queries: t.List[Query] = [] def add(self, *query: Query): self.queries += list(query) async def run(self): from piccolo.query.methods.objects import Create, GetOrCreate try: async with self.engine.transaction( transaction_type=self.transaction_type ): for query in self.queries: if isinstance(query, (Query, DDL, Create, GetOrCreate)): await query.run() else: raise ValueError("Unrecognised query") self.queries = [] except Exception as exception: self.queries = [] raise exception from exception def run_sync(self): return run_sync(self.run()) def __await__(self): return self.run().__await__() ############################################################################### class Savepoint: def __init__(self, name: str, transaction: SQLiteTransaction): self.name = name self.transaction = transaction async def rollback_to(self): await self.transaction.connection.execute( f"ROLLBACK TO SAVEPOINT {self.name}" ) async def release(self): await self.transaction.connection.execute( f"RELEASE SAVEPOINT {self.name}" ) class SQLiteTransaction: """ Used for wrapping queries in a transaction, using a context manager. Currently it's async only. Usage:: async with engine.transaction(): # Run some queries: await Band.select().run() """ __slots__ = ( "engine", "context", "connection", "transaction_type", "allow_nested", "_savepoint_id", "_parent", "_committed", "_rolled_back", ) def __init__( self, engine: SQLiteEngine, transaction_type: TransactionType = TransactionType.deferred, allow_nested: bool = True, ): """ :param transaction_type: If your transaction just contains ``SELECT`` queries, then use ``TransactionType.deferred``. This will give you the best performance. When performing ``INSERT``, ``UPDATE``, ``DELETE`` queries, we recommend using ``TransactionType.immediate`` to avoid database locks. """ self.engine = engine self.transaction_type = transaction_type current_transaction = self.engine.current_transaction.get() self._savepoint_id = 0 self._parent = None self._committed = False self._rolled_back = False if current_transaction: if allow_nested: self._parent = current_transaction else: raise TransactionError( "A transaction is already active - nested transactions " "aren't allowed." ) async def __aenter__(self) -> SQLiteTransaction: if self._parent is not None: return self._parent self.connection = await self.get_connection() await self.begin() self.context = self.engine.current_transaction.set(self) return self async def get_connection(self): return await self.engine.get_connection() async def begin(self): await self.connection.execute(f"BEGIN {self.transaction_type.value}") async def commit(self): await self.connection.execute("COMMIT") self._committed = True async def rollback(self): await self.connection.execute("ROLLBACK") self._rolled_back = True async def rollback_to(self, savepoint_name: str): """ Used to rollback to a savepoint just using the name. """ await Savepoint(name=savepoint_name, transaction=self).rollback_to() ########################################################################### def get_savepoint_id(self) -> int: self._savepoint_id += 1 return self._savepoint_id async def savepoint(self, name: t.Optional[str] = None) -> Savepoint: name = name or f"savepoint_{self.get_savepoint_id()}" await self.connection.execute(f"SAVEPOINT {name}") return Savepoint(name=name, transaction=self) ########################################################################### async def __aexit__(self, exception_type, exception, traceback): if self._parent: return exception is None if exception: # The user may have manually rolled it back. if not self._rolled_back: await self.rollback() else: # The user may have manually committed it. if not self._committed and not self._rolled_back: await self.commit() await self.connection.close() self.engine.current_transaction.reset(self.context) return exception is None ############################################################################### def dict_factory(cursor, row) -> t.Dict: return {col[0]: row[idx] for idx, col in enumerate(cursor.description)} class SQLiteEngine(Engine[t.Optional[SQLiteTransaction]]): __slots__ = ( "connection_kwargs", "current_transaction", "log_queries", "log_responses", ) engine_type = "sqlite" min_version_number = 3.25 def __init__( self, path: str = "piccolo.sqlite", log_queries: bool = False, log_responses: bool = False, **connection_kwargs, ) -> None: """ :param path: A relative or absolute path to the the SQLite database file (it will be created if it doesn't already exist). :param log_queries: If ``True``, all SQL and DDL statements are printed out before being run. Useful for debugging. :param log_responses: If ``True``, the raw response from each query is printed out. Useful for debugging. :param connection_kwargs: These are passed directly to the database adapter. We recommend setting ``timeout`` if you expect your application to process a large number of concurrent writes, to prevent queries timing out. See Python's `sqlite3 docs <https://docs.python.org/3/library/sqlite3.html#sqlite3.connect>`_ for more info. """ # noqa: E501 default_connection_kwargs = { "database": path, "detect_types": sqlite3.PARSE_DECLTYPES | sqlite3.PARSE_COLNAMES, "isolation_level": None, } self.log_queries = log_queries self.log_responses = log_responses self.connection_kwargs = { **default_connection_kwargs, **connection_kwargs, } self.current_transaction = contextvars.ContextVar( f"sqlite_current_transaction_{path}", default=None ) super().__init__() @property def path(self): return self.connection_kwargs["database"] @path.setter def path(self, value: str): self.connection_kwargs["database"] = value async def get_version(self) -> float: return self.get_version_sync() def get_version_sync(self) -> float: major, minor, _ = sqlite3.sqlite_version_info return float(f"{major}.{minor}") async def prep_database(self): pass ########################################################################### def remove_db_file(self): """ Use with caution - removes the SQLite file. Useful for testing purposes. """ if os.path.exists(self.path): os.unlink(self.path) def create_db_file(self): """ Create the database file. Useful for testing purposes. """ if os.path.exists(self.path): raise Exception(f"Database at {self.path} already exists") with open(self.path, "w"): pass ########################################################################### async def batch( self, query: Query, batch_size: int = 100, node: t.Optional[str] = None ) -> AsyncBatch: """ :param query: The database query to run. :param batch_size: How many rows to fetch on each iteration. :param node: This is ignored currently, as SQLite runs off a single node. The value is here so the API is consistent with Postgres. """ connection = await self.get_connection() return AsyncBatch( connection=connection, query=query, batch_size=batch_size ) ########################################################################### async def get_connection(self) -> Connection: connection = await aiosqlite.connect(**self.connection_kwargs) connection.row_factory = dict_factory # type: ignore await connection.execute("PRAGMA foreign_keys = 1") return connection ########################################################################### async def _get_inserted_pk(self, cursor, table: t.Type[Table]) -> t.Any: """ If the `pk` column is a non-integer then `ROWID` and `pk` will return different types. Need to query by `lastrowid` to get `pk`s in SQLite prior to 3.35.0. """ await cursor.execute( f"SELECT {table._meta.primary_key._meta.db_column_name} FROM " f"{table._meta.tablename} WHERE ROWID = {cursor.lastrowid}" ) response = await cursor.fetchone() return response[table._meta.primary_key._meta.db_column_name] async def _run_in_new_connection( self, query: str, args: t.List[t.Any] = None, query_type: str = "generic", table: t.Optional[t.Type[Table]] = None, ): if args is None: args = [] async with aiosqlite.connect(**self.connection_kwargs) as connection: await connection.execute("PRAGMA foreign_keys = 1") connection.row_factory = dict_factory # type: ignore async with connection.execute(query, args) as cursor: await connection.commit() if query_type == "insert" and self.get_version_sync() < 3.35: # We can't use the RETURNING clause on older versions # of SQLite. assert table is not None pk = await self._get_inserted_pk(cursor, table) return [{table._meta.primary_key._meta.db_column_name: pk}] else: return await cursor.fetchall() async def _run_in_existing_connection( self, connection, query: str, args: t.List[t.Any] = None, query_type: str = "generic", table: t.Optional[t.Type[Table]] = None, ): """ This is used when a transaction is currently active. """ if args is None: args = [] await connection.execute("PRAGMA foreign_keys = 1") connection.row_factory = dict_factory async with connection.execute(query, args) as cursor: response = await cursor.fetchall() if query_type == "insert" and self.get_version_sync() < 3.35: # We can't use the RETURNING clause on older versions # of SQLite. assert table is not None pk = await self._get_inserted_pk(cursor, table) return [{table._meta.primary_key._meta.db_column_name: pk}] else: return response async def run_querystring( self, querystring: QueryString, in_pool: bool = False ): """ Connection pools aren't currently supported - the argument is there for consistency with other engines. """ query_id = self.get_query_id() if self.log_queries: self.print_query(query_id=query_id, query=querystring.__str__()) query, query_args = querystring.compile_string( engine_type=self.engine_type ) # If running inside a transaction: current_transaction = self.current_transaction.get() if current_transaction: response = await self._run_in_existing_connection( connection=current_transaction.connection, query=query, args=query_args, query_type=querystring.query_type, table=querystring.table, ) else: response = await self._run_in_new_connection( query=query, args=query_args, query_type=querystring.query_type, table=querystring.table, ) if self.log_responses: self.print_response(query_id=query_id, response=response) return response async def run_ddl(self, ddl: str, in_pool: bool = False): """ Connection pools aren't currently supported - the argument is there for consistency with other engines. """ query_id = self.get_query_id() if self.log_queries: self.print_query(query_id=query_id, query=ddl) # If running inside a transaction: current_transaction = self.current_transaction.get() if current_transaction: response = await self._run_in_existing_connection( connection=current_transaction.connection, query=ddl, ) else: response = await self._run_in_new_connection( query=ddl, ) if self.log_responses: self.print_response(query_id=query_id, response=response) return response def atomic( self, transaction_type: TransactionType = TransactionType.deferred ) -> Atomic: return Atomic(engine=self, transaction_type=transaction_type) def transaction( self, transaction_type: TransactionType = TransactionType.deferred, allow_nested: bool = True, ) -> SQLiteTransaction: """ Create a new database transaction. See :class:`Transaction`. """ return SQLiteTransaction( engine=self, transaction_type=transaction_type, allow_nested=allow_nested, )

nvidia__dali

augmentations.rst

Module doc

Generate documentation for this module

Apache License 2.0

nvidia__dali/docs/auto_aug/augmentations.rst

Augmentation operations In terms of the automatic augmentations, the augmentation is image processing function that meets following requirements: 1. Its first argument is the input batch for the processing 2. The second argument is the parameter controlling the operation (for example angle of rotation). 3. It can take additional keyword arguments. 4. It is implemented in terms of DALI operators <operation reference>. 5. It is decorated with @augmentation <nvidia.dali.auto_aug.core.augmentation> Here is an example of defining a simplified rotate augmentation: from nvidia.dali.auto_aug.core import augmentation from nvidia.dali import fn @augmentation(mag_range=(0, 30), randomly_negate=True) def rotate_aug(data, angle, fill_value=128, rotate_keep_size=True): return fn.rotate(data, angle=angle, fill_value=fill_value, keep_size=True) Based on the exsiting augmentation, a new one, with adjusted parameters, can be created: rotate_aug_60 = rotate_aug.augmentation(mag_range=(0, 60), randomly_negate=False) To learn more how to build a policy using augmentations listed here, check the documentation for specific automatic augmentation scheme: AutoAugment, RandAugment, or TrivialAugment. Decorator nvidia.dali.auto_aug.core.augmentation nvidia.dali.auto_aug._augmentation The result of decorating a function with @augmentation <nvidia.dali.auto_aug.core.augmentation> is an instance of class ~nvidia.dali.auto_aug.core._augmentation.Augmentation. The class should not be instantiated directly, it needs to be created with the decorator. Once obtained, those objects become callables that can be used to speicfy a policy for AutoAugment, RandAugment or TrivialAugment. def augmentation(self, mag_range, randomly_negate, mag_to_param, param_device, name) -> Augmentation You can call this method to create new ~nvidia.dali.auto_aug.core._augmentation.Augmentation instance based on an existing one, with the paramenters adjusted. All parameters are optional - those that were specifed replace the ones that were previouslly passed to @augmentation <nvidia.dali.auto_aug.core.augmentation>. param mag_range optional, see @augmentation <nvidia.dali.auto_aug.core.augmentation>. param randomly_negate optional, see @augmentation <nvidia.dali.auto_aug.core.augmentation>. param mag_to_param optional, see @augmentation <nvidia.dali.auto_aug.core.augmentation>. param param_device optional, see @augmentation <nvidia.dali.auto_aug.core.augmentation>. param name optional, see @augmentation <nvidia.dali.auto_aug.core.augmentation>. Augmentations Here is a list of callable ~nvidia.dali.auto_aug.core._augmentation.Augmentation instances defined by DALI. Note that the mag_to_param, param_device and name parameters were ommited from the @augmentation <nvidia.dali.auto_aug.core.augmentation> decorator listing for simplicity. To adjust the range of parameter, use the augmentation method on the existing ~nvidia.dali.auto_aug.core._augmentation.Augmentation instance listed below, for example: # Create a steeper sheer operation based on existing one steep_shear_x = shear_x.augmentation(mag_range=(0, 0.5), name="steep_shear_x") nvidia.dali.auto_aug.augmentations shear_x(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Applies nvidia.dali.fn.transforms.shear with shear_x factor using nvidia.dali.fn.warp_affine. @augmentation(mag_range=(0, 0.3), randomly_negate=True, ...) def shear_x(data, shear, fill_value=128, interp_type=None) shear_y(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Applies nvidia.dali.fn.transforms.shear with shear_y factor using nvidia.dali.fn.warp_affine. @augmentation(mag_range=(0, 0.3), randomly_negate=True, ...) def shear_y(data, shear, fill_value=128, interp_type=None) translate_x(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Applies nvidia.dali.fn.transforms.translation with shape-relative offset in x-axis using nvidia.dali.fn.warp_affine. @augmentation(mag_range=(0., 1.), randomly_negate=True, ...) def translate_x(data, rel_offset, shape, fill_value=128, interp_type=None) translate_x_no_shape(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Applies nvidia.dali.fn.transforms.translation with absolute offset in x-axis using nvidia.dali.fn.warp_affine. @augmentation(mag_range=(0, 250), randomly_negate=True, ...) def translate_x_no_shape(data, offset, fill_value=128, interp_type=None) translate_y(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Applies nvidia.dali.fn.transforms.translation with shape-relative offset in y-axis using nvidia.dali.fn.warp_affine. @augmentation(mag_range=(0., 1.), randomly_negate=True, ...) def translate_y(data, rel_offset, shape, fill_value=128, interp_type=None) translate_y_no_shape(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Applies nvidia.dali.fn.transforms.translation with absolute offset in y-axis using nvidia.dali.fn.warp_affine. @augmentation(mag_range=(0, 250), randomly_negate=True, ...) def translate_y_no_shape(data, offset, fill_value=128, interp_type=None) rotate(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Rotates the image using nvidia.dali.fn.rotate. @augmentation(mag_range=(0, 30), randomly_negate=True) def rotate(data, angle, fill_value=128, interp_type=None, rotate_keep_size=True) brightness(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Adjusts the brightness with nvidia.dali.fn.brightness. The magnitude is mapped to a [0, 2] parameter range. @augmentation(mag_range=(0, 0.9), randomly_negate=True, ...) def brightness(data, parameter) contrast(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Adjusts the contrasts using a channel-weighted mean as a contrast center. The magnitude is mapped to a [0, 2] parameter range. @augmentation(mag_range=(0, 0.9), randomly_negate=True, ...) def contrast(data, parameter) color(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Adjusts the color with nvidia.dali.fn.saturation. The magnitude is mapped to a [0, 2] parameter range. @augmentation(mag_range=(0, 0.9), randomly_negate=True, ...) def color(data, parameter) sharpness(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) The outputs correspond to PIL's ImageEnhance.Sharpness. @augmentation(mag_range=(0, 0.9), randomly_negate=True, ...) def sharpness(data, kernel) posterize(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Posterizes the image by masking out the lower input bits. @augmentation(mag_range=(0, 4), ...) def posterize(data, mask) solarize(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Inverts the pixels that lie below a threshold. @augmentation(mag_range=(256, 0), ...) def solarize(data, threshold) solarize_add(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Applies the shift to the pixels of value lower than 128. @augmentation(mag_range=(0, 110), ...) def solarize_add(data, shift) invert(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Inverts the image. @augmentation def invert(data, _) equalize(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Applies histogram equalization using nvidia.dali.fn.experimental.equalize. @augmentation def equalize(data, _) """ DALI's equalize follows OpenCV's histogram equalization. The PIL uses slightly different formula when transforming histogram's cumulative sum into lookup table. auto_contrast(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Applies automatic contrast adjustment. @augmentation def auto_contrast(data, _) identity(data, , magnitude_bin=None, num_magnitude_bins=None,*kwargs) Identity operation - no processing is applied. @augmentation def identity(data, _)

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. try: import numpy as np except ImportError: raise RuntimeError( "Could not import numpy. DALI's automatic augmentation examples depend on numpy. " "Please install numpy to use the examples.") from nvidia.dali import fn from nvidia.dali import types from nvidia.dali.auto_aug.core import augmentation """ This module contains a standard suite of augmentations used by AutoAugment policy for ImageNet, RandAugment and TrivialAugmentWide. The augmentations are implemented in terms of DALI operators. The `@augmentation` decorator handles computation of the decorated transformations's parameter. When called, the decorated augmentation expects: * a single positional argument: batch of samples * `magnitude_bin` and `num_magnitude_bins` instead of the parameter. The parameter is computed as if by calling `mag_to_param(magnitudes[magnitude_bin] * ((-1) ** random_sign))`, where `magnitudes=linspace(mag_range[0], mag_range[1], num_magnitude_bins)`. The augmentations in this module are defined with example setups passed to `@augmentation`. The parameters can be easily adjusted. For instance, to increase the magnitudes range of `shear_x` from 0.3 to 0.5, you can create `my_shear_x = shear_x.augmentation(mag_range=(0, 0.5))`. """ def warp_x_param(magnitude): return [magnitude, 0] def warp_y_param(magnitude): return [0, magnitude] @augmentation(mag_range=(0, 0.3), randomly_negate=True, mag_to_param=warp_x_param) def shear_x(data, shear, fill_value=128, interp_type=None): mt = fn.transforms.shear(shear=shear) return fn.warp_affine(data, matrix=mt, fill_value=fill_value, interp_type=interp_type, inverse_map=False) @augmentation(mag_range=(0, 0.3), randomly_negate=True, mag_to_param=warp_y_param) def shear_y(data, shear, fill_value=128, interp_type=None): mt = fn.transforms.shear(shear=shear) return fn.warp_affine(data, matrix=mt, fill_value=fill_value, interp_type=interp_type, inverse_map=False) @augmentation(mag_range=(0., 1.), randomly_negate=True, mag_to_param=warp_x_param) def translate_x(data, rel_offset, shape, fill_value=128, interp_type=None): offset = rel_offset * shape[1] mt = fn.transforms.translation(offset=offset) return fn.warp_affine(data, matrix=mt, fill_value=fill_value, interp_type=interp_type, inverse_map=False) @augmentation(mag_range=(0, 250), randomly_negate=True, mag_to_param=warp_x_param, name="translate_x") def translate_x_no_shape(data, offset, fill_value=128, interp_type=None): mt = fn.transforms.translation(offset=offset) return fn.warp_affine(data, matrix=mt, fill_value=fill_value, interp_type=interp_type, inverse_map=False) @augmentation(mag_range=(0., 1.), randomly_negate=True, mag_to_param=warp_y_param) def translate_y(data, rel_offset, shape, fill_value=128, interp_type=None): offset = rel_offset * shape[0] mt = fn.transforms.translation(offset=offset) return fn.warp_affine(data, matrix=mt, fill_value=fill_value, interp_type=interp_type, inverse_map=False) @augmentation(mag_range=(0, 250), randomly_negate=True, mag_to_param=warp_y_param, name="translate_y") def translate_y_no_shape(data, offset, fill_value=128, interp_type=None): mt = fn.transforms.translation(offset=offset) return fn.warp_affine(data, matrix=mt, fill_value=fill_value, interp_type=interp_type, inverse_map=False) @augmentation(mag_range=(0, 30), randomly_negate=True) def rotate(data, angle, fill_value=128, interp_type=None, rotate_keep_size=True): return fn.rotate(data, angle=angle, fill_value=fill_value, interp_type=interp_type, keep_size=rotate_keep_size) def shift_enhance_range(magnitude): """The `enhance` operations (brightness, contrast, color, sharpness) accept magnitudes from [0, 2] range. However, the neutral magnitude is not 0 but 1 and the intuitive strength of the operation increases the further the magnitude is from 1. So, we specify magnitudes range to be in [0, 1] range, expect it to be randomly negated and then shift it by 1""" return 1 + magnitude @augmentation(mag_range=(0, 0.9), randomly_negate=True, mag_to_param=shift_enhance_range) def brightness(data, parameter): return fn.brightness(data, brightness=parameter) @augmentation(mag_range=(0, 0.9), randomly_negate=True, mag_to_param=shift_enhance_range) def contrast(data, parameter): """ It follows PIL implementation of Contrast enhancement which uses a channel-weighted mean as a contrast center. """ # assumes FHWC or HWC layout mean = fn.reductions.mean(data, axis_names="HW", keep_dims=True) rgb_weights = types.Constant(np.array([0.299, 0.587, 0.114], dtype=np.float32)) center = fn.reductions.sum(mean * rgb_weights, axis_names="C", keep_dims=True) # it could be just `fn.contrast(data, contrast=parameter, contrast_center=center)` # but for GPU `data` the `center` is in GPU mem, and that cannot be passed # as named arg (i.e. `contrast_center`) to the operator return fn.cast_like(center + (data - center) * parameter, data) @augmentation(mag_range=(0, 0.9), randomly_negate=True, mag_to_param=shift_enhance_range) def color(data, parameter): return fn.saturation(data, saturation=parameter) def sharpness_kernel(magnitude): # assumes magnitude: [-1, 1] blur = np.array([[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=np.float32) / 13 ident = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=np.float32) return -magnitude * blur + (1 + magnitude) * ident def sharpness_kernel_shifted(magnitude): # assumes magnitude: [0, 2] return sharpness_kernel(magnitude - 1) @augmentation(mag_range=(0, 0.9), randomly_negate=True, mag_to_param=sharpness_kernel, param_device="auto") def sharpness(data, kernel): """ The outputs correspond to PIL's ImageEnhance.Sharpness with the exception for 1px border around the output. PIL computes convolution with smoothing filter only for valid positions (no out-of-bounds filter positions) and pads the output with the input. """ return fn.experimental.filter(data, kernel) def poster_mask_uint8(magnitude): # expects [0..8] where 8 yields identity mask and a 0 # would be a mask that zeros all bits, # however, following the implementation for AA and RA referred # in the paper https://arxiv.org/pdf/1909.13719.pdf, we remap 0 to 1, # to avoid completely blank images magnitude = np.round(magnitude).astype(np.uint32) if magnitude <= 0: magnitude = 1 elif magnitude > 8: magnitude = 8 nbits = np.round(8 - magnitude).astype(np.uint32) removal_mask = np.uint8(2)**nbits - 1 return np.array(np.uint8(255) ^ removal_mask, dtype=np.uint8) @augmentation(mag_range=(0, 4), mag_to_param=poster_mask_uint8, param_device="auto") def posterize(data, mask): return data & mask @augmentation(mag_range=(256, 0), param_device="auto") def solarize(data, threshold): sample_inv = types.Constant(255, dtype=types.UINT8) - data mask_unchanged = data < threshold mask_inverted = mask_unchanged ^ True return mask_unchanged * data + mask_inverted * sample_inv def solarize_add_shift(shift): if shift >= 128: raise Exception("The solarize_add augmentation accepts shifts from 0 to 128") return np.uint8(shift) @augmentation(mag_range=(0, 110), param_device="auto", mag_to_param=solarize_add_shift) def solarize_add(data, shift): mask_shifted = data < types.Constant(128, dtype=types.UINT8) mask_id = mask_shifted ^ True sample_shifted = data + shift return mask_shifted * sample_shifted + mask_id * data @augmentation def invert(data, _): return types.Constant(255, dtype=types.UINT8) - data @augmentation def equalize(data, _): """ DALI's equalize follows OpenCV's histogram equalization. The PIL uses slightly different formula when transforming histogram's cumulative sum into lookup table. """ return fn.experimental.equalize(data) @augmentation def auto_contrast(data, _): # assumes FHWC or HWC layout lo = fn.reductions.min(data, axis_names="HW", keep_dims=True) hi = fn.reductions.max(data, axis_names="HW", keep_dims=True) diff = hi - lo mask_scale = diff > 0 mask_id = mask_scale ^ True # choose div so that scale ends up being 255 / diff if diff > 0 and 1 otherwise div_by = diff * mask_scale + types.Constant(255, dtype=types.UINT8) * mask_id scale = 255 / div_by scaled = (data - lo * mask_scale) * scale return fn.cast_like(scaled, data) @augmentation def identity(data, _): return data

nvidia__dali

auto_augment.rst

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nvidia__dali/docs/auto_aug/auto_augment.rst

AutoAugment AutoAugment, as described in https://arxiv.org/abs/1805.09501, builds policies out of pairs of augmentations <Augmentation operations> called subpolicies. Each subpolicy specifies sequence of operations with the probability of application and the magnitude parameter. When AutoAugment is used, for each sample a random subpolicy is selected and applied. To use the predefined policy that was discovered on ImageNet, import and invoke ~nvidia.dali.auto_aug.auto_augment.auto_augment inside the pipeline definition, for example: from nvidia.dali import pipeline_def from nvidia.dali.auto_aug import auto_augment @pipeline_def(enable_conditionals=True) def training_pipe(data_dir, image_size): jpegs, labels = fn.readers.file(file_root=data_dir, ...) shapes = fn.peek_image_shape(jpegs) images = fn.decoders.image(jpegs, device="mixed", output_type=types.RGB) augmented_images = auto_augment.auto_augment(images, shape=shapes) resized_images = fn.resize(augmented_images, size=[image_size, image_size]) return resized_images, labels Warning You need to define the pipeline with the @pipeline_def <nvidia.dali.pipeline_def> decorator and set enable_conditionals to True to use automatic augmentations. Refer to this <Building and invoking custom policies> section to read more about using custom policies. Invoking predefined AutoAugment policies To invoke one of the predefined policies use the following functions. Building and invoking custom policies DALI's AutoAugment implementation relies on ~nvidia.dali.auto_aug.core.Policy class to define the policies to execute, which can be invoked within the pipeline using ~nvidia.dali.auto_aug.auto_augment.apply_auto_augment function. The best way is to wrap your policy creation into a function: from nvidia.dali.auto_aug import augmentations from nvidia.dali.auto_aug.core import Policy def my_custom_policy() -> Policy: """ Creates a simple AutoAugment policy with 3 sub-policies using custom magnitude ranges. """ shear_x = augmentations.shear_x.augmentation((0, 0.5), True) shear_y = augmentations.shear_y.augmentation((0, 0.5), True) rotate = augmentations.rotate.augmentation((0, 40), True) invert = augmentations.invert return Policy( name="SimplePolicy", num_magnitude_bins=11, sub_policies=[ [(shear_x, 0.8, 7), (shear_y, 0.8, 4)], [(invert, 0.4, None), (rotate, 0.6, 8)], [(rotate, 0.6, 7), (shear_y, 0.6, 3)], ]) The tuple within the subpolicy definition specifies: - the augmentation to use, - the probability of applying that augmentation (if this subpolicy is selected), - the magnitude to be used. Accessing predefined policies To obtain the predefined policy definition refer to the following functions.

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List, Optional, Tuple, Union from nvidia.dali import fn from nvidia.dali import types from nvidia.dali.auto_aug import augmentations as a from nvidia.dali.auto_aug.core import _Augmentation, Policy, signed_bin from nvidia.dali.auto_aug.core._args import forbid_unused_kwargs as _forbid_unused_kwargs from nvidia.dali.auto_aug.core._utils import \ get_translations as _get_translations, \ pretty_select as _pretty_select from nvidia.dali.data_node import DataNode as _DataNode try: import numpy as np except ImportError: raise RuntimeError( "Could not import numpy. DALI's automatic augmentation examples depend on numpy. " "Please install numpy to use the examples.") def auto_augment( data: _DataNode, policy_name: str = 'image_net', shape: Optional[Union[_DataNode, Tuple[int, int]]] = None, fill_value: Optional[int] = 128, interp_type: Optional[types.DALIInterpType] = None, max_translate_abs: Optional[int] = None, max_translate_rel: Optional[float] = None, seed: Optional[int] = None, ) -> _DataNode: """ Applies one of the predefined policies from the AutoAugment paper (https://arxiv.org/abs/1805.09501) to the provided batch of samples. Args ---- data : DataNode A batch of samples to be processed. The supported samples are images of `HWC` layout and videos of `FHWC` layout, the supported data type is `uint8`. policy_name : str, optional The name of predefined policy. Acceptable values are: `image_net`, `reduced_image_net`, `svhn`, `reduced_cifar10`. Defaults to `image_net`. shape: DataNode or Tuple[int, int], optional The size (height and width) of the image or frames in the video sequence passed as the `data`. If specified, the magnitude of `translation` operations depends on the image/frame shape and spans from 0 to `max_translate_rel * shape`. Otherwise, the magnitude range is `[0, max_translate_abs]` for any sample. fill_value: int, optional A value to be used as a padding for images/frames transformed with warp_affine ops (translation, shear and rotate). If `None` is specified, the images/frames are padded with the border value repeated (clamped). interp_type: types.DALIInterpType, optional Interpolation method used by the warp_affine ops (translation, shear and rotate). Supported values are `types.INTERP_LINEAR` (default) and `types.INTERP_NN`. max_translate_abs: int or (int, int), optional Only valid when `shape` is not provided. Specifies the maximal shift (in pixels) in the translation augmentation. If a tuple is specified, the first component limits height, the second the width. Defaults to 250, which means the maximal magnitude shifts the image by 250 pixels. max_translate_rel: float or (float, float), optional Only valid when `shape` argument is provided. Specifies the maximal shift as a fraction of image shape in the translation augmentations. If a tuple is specified, the first component limits the height, the second the width. Defaults to 1, which means the maximal magnitude shifts the image entirely out of the canvas. seed: int, optional Seed to be used to randomly sample operations (and to negate magnitudes). Returns ------- DataNode A batch of transformed samples. """ predefined_policies = { 'image_net': get_image_net_policy, 'reduced_image_net': get_reduced_image_net_policy, 'svhn': get_svhn_policy, 'reduced_cifar10': get_reduced_cifar10_policy, } policies_without_translation = ('reduced_image_net', ) shape_related_args = ( (shape,'shape'), (max_translate_abs,'max_translate_abs'), (max_translate_rel,'max_translate_rel'), ) if not isinstance(policy_name, str) or policy_name not in predefined_policies: policies_str = ", ".join([f"`{name}`" for name in predefined_policies.keys()]) raise Exception( f"The `policy_name` must be a string that takes one of the values: {policies_str}") if policy_name in policies_without_translation: shape_arg = next((name for arg, name in shape_related_args if arg is not None), None) if shape_arg is not None: raise Exception( f"The policy `{policy_name}` does not contain any augmentations that rely on the " f"image shape. The `{shape_arg}` argument must not be specified in that case.") aug_kwargs = {"fill_value": fill_value, "interp_type": interp_type} use_shape = shape is not None if use_shape: aug_kwargs["shape"] = shape if policy_name in policies_without_translation: policy = predefined_policies[policy_name]() else: policy = predefined_policies[policy_name](use_shape=use_shape, max_translate_abs=max_translate_abs, max_translate_rel=max_translate_rel) return apply_auto_augment(policy, data, seed, **aug_kwargs) def auto_augment_image_net( data: _DataNode, shape: Optional[Union[_DataNode, Tuple[int, int]]] = None, fill_value: Optional[int] = 128, interp_type: Optional[types.DALIInterpType] = None, max_translate_abs: Optional[int] = None, max_translate_rel: Optional[float] = None, seed: Optional[int] = None, ) -> _DataNode: """ Applies `image_net_policy` in AutoAugment (https://arxiv.org/abs/1805.09501) fashion to the provided batch of samples. Equivalent to :meth:`~nvidia.dali.auto_aug.auto_augment.auto_augment` call with ``policy_name`` specified to ``'image_net'``. See :meth:`~nvidia.dali.auto_aug.auto_augment.auto_augment` function for details. """ return auto_augment(data, "image_net", shape, fill_value, interp_type, max_translate_abs, max_translate_rel, seed) def apply_auto_augment(policy: Policy, data: _DataNode, seed: Optional[int] = None, **kwargs) -> _DataNode: """ Applies AutoAugment (https://arxiv.org/abs/1805.09501) augmentation scheme to the provided batch of samples. Args ---- policy: Policy Set of sequences of augmentations to be applied in AutoAugment fashion. data : DataNode A batch of samples to be processed. seed: int, optional Seed to be used to randomly sample operations (and to negate magnitudes). kwargs: A dictionary of extra parameters to be passed when calling augmentations. The signature of each augmentation is checked for any extra arguments and if the name of the argument matches one from the `kwargs`, the value is passed as an argument. For example, some augmentations from the default AutoAugment suite accept ``shape``, ``fill_value`` and ``interp_type``. Returns ------- DataNode A batch of transformed samples. """ if len(policy.sub_policies) == 0: raise Exception(f"Cannot run empty policy. Got {policy} in `apply_auto_augment` call.") max_policy_len = max(len(sub_policy) for sub_policy in policy.sub_policies) should_run = fn.random.uniform(range=[0, 1], shape=(max_policy_len, ), dtype=types.FLOAT, seed=seed) sub_policy_id = fn.random.uniform(values=list(range(len(policy.sub_policies))), seed=seed, dtype=types.INT32) run_probabilities = _sub_policy_to_probability_map(policy)[sub_policy_id] magnitude_bins = _sub_policy_to_magnitude_bin_map(policy)[sub_policy_id] aug_ids, augmentations = _sub_policy_to_augmentation_map(policy) aug_ids = aug_ids[sub_policy_id] if any(aug.randomly_negate for aug in policy.augmentations.values()): magnitude_bins = signed_bin(magnitude_bins, seed=seed, shape=(max_policy_len, )) _forbid_unused_kwargs(policy.augmentations.values(), kwargs, 'apply_auto_augment') for stage_id in range(max_policy_len): if should_run[stage_id] < run_probabilities[stage_id]: op_kwargs = dict(data=data, magnitude_bin=magnitude_bins[stage_id], num_magnitude_bins=policy.num_magnitude_bins, **kwargs) data = _pretty_select(augmentations[stage_id], aug_ids[stage_id], op_kwargs, auto_aug_name='apply_auto_augment', ref_suite_name='get_image_net_policy') return data def get_image_net_policy(use_shape: bool = False, max_translate_abs: Optional[int] = None, max_translate_rel: Optional[float] = None) -> Policy: """ Creates augmentation policy tuned for the ImageNet as described in AutoAugment paper (https://arxiv.org/abs/1805.09501). The returned policy can be run with :meth:`~nvidia.dali.auto_aug.auto_augment.apply_auto_augment`. Args ---- use_shape : bool If true, the translation offset is computed as a percentage of the image/frame shape. Useful if the samples processed with the auto augment have different shapes. If false, the offsets range is bounded by a constant (`max_translate_abs`). max_translate_abs: int or (int, int), optional Only valid with use_shape=False, specifies the maximal shift (in pixels) in the translation augmentations. If a tuple is specified, the first component limits height, the second the width. Defaults to 250. max_translate_rel: float or (float, float), optional Only valid with use_shape=True, specifies the maximal shift as a fraction of image/frame shape in the translation augmentations. If a tuple is specified, the first component limits height, the second the width. Defaults to 1. """ default_translate_abs, default_translate_rel = 250, 1. _, translate_y = _get_translations(use_shape, default_translate_abs, default_translate_rel, max_translate_abs, max_translate_rel) shear_x = a.shear_x.augmentation((0, 0.3), True) shear_y = a.shear_y.augmentation((0, 0.3), True) rotate = a.rotate.augmentation((0, 30), True) color = a.color.augmentation((0.1, 1.9), False, None) posterize = a.posterize.augmentation((0, 4), False, a.poster_mask_uint8) solarize = a.solarize.augmentation((0, 256), False) solarize_add = a.solarize_add.augmentation((0, 110), False) invert = a.invert equalize = a.equalize auto_contrast = a.auto_contrast return Policy( name="ImageNetPolicy", num_magnitude_bins=11, sub_policies=[ [(equalize, 0.8, None), (shear_y, 0.8, 4)], [(color, 0.4, 9), (equalize, 0.6, None)], [(color, 0.4, 1), (rotate, 0.6, 8)], [(solarize, 0.8, 3), (equalize, 0.4, None)], [(solarize, 0.4, 2), (solarize, 0.6, 2)], [(color, 0.2, 0), (equalize, 0.8, None)], [(equalize, 0.4, None), (solarize_add, 0.8, 3)], [(shear_x, 0.2, 9), (rotate, 0.6, 8)], [(color, 0.6, 1), (equalize, 1.0, None)], [(invert, 0.4, None), (rotate, 0.6, 0)], [(equalize, 1.0, None), (shear_y, 0.6, 3)], [(color, 0.4, 7), (equalize, 0.6, None)], [(posterize, 0.4, 6), (auto_contrast, 0.4, None)], [(solarize, 0.6, 8), (color, 0.6, 9)], [(solarize, 0.2, 4), (rotate, 0.8, 9)], [(rotate, 1.0, 7), (translate_y, 0.8, 9)], [(solarize, 0.8, 4)], [(shear_y, 0.8, 0), (color, 0.6, 4)], [(color, 1.0, 0), (rotate, 0.6, 2)], [(equalize, 0.8, None)], [(equalize, 1.0, None), (auto_contrast, 0.6, None)], [(shear_y, 0.4, 7), (solarize_add, 0.6, 7)], [(posterize, 0.8, 2), (solarize, 0.6, 10)], [(solarize, 0.6, 8), (equalize, 0.6, None)], [(color, 0.8, 6), (rotate, 0.4, 5)], ]) def get_reduced_cifar10_policy(use_shape: bool = False, max_translate_abs: Optional[int] = None, max_translate_rel: Optional[float] = None) -> Policy: """ Creates augmentation policy tuned with the reduced CIFAR-10 as described in AutoAugment paper (https://arxiv.org/abs/1805.09501). The returned policy can be run with :meth:`~nvidia.dali.auto_aug.auto_augment.apply_auto_augment`. Args ---- use_shape : bool If true, the translation offset is computed as a percentage of the image/frame shape. Useful if the samples processed with the auto augment have different shapes. If false, the offsets range is bounded by a constant (`max_translate_abs`). max_translate_abs: int or (int, int), optional Only valid with use_shape=False, specifies the maximal shift (in pixels) in the translation augmentations. If a tuple is specified, the first component limits height, the second the width. Defaults to 250. max_translate_rel: float or (float, float), optional Only valid with use_shape=True, specifies the maximal shift as a fraction of image/frame shape in the translation augmentations. If a tuple is specified, the first component limits height, the second the width. Defaults to 1. """ default_translate_abs, default_translate_rel = 250, 1. translate_x, translate_y = _get_translations(use_shape, default_translate_abs, default_translate_rel, max_translate_abs, max_translate_rel) shear_y = a.shear_y.augmentation((0, 0.3), True) rotate = a.rotate.augmentation((0, 30), True) brightness = a.brightness.augmentation((0.1, 1.9), False, None) color = a.color.augmentation((0.1, 1.9), False, None) contrast = a.contrast.augmentation((0.1, 1.9), False, None) sharpness = a.sharpness.augmentation((0.1, 1.9), False, a.sharpness_kernel_shifted) posterize = a.posterize.augmentation((0, 4), False, a.poster_mask_uint8) solarize = a.solarize.augmentation((0, 256), False) invert = a.invert equalize = a.equalize auto_contrast = a.auto_contrast return Policy( name="ReducedCifar10Policy", num_magnitude_bins=11, sub_policies=[ [(invert, 0.1, None), (contrast, 0.2, 6)], [(rotate, 0.7, 2), (translate_x, 0.3, 9)], [(sharpness, 0.8, 1), (sharpness, 0.9, 3)], [(shear_y, 0.5, 8), (translate_y, 0.7, 9)], [(auto_contrast, 0.5, None), (equalize, 0.9, None)], [(shear_y, 0.2, 7), (posterize, 0.3, 7)], [(color, 0.4, 3), (brightness, 0.6, 7)], [(sharpness, 0.3, 9), (brightness, 0.7, 9)], [(equalize, 0.6, None), (equalize, 0.5, None)], [(contrast, 0.6, 7), (sharpness, 0.6, 5)], [(color, 0.7, 7), (translate_x, 0.5, 8)], [(equalize, 0.3, None), (auto_contrast, 0.4, None)], [(translate_y, 0.4, 3), (sharpness, 0.2, 6)], [(brightness, 0.9, 6), (color, 0.2, 8)], [(solarize, 0.5, 2)], [(equalize, 0.2, None), (auto_contrast, 0.6, None)], [(equalize, 0.2, None), (equalize, 0.6, None)], [(color, 0.9, 9), (equalize, 0.6, None)], [(auto_contrast, 0.8, None), (solarize, 0.2, 8)], [(brightness, 0.1, 3), (color, 0.7, 0)], [(solarize, 0.4, 5), (auto_contrast, 0.9, None)], [(translate_y, 0.9, 9), (translate_y, 0.7, 9)], [(auto_contrast, 0.9, None), (solarize, 0.8, 3)], [(equalize, 0.8, None), (invert, 0.1, None)], [(translate_y, 0.7, 9), (auto_contrast, 0.9, None)], ]) def get_svhn_policy(use_shape: bool = False, max_translate_abs: Optional[int] = None, max_translate_rel: Optional[float] = None) -> Policy: """ Creates augmentation policy tuned with the SVHN as described in AutoAugment paper (https://arxiv.org/abs/1805.09501). The returned policy can be run with :meth:`~nvidia.dali.auto_aug.auto_augment.apply_auto_augment`. Args ---- use_shape : bool If true, the translation offset is computed as a percentage of the image/frame shape. Useful if the samples processed with the auto augment have different shapes. If false, the offsets range is bounded by a constant (`max_translate_abs`). max_translate_abs: int or (int, int), optional Only valid with use_shape=False, specifies the maximal shift (in pixels) in the translation augmentations. If a tuple is specified, the first component limits height, the second the width. Defaults to 250. max_translate_rel: float or (float, float), optional Only valid with use_shape=True, specifies the maximal shift as a fraction of image/frame shape in the translation augmentations. If a tuple is specified, the first component limits height, the second the width. Defaults to 1. """ default_translate_abs, default_translate_rel = 250, 1. translate_x, translate_y = _get_translations(use_shape, default_translate_abs, default_translate_rel, max_translate_abs, max_translate_rel) shear_x = a.shear_x.augmentation((0, 0.3), True) shear_y = a.shear_y.augmentation((0, 0.3), True) rotate = a.rotate.augmentation((0, 30), True) contrast = a.contrast.augmentation((0.1, 1.9), False, None) solarize = a.solarize.augmentation((0, 256), False) invert = a.invert equalize = a.equalize auto_contrast = a.auto_contrast return Policy( name="SvhnPolicy", num_magnitude_bins=11, sub_policies=[ [(shear_x, 0.9, 4), (invert, 0.2, None)], [(shear_y, 0.9, 8), (invert, 0.7, None)], [(equalize, 0.6, None), (solarize, 0.6, 6)], [(invert, 0.9, None), (equalize, 0.6, None)], [(equalize, 0.6, None), (rotate, 0.9, 3)], [(shear_x, 0.9, 4), (auto_contrast, 0.8, None)], [(shear_y, 0.9, 8), (invert, 0.4, None)], [(shear_y, 0.9, 5), (solarize, 0.2, 6)], [(invert, 0.9, None), (auto_contrast, 0.8, None)], [(equalize, 0.6, None), (rotate, 0.9, 3)], [(shear_x, 0.9, 4), (solarize, 0.3, 3)], [(shear_y, 0.8, 8), (invert, 0.7, None)], [(equalize, 0.9, None), (translate_y, 0.6, 6)], [(invert, 0.9, None), (equalize, 0.6, None)], [(contrast, 0.3, 3), (rotate, 0.8, 4)], [(invert, 0.8, None)], [(shear_y, 0.7, 6), (solarize, 0.4, 8)], [(invert, 0.6, None), (rotate, 0.8, 4)], [(shear_y, 0.3, 7), (translate_x, 0.9, 3)], [(shear_x, 0.1, 6), (invert, 0.6, None)], [(solarize, 0.7, 2), (translate_y, 0.6, 7)], [(shear_y, 0.8, 4), (invert, 0.8, None)], [(shear_x, 0.7, 9), (translate_y, 0.8, 3)], [(shear_y, 0.8, 5), (auto_contrast, 0.7, None)], [(shear_x, 0.7, 2), (invert, 0.1, None)], ]) def get_reduced_image_net_policy() -> Policy: """ Creates augmentation policy tuned with the reduced ImageNet as described in AutoAugment paper (https://arxiv.org/abs/1805.09501). The returned policy can be run with :meth:`~nvidia.dali.auto_aug.auto_augment.apply_auto_augment`. """ shear_x = a.shear_x.augmentation((0, 0.3), True) rotate = a.rotate.augmentation((0, 30), True) color = a.color.augmentation((0.1, 1.9), False, None) contrast = a.contrast.augmentation((0.1, 1.9), False, None) sharpness = a.sharpness.augmentation((0.1, 1.9), False, a.sharpness_kernel_shifted) posterize = a.posterize.augmentation((0, 4), False, a.poster_mask_uint8) solarize = a.solarize.augmentation((0, 256), False) invert = a.invert equalize = a.equalize auto_contrast = a.auto_contrast return Policy( name="ReducedImageNetPolicy", num_magnitude_bins=11, sub_policies=[[(posterize, 0.4, 8), (rotate, 0.6, 9)], [(solarize, 0.6, 5), (auto_contrast, 0.6, None)], [(equalize, 0.8, None), (equalize, 0.6, None)], [(posterize, 0.6, 7), (posterize, 0.6, 6)], [(equalize, 0.4, None), (solarize, 0.2, 4)], [(equalize, 0.4, None), (rotate, 0.8, 8)], [(solarize, 0.6, 3), (equalize, 0.6, None)], [(posterize, 0.8, 5), (equalize, 1.0, None)], [(rotate, 0.2, 3), (solarize, 0.6, 8)], [(equalize, 0.6, None), (posterize, 0.4, 6)], [(rotate, 0.8, 8), (color, 0.4, 0)], [(rotate, 0.4, 9), (equalize, 0.6, None)], [(equalize, 0.8, None)], [(invert, 0.6, None), (equalize, 1.0, None)], [(color, 0.6, 4), (contrast, 1.0, 8)], [(rotate, 0.8, 8), (color, 1.0, 2)], [(color, 0.8, 8), (solarize, 0.8, 7)], [(sharpness, 0.4, 7), (invert, 0.6, None)], [(shear_x, 0.6, 5), (equalize, 1.0, None)], [(color, 0.4, 0), (equalize, 0.6, None)], [(equalize, 0.4, None), (solarize, 0.2, 4)], [(solarize, 0.6, 5), (auto_contrast, 0.6, None)], [(invert, 0.6, None), (equalize, 1.0, None)], [(color, 0.6, 4), (contrast, 1.0, 8)], [(equalize, 0.8, None), (equalize, 0.6, None)]]) def _sub_policy_to_probability_map(policy: Policy) -> _DataNode: sub_policies = policy.sub_policies max_policy_len = max(len(sub_policy) for sub_policy in sub_policies) prob = np.array([[0. for _ in range(max_policy_len)] for _ in range(len(sub_policies))], dtype=np.float32) for sub_policy_id, sub_policy in enumerate(sub_policies): for stage_idx, (aug_name, p, mag) in enumerate(sub_policy): prob[sub_policy_id, stage_idx] = p return types.Constant(prob) def _sub_policy_to_magnitude_bin_map(policy: Policy) -> _DataNode: sub_policies = policy.sub_policies max_policy_len = max(len(sub_policy) for sub_policy in sub_policies) magnitude_bin = np.array([[0 for _ in range(max_policy_len)] for _ in range(len(sub_policies))], dtype=np.int32) for sub_policy_id, sub_policy in enumerate(sub_policies): for stage_idx, (aug_name, p, mag) in enumerate(sub_policy): # use dummy value instead of None, it will be ignored anyway val = mag if mag is not None else -999 magnitude_bin[sub_policy_id, stage_idx] = val return types.Constant(magnitude_bin) def _sub_policy_to_augmentation_matrix_map( policy: Policy) -> Tuple[np.ndarray, List[List[_Augmentation]]]: """ Creates a matrix of operators to be called for given sub policy at given stage. The output is a tuple `(m, augments)`, where `augments` is a list of augmentations per stage - each entry contains a reduced list of unique augmentations used in a corresponding stage. The `m` matrix contains the mapping from the original sub_policy_id, to the index within the reduced list, for every stage. I.e., for policy `sub_policy_idx`, as the `stage_idx`-ith operation in a sequence, the `augments[stage_idx][m[sub_policy_idx][stage_idx]]` operator should be called. """ sub_policies = policy.sub_policies max_policy_len = max(len(sub_policy) for sub_policy in sub_policies) augmentations = [] # list of augmentations in each stage for stage_idx in range(max_policy_len): stage_augments = set() stage_augments_list = [] for sub_policy in sub_policies: if stage_idx < len(sub_policy): aug, _, _ = sub_policy[stage_idx] if aug not in stage_augments: stage_augments.add(aug) stage_augments_list.append(aug) augmentations.append(stage_augments_list + [a.identity]) identity_id = [len(stage_augments) - 1 for stage_augments in augmentations] augment_to_id = [{augmentation: i for i, augmentation in enumerate(stage_augments)} for stage_augments in augmentations] augments_by_id = np.array([[identity_id[stage_idx] for stage_idx in range(max_policy_len)] for _ in range(len(sub_policies))], dtype=np.int32) for sub_policy_id, sub_policy in enumerate(sub_policies): for stage_idx, (augment, p, mag) in enumerate(sub_policy): augments_by_id[sub_policy_id, stage_idx] = augment_to_id[stage_idx][augment] return augments_by_id, augmentations def _sub_policy_to_augmentation_map(policy: Policy) -> Tuple[_DataNode, List[List[_Augmentation]]]: matrix, augments = _sub_policy_to_augmentation_matrix_map(policy) return types.Constant(matrix), augments

nvidia__dali

rand_augment.rst

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nvidia__dali/docs/auto_aug/rand_augment.rst

RandAugment RandAugment, as described in https://arxiv.org/abs/1909.13719, is an automatic augmentation scheme that simplified the AutoAugment. For RandAugment the policy is just a list of augmentations <Augmentation operations> with a search space limited to two parameters n and m. - n describes how many randomly selected augmentations should we apply to an input sample. - m is a fixed magnitude used for all of the augmentations. For example, to use 3 random operations for each sample, each with fixed magnitude 17, you can call ~nvidia.dali.auto_aug.rand_augment.rand_augment, as follows: from nvidia.dali import pipeline_def from nvidia.dali.auto_aug import rand_augment @pipeline_def(enable_conditionals=True) def training_pipe(data_dir, image_size): jpegs, labels = fn.readers.file(file_root=data_dir, ...) shapes = fn.peek_image_shape(jpegs) images = fn.decoders.image(jpegs, device="mixed", output_type=types.RGB) augmented_images = rand_augment.rand_augment(images, shape=shapes, n=3, m=17) resized_images = fn.resize(augmented_images, size=[image_size, image_size]) return resized_images, labels The ~nvidia.dali.auto_aug.rand_augment.rand_augment uses set of augmentations described in the paper. To apply custom augmentations refer to this section <Invoking custom RandAugment policies>. Warning You need to define the pipeline with the @pipeline_def <nvidia.dali.pipeline_def> decorator and set enable_conditionals to True to use automatic augmentations. Invoking custom RandAugment policies Thanks to the simpler nature of RandAugment, its policies are defined as lists of augmentations <Augmentation operations>, that can be passed as a first argument to the ~nvidia.dali.auto_aug.rand_augment.apply_rand_augment when invoked inside a pipeline definition.

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from typing import List, Optional, Tuple, Union from nvidia.dali import fn from nvidia.dali import types from nvidia.dali.auto_aug import augmentations as a from nvidia.dali.auto_aug.core import signed_bin, _Augmentation from nvidia.dali.auto_aug.core._args import \ forbid_unused_kwargs as _forbid_unused_kwargs from nvidia.dali.auto_aug.core._utils import \ get_translations as _get_translations, \ pretty_select as _pretty_select from nvidia.dali.data_node import DataNode as _DataNode def rand_augment( data: _DataNode, n: int, m: int, num_magnitude_bins: int = 31, shape: Optional[Union[_DataNode, Tuple[int, int]]] = None, fill_value: Optional[int] = 128, interp_type: Optional[types.DALIInterpType] = None, max_translate_abs: Optional[int] = None, max_translate_rel: Optional[float] = None, seed: Optional[int] = None, monotonic_mag: bool = True, excluded: Optional[List[str]] = None, ) -> _DataNode: """ Applies RandAugment (https://arxiv.org/abs/1909.13719) augmentation scheme to the provided batch of samples. Args ---- data : DataNode A batch of samples to be processed. The supported samples are images of `HWC` layout and videos of `FHWC` layout, the supported data type is `uint8`. n: int The number of randomly sampled operations to be applied to a sample. m: int A magnitude (strength) of each operation to be applied, it must be an integer within ``[0, num_magnitude_bins - 1]``. num_magnitude_bins: int, optional The number of bins to divide the magnitude ranges into. shape: DataNode or Tuple[int, int], optional The size (height and width) of the image or frames in the video sequence passed as the `data`. If specified, the magnitude of `translation` operations depends on the image/frame shape and spans from 0 to `max_translate_rel * shape`. Otherwise, the magnitude range is `[0, max_translate_abs]` for any sample. fill_value: int, optional A value to be used as a padding for images/frames transformed with warp_affine ops (translation, shear and rotate). If `None` is specified, the images/frames are padded with the border value repeated (clamped). interp_type: types.DALIInterpType, optional Interpolation method used by the warp_affine ops (translation, shear and rotate). Supported values are `types.INTERP_LINEAR` (default) and `types.INTERP_NN`. max_translate_abs: int or (int, int), optional Only valid when ``shapes`` is not provided. Specifies the maximal shift (in pixels) in the translation augmentation. If a tuple is specified, the first component limits height, the second the width. Defaults to 100, which means the maximal magnitude shifts the image by 100 pixels. max_translate_rel: float or (float, float), optional Only valid when ``shapes`` argument is provided. Specifies the maximal shift as a fraction of image shape in the translation augmentations. If a tuple is specified, the first component limits the height, the second the width. Defaults to around `0.45` (100/224). seed: int, optional Seed to be used to randomly sample operations (and to negate magnitudes). monotonic_mag: bool, optional There are two flavours of RandAugment available in different frameworks. For the default ``monotonic_mag=True`` the strength of operations that accept magnitude bins increases with the increasing bins. If set to False, the magnitude ranges for some color operations differ. There, the :meth:`~nvidia.dali.auto_aug.augmentations.posterize` and :meth:`~nvidia.dali.auto_aug.augmentations.solarize` strength decreases with increasing magnitude bins and enhance operations ( :meth:`~nvidia.dali.auto_aug.augmentations.brightness`, :meth:`~nvidia.dali.auto_aug.augmentations.contrast`, :meth:`~nvidia.dali.auto_aug.augmentations.color`, :meth:`~nvidia.dali.auto_aug.augmentations.sharpness`) use (0.1, 1.9) range, which means that the strength decreases the closer the magnitudes are to the center of the range. See :meth:`~nvidia.dali.auto_aug.rand_augment.get_rand_augment_non_monotonic_suite`. excluded: List[str], optional A list of names of the operations to be excluded from the default suite of augmentations. If, instead of just limiting the set of operations, you need to include some custom operations or fine-tune the existing ones, you can use the :meth:`~nvidia.dali.auto_aug.rand_augment.apply_rand_augment` directly, which accepts a list of augmentations. Returns ------- DataNode A batch of transformed samples. """ aug_kwargs = {"fill_value": fill_value, "interp_type": interp_type} use_shape = shape is not None if use_shape: aug_kwargs["shape"] = shape if monotonic_mag: augmentations = get_rand_augment_suite(use_shape, max_translate_abs, max_translate_rel) else: augmentations = get_rand_augment_non_monotonic_suite(use_shape, max_translate_abs, max_translate_rel) augmentation_names = set(aug.name for aug in augmentations) assert len(augmentation_names) == len(augmentations) excluded = excluded or [] for name in excluded: if name not in augmentation_names: raise Exception(f"The `{name}` was specified in `excluded`, but the RandAugment suite " f"does not contain augmentation with this name. " f"The augmentations in the suite are: {', '.join(augmentation_names)}.") selected_augments = [aug for aug in augmentations if aug.name not in excluded] return apply_rand_augment(selected_augments, data, n, m, num_magnitude_bins=num_magnitude_bins, seed=seed, **aug_kwargs) def apply_rand_augment(augmentations: List[_Augmentation], data: _DataNode, n: int, m: int, num_magnitude_bins: int = 31, seed: Optional[int] = None, **kwargs) -> _DataNode: """ Applies the list of ``augmentations`` in RandAugment (https://arxiv.org/abs/1909.13719) fashion. Each sample is transformed with ``n`` operations in a sequence randomly selected from the ``augmentations`` list. Each operation uses ``m`` as the magnitude bin. Args ---- augmentations : List[core._Augmentation] List of augmentations to be sampled and applied in RandAugment fashion. data : DataNode A batch of samples to be processed. n: int The number of randomly sampled operations to be applied to a sample. m: int A magnitude bin (strength) of each operation to be applied, it must be an integer within ``[0, num_magnitude_bins - 1]``. num_magnitude_bins: int The number of bins to divide the magnitude ranges into. seed: int Seed to be used to randomly sample operations (and to negate magnitudes). kwargs: Any extra parameters to be passed when calling `augmentations`. The signature of each augmentation is checked for any extra arguments and if the name of the argument matches one from the `kwargs`, the value is passed as an argument. For example, some augmentations from the default RandAugment suite accept ``shapes``, ``fill_value`` and ``interp_type``. Returns ------- DataNode A batch of transformed samples. """ if not isinstance(n, int) or n < 0: raise Exception( f"The number of operations to apply `n` must be a non-negative integer, got {n}.") if not isinstance(num_magnitude_bins, int) or num_magnitude_bins < 1: raise Exception( f"The `num_magnitude_bins` must be a positive integer, got {num_magnitude_bins}.") if not isinstance(m, int) or not 0 <= m < num_magnitude_bins: raise Exception(f"The magnitude bin `m` must be an integer from " f"`[0, {num_magnitude_bins - 1}]` range. Got {m}.") if n == 0: warnings.warn( "The `apply_rand_augment` was called with `n=0`, " "no augmentation will be applied.", Warning) return data if len(augmentations) == 0: raise Exception("The `augmentations` list cannot be empty, unless n=0. " "Got empty list in `apply_rand_augment` call.") shape = tuple() if n == 1 else (n, ) op_idx = fn.random.uniform(values=list(range(len(augmentations))), seed=seed, shape=shape, dtype=types.INT32) use_signed_magnitudes = any(aug.randomly_negate for aug in augmentations) mag_bin = signed_bin(m, seed=seed, shape=shape) if use_signed_magnitudes else m _forbid_unused_kwargs(augmentations, kwargs, 'apply_rand_augment') for level_idx in range(n): level_mag_bin = mag_bin if not use_signed_magnitudes or n == 1 else mag_bin[level_idx] op_kwargs = dict(data=data, magnitude_bin=level_mag_bin, num_magnitude_bins=num_magnitude_bins, **kwargs) level_op_idx = op_idx if n == 1 else op_idx[level_idx] data = _pretty_select(augmentations, level_op_idx, op_kwargs, auto_aug_name='apply_rand_augment', ref_suite_name='get_rand_augment_suite') return data def get_rand_augment_suite(use_shape: bool = False, max_translate_abs: Optional[int] = None, max_translate_rel: Optional[float] = None) -> List[_Augmentation]: """ Creates a list of RandAugment augmentations. Args ---- use_shape : bool If true, the translation offset is computed as a percentage of the image/frame shape. Useful if the samples processed with the auto augment have different shapes. If false, the offsets range is bounded by a constant (`max_translate_abs`). max_translate_abs: int or (int, int), optional Only valid with use_shape=False, specifies the maximal shift (in pixels) in the translation augmentations. If a tuple is specified, the first component limits height, the second the width. Defaults 100. max_translate_rel: float or (float, float), optional Only valid with use_shape=True, specifies the maximal shift as a fraction of image/frame shape in the translation augmentations. If a tuple is specified, the first component limits height, the second the width. Defaults to around `0.45` (100/224). """ default_translate_abs, default_translate_rel = 100, 100 / 224 # translations = [translate_x, translate_y] with adjusted magnitude range translations = _get_translations(use_shape, default_translate_abs, default_translate_rel, max_translate_abs, max_translate_rel) # [.augmentation((mag_low, mag_high), randomly_negate_mag, magnitude_to_param_custom_mapping] return translations + [ a.shear_x.augmentation((0, 0.3), True), a.shear_y.augmentation((0, 0.3), True), a.rotate.augmentation((0, 30), True), a.brightness.augmentation((0, 0.9), True, a.shift_enhance_range), a.contrast.augmentation((0, 0.9), True, a.shift_enhance_range), a.color.augmentation((0, 0.9), True, a.shift_enhance_range), a.sharpness.augmentation((0, 0.9), True, a.sharpness_kernel), a.posterize.augmentation((8, 4), False, a.poster_mask_uint8), # solarization strength increases with decreasing magnitude (threshold) a.solarize.augmentation((256, 0)), a.equalize, a.auto_contrast, a.identity, ] def get_rand_augment_non_monotonic_suite( use_shape: bool = False, max_translate_abs: Optional[int] = None, max_translate_rel: Optional[float] = None) -> List[_Augmentation]: """ Similarly to :meth:`~nvidia.dali.auto_aug.rand_augment.get_rand_augment_suite` creates a list of RandAugment augmentations. This variant uses brightness, contrast, color, sharpness, posterize, and solarize with magnitude ranges as used by the AutoAugment. However, those ranges do not meet the intuition that the bigger magnitude bin corresponds to stronger operation. """ default_translate_abs, default_translate_rel = 100, 100 / 224 # translations = [translate_x, translate_y] with adjusted magnitude range translations = _get_translations(use_shape, default_translate_abs, default_translate_rel, max_translate_abs, max_translate_rel) return translations + [ a.shear_x.augmentation((0, 0.3), True), a.shear_y.augmentation((0, 0.3), True), a.rotate.augmentation((0, 30), True), a.brightness.augmentation((0.1, 1.9), False, None), a.contrast.augmentation((0.1, 1.9), False, None), a.color.augmentation((0.1, 1.9), False, None), a.sharpness.augmentation((0.1, 1.9), False, a.sharpness_kernel_shifted), a.posterize.augmentation((0, 4), False, a.poster_mask_uint8), a.solarize.augmentation((0, 256), False, None), a.equalize, a.auto_contrast, a.identity, ]

numpy__numpy

distutils.rst

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numpy__numpy/doc/source/reference/distutils.rst

numpy__numpy/numpy/distutils

NumPy provides enhanced distutils functionality to make it easier to build and install sub-packages, auto-generate code, and extension modules that use Fortran-compiled libraries. To use features of NumPy distutils, use the setup <core.setup> command from numpy.distutils.core. A useful Configuration <misc_util.Configuration> class is also provided in numpy.distutils.misc_util that can make it easier to construct keyword arguments to pass to the setup function (by passing the dictionary obtained from the todict() method of the class). More information is available in the distutils-user-guide. The choice and location of linked libraries such as BLAS and LAPACK as well as include paths and other such build options can be specified in a site.cfg file located in the NumPy root repository or a .numpy-site.cfg file in your home directory. See the site.cfg.example example file included in the NumPy repository or sdist for documentation. Configuration class Construct a configuration instance for the given package name. If parent_name is not None, then construct the package as a sub-package of the parent_name package. If top_path and package_path are None then they are assumed equal to the path of the file this instance was created in. The setup.py files in the numpy distribution are good examples of how to use the Configuration instance. Building Installable C libraries Conventional C libraries (installed through add_library) are not installed, and are just used during the build (they are statically linked). An installable C library is a pure C library, which does not depend on the python C runtime, and is installed such that it may be used by third-party packages. To build and install the C library, you just use the method add_installed_library instead of add_library, which takes the same arguments except for an additional install_dir argument: .. hidden in a comment so as to be included in refguide but not rendered documentation >>> import numpy.distutils.misc_util >>> config = np.distutils.misc_util.Configuration(None, '', '.') >>> with open('foo.c', 'w') as f: pass >>> config.add_installed_library('foo', sources=['foo.c'], install_dir='lib') npy-pkg-config files To make the necessary build options available to third parties, you could use the npy-pkg-config mechanism implemented in numpy.distutils. This mechanism is based on a .ini file which contains all the options. A .ini file is very similar to .pc files as used by the pkg-config unix utility: [meta] Name: foo Version: 1.0 Description: foo library [variables] prefix = /home/user/local libdir = ${prefix}/lib includedir = ${prefix}/include [default] cflags = -I${includedir} libs = -L${libdir} -lfoo Generally, the file needs to be generated during the build, since it needs some information known at build time only (e.g. prefix). This is mostly automatic if one uses the Configuration method add_npy_pkg_config. Assuming we have a template file foo.ini.in as follows: [meta] Name: foo Version: @version@ Description: foo library [variables] prefix = @prefix@ libdir = ${prefix}/lib includedir = ${prefix}/include [default] cflags = -I${includedir} libs = -L${libdir} -lfoo and the following code in setup.py: >>> config.add_installed_library('foo', sources=['foo.c'], install_dir='lib') >>> subst = {'version': '1.0'} >>> config.add_npy_pkg_config('foo.ini.in', 'lib', subst_dict=subst) This will install the file foo.ini into the directory package_dir/lib, and the foo.ini file will be generated from foo.ini.in, where each @version@ will be replaced by subst_dict['version']. The dictionary has an additional prefix substitution rule automatically added, which contains the install prefix (since this is not easy to get from setup.py). npy-pkg-config files can also be installed at the same location as used for numpy, using the path returned from get_npy_pkg_dir function. Reusing a C library from another package Info are easily retrieved from the get_info function in `numpy.distutils.misc_util`: >>> info = np.distutils.misc_util.get_info('npymath') >>> config.add_extension('foo', sources=['foo.c'], extra_info=info) <numpy.distutils.extension.Extension('foo') at 0x...> An additional list of paths to look for .ini files can be given to get_info. Conversion of .src files NumPy distutils supports automatic conversion of source files named <somefile>.src. This facility can be used to maintain very similar code blocks requiring only simple changes between blocks. During the build phase of setup, if a template file named <somefile>.src is encountered, a new file named <somefile> is constructed from the template and placed in the build directory to be used instead. Two forms of template conversion are supported. The first form occurs for files named <file>.ext.src where ext is a recognized Fortran extension (f, f90, f95, f77, for, ftn, pyf). The second form is used for all other cases. See templating.

import os import re import sys import copy import glob import atexit import tempfile import subprocess import shutil import multiprocessing import textwrap import importlib.util from threading import local as tlocal from functools import reduce import distutils from distutils.errors import DistutilsError # stores temporary directory of each thread to only create one per thread _tdata = tlocal() # store all created temporary directories so they can be deleted on exit _tmpdirs = [] def clean_up_temporary_directory(): if _tmpdirs is not None: for d in _tmpdirs: try: shutil.rmtree(d) except OSError: pass atexit.register(clean_up_temporary_directory) __all__ = ['Configuration', 'get_numpy_include_dirs', 'default_config_dict', 'dict_append', 'appendpath', 'generate_config_py', 'get_cmd', 'allpath', 'get_mathlibs', 'terminal_has_colors','red_text', 'green_text', 'yellow_text', 'blue_text', 'cyan_text', 'cyg2win32','mingw32', 'all_strings', 'has_f_sources', 'has_cxx_sources', 'filter_sources', 'get_dependencies', 'is_local_src_dir', 'get_ext_source_files', 'get_script_files', 'get_lib_source_files', 'get_data_files', 'dot_join', 'get_frame','minrelpath', 'njoin', 'is_sequence', 'is_string', 'as_list', 'gpaths', 'get_language', 'get_build_architecture', 'get_info', 'get_pkg_info', 'get_num_build_jobs','sanitize_cxx_flags', 'exec_mod_from_location'] class InstallableLib: """ Container to hold information on an installable library. Parameters ---------- name : str Name of the installed library. build_info : dict Dictionary holding build information. target_dir : str Absolute path specifying where to install the library. See Also -------- Configuration.add_installed_library Notes ----- The three parameters are stored as attributes with the same names. """ def __init__(self, name, build_info, target_dir): self.name = name self.build_info = build_info self.target_dir = target_dir def get_num_build_jobs(): """ Get number of parallel build jobs set by the --parallel command line argument of setup.py If the command did not receive a setting the environment variable NPY_NUM_BUILD_JOBS is checked. If that is unset, return the number of processors on the system, with a maximum of 8 (to prevent overloading the system if there a lot of CPUs). Returns ------- out : int number of parallel jobs that can be run """ from numpy.distutils.core import get_distribution try: cpu_count = len(os.sched_getaffinity(0)) except AttributeError: cpu_count = multiprocessing.cpu_count() cpu_count = min(cpu_count, 8) envjobs = int(os.environ.get("NPY_NUM_BUILD_JOBS", cpu_count)) dist = get_distribution() # may be None during configuration if dist is None: return envjobs # any of these three may have the job set, take the largest cmdattr = (getattr(dist.get_command_obj('build'), 'parallel', None), getattr(dist.get_command_obj('build_ext'), 'parallel', None), getattr(dist.get_command_obj('build_clib'), 'parallel', None)) if all(x is None for x in cmdattr): return envjobs else: return max(x for x in cmdattr if x is not None) def quote_args(args): """Quote list of arguments. .. deprecated:: 1.22. """ import warnings warnings.warn('"quote_args" is deprecated.', DeprecationWarning, stacklevel=2) # don't used _nt_quote_args as it does not check if # args items already have quotes or not. args = list(args) for i in range(len(args)): a = args[i] if'' in a and a[0] not in '"\'': args[i] = '"%s"' % (a) return args def allpath(name): "Convert a /-separated pathname to one using the OS's path separator." split = name.split('/') return os.path.join(*split) def rel_path(path, parent_path): """Return path relative to parent_path.""" # Use realpath to avoid issues with symlinked dirs (see gh-7707) pd = os.path.realpath(os.path.abspath(parent_path)) apath = os.path.realpath(os.path.abspath(path)) if len(apath) < len(pd): return path if apath == pd: return '' if pd == apath[:len(pd)]: assert apath[len(pd)] in [os.sep], repr((path, apath[len(pd)])) path = apath[len(pd)+1:] return path def get_path_from_frame(frame, parent_path=None): """Return path of the module given a frame object from the call stack. Returned path is relative to parent_path when given, otherwise it is absolute path. """ # First, try to find if the file name is in the frame. try: caller_file = eval('__file__', frame.f_globals, frame.f_locals) d = os.path.dirname(os.path.abspath(caller_file)) except NameError: # __file__ is not defined, so let's try __name__. We try this second # because setuptools spoofs __name__ to be '__main__' even though # sys.modules['__main__'] might be something else, like easy_install(1). caller_name = eval('__name__', frame.f_globals, frame.f_locals) __import__(caller_name) mod = sys.modules[caller_name] if hasattr(mod, '__file__'): d = os.path.dirname(os.path.abspath(mod.__file__)) else: # we're probably running setup.py as execfile("setup.py") # (likely we're building an egg) d = os.path.abspath('.') if parent_path is not None: d = rel_path(d, parent_path) return d or '.' def njoin(*path): """Join two or more pathname components + - convert a /-separated pathname to one using the OS's path separator. - resolve `..` and `.` from path. Either passing n arguments as in njoin('a','b'), or a sequence of n names as in njoin(['a','b']) is handled, or a mixture of such arguments. """ paths = [] for p in path: if is_sequence(p): # njoin(['a', 'b'], 'c') paths.append(njoin(*p)) else: assert is_string(p) paths.append(p) path = paths if not path: # njoin() joined = '' else: # njoin('a', 'b') joined = os.path.join(*path) if os.path.sep!= '/': joined = joined.replace('/', os.path.sep) return minrelpath(joined) def get_mathlibs(path=None): """Return the MATHLIB line from numpyconfig.h """ if path is not None: config_file = os.path.join(path, '_numpyconfig.h') else: # Look for the file in each of the numpy include directories. dirs = get_numpy_include_dirs() for path in dirs: fn = os.path.join(path, '_numpyconfig.h') if os.path.exists(fn): config_file = fn break else: raise DistutilsError('_numpyconfig.h not found in numpy include ' 'dirs %r' % (dirs,)) with open(config_file) as fid: mathlibs = [] s = '#define MATHLIB' for line in fid: if line.startswith(s): value = line[len(s):].strip() if value: mathlibs.extend(value.split(',')) return mathlibs def minrelpath(path): """Resolve `..` and '.' from path. """ if not is_string(path): return path if '.' not in path: return path l = path.split(os.sep) while l: try: i = l.index('.', 1) except ValueError: break del l[i] j = 1 while l: try: i = l.index('..', j) except ValueError: break if l[i-1]=='..': j += 1 else: del l[i], l[i-1] j = 1 if not l: return '' return os.sep.join(l) def sorted_glob(fileglob): """sorts output of python glob for https://bugs.python.org/issue30461 to allow extensions to have reproducible build results""" return sorted(glob.glob(fileglob)) def _fix_paths(paths, local_path, include_non_existing): assert is_sequence(paths), repr(type(paths)) new_paths = [] assert not is_string(paths), repr(paths) for n in paths: if is_string(n): if '*' in n or '?' in n: p = sorted_glob(n) p2 = sorted_glob(njoin(local_path, n)) if p2: new_paths.extend(p2) elif p: new_paths.extend(p) else: if include_non_existing: new_paths.append(n) print('could not resolve pattern in %r: %r' % (local_path, n)) else: n2 = njoin(local_path, n) if os.path.exists(n2): new_paths.append(n2) else: if os.path.exists(n): new_paths.append(n) elif include_non_existing: new_paths.append(n) if not os.path.exists(n): print('non-existing path in %r: %r' % (local_path, n)) elif is_sequence(n): new_paths.extend(_fix_paths(n, local_path, include_non_existing)) else: new_paths.append(n) return [minrelpath(p) for p in new_paths] def gpaths(paths, local_path='', include_non_existing=True): """Apply glob to paths and prepend local_path if needed. """ if is_string(paths): paths = (paths,) return _fix_paths(paths, local_path, include_non_existing) def make_temp_file(suffix='', prefix='', text=True): if not hasattr(_tdata, 'tempdir'): _tdata.tempdir = tempfile.mkdtemp() _tmpdirs.append(_tdata.tempdir) fid, name = tempfile.mkstemp(suffix=suffix, prefix=prefix, dir=_tdata.tempdir, text=text) fo = os.fdopen(fid, 'w') return fo, name # Hooks for colored terminal output. # See also https://web.archive.org/web/20100314204946/http://www.livinglogic.de/Python/ansistyle def terminal_has_colors(): if sys.platform=='cygwin' and 'USE_COLOR' not in os.environ: # Avoid importing curses that causes illegal operation # with a message: # PYTHON2 caused an invalid page fault in # module CYGNURSES7.DLL as 015f:18bbfc28 # Details: Python 2.3.3 [GCC 3.3.1 (cygming special)] # ssh to Win32 machine from debian # curses.version is 2.2 # CYGWIN_98-4.10, release 1.5.7(0.109/3/2)) return 0 if hasattr(sys.stdout, 'isatty') and sys.stdout.isatty(): try: import curses curses.setupterm() if (curses.tigetnum("colors") >= 0 and curses.tigetnum("pairs") >= 0 and ((curses.tigetstr("setf") is not None and curses.tigetstr("setb") is not None) or (curses.tigetstr("setaf") is not None and curses.tigetstr("setab") is not None) or curses.tigetstr("scp") is not None)): return 1 except Exception: pass return 0 if terminal_has_colors(): _colour_codes = dict(black=0, red=1, green=2, yellow=3, blue=4, magenta=5, cyan=6, white=7, default=9) def colour_text(s, fg=None, bg=None, bold=False): seq = [] if bold: seq.append('1') if fg: fgcode = 30 + _colour_codes.get(fg.lower(), 0) seq.append(str(fgcode)) if bg: bgcode = 40 + _colour_codes.get(bg.lower(), 7) seq.append(str(bgcode)) if seq: return '\x1b[%sm%s\x1b[0m' % (';'.join(seq), s) else: return s else: def colour_text(s, fg=None, bg=None): return s def default_text(s): return colour_text(s, 'default') def red_text(s): return colour_text(s,'red') def green_text(s): return colour_text(s, 'green') def yellow_text(s): return colour_text(s, 'yellow') def cyan_text(s): return colour_text(s, 'cyan') def blue_text(s): return colour_text(s, 'blue') ######################### def cyg2win32(path: str) -> str: """Convert a path from Cygwin-native to Windows-native. Uses the cygpath utility (part of the Base install) to do the actual conversion. Falls back to returning the original path if this fails. Handles the default ``/cygdrive`` mount prefix as well as the ``/proc/cygdrive`` portable prefix, custom cygdrive prefixes such as ``/`` or ``/mnt``, and absolute paths such as ``/usr/src/`` or ``/home/username`` Parameters ---------- path : str The path to convert Returns ------- converted_path : str The converted path Notes ----- Documentation for cygpath utility: https://cygwin.com/cygwin-ug-net/cygpath.html Documentation for the C function it wraps: https://cygwin.com/cygwin-api/func-cygwin-conv-path.html """ if sys.platform!= "cygwin": return path return subprocess.check_output( ["/usr/bin/cygpath", "--windows", path], text=True ) def mingw32(): """Return true when using mingw32 environment. """ if sys.platform=='win32': if os.environ.get('OSTYPE', '')=='msys': return True if os.environ.get('MSYSTEM', '')=='MINGW32': return True return False def msvc_runtime_version(): "Return version of MSVC runtime library, as defined by __MSC_VER__ macro" msc_pos = sys.version.find('MSC v.') if msc_pos!= -1: msc_ver = int(sys.version[msc_pos+6:msc_pos+10]) else: msc_ver = None return msc_ver def msvc_runtime_library(): "Return name of MSVC runtime library if Python was built with MSVC >= 7" ver = msvc_runtime_major () if ver: if ver < 140: return "msvcr%i" % ver else: return "vcruntime%i" % ver else: return None def msvc_runtime_major(): "Return major version of MSVC runtime coded like get_build_msvc_version" major = {1300: 70, # MSVC 7.0 1310: 71, # MSVC 7.1 1400: 80, # MSVC 8 1500: 90, # MSVC 9 (aka 2008) 1600: 100, # MSVC 10 (aka 2010) 1900: 140, # MSVC 14 (aka 2015) }.get(msvc_runtime_version(), None) return major ######################### #XXX need support for.C that is also C++ cxx_ext_match = re.compile(r'.*\.(cpp|cxx|cc)\Z', re.I).match fortran_ext_match = re.compile(r'.*\.(f90|f95|f77|for|ftn|f)\Z', re.I).match f90_ext_match = re.compile(r'.*\.(f90|f95)\Z', re.I).match f90_module_name_match = re.compile(r'\s*module\s*(?P<name>[\w_]+)', re.I).match def _get_f90_modules(source): """Return a list of Fortran f90 module names that given source file defines. """ if not f90_ext_match(source): return [] modules = [] with open(source) as f: for line in f: m = f90_module_name_match(line) if m: name = m.group('name') modules.append(name) # break # XXX can we assume that there is one module per file? return modules def is_string(s): return isinstance(s, str) def all_strings(lst): """Return True if all items in lst are string objects. """ for item in lst: if not is_string(item): return False return True def is_sequence(seq): if is_string(seq): return False try: len(seq) except Exception: return False return True def is_glob_pattern(s): return is_string(s) and ('*' in s or '?' in s) def as_list(seq): if is_sequence(seq): return list(seq) else: return [seq] def get_language(sources): # not used in numpy/scipy packages, use build_ext.detect_language instead """Determine language value (c,f77,f90) from sources """ language = None for source in sources: if isinstance(source, str): if f90_ext_match(source): language = 'f90' break elif fortran_ext_match(source): language = 'f77' return language def has_f_sources(sources): """Return True if sources contains Fortran files """ for source in sources: if fortran_ext_match(source): return True return False def has_cxx_sources(sources): """Return True if sources contains C++ files """ for source in sources: if cxx_ext_match(source): return True return False def filter_sources(sources): """Return four lists of filenames containing C, C++, Fortran, and Fortran 90 module sources, respectively. """ c_sources = [] cxx_sources = [] f_sources = [] fmodule_sources = [] for source in sources: if fortran_ext_match(source): modules = _get_f90_modules(source) if modules: fmodule_sources.append(source) else: f_sources.append(source) elif cxx_ext_match(source): cxx_sources.append(source) else: c_sources.append(source) return c_sources, cxx_sources, f_sources, fmodule_sources def _get_headers(directory_list): # get *.h files from list of directories headers = [] for d in directory_list: head = sorted_glob(os.path.join(d, "*.h")) #XXX: *.hpp files?? headers.extend(head) return headers def _get_directories(list_of_sources): # get unique directories from list of sources. direcs = [] for f in list_of_sources: d = os.path.split(f) if d[0]!= '' and not d[0] in direcs: direcs.append(d[0]) return direcs def _commandline_dep_string(cc_args, extra_postargs, pp_opts): """ Return commandline representation used to determine if a file needs to be recompiled """ cmdline = 'commandline: ' cmdline +=''.join(cc_args) cmdline +=''.join(extra_postargs) cmdline +=''.join(pp_opts) + '\n' return cmdline def get_dependencies(sources): #XXX scan sources for include statements return _get_headers(_get_directories(sources)) def is_local_src_dir(directory): """Return true if directory is local directory. """ if not is_string(directory): return False abs_dir = os.path.abspath(directory) c = os.path.commonprefix([os.getcwd(), abs_dir]) new_dir = abs_dir[len(c):].split(os.sep) if new_dir and not new_dir[0]: new_dir = new_dir[1:] if new_dir and new_dir[0]=='build': return False new_dir = os.sep.join(new_dir) return os.path.isdir(new_dir) def general_source_files(top_path): pruned_directories = {'CVS':1, '.svn':1, 'build':1} prune_file_pat = re.compile(r'(?:[~#]|\.py[co]|\.o)$') for dirpath, dirnames, filenames in os.walk(top_path, topdown=True): pruned = [ d for d in dirnames if d not in pruned_directories ] dirnames[:] = pruned for f in filenames: if not prune_file_pat.search(f): yield os.path.join(dirpath, f) def general_source_directories_files(top_path): """Return a directory name relative to top_path and files contained. """ pruned_directories = ['CVS', '.svn', 'build'] prune_file_pat = re.compile(r'(?:[~#]|\.py[co]|\.o)$') for dirpath, dirnames, filenames in os.walk(top_path, topdown=True): pruned = [ d for d in dirnames if d not in pruned_directories ] dirnames[:] = pruned for d in dirnames: dpath = os.path.join(dirpath, d) rpath = rel_path(dpath, top_path) files = [] for f in os.listdir(dpath): fn = os.path.join(dpath, f) if os.path.isfile(fn) and not prune_file_pat.search(fn): files.append(fn) yield rpath, files dpath = top_path rpath = rel_path(dpath, top_path) filenames = [os.path.join(dpath, f) for f in os.listdir(dpath) \ if not prune_file_pat.search(f)] files = [f for f in filenames if os.path.isfile(f)] yield rpath, files def get_ext_source_files(ext): # Get sources and any include files in the same directory. filenames = [] sources = [_m for _m in ext.sources if is_string(_m)] filenames.extend(sources) filenames.extend(get_dependencies(sources)) for d in ext.depends: if is_local_src_dir(d): filenames.extend(list(general_source_files(d))) elif os.path.isfile(d): filenames.append(d) return filenames def get_script_files(scripts): scripts = [_m for _m in scripts if is_string(_m)] return scripts def get_lib_source_files(lib): filenames = [] sources = lib[1].get('sources', []) sources = [_m for _m in sources if is_string(_m)] filenames.extend(sources) filenames.extend(get_dependencies(sources)) depends = lib[1].get('depends', []) for d in depends: if is_local_src_dir(d): filenames.extend(list(general_source_files(d))) elif os.path.isfile(d): filenames.append(d) return filenames def get_shared_lib_extension(is_python_ext=False): """Return the correct file extension for shared libraries. Parameters ---------- is_python_ext : bool, optional Whether the shared library is a Python extension. Default is False. Returns ------- so_ext : str The shared library extension. Notes ----- For Python shared libs, `so_ext` will typically be '.so' on Linux and OS X, and '.pyd' on Windows. For Python >= 3.2 `so_ext` has a tag prepended on POSIX systems according to PEP 3149. """ confvars = distutils.sysconfig.get_config_vars() so_ext = confvars.get('EXT_SUFFIX', '') if not is_python_ext: # hardcode known values, config vars (including SHLIB_SUFFIX) are # unreliable (see #3182) # darwin, windows and debug linux are wrong in 3.3.1 and older if (sys.platform.startswith('linux') or sys.platform.startswith('gnukfreebsd')): so_ext = '.so' elif sys.platform.startswith('darwin'): so_ext = '.dylib' elif sys.platform.startswith('win'): so_ext = '.dll' else: # fall back to config vars for unknown platforms # fix long extension for Python >=3.2, see PEP 3149. if 'SOABI' in confvars: # Does nothing unless SOABI config var exists so_ext = so_ext.replace('.' + confvars.get('SOABI'), '', 1) return so_ext def get_data_files(data): if is_string(data): return [data] sources = data[1] filenames = [] for s in sources: if hasattr(s, '__call__'): continue if is_local_src_dir(s): filenames.extend(list(general_source_files(s))) elif is_string(s): if os.path.isfile(s): filenames.append(s) else: print('Not existing data file:', s) else: raise TypeError(repr(s)) return filenames def dot_join(*args): return '.'.join([a for a in args if a]) def get_frame(level=0): """Return frame object from call stack with given level. """ try: return sys._getframe(level+1) except AttributeError: frame = sys.exc_info()[2].tb_frame for _ in range(level+1): frame = frame.f_back return frame ###################### class Configuration: _list_keys = ['packages', 'ext_modules', 'data_files', 'include_dirs', 'libraries', 'headers','scripts', 'py_modules', 'installed_libraries', 'define_macros'] _dict_keys = ['package_dir', 'installed_pkg_config'] _extra_keys = ['name','version'] numpy_include_dirs = [] def __init__(self, package_name=None, parent_name=None, top_path=None, package_path=None, caller_level=1, setup_name='setup.py', **attrs): """Construct configuration instance of a package. package_name -- name of the package Ex.: 'distutils' parent_name -- name of the parent package Ex.: 'numpy' top_path -- directory of the toplevel package Ex.: the directory where the numpy package source sits package_path -- directory of package. Will be computed by magic from the directory of the caller module if not specified Ex.: the directory where numpy.distutils is caller_level -- frame level to caller namespace, internal parameter. """ self.name = dot_join(parent_name, package_name) self.version = None caller_frame = get_frame(caller_level) self.local_path = get_path_from_frame(caller_frame, top_path) # local_path -- directory of a file (usually setup.py) that # defines a configuration() function. # local_path -- directory of a file (usually setup.py) that # defines a configuration() function. if top_path is None: top_path = self.local_path self.local_path = '' if package_path is None: package_path = self.local_path elif os.path.isdir(njoin(self.local_path, package_path)): package_path = njoin(self.local_path, package_path) if not os.path.isdir(package_path or '.'): raise ValueError("%r is not a directory" % (package_path,)) self.top_path = top_path self.package_path = package_path # this is the relative path in the installed package self.path_in_package = os.path.join(*self.name.split('.')) self.list_keys = self._list_keys[:] self.dict_keys = self._dict_keys[:] for n in self.list_keys: v = copy.copy(attrs.get(n, [])) setattr(self, n, as_list(v)) for n in self.dict_keys: v = copy.copy(attrs.get(n, {})) setattr(self, n, v) known_keys = self.list_keys + self.dict_keys self.extra_keys = self._extra_keys[:] for n in attrs.keys(): if n in known_keys: continue a = attrs[n] setattr(self, n, a) if isinstance(a, list): self.list_keys.append(n) elif isinstance(a, dict): self.dict_keys.append(n) else: self.extra_keys.append(n) if os.path.exists(njoin(package_path, '__init__.py')): self.packages.append(self.name) self.package_dir[self.name] = package_path self.options = dict( ignore_setup_xxx_py = False, assume_default_configuration = False, delegate_options_to_subpackages = False, quiet = False, ) caller_instance = None for i in range(1, 3): try: f = get_frame(i) except ValueError: break try: caller_instance = eval('self', f.f_globals, f.f_locals) break except NameError: pass if isinstance(caller_instance, self.__class__): if caller_instance.options['delegate_options_to_subpackages']: self.set_options(**caller_instance.options) self.setup_name = setup_name def todict(self): """ Return a dictionary compatible with the keyword arguments of distutils setup function. Examples -------- >>> setup(**config.todict()) #doctest: +SKIP """ self._optimize_data_files() d = {} known_keys = self.list_keys + self.dict_keys + self.extra_keys for n in known_keys: a = getattr(self, n) if a: d[n] = a return d def info(self, message): if not self.options['quiet']: print(message) def warn(self, message): sys.stderr.write('Warning: %s\n' % (message,)) def set_options(self, **options): """ Configure Configuration instance. The following options are available: - ignore_setup_xxx_py - assume_default_configuration - delegate_options_to_subpackages - quiet """ for key, value in options.items(): if key in self.options: self.options[key] = value else: raise ValueError('Unknown option: '+key) def get_distribution(self): """Return the distutils distribution object for self.""" from numpy.distutils.core import get_distribution return get_distribution() def _wildcard_get_subpackage(self, subpackage_name, parent_name, caller_level = 1): l = subpackage_name.split('.') subpackage_path = njoin([self.local_path]+l) dirs = [_m for _m in sorted_glob(subpackage_path) if os.path.isdir(_m)] config_list = [] for d in dirs: if not os.path.isfile(njoin(d, '__init__.py')): continue if 'build' in d.split(os.sep): continue n = '.'.join(d.split(os.sep)[-len(l):]) c = self.get_subpackage(n, parent_name = parent_name, caller_level = caller_level+1) config_list.extend(c) return config_list def _get_configuration_from_setup_py(self, setup_py, subpackage_name, subpackage_path, parent_name, caller_level = 1): # In case setup_py imports local modules: sys.path.insert(0, os.path.dirname(setup_py)) try: setup_name = os.path.splitext(os.path.basename(setup_py))[0] n = dot_join(self.name, subpackage_name, setup_name) setup_module = exec_mod_from_location( '_'.join(n.split('.')), setup_py) if not hasattr(setup_module, 'configuration'): if not self.options['assume_default_configuration']: self.warn('Assuming default configuration '\ '(%s does not define configuration())'\ % (setup_module)) config = Configuration(subpackage_name, parent_name, self.top_path, subpackage_path, caller_level = caller_level + 1) else: pn = dot_join(*([parent_name] + subpackage_name.split('.')[:-1])) args = (pn,) if setup_module.configuration.__code__.co_argcount > 1: args = args + (self.top_path,) config = setup_module.configuration(*args) if config.name!=dot_join(parent_name, subpackage_name): self.warn('Subpackage %r configuration returned as %r' % \ (dot_join(parent_name, subpackage_name), config.name)) finally: del sys.path[0] return config def get_subpackage(self,subpackage_name, subpackage_path=None, parent_name=None, caller_level = 1): """Return list of subpackage configurations. Parameters ---------- subpackage_name : str or None Name of the subpackage to get the configuration. '*' in subpackage_name is handled as a wildcard. subpackage_path : str If None, then the path is assumed to be the local path plus the subpackage_name. If a setup.py file is not found in the subpackage_path, then a default configuration is used. parent_name : str Parent name. """ if subpackage_name is None: if subpackage_path is None: raise ValueError( "either subpackage_name or subpackage_path must be specified") subpackage_name = os.path.basename(subpackage_path) # handle wildcards l = subpackage_name.split('.') if subpackage_path is None and '*' in subpackage_name: return self._wildcard_get_subpackage(subpackage_name, parent_name, caller_level = caller_level+1) assert '*' not in subpackage_name, repr((subpackage_name, subpackage_path, parent_name)) if subpackage_path is None: subpackage_path = njoin([self.local_path] + l) else: subpackage_path = njoin([subpackage_path] + l[:-1]) subpackage_path = self.paths([subpackage_path])[0] setup_py = njoin(subpackage_path, self.setup_name) if not self.options['ignore_setup_xxx_py']: if not os.path.isfile(setup_py): setup_py = njoin(subpackage_path, 'setup_%s.py' % (subpackage_name)) if not os.path.isfile(setup_py): if not self.options['assume_default_configuration']: self.warn('Assuming default configuration '\ '(%s/{setup_%s,setup}.py was not found)' \ % (os.path.dirname(setup_py), subpackage_name)) config = Configuration(subpackage_name, parent_name, self.top_path, subpackage_path, caller_level = caller_level+1) else: config = self._get_configuration_from_setup_py( setup_py, subpackage_name, subpackage_path, parent_name, caller_level = caller_level + 1) if config: return [config] else: return [] def add_subpackage(self,subpackage_name, subpackage_path=None, standalone = False): """Add a sub-package to the current Configuration instance. This is useful in a setup.py script for adding sub-packages to a package. Parameters ---------- subpackage_name : str name of the subpackage subpackage_path : str if given, the subpackage path such as the subpackage is in subpackage_path / subpackage_name. If None,the subpackage is assumed to be located in the local path / subpackage_name. standalone : bool """ if standalone: parent_name = None else: parent_name = self.name config_list = self.get_subpackage(subpackage_name, subpackage_path, parent_name = parent_name, caller_level = 2) if not config_list: self.warn('No configuration returned, assuming unavailable.') for config in config_list: d = config if isinstance(config, Configuration): d = config.todict() assert isinstance(d, dict), repr(type(d)) self.info('Appending %s configuration to %s' \ % (d.get('name'), self.name)) self.dict_append(**d) dist = self.get_distribution() if dist is not None: self.warn('distutils distribution has been initialized,'\ 'it may be too late to add a subpackage '+ subpackage_name) def add_data_dir(self, data_path): """Recursively add files under data_path to data_files list. Recursively add files under data_path to the list of data_files to be installed (and distributed). The data_path can be either a relative path-name, or an absolute path-name, or a 2-tuple where the first argument shows where in the install directory the data directory should be installed to. Parameters ---------- data_path : seq or str Argument can be either * 2-sequence (<datadir suffix>, <path to data directory>) * path to data directory where python datadir suffix defaults to package dir. Notes ----- Rules for installation paths:: foo/bar -> (foo/bar, foo/bar) -> parent/foo/bar (gun, foo/bar) -> parent/gun foo/* -> (foo/a, foo/a), (foo/b, foo/b) -> parent/foo/a, parent/foo/b (gun, foo/*) -> (gun, foo/a), (gun, foo/b) -> gun (gun/*, foo/*) -> parent/gun/a, parent/gun/b /foo/bar -> (bar, /foo/bar) -> parent/bar (gun, /foo/bar) -> parent/gun (fun/*/gun/*, sun/foo/bar) -> parent/fun/foo/gun/bar Examples -------- For example suppose the source directory contains fun/foo.dat and fun/bar/car.dat: >>> self.add_data_dir('fun') #doctest: +SKIP >>> self.add_data_dir(('sun', 'fun')) #doctest: +SKIP >>> self.add_data_dir(('gun', '/full/path/to/fun'))#doctest: +SKIP Will install data-files to the locations:: <package install directory>/ fun/ foo.dat bar/ car.dat sun/ foo.dat bar/ car.dat gun/ foo.dat car.dat """ if is_sequence(data_path): d, data_path = data_path else: d = None if is_sequence(data_path): [self.add_data_dir((d, p)) for p in data_path] return if not is_string(data_path): raise TypeError("not a string: %r" % (data_path,)) if d is None: if os.path.isabs(data_path): return self.add_data_dir((os.path.basename(data_path), data_path)) return self.add_data_dir((data_path, data_path)) paths = self.paths(data_path, include_non_existing=False) if is_glob_pattern(data_path): if is_glob_pattern(d): pattern_list = allpath(d).split(os.sep) pattern_list.reverse() # /a/*//b/ -> /a/*/b rl = list(range(len(pattern_list)-1)); rl.reverse() for i in rl: if not pattern_list[i]: del pattern_list[i] # for path in paths: if not os.path.isdir(path): print('Not a directory, skipping', path) continue rpath = rel_path(path, self.local_path) path_list = rpath.split(os.sep) path_list.reverse() target_list = [] i = 0 for s in pattern_list: if is_glob_pattern(s): if i>=len(path_list): raise ValueError('cannot fill pattern %r with %r' \ % (d, path)) target_list.append(path_list[i]) else: assert s==path_list[i], repr((s, path_list[i], data_path, d, path, rpath)) target_list.append(s) i += 1 if path_list[i:]: self.warn('mismatch of pattern_list=%s and path_list=%s'\ % (pattern_list, path_list)) target_list.reverse() self.add_data_dir((os.sep.join(target_list), path)) else: for path in paths: self.add_data_dir((d, path)) return assert not is_glob_pattern(d), repr(d) dist = self.get_distribution() if dist is not None and dist.data_files is not None: data_files = dist.data_files else: data_files = self.data_files for path in paths: for d1, f in list(general_source_directories_files(path)): target_path = os.path.join(self.path_in_package, d, d1) data_files.append((target_path, f)) def _optimize_data_files(self): data_dict = {} for p, files in self.data_files: if p not in data_dict: data_dict[p] = set() for f in files: data_dict[p].add(f) self.data_files[:] = [(p, list(files)) for p, files in data_dict.items()] def add_data_files(self,*files): """Add data files to configuration data_files. Parameters ---------- files : sequence Argument(s) can be either * 2-sequence (<datadir prefix>,<path to data file(s)>) * paths to data files where python datadir prefix defaults to package dir. Notes ----- The form of each element of the files sequence is very flexible allowing many combinations of where to get the files from the package and where they should ultimately be installed on the system. The most basic usage is for an element of the files argument sequence to be a simple filename. This will cause that file from the local path to be installed to the installation path of the self.name package (package path). The file argument can also be a relative path in which case the entire relative path will be installed into the package directory. Finally, the file can be an absolute path name in which case the file will be found at the absolute path name but installed to the package path. This basic behavior can be augmented by passing a 2-tuple in as the file argument. The first element of the tuple should specify the relative path (under the package install directory) where the remaining sequence of files should be installed to (it has nothing to do with the file-names in the source distribution). The second element of the tuple is the sequence of files that should be installed. The files in this sequence can be filenames, relative paths, or absolute paths. For absolute paths the file will be installed in the top-level package installation directory (regardless of the first argument). Filenames and relative path names will be installed in the package install directory under the path name given as the first element of the tuple. Rules for installation paths: #. file.txt -> (., file.txt)-> parent/file.txt #. foo/file.txt -> (foo, foo/file.txt) -> parent/foo/file.txt #. /foo/bar/file.txt -> (., /foo/bar/file.txt) -> parent/file.txt #. ``*``.txt -> parent/a.txt, parent/b.txt #. foo/``*``.txt`` -> parent/foo/a.txt, parent/foo/b.txt #. ``*/*.txt`` -> (``*``, ``*``/``*``.txt) -> parent/c/a.txt, parent/d/b.txt #. (sun, file.txt) -> parent/sun/file.txt #. (sun, bar/file.txt) -> parent/sun/file.txt #. (sun, /foo/bar/file.txt) -> parent/sun/file.txt #. (sun, ``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt #. (sun, bar/``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt #. (sun/``*``, ``*``/``*``.txt) -> parent/sun/c/a.txt, parent/d/b.txt An additional feature is that the path to a data-file can actually be a function that takes no arguments and returns the actual path(s) to the data-files. This is useful when the data files are generated while building the package. Examples -------- Add files to the list of data_files to be included with the package. >>> self.add_data_files('foo.dat', ... ('fun', ['gun.dat', 'nun/pun.dat', '/tmp/sun.dat']), ... 'bar/cat.dat', ... '/full/path/to/can.dat') #doctest: +SKIP will install these data files to:: <package install directory>/ foo.dat fun/ gun.dat nun/ pun.dat sun.dat bar/ car.dat can.dat where <package install directory> is the package (or sub-package) directory such as '/usr/lib/python2.4/site-packages/mypackage' ('C: \\Python2.4 \\Lib \\site-packages \\mypackage') or '/usr/lib/python2.4/site- packages/mypackage/mysubpackage' ('C: \\Python2.4 \\Lib \\site-packages \\mypackage \\mysubpackage'). """ if len(files)>1: for f in files: self.add_data_files(f) return assert len(files)==1 if is_sequence(files[0]): d, files = files[0] else: d = None if is_string(files): filepat = files elif is_sequence(files): if len(files)==1: filepat = files[0] else: for f in files: self.add_data_files((d, f)) return else: raise TypeError(repr(type(files))) if d is None: if hasattr(filepat, '__call__'): d = '' elif os.path.isabs(filepat): d = '' else: d = os.path.dirname(filepat) self.add_data_files((d, files)) return paths = self.paths(filepat, include_non_existing=False) if is_glob_pattern(filepat): if is_glob_pattern(d): pattern_list = d.split(os.sep) pattern_list.reverse() for path in paths: path_list = path.split(os.sep) path_list.reverse() path_list.pop() # filename target_list = [] i = 0 for s in pattern_list: if is_glob_pattern(s): target_list.append(path_list[i]) i += 1 else: target_list.append(s) target_list.reverse() self.add_data_files((os.sep.join(target_list), path)) else: self.add_data_files((d, paths)) return assert not is_glob_pattern(d), repr((d, filepat)) dist = self.get_distribution() if dist is not None and dist.data_files is not None: data_files = dist.data_files else: data_files = self.data_files data_files.append((os.path.join(self.path_in_package, d), paths)) ### XXX Implement add_py_modules def add_define_macros(self, macros): """Add define macros to configuration Add the given sequence of macro name and value duples to the beginning of the define_macros list This list will be visible to all extension modules of the current package. """ dist = self.get_distribution() if dist is not None: if not hasattr(dist, 'define_macros'): dist.define_macros = [] dist.define_macros.extend(macros) else: self.define_macros.extend(macros) def add_include_dirs(self,*paths): """Add paths to configuration include directories. Add the given sequence of paths to the beginning of the include_dirs list. This list will be visible to all extension modules of the current package. """ include_dirs = self.paths(paths) dist = self.get_distribution() if dist is not None: if dist.include_dirs is None: dist.include_dirs = [] dist.include_dirs.extend(include_dirs) else: self.include_dirs.extend(include_dirs) def add_headers(self,*files): """Add installable headers to configuration. Add the given sequence of files to the beginning of the headers list. By default, headers will be installed under <python- include>/<self.name.replace('.','/')>/ directory. If an item of files is a tuple, then its first argument specifies the actual installation location relative to the <python-include> path. Parameters ---------- files : str or seq Argument(s) can be either: * 2-sequence (<includedir suffix>,<path to header file(s)>) * path(s) to header file(s) where python includedir suffix will default to package name. """ headers = [] for path in files: if is_string(path): [headers.append((self.name, p)) for p in self.paths(path)] else: if not isinstance(path, (tuple, list)) or len(path)!= 2: raise TypeError(repr(path)) [headers.append((path[0], p)) for p in self.paths(path[1])] dist = self.get_distribution() if dist is not None: if dist.headers is None: dist.headers = [] dist.headers.extend(headers) else: self.headers.extend(headers) def paths(self,*paths,**kws): """Apply glob to paths and prepend local_path if needed. Applies glob.glob(...) to each path in the sequence (if needed) and pre-pends the local_path if needed. Because this is called on all source lists, this allows wildcard characters to be specified in lists of sources for extension modules and libraries and scripts and allows path-names be relative to the source directory. """ include_non_existing = kws.get('include_non_existing', True) return gpaths(paths, local_path = self.local_path, include_non_existing=include_non_existing) def _fix_paths_dict(self, kw): for k in kw.keys(): v = kw[k] if k in ['sources', 'depends', 'include_dirs', 'library_dirs', 'module_dirs', 'extra_objects']: new_v = self.paths(v) kw[k] = new_v def add_extension(self,name,sources,**kw): """Add extension to configuration. Create and add an Extension instance to the ext_modules list. This method also takes the following optional keyword arguments that are passed on to the Extension constructor. Parameters ---------- name : str name of the extension sources : seq list of the sources. The list of sources may contain functions (called source generators) which must take an extension instance and a build directory as inputs and return a source file or list of source files or None. If None is returned then no sources are generated. If the Extension instance has no sources after processing all source generators, then no extension module is built. include_dirs : define_macros : undef_macros : library_dirs : libraries : runtime_library_dirs : extra_objects : extra_compile_args : extra_link_args : extra_f77_compile_args : extra_f90_compile_args : export_symbols : swig_opts : depends : The depends list contains paths to files or directories that the sources of the extension module depend on. If any path in the depends list is newer than the extension module, then the module will be rebuilt. language : f2py_options : module_dirs : extra_info : dict or list dict or list of dict of keywords to be appended to keywords. Notes ----- The self.paths(...) method is applied to all lists that may contain paths. """ ext_args = copy.copy(kw) ext_args['name'] = dot_join(self.name, name) ext_args['sources'] = sources if 'extra_info' in ext_args: extra_info = ext_args['extra_info'] del ext_args['extra_info'] if isinstance(extra_info, dict): extra_info = [extra_info] for info in extra_info: assert isinstance(info, dict), repr(info) dict_append(ext_args,**info) self._fix_paths_dict(ext_args) # Resolve out-of-tree dependencies libraries = ext_args.get('libraries', []) libnames = [] ext_args['libraries'] = [] for libname in libraries: if isinstance(libname, tuple): self._fix_paths_dict(libname[1]) # Handle library names of the form libname@relative/path/to/library if '@' in libname: lname, lpath = libname.split('@', 1) lpath = os.path.abspath(njoin(self.local_path, lpath)) if os.path.isdir(lpath): c = self.get_subpackage(None, lpath, caller_level = 2) if isinstance(c, Configuration): c = c.todict() for l in [l[0] for l in c.get('libraries', [])]: llname = l.split('__OF__', 1)[0] if llname == lname: c.pop('name', None) dict_append(ext_args,**c) break continue libnames.append(libname) ext_args['libraries'] = libnames + ext_args['libraries'] ext_args['define_macros'] = \ self.define_macros + ext_args.get('define_macros', []) from numpy.distutils.core import Extension ext = Extension(**ext_args) self.ext_modules.append(ext) dist = self.get_distribution() if dist is not None: self.warn('distutils distribution has been initialized,'\ 'it may be too late to add an extension '+name) return ext def add_library(self,name,sources,**build_info): """ Add library to configuration. Parameters ---------- name : str Name of the extension. sources : sequence List of the sources. The list of sources may contain functions (called source generators) which must take an extension instance and a build directory as inputs and return a source file or list of source files or None. If None is returned then no sources are generated. If the Extension instance has no sources after processing all source generators, then no extension module is built. build_info : dict, optional The following keys are allowed: * depends * macros * include_dirs * extra_compiler_args * extra_f77_compile_args * extra_f90_compile_args * f2py_options * language """ self._add_library(name, sources, None, build_info) dist = self.get_distribution() if dist is not None: self.warn('distutils distribution has been initialized,'\ 'it may be too late to add a library '+ name) def _add_library(self, name, sources, install_dir, build_info): """Common implementation for add_library and add_installed_library. Do not use directly""" build_info = copy.copy(build_info) build_info['sources'] = sources # Sometimes, depends is not set up to an empty list by default, and if # depends is not given to add_library, distutils barfs (#1134) if not 'depends' in build_info: build_info['depends'] = [] self._fix_paths_dict(build_info) # Add to libraries list so that it is build with build_clib self.libraries.append((name, build_info)) def add_installed_library(self, name, sources, install_dir, build_info=None): """ Similar to add_library, but the specified library is installed. Most C libraries used with `distutils` are only used to build python extensions, but libraries built through this method will be installed so that they can be reused by third-party packages. Parameters ---------- name : str Name of the installed library. sources : sequence List of the library's source files. See `add_library` for details. install_dir : str Path to install the library, relative to the current sub-package. build_info : dict, optional The following keys are allowed: * depends * macros * include_dirs * extra_compiler_args * extra_f77_compile_args * extra_f90_compile_args * f2py_options * language Returns ------- None See Also -------- add_library, add_npy_pkg_config, get_info Notes ----- The best way to encode the options required to link against the specified C libraries is to use a "libname.ini" file, and use `get_info` to retrieve the required options (see `add_npy_pkg_config` for more information). """ if not build_info: build_info = {} install_dir = os.path.join(self.package_path, install_dir) self._add_library(name, sources, install_dir, build_info) self.installed_libraries.append(InstallableLib(name, build_info, install_dir)) def add_npy_pkg_config(self, template, install_dir, subst_dict=None): """ Generate and install a npy-pkg config file from a template. The config file generated from `template` is installed in the given install directory, using `subst_dict` for variable substitution. Parameters ---------- template : str The path of the template, relatively to the current package path. install_dir : str Where to install the npy-pkg config file, relatively to the current package path. subst_dict : dict, optional If given, any string of the form ``@key@`` will be replaced by ``subst_dict[key]`` in the template file when installed. The install prefix is always available through the variable ``@prefix@``, since the install prefix is not easy to get reliably from setup.py. See also -------- add_installed_library, get_info Notes ----- This works for both standard installs and in-place builds, i.e. the ``@prefix@`` refer to the source directory for in-place builds. Examples -------- :: config.add_npy_pkg_config('foo.ini.in', 'lib', {'foo': bar}) Assuming the foo.ini.in file has the following content:: [meta] Name=@foo@ Version=1.0 Description=dummy description [default] Cflags=-I@prefix@/include Libs= The generated file will have the following content:: [meta] Name=bar Version=1.0 Description=dummy description [default] Cflags=-Iprefix_dir/include Libs= and will be installed as foo.ini in the 'lib' subpath. When cross-compiling with numpy distutils, it might be necessary to use modified npy-pkg-config files. Using the default/generated files will link with the host libraries (i.e. libnpymath.a). For cross-compilation you of-course need to link with target libraries, while using the host Python installation. You can copy out the numpy/core/lib/npy-pkg-config directory, add a pkgdir value to the.ini files and set NPY_PKG_CONFIG_PATH environment variable to point to the directory with the modified npy-pkg-config files. Example npymath.ini modified for cross-compilation:: [meta] Name=npymath Description=Portable, core math library implementing C99 standard Version=0.1 [variables] pkgname=numpy.core pkgdir=/build/arm-linux-gnueabi/sysroot/usr/lib/python3.7/site-packages/numpy/core prefix=${pkgdir} libdir=${prefix}/lib includedir=${prefix}/include [default] Libs=-L${libdir} -lnpymath Cflags=-I${includedir} Requires=mlib [msvc] Libs=/LIBPATH:${libdir} npymath.lib Cflags=/INCLUDE:${includedir} Requires=mlib """ if subst_dict is None: subst_dict = {} template = os.path.join(self.package_path, template) if self.name in self.installed_pkg_config: self.installed_pkg_config[self.name].append((template, install_dir, subst_dict)) else: self.installed_pkg_config[self.name] = [(template, install_dir, subst_dict)] def add_scripts(self,*files): """Add scripts to configuration. Add the sequence of files to the beginning of the scripts list. Scripts will be installed under the <prefix>/bin/ directory. """ scripts = self.paths(files) dist = self.get_distribution() if dist is not None: if dist.scripts is None: dist.scripts = [] dist.scripts.extend(scripts) else: self.scripts.extend(scripts) def dict_append(self,**dict): for key in self.list_keys: a = getattr(self, key) a.extend(dict.get(key, [])) for key in self.dict_keys: a = getattr(self, key) a.update(dict.get(key, {})) known_keys = self.list_keys + self.dict_keys + self.extra_keys for key in dict.keys(): if key not in known_keys: a = getattr(self, key, None) if a and a==dict[key]: continue self.warn('Inheriting attribute %r=%r from %r' \ % (key, dict[key], dict.get('name', '?'))) setattr(self, key, dict[key]) self.extra_keys.append(key) elif key in self.extra_keys: self.info('Ignoring attempt to set %r (from %r to %r)' \ % (key, getattr(self, key), dict[key])) elif key in known_keys: # key is already processed above pass else: raise ValueError("Don't know about key=%r" % (key)) def __str__(self): from pprint import pformat known_keys = self.list_keys + self.dict_keys + self.extra_keys s = '<'+5*'-' + '\n' s += 'Configuration of '+self.name+':\n' known_keys.sort() for k in known_keys: a = getattr(self, k, None) if a: s += '%s = %s\n' % (k, pformat(a)) s += 5*'-' + '>' return s def get_config_cmd(self): """ Returns the numpy.distutils config command instance. """ cmd = get_cmd('config') cmd.ensure_finalized() cmd.dump_source = 0 cmd.noisy = 0 old_path = os.environ.get('PATH') if old_path: path = os.pathsep.join(['.', old_path]) os.environ['PATH'] = path return cmd def get_build_temp_dir(self): """ Return a path to a temporary directory where temporary files should be placed. """ cmd = get_cmd('build') cmd.ensure_finalized() return cmd.build_temp def have_f77c(self): """Check for availability of Fortran 77 compiler. Use it inside source generating function to ensure that setup distribution instance has been initialized. Notes ----- True if a Fortran 77 compiler is available (because a simple Fortran 77 code was able to be compiled successfully). """ simple_fortran_subroutine = ''' subroutine simple end ''' config_cmd = self.get_config_cmd() flag = config_cmd.try_compile(simple_fortran_subroutine, lang='f77') return flag def have_f90c(self): """Check for availability of Fortran 90 compiler. Use it inside source generating function to ensure that setup distribution instance has been initialized. Notes ----- True if a Fortran 90 compiler is available (because a simple Fortran 90 code was able to be compiled successfully) """ simple_fortran_subroutine = ''' subroutine simple end ''' config_cmd = self.get_config_cmd() flag = config_cmd.try_compile(simple_fortran_subroutine, lang='f90') return flag def append_to(self, extlib): """Append libraries, include_dirs to extension or library item. """ if is_sequence(extlib): lib_name, build_info = extlib dict_append(build_info, libraries=self.libraries, include_dirs=self.include_dirs) else: from numpy.distutils.core import Extension assert isinstance(extlib, Extension), repr(extlib) extlib.libraries.extend(self.libraries) extlib.include_dirs.extend(self.include_dirs) def _get_svn_revision(self, path): """Return path's SVN revision number. """ try: output = subprocess.check_output(['svnversion'], cwd=path) except (subprocess.CalledProcessError, OSError): pass else: m = re.match(rb'(?P<revision>\d+)', output) if m: return int(m.group('revision')) if sys.platform=='win32' and os.environ.get('SVN_ASP_DOT_NET_HACK', None): entries = njoin(path, '_svn', 'entries') else: entries = njoin(path, '.svn', 'entries') if os.path.isfile(entries): with open(entries) as f: fstr = f.read() if fstr[:5] == '<?xml': # pre 1.4 m = re.search(r'revision="(?P<revision>\d+)"', fstr) if m: return int(m.group('revision')) else: # non-xml entries file --- check to be sure that m = re.search(r'dir[\n\r]+(?P<revision>\d+)', fstr) if m: return int(m.group('revision')) return None def _get_hg_revision(self, path): """Return path's Mercurial revision number. """ try: output = subprocess.check_output( ['hg', 'identify', '--num'], cwd=path) except (subprocess.CalledProcessError, OSError): pass else: m = re.match(rb'(?P<revision>\d+)', output) if m: return int(m.group('revision')) branch_fn = njoin(path, '.hg', 'branch') branch_cache_fn = njoin(path, '.hg', 'branch.cache') if os.path.isfile(branch_fn): branch0 = None with open(branch_fn) as f: revision0 = f.read().strip() branch_map = {} with open(branch_cache_fn) as f: for line in f: branch1, revision1 = line.split()[:2] if revision1==revision0: branch0 = branch1 try: revision1 = int(revision1) except ValueError: continue branch_map[branch1] = revision1 return branch_map.get(branch0) return None def get_version(self, version_file=None, version_variable=None): """Try to get version string of a package. Return a version string of the current package or None if the version information could not be detected. Notes ----- This method scans files named __version__.py, <packagename>_version.py, version.py, and __svn_version__.py for string variables version, __version__, and <packagename>_version, until a version number is found. """ version = getattr(self,'version', None) if version is not None: return version # Get version from version file. if version_file is None: files = ['__version__.py', self.name.split('.')[-1]+'_version.py', 'version.py', '__svn_version__.py', '__hg_version__.py'] else: files = [version_file] if version_variable is None: version_vars = ['version', '__version__', self.name.split('.')[-1]+'_version'] else: version_vars = [version_variable] for f in files: fn = njoin(self.local_path, f) if os.path.isfile(fn): info = ('.py', 'U', 1) name = os.path.splitext(os.path.basename(fn))[0] n = dot_join(self.name, name) try: version_module = exec_mod_from_location( '_'.join(n.split('.')), fn) except ImportError as e: self.warn(str(e)) version_module = None if version_module is None: continue for a in version_vars: version = getattr(version_module, a, None) if version is not None: break # Try if versioneer module try: version = version_module.get_versions()['version'] except AttributeError: pass if version is not None: break if version is not None: self.version = version return version # Get version as SVN or Mercurial revision number revision = self._get_svn_revision(self.local_path) if revision is None: revision = self._get_hg_revision(self.local_path) if revision is not None: version = str(revision) self.version = version return version def make_svn_version_py(self, delete=True): """Appends a data function to the data_files list that will generate __svn_version__.py file to the current package directory. Generate package __svn_version__.py file from SVN revision number, it will be removed after python exits but will be available when sdist, etc commands are executed. Notes ----- If __svn_version__.py existed before, nothing is done. This is intended for working with source directories that are in an SVN repository. """ target = njoin(self.local_path, '__svn_version__.py') revision = self._get_svn_revision(self.local_path) if os.path.isfile(target) or revision is None: return else: def generate_svn_version_py(): if not os.path.isfile(target): version = str(revision) self.info('Creating %s (version=%r)' % (target, version)) with open(target, 'w') as f: f.write('version = %r\n' % (version)) def rm_file(f=target,p=self.info): if delete: try: os.remove(f); p('removed '+f) except OSError: pass try: os.remove(f+'c'); p('removed '+f+'c') except OSError: pass atexit.register(rm_file) return target self.add_data_files(('', generate_svn_version_py())) def make_hg_version_py(self, delete=True): """Appends a data function to the data_files list that will generate __hg_version__.py file to the current package directory. Generate package __hg_version__.py file from Mercurial revision, it will be removed after python exits but will be available when sdist, etc commands are executed. Notes ----- If __hg_version__.py existed before, nothing is done. This is intended for working with source directories that are in an Mercurial repository. """ target = njoin(self.local_path, '__hg_version__.py') revision = self._get_hg_revision(self.local_path) if os.path.isfile(target) or revision is None: return else: def generate_hg_version_py(): if not os.path.isfile(target): version = str(revision) self.info('Creating %s (version=%r)' % (target, version)) with open(target, 'w') as f: f.write('version = %r\n' % (version)) def rm_file(f=target,p=self.info): if delete: try: os.remove(f); p('removed '+f) except OSError: pass try: os.remove(f+'c'); p('removed '+f+'c') except OSError: pass atexit.register(rm_file) return target self.add_data_files(('', generate_hg_version_py())) def make_config_py(self,name='__config__'): """Generate package __config__.py file containing system_info information used during building the package. This file is installed to the package installation directory. """ self.py_modules.append((self.name, name, generate_config_py)) def get_info(self,*names): """Get resources information. Return information (from system_info.get_info) for all of the names in the argument list in a single dictionary. """ from.system_info import get_info, dict_append info_dict = {} for a in names: dict_append(info_dict,**get_info(a)) return info_dict def get_cmd(cmdname, _cache={}): if cmdname not in _cache: import distutils.core dist = distutils.core._setup_distribution if dist is None: from distutils.errors import DistutilsInternalError raise DistutilsInternalError( 'setup distribution instance not initialized') cmd = dist.get_command_obj(cmdname) _cache[cmdname] = cmd return _cache[cmdname] def get_numpy_include_dirs(): # numpy_include_dirs are set by numpy/core/setup.py, otherwise [] include_dirs = Configuration.numpy_include_dirs[:] if not include_dirs: import numpy include_dirs = [ numpy.get_include() ] # else running numpy/core/setup.py return include_dirs def get_npy_pkg_dir(): """Return the path where to find the npy-pkg-config directory. If the NPY_PKG_CONFIG_PATH environment variable is set, the value of that is returned. Otherwise, a path inside the location of the numpy module is returned. The NPY_PKG_CONFIG_PATH can be useful when cross-compiling, maintaining customized npy-pkg-config.ini files for the cross-compilation environment, and using them when cross-compiling. """ d = os.environ.get('NPY_PKG_CONFIG_PATH') if d is not None: return d spec = importlib.util.find_spec('numpy') d = os.path.join(os.path.dirname(spec.origin), 'core', 'lib', 'npy-pkg-config') return d def get_pkg_info(pkgname, dirs=None): """ Return library info for the given package. Parameters ---------- pkgname : str Name of the package (should match the name of the.ini file, without the extension, e.g. foo for the file foo.ini). dirs : sequence, optional If given, should be a sequence of additional directories where to look for npy-pkg-config files. Those directories are searched prior to the NumPy directory. Returns ------- pkginfo : class instance The `LibraryInfo` instance containing the build information. Raises ------ PkgNotFound If the package is not found. See Also -------- Configuration.add_npy_pkg_config, Configuration.add_installed_library, get_info """ from numpy.distutils.npy_pkg_config import read_config if dirs: dirs.append(get_npy_pkg_dir()) else: dirs = [get_npy_pkg_dir()] return read_config(pkgname, dirs) def get_info(pkgname, dirs=None): """ Return an info dict for a given C library. The info dict contains the necessary options to use the C library. Parameters ---------- pkgname : str Name of the package (should match the name of the.ini file, without the extension, e.g. foo for the file foo.ini). dirs : sequence, optional If given, should be a sequence of additional directories where to look for npy-pkg-config files. Those directories are searched prior to the NumPy directory. Returns ------- info : dict The dictionary with build information. Raises ------ PkgNotFound If the package is not found. See Also -------- Configuration.add_npy_pkg_config, Configuration.add_installed_library, get_pkg_info Examples -------- To get the necessary information for the npymath library from NumPy: >>> npymath_info = np.distutils.misc_util.get_info('npymath') >>> npymath_info #doctest: +SKIP {'define_macros': [], 'libraries': ['npymath'], 'library_dirs': ['.../numpy/core/lib'], 'include_dirs': ['.../numpy/core/include']} This info dict can then be used as input to a `Configuration` instance:: config.add_extension('foo', sources=['foo.c'], extra_info=npymath_info) """ from numpy.distutils.npy_pkg_config import parse_flags pkg_info = get_pkg_info(pkgname, dirs) # Translate LibraryInfo instance into a build_info dict info = parse_flags(pkg_info.cflags()) for k, v in parse_flags(pkg_info.libs()).items(): info[k].extend(v) # add_extension extra_info argument is ANAL info['define_macros'] = info['macros'] del info['macros'] del info['ignored'] return info def is_bootstrapping(): import builtins try: builtins.__NUMPY_SETUP__ return True except AttributeError: return False ######################### def default_config_dict(name = None, parent_name = None, local_path=None): """Return a configuration dictionary for usage in configuration() function defined in file setup_<name>.py. """ import warnings warnings.warn('Use Configuration(%r,%r,top_path=%r) instead of '\ 'deprecated default_config_dict(%r,%r,%r)' % (name, parent_name, local_path, name, parent_name, local_path, ), stacklevel=2) c = Configuration(name, parent_name, local_path) return c.todict() def dict_append(d, **kws): for k, v in kws.items(): if k in d: ov = d[k] if isinstance(ov, str): d[k] = v else: d[k].extend(v) else: d[k] = v def appendpath(prefix, path): if os.path.sep!= '/': prefix = prefix.replace('/', os.path.sep) path = path.replace('/', os.path.sep) drive = '' if os.path.isabs(path): drive = os.path.splitdrive(prefix)[0] absprefix = os.path.splitdrive(os.path.abspath(prefix))[1] pathdrive, path = os.path.splitdrive(path) d = os.path.commonprefix([absprefix, path]) if os.path.join(absprefix[:len(d)], absprefix[len(d):])!= absprefix \ or os.path.join(path[:len(d)], path[len(d):])!= path: # Handle invalid paths d = os.path.dirname(d) subpath = path[len(d):] if os.path.isabs(subpath): subpath = subpath[1:] else: subpath = path return os.path.normpath(njoin(drive + prefix, subpath)) def generate_config_py(target): """Generate config.py file containing system_info information used during building the package. Usage: config['py_modules'].append((packagename, '__config__',generate_config_py)) """ from numpy.distutils.system_info import system_info from distutils.dir_util import mkpath mkpath(os.path.dirname(target)) with open(target, 'w') as f: f.write('# This file is generated by numpy\'s %s\n' % (os.path.basename(sys.argv[0]))) f.write('# It contains system_info results at the time of building this package.\n') f.write('__all__ = ["get_info","show"]\n\n') # For gfortran+msvc combination, extra shared libraries may exist f.write(textwrap.dedent(""" import os import sys extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs') if sys.platform == 'win32' and os.path.isdir(extra_dll_dir): os.add_dll_directory(extra_dll_dir) """)) for k, i in system_info.saved_results.items(): f.write('%s=%r\n' % (k, i)) f.write(textwrap.dedent(r''' def get_info(name): g = globals() return g.get(name, g.get(name + "_info", {})) def show(): """ Show libraries in the system on which NumPy was built. Print information about various resources (libraries, library directories, include directories, etc.) in the system on which NumPy was built. See Also -------- get_include : Returns the directory containing NumPy C header files. Notes ----- 1. Classes specifying the information to be printed are defined in the `numpy.distutils.system_info` module. Information may include: * ``language``: language used to write the libraries (mostly C or f77) * ``libraries``: names of libraries found in the system * ``library_dirs``: directories containing the libraries * ``include_dirs``: directories containing library header files * ``src_dirs``: directories containing library source files * ``define_macros``: preprocessor macros used by ``distutils.setup`` * ``baseline``: minimum CPU features required * ``found``: dispatched features supported in the system * ``not found``: dispatched features that are not supported in the system 2. NumPy BLAS/LAPACK Installation Notes Installing a numpy wheel (``pip install numpy`` or force it via ``pip install numpy --only-binary :numpy: numpy``) includes an OpenBLAS implementation of the BLAS and LAPACK linear algebra APIs. In this case, ``library_dirs`` reports the original build time configuration as compiled with gcc/gfortran; at run time the OpenBLAS library is in ``site-packages/numpy.libs/`` (linux), or ``site-packages/numpy/.dylibs/`` (macOS), or ``site-packages/numpy/.libs/`` (windows). Installing numpy from source (``pip install numpy --no-binary numpy``) searches for BLAS and LAPACK dynamic link libraries at build time as influenced by environment variables NPY_BLAS_LIBS, NPY_CBLAS_LIBS, and NPY_LAPACK_LIBS; or NPY_BLAS_ORDER and NPY_LAPACK_ORDER; or the optional file ``~/.numpy-site.cfg``. NumPy remembers those locations and expects to load the same libraries at run-time. In NumPy 1.21+ on macOS, 'accelerate' (Apple's Accelerate BLAS library) is in the default build-time search order after 'openblas'. Examples -------- >>> import numpy as np >>> np.show_config() blas_opt_info: language = c define_macros = [('HAVE_CBLAS', None)] libraries = ['openblas', 'openblas'] library_dirs = ['/usr/local/lib'] """ from numpy.core._multiarray_umath import ( __cpu_features__, __cpu_baseline__, __cpu_dispatch__ ) for name,info_dict in globals().items(): if name[0] == "_" or type(info_dict) is not type({}): continue print(name + ":") if not info_dict: print(" NOT AVAILABLE") for k,v in info_dict.items(): v = str(v) if k == "sources" and len(v) > 200: v = v[:60] + "...\n... " + v[-60:] print(" %s = %s" % (k,v)) features_found, features_not_found = [], [] for feature in __cpu_dispatch__: if __cpu_features__[feature]: features_found.append(feature) else: features_not_found.append(feature) print("Supported SIMD extensions in this NumPy install:") print(" baseline = %s" % (','.join(__cpu_baseline__))) print(" found = %s" % (','.join(features_found))) print(" not found = %s" % (','.join(features_not_found))) ''')) return target def msvc_version(compiler): """Return version major and minor of compiler instance if it is MSVC, raise an exception otherwise.""" if not compiler.compiler_type == "msvc": raise ValueError("Compiler instance is not msvc (%s)"\ % compiler.compiler_type) return compiler._MSVCCompiler__version def get_build_architecture(): # Importing distutils.msvccompiler triggers a warning on non-Windows # systems, so delay the import to here. from distutils.msvccompiler import get_build_architecture return get_build_architecture() _cxx_ignore_flags = {'-Werror=implicit-function-declaration', '-std=c99'} def sanitize_cxx_flags(cxxflags): ''' Some flags are valid for C but not C++. Prune them. ''' return [flag for flag in cxxflags if flag not in _cxx_ignore_flags] def exec_mod_from_location(modname, modfile): ''' Use importlib machinery to import a module `modname` from the file `modfile`. Depending on the `spec.loader`, the module may not be registered in sys.modules. ''' spec = importlib.util.spec_from_file_location(modname, modfile) foo = importlib.util.module_from_spec(spec) spec.loader.exec_module(foo) return foo

numpy__numpy

basics.indexing.rst

Module doc

Generate documentation for this module

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numpy__numpy/doc/source/user/basics.indexing.rst

numpy__numpy/numpy

Structured arrays Introduction Structured arrays are ndarrays whose datatype is a composition of simpler datatypes organized as a sequence of named fields <field>. For example, : >>> x = np.array([('Rex', 9, 81.0), ('Fido', 3, 27.0)], ... dtype=[('name', 'U10'), ('age', 'i4'), ('weight', 'f4')]) >>> x array([('Rex', 9, 81.), ('Fido', 3, 27.)], dtype=[('name', '<U10'), ('age', '<i4'), ('weight', '<f4')]) Here x is a one-dimensional array of length two whose datatype is a structure with three fields: 1. A string of length 10 or less named 'name', 2. a 32-bit integer named 'age', and 3. a 32-bit float named 'weight'. If you index x at position 1 you get a structure: >>> x[1] np.void(('Fido', 3, 27.0), dtype=[('name', '<U10'), ('age', '<i4'), ('weight', '<f4')]) You can access and modify individual fields of a structured array by indexing with the field name: >>> x['age'] array([9, 3], dtype=int32) >>> x['age'] = 5 >>> x array([('Rex', 5, 81.), ('Fido', 5, 27.)], dtype=[('name', '<U10'), ('age', '<i4'), ('weight', '<f4')]) Structured datatypes are designed to be able to mimic 'structs' in the C language, and share a similar memory layout. They are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure. Users looking to manipulate tabular data, such as stored in csv files, may find other pydata projects more suitable, such as xarray, pandas, or DataArray. These provide a high-level interface for tabular data analysis and are better optimized for that use. For instance, the C-struct-like memory layout of structured arrays in numpy can lead to poor cache behavior in comparison. Structured Datatypes A structured datatype can be thought of as a sequence of bytes of a certain length (the structure's itemsize) which is interpreted as a collection of fields. Each field has a name, a datatype, and a byte offset within the structure. The datatype of a field may be any numpy datatype including other structured datatypes, and it may also be a subarray data type which behaves like an ndarray of a specified shape. The offsets of the fields are arbitrary, and fields may even overlap. These offsets are usually determined automatically by numpy, but can also be specified. Structured Datatype Creation Structured datatypes may be created using the function numpy.dtype. There are 4 alternative forms of specification which vary in flexibility and conciseness. These are further documented in the Data Type Objects <arrays.dtypes.constructing> reference page, and in summary they are: 1. A list of tuples, one tuple per field Each tuple has the form (fieldname, datatype, shape) where shape is optional. fieldname is a string (or tuple if titles are used, see Field Titles <titles> below), datatype may be any object convertible to a datatype, and shape is a tuple of integers specifying subarray shape. >>> np.dtype([('x', 'f4'), ('y', np.float32), ('z', 'f4', (2, 2))]) dtype([('x', '<f4'), ('y', '<f4'), ('z', '<f4', (2, 2))]) If fieldname is the empty string '', the field will be given a default name of the form f#, where # is the integer index of the field, counting from 0 from the left: >>> np.dtype([('x', 'f4'), ('', 'i4'), ('z', 'i8')]) dtype([('x', '<f4'), ('f1', '<i4'), ('z', '<i8')]) The byte offsets of the fields within the structure and the total structure itemsize are determined automatically. 2. A string of comma-separated dtype specifications In this shorthand notation any of the string dtype specifications <arrays.dtypes.constructing> may be used in a string and separated by commas. The itemsize and byte offsets of the fields are determined automatically, and the field names are given the default names f0, f1, etc. : >>> np.dtype('i8, f4, S3') dtype([('f0', '<i8'), ('f1', '<f4'), ('f2', 'S3')]) >>> np.dtype('3int8, float32, (2, 3)float64') dtype([('f0', 'i1', (3,)), ('f1', '<f4'), ('f2', '<f8', (2, 3))]) 3. A dictionary of field parameter arrays This is the most flexible form of specification since it allows control over the byte-offsets of the fields and the itemsize of the structure. The dictionary has two required keys, 'names' and 'formats', and four optional keys, 'offsets', 'itemsize', 'aligned' and 'titles'. The values for 'names' and 'formats' should respectively be a list of field names and a list of dtype specifications, of the same length. The optional 'offsets' value should be a list of integer byte-offsets, one for each field within the structure. If 'offsets' is not given the offsets are determined automatically. The optional 'itemsize' value should be an integer describing the total size in bytes of the dtype, which must be large enough to contain all the fields. : >>> np.dtype({'names': ['col1', 'col2'], 'formats': ['i4', 'f4']}) dtype([('col1', '<i4'), ('col2', '<f4')]) >>> np.dtype({'names': ['col1', 'col2'], ... 'formats': ['i4', 'f4'], ... 'offsets': [0, 4], ... 'itemsize': 12}) dtype({'names': ['col1', 'col2'], 'formats': ['<i4', '<f4'], 'offsets': [0, 4], 'itemsize': 12}) Offsets may be chosen such that the fields overlap, though this will mean that assigning to one field may clobber any overlapping field's data. As an exception, fields of numpy.object_ type cannot overlap with other fields, because of the risk of clobbering the internal object pointer and then dereferencing it. The optional 'aligned' value can be set to True to make the automatic offset computation use aligned offsets (see offsets-and-alignment), as if the 'align' keyword argument of numpy.dtype had been set to True. The optional 'titles' value should be a list of titles of the same length as 'names', see Field Titles <titles> below. 4. A dictionary of field names The keys of the dictionary are the field names and the values are tuples specifying type and offset: >>> np.dtype({'col1': ('i1', 0), 'col2': ('f4', 1)}) dtype([('col1', 'i1'), ('col2', '<f4')]) This form was discouraged because Python dictionaries did not preserve order in Python versions before Python 3.6. Field Titles <titles> may be specified by using a 3-tuple, see below. Manipulating and Displaying Structured Datatypes The list of field names of a structured datatype can be found in the names attribute of the dtype object: >>> d = np.dtype([('x', 'i8'), ('y', 'f4')]) >>> d.names ('x', 'y') The dtype of each individual field can be looked up by name: >>> d['x'] dtype('int64') The field names may be modified by assigning to the names attribute using a sequence of strings of the same length. The dtype object also has a dictionary-like attribute, fields, whose keys are the field names (and Field Titles <titles>, see below) and whose values are tuples containing the dtype and byte offset of each field. : >>> d.fields mappingproxy({'x': (dtype('int64'), 0), 'y': (dtype('float32'), 8)}) Both the names and fields attributes will equal None for unstructured arrays. The recommended way to test if a dtype is structured is with if dt.names is not None rather than if dt.names, to account for dtypes with 0 fields. The string representation of a structured datatype is shown in the "list of tuples" form if possible, otherwise numpy falls back to using the more general dictionary form. Automatic Byte Offsets and Alignment Numpy uses one of two methods to automatically determine the field byte offsets and the overall itemsize of a structured datatype, depending on whether align=True was specified as a keyword argument to numpy.dtype. By default (align=False), numpy will pack the fields together such that each field starts at the byte offset the previous field ended, and the fields are contiguous in memory. : >>> def print_offsets(d): ... print("offsets:", [d.fields[name][1] for name in d.names]) ... print("itemsize:", d.itemsize) >>> print_offsets(np.dtype('u1, u1, i4, u1, i8, u2')) offsets: [0, 1, 2, 6, 7, 15] itemsize: 17 If align=True is set, numpy will pad the structure in the same way many C compilers would pad a C-struct. Aligned structures can give a performance improvement in some cases, at the cost of increased datatype size. Padding bytes are inserted between fields such that each field's byte offset will be a multiple of that field's alignment, which is usually equal to the field's size in bytes for simple datatypes, see PyArray_Descr.alignment. The structure will also have trailing padding added so that its itemsize is a multiple of the largest field's alignment. : >>> print_offsets(np.dtype('u1, u1, i4, u1, i8, u2', align=True)) offsets: [0, 1, 4, 8, 16, 24] itemsize: 32 Note that although almost all modern C compilers pad in this way by default, padding in C structs is C-implementation-dependent so this memory layout is not guaranteed to exactly match that of a corresponding struct in a C program. Some work may be needed, either on the numpy side or the C side, to obtain exact correspondence. If offsets were specified using the optional offsets key in the dictionary-based dtype specification, setting align=True will check that each field's offset is a multiple of its size and that the itemsize is a multiple of the largest field size, and raise an exception if not. If the offsets of the fields and itemsize of a structured array satisfy the alignment conditions, the array will have the ALIGNED flag <numpy.ndarray.flags> set. A convenience function numpy.lib.recfunctions.repack_fields converts an aligned dtype or array to a packed one and vice versa. It takes either a dtype or structured ndarray as an argument, and returns a copy with fields re-packed, with or without padding bytes. Field Titles In addition to field names, fields may also have an associated title, an alternate name, which is sometimes used as an additional description or alias for the field. The title may be used to index an array, just like a field name. To add titles when using the list-of-tuples form of dtype specification, the field name may be specified as a tuple of two strings instead of a single string, which will be the field's title and field name respectively. For example: >>> np.dtype([(('my title', 'name'), 'f4')]) dtype([(('my title', 'name'), '<f4')]) When using the first form of dictionary-based specification, the titles may be supplied as an extra 'titles' key as described above. When using the second (discouraged) dictionary-based specification, the title can be supplied by providing a 3-element tuple (datatype, offset, title) instead of the usual 2-element tuple: >>> np.dtype({'name': ('i4', 0, 'my title')}) dtype([(('my title', 'name'), '<i4')]) The dtype.fields dictionary will contain titles as keys, if any titles are used. This means effectively that a field with a title will be represented twice in the fields dictionary. The tuple values for these fields will also have a third element, the field title. Because of this, and because the names attribute preserves the field order while the fields attribute may not, it is recommended to iterate through the fields of a dtype using the names attribute of the dtype, which will not list titles, as in: >>> for name in d.names: ... print(d.fields[name][:2]) (dtype('int64'), 0) (dtype('float32'), 8) Union types Structured datatypes are implemented in numpy to have base type numpy.void by default, but it is possible to interpret other numpy types as structured types using the (base_dtype, dtype) form of dtype specification described in Data Type Objects <arrays.dtypes.constructing>. Here, base_dtype is the desired underlying dtype, and fields and flags will be copied from dtype. This dtype is similar to a 'union' in C. Indexing and Assignment to Structured arrays Assigning data to a Structured Array There are a number of ways to assign values to a structured array: Using python tuples, using scalar values, or using other structured arrays. Assignment from Python Native Types (Tuples) The simplest way to assign values to a structured array is using python tuples. Each assigned value should be a tuple of length equal to the number of fields in the array, and not a list or array as these will trigger numpy's broadcasting rules. The tuple's elements are assigned to the successive fields of the array, from left to right: >>> x = np.array([(1, 2, 3), (4, 5, 6)], dtype='i8, f4, f8') >>> x[1] = (7, 8, 9) >>> x array([(1, 2., 3.), (7, 8., 9.)], dtype=[('f0', '<i8'), ('f1', '<f4'), ('f2', '<f8')]) Assignment from Scalars A scalar assigned to a structured element will be assigned to all fields. This happens when a scalar is assigned to a structured array, or when an unstructured array is assigned to a structured array: >>> x = np.zeros(2, dtype='i8, f4, ?, S1') >>> x[:] = 3 >>> x array([(3, 3., True, b'3'), (3, 3., True, b'3')], dtype=[('f0', '<i8'), ('f1', '<f4'), ('f2', '?'), ('f3', 'S1')]) >>> x[:] = np.arange(2) >>> x array([(0, 0., False, b'0'), (1, 1., True, b'1')], dtype=[('f0', '<i8'), ('f1', '<f4'), ('f2', '?'), ('f3', 'S1')]) Structured arrays can also be assigned to unstructured arrays, but only if the structured datatype has just a single field: >>> twofield = np.zeros(2, dtype=[('A', 'i4'), ('B', 'i4')]) >>> onefield = np.zeros(2, dtype=[('A', 'i4')]) >>> nostruct = np.zeros(2, dtype='i4') >>> nostruct[:] = twofield Traceback (most recent call last): ... TypeError: Cannot cast array data from dtype([('A', '<i4'), ('B', '<i4')]) to dtype('int32') according to the rule 'unsafe' Assignment from other Structured Arrays Assignment between two structured arrays occurs as if the source elements had been converted to tuples and then assigned to the destination elements. That is, the first field of the source array is assigned to the first field of the destination array, and the second field likewise, and so on, regardless of field names. Structured arrays with a different number of fields cannot be assigned to each other. Bytes of the destination structure which are not included in any of the fields are unaffected. : >>> a = np.zeros(3, dtype=[('a', 'i8'), ('b', 'f4'), ('c', 'S3')]) >>> b = np.ones(3, dtype=[('x', 'f4'), ('y', 'S3'), ('z', 'O')]) >>> b[:] = a >>> b array([(0., b'0.0', b''), (0., b'0.0', b''), (0., b'0.0', b'')], dtype=[('x', '<f4'), ('y', 'S3'), ('z', 'O')]) Assignment involving subarrays When assigning to fields which are subarrays, the assigned value will first be broadcast to the shape of the subarray. Indexing Structured Arrays Accessing Individual Fields Individual fields of a structured array may be accessed and modified by indexing the array with the field name. : >>> x = np.array([(1, 2), (3, 4)], dtype=[('foo', 'i8'), ('bar', 'f4')]) >>> x['foo'] array([1, 3]) >>> x['foo'] = 10 >>> x array([(10, 2.), (10, 4.)], dtype=[('foo', '<i8'), ('bar', '<f4')]) The resulting array is a view into the original array. It shares the same memory locations and writing to the view will modify the original array. : >>> y = x['bar'] >>> y[:] = 11 >>> x array([(10, 11.), (10, 11.)], dtype=[('foo', '<i8'), ('bar', '<f4')]) This view has the same dtype and itemsize as the indexed field, so it is typically a non-structured array, except in the case of nested structures. >>> y.dtype, y.shape, y.strides (dtype('float32'), (2,), (12,)) If the accessed field is a subarray, the dimensions of the subarray are appended to the shape of the result: >>> x = np.zeros((2, 2), dtype=[('a', np.int32), ('b', np.float64, (3, 3))]) >>> x['a'].shape (2, 2) >>> x['b'].shape (2, 2, 3, 3) Accessing Multiple Fields One can index and assign to a structured array with a multi-field index, where the index is a list of field names. Warning The behavior of multi-field indexes changed from Numpy 1.15 to Numpy 1.16. The result of indexing with a multi-field index is a view into the original array, as follows: >>> a = np.zeros(3, dtype=[('a', 'i4'), ('b', 'i4'), ('c', 'f4')]) >>> a[['a', 'c']] array([(0, 0.), (0, 0.), (0, 0.)], dtype={'names': ['a', 'c'], 'formats': ['<i4', '<f4'], 'offsets': [0, 8], 'itemsize': 12}) Assignment to the view modifies the original array. The view's fields will be in the order they were indexed. Note that unlike for single-field indexing, the dtype of the view has the same itemsize as the original array, and has fields at the same offsets as in the original array, and unindexed fields are merely missing. Warning In Numpy 1.15, indexing an array with a multi-field index returned a copy of the result above, but with fields packed together in memory as if passed through numpy.lib.recfunctions.repack_fields. The new behavior as of Numpy 1.16 leads to extra "padding" bytes at the location of unindexed fields compared to 1.15. You will need to update any code which depends on the data having a "packed" layout. For instance code such as: >>> a[['a', 'c']].view('i8') # Fails in Numpy 1.16 Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: When changing to a smaller dtype, its size must be a divisor of the size of original dtype will need to be changed. This code has raised a FutureWarning since Numpy 1.12, and similar code has raised FutureWarning since 1.7. In 1.16 a number of functions have been introduced in the numpy.lib.recfunctions module to help users account for this change. These are numpy.lib.recfunctions.repack_fields. numpy.lib.recfunctions.structured_to_unstructured, numpy.lib.recfunctions.unstructured_to_structured, numpy.lib.recfunctions.apply_along_fields, numpy.lib.recfunctions.assign_fields_by_name, and numpy.lib.recfunctions.require_fields. The function numpy.lib.recfunctions.repack_fields can always be used to reproduce the old behavior, as it will return a packed copy of the structured array. The code above, for example, can be replaced with: >>> from numpy.lib.recfunctions import repack_fields >>> repack_fields(a[['a', 'c']]).view('i8') # supported in 1.16 array([0, 0, 0]) Furthermore, numpy now provides a new function numpy.lib.recfunctions.structured_to_unstructured which is a safer and more efficient alternative for users who wish to convert structured arrays to unstructured arrays, as the view above is often intended to do. This function allows safe conversion to an unstructured type taking into account padding, often avoids a copy, and also casts the datatypes as needed, unlike the view. Code such as: >>> b = np.zeros(3, dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4')]) >>> b[['x', 'z']].view('f4') array([0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32) can be made safer by replacing with: >>> from numpy.lib.recfunctions import structured_to_unstructured >>> structured_to_unstructured(b[['x', 'z']]) array([[0., 0.], [0., 0.], [0., 0.]], dtype=float32) Assignment to an array with a multi-field index modifies the original array: >>> a[['a', 'c']] = (2, 3) >>> a array([(2, 0, 3.), (2, 0, 3.), (2, 0, 3.)], dtype=[('a', '<i4'), ('b', '<i4'), ('c', '<f4')]) This obeys the structured array assignment rules described above. For example, this means that one can swap the values of two fields using appropriate multi-field indexes: >>> a[['a', 'c']] = a[['c', 'a']] Indexing with an Integer to get a Structured Scalar Indexing a single element of a structured array (with an integer index) returns a structured scalar: >>> x = np.array([(1, 2., 3.)], dtype='i, f, f') >>> scalar = x[0] >>> scalar np.void((1, 2.0, 3.0), dtype=[('f0', '<i4'), ('f1', '<f4'), ('f2', '<f4')]) >>> type(scalar) <class 'numpy.void'> Unlike other numpy scalars, structured scalars are mutable and act like views into the original array, such that modifying the scalar will modify the original array. Structured scalars also support access and assignment by field name: >>> x = np.array([(1, 2), (3, 4)], dtype=[('foo', 'i8'), ('bar', 'f4')]) >>> s = x[0] >>> s['bar'] = 100 >>> x array([(1, 100.), (3, 4.)], dtype=[('foo', '<i8'), ('bar', '<f4')]) Similarly to tuples, structured scalars can also be indexed with an integer: >>> scalar = np.array([(1, 2., 3.)], dtype='i, f, f')[0] >>> scalar[0] 1 >>> scalar[1] = 4 Thus, tuples might be thought of as the native Python equivalent to numpy's structured types, much like native python integers are the equivalent to numpy's integer types. Structured scalars may be converted to a tuple by calling `numpy.ndarray.item`: >>> scalar.item(), type(scalar.item()) ((1, 4.0, 3.0), <class 'tuple'>) Viewing Structured Arrays Containing Objects In order to prevent clobbering object pointers in fields of object type, numpy currently does not allow views of structured arrays containing objects. Structure Comparison and Promotion If the dtypes of two void structured arrays are equal, testing the equality of the arrays will result in a boolean array with the dimensions of the original arrays, with elements set to True where all fields of the corresponding structures are equal: >>> a = np.array([(1, 1), (2, 2)], dtype=[('a', 'i4'), ('b', 'i4')]) >>> b = np.array([(1, 1), (2, 3)], dtype=[('a', 'i4'), ('b', 'i4')]) >>> a == b array([True, False]) NumPy will promote individual field datatypes to perform the comparison. So the following is also valid (note the 'f4' dtype for the 'a' field): >>> b = np.array([(1.0, 1), (2.5, 2)], dtype=[("a", "f4"), ("b", "i4")]) >>> a == b array([True, False]) To compare two structured arrays, it must be possible to promote them to a common dtype as returned by numpy.result_type and numpy.promote_types. This enforces that the number of fields, the field names, and the field titles must match precisely. When promotion is not possible, for example due to mismatching field names, NumPy will raise an error. Promotion between two structured dtypes results in a canonical dtype that ensures native byte-order for all fields: >>> np.result_type(np.dtype("i,>i")) dtype([('f0', '<i4'), ('f1', '<i4')]) >>> np.result_type(np.dtype("i,>i"), np.dtype("i,i")) dtype([('f0', '<i4'), ('f1', '<i4')]) The resulting dtype from promotion is also guaranteed to be packed, meaning that all fields are ordered contiguously and any unnecessary padding is removed: >>> dt = np.dtype("i1,V3,i4,V1")[["f0", "f2"]] >>> dt dtype({'names':['f0','f2'], 'formats':['i1','<i4'], 'offsets':[0,4], 'itemsize':9}) >>> np.result_type(dt) dtype([('f0', 'i1'), ('f2', '<i4')]) Note that the result prints without offsets or itemsize indicating no additional padding. If a structured dtype is created with align=True ensuring that dtype.isalignedstruct is true, this property is preserved: >>> dt = np.dtype("i1,V3,i4,V1", align=True)[["f0", "f2"]] >>> dt dtype({'names':['f0','f2'], 'formats':['i1','<i4'], 'offsets':[0,4], 'itemsize':12}, align=True) >>> np.result_type(dt) dtype([('f0', 'i1'), ('f2', '<i4')], align=True) >>> np.result_type(dt).isalignedstruct True When promoting multiple dtypes, the result is aligned if any of the inputs is: >>> np.result_type(np.dtype("i,i"), np.dtype("i,i", align=True)) dtype([('f0', '<i4'), ('f1', '<i4')], align=True) The < and > operators always return False when comparing void structured arrays, and arithmetic and bitwise operations are not supported. 1.23 Before NumPy 1.23, a warning was given and False returned when promotion to a common dtype failed. Further, promotion was much more restrictive: It would reject the mixed float/integer comparison example above. Record Arrays As an optional convenience numpy provides an ndarray subclass, numpy.recarray that allows access to fields of structured arrays by attribute instead of only by index. Record arrays use a special datatype, numpy.record, that allows field access by attribute on the structured scalars obtained from the array. The numpy.rec module provides functions for creating recarrays from various objects. Additional helper functions for creating and manipulating structured arrays can be found in numpy.lib.recfunctions. The simplest way to create a record array is with numpy.rec.array <numpy.core.records.array>: >>> recordarr = np.rec.array([(1, 2., 'Hello'), (2, 3., "World")], ... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')]) >>> recordarr.bar array([2., 3.], dtype=float32) >>> recordarr[1:2] rec.array([(2, 3., b'World')], dtype=[('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')]) >>> recordarr[1:2].foo array([2], dtype=int32) >>> recordarr.foo[1:2] array([2], dtype=int32) >>> recordarr[1].baz b'World' numpy.rec.array <numpy.core.records.array> can convert a wide variety of arguments into record arrays, including structured arrays: >>> arr = np.array([(1, 2., 'Hello'), (2, 3., "World")], ... dtype=[('foo', 'i4'), ('bar', 'f4'), ('baz', 'S10')]) >>> recordarr = np.rec.array(arr) The numpy.rec module provides a number of other convenience functions for creating record arrays, see record array creation routines <routines.array-creation.rec>. A record array representation of a structured array can be obtained using the appropriate view: >>> arr = np.array([(1, 2., 'Hello'), (2, 3., "World")], ... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'a10')]) >>> recordarr = arr.view(dtype=np.dtype((np.record, arr.dtype)), ... type=np.recarray) For convenience, viewing an ndarray as type numpy.recarray will automatically convert to numpy.record datatype, so the dtype can be left out of the view: >>> recordarr = arr.view(np.recarray) >>> recordarr.dtype dtype((numpy.record, [('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')])) To get back to a plain ndarray both the dtype and type must be reset. The following view does so, taking into account the unusual case that the recordarr was not a structured type: >>> arr2 = recordarr.view(recordarr.dtype.fields or recordarr.dtype, np.ndarray) Record array fields accessed by index or by attribute are returned as a record array if the field has a structured type but as a plain ndarray otherwise. : >>> recordarr = np.rec.array([('Hello', (1, 2)), ("World", (3, 4))], ... dtype=[('foo', 'S6'),('bar', [('A', int), ('B', int)])]) >>> type(recordarr.foo) <class 'numpy.ndarray'> >>> type(recordarr.bar) <class 'numpy.recarray'> Note that if a field has the same name as an ndarray attribute, the ndarray attribute takes precedence. Such fields will be inaccessible by attribute but will still be accessible by index.

""" Collection of utilities to manipulate structured arrays. Most of these functions were initially implemented by John Hunter for matplotlib. They have been rewritten and extended for convenience. """ import itertools import numpy as np import numpy.ma as ma from numpy import ndarray, recarray from numpy.ma import MaskedArray from numpy.ma.mrecords import MaskedRecords from numpy.core.overrides import array_function_dispatch from numpy.lib._iotools import _is_string_like _check_fill_value = np.ma.core._check_fill_value __all__ = [ 'append_fields', 'apply_along_fields', 'assign_fields_by_name', 'drop_fields', 'find_duplicates', 'flatten_descr', 'get_fieldstructure', 'get_names', 'get_names_flat', 'join_by','merge_arrays','rec_append_fields', 'rec_drop_fields','rec_join','recursive_fill_fields', 'rename_fields','repack_fields','require_fields', 'stack_arrays','structured_to_unstructured', 'unstructured_to_structured', ] def _recursive_fill_fields_dispatcher(input, output): return (input, output) @array_function_dispatch(_recursive_fill_fields_dispatcher) def recursive_fill_fields(input, output): """ Fills fields from output with fields from input, with support for nested structures. Parameters ---------- input : ndarray Input array. output : ndarray Output array. Notes ----- * `output` should be at least the same size as `input` Examples -------- >>> from numpy.lib import recfunctions as rfn >>> a = np.array([(1, 10.), (2, 20.)], dtype=[('A', np.int64), ('B', np.float64)]) >>> b = np.zeros((3,), dtype=a.dtype) >>> rfn.recursive_fill_fields(a, b) array([(1, 10.), (2, 20.), (0, 0.)], dtype=[('A', '<i8'), ('B', '<f8')]) """ newdtype = output.dtype for field in newdtype.names: try: current = input[field] except ValueError: continue if current.dtype.names is not None: recursive_fill_fields(current, output[field]) else: output[field][:len(current)] = current return output def _get_fieldspec(dtype): """ Produce a list of name/dtype pairs corresponding to the dtype fields Similar to dtype.descr, but the second item of each tuple is a dtype, not a string. As a result, this handles subarray dtypes Can be passed to the dtype constructor to reconstruct the dtype, noting that this (deliberately) discards field offsets. Examples -------- >>> dt = np.dtype([(('a', 'A'), np.int64), ('b', np.double, 3)]) >>> dt.descr [(('a', 'A'), '<i8'), ('b', '<f8', (3,))] >>> _get_fieldspec(dt) [(('a', 'A'), dtype('int64')), ('b', dtype(('<f8', (3,))))] """ if dtype.names is None: #.descr returns a nameless field, so we should too return [('', dtype)] else: fields = ((name, dtype.fields[name]) for name in dtype.names) # keep any titles, if present return [ (name if len(f) == 2 else (f[2], name), f[0]) for name, f in fields ] def get_names(adtype): """ Returns the field names of the input datatype as a tuple. Input datatype must have fields otherwise error is raised. Parameters ---------- adtype : dtype Input datatype Examples -------- >>> from numpy.lib import recfunctions as rfn >>> rfn.get_names(np.empty((1,), dtype=[('A', int)]).dtype) ('A',) >>> rfn.get_names(np.empty((1,), dtype=[('A',int), ('B', float)]).dtype) ('A', 'B') >>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])]) >>> rfn.get_names(adtype) ('a', ('b', ('ba', 'bb'))) """ listnames = [] names = adtype.names for name in names: current = adtype[name] if current.names is not None: listnames.append((name, tuple(get_names(current)))) else: listnames.append(name) return tuple(listnames) def get_names_flat(adtype): """ Returns the field names of the input datatype as a tuple. Input datatype must have fields otherwise error is raised. Nested structure are flattened beforehand. Parameters ---------- adtype : dtype Input datatype Examples -------- >>> from numpy.lib import recfunctions as rfn >>> rfn.get_names_flat(np.empty((1,), dtype=[('A', int)]).dtype) is None False >>> rfn.get_names_flat(np.empty((1,), dtype=[('A',int), ('B', str)]).dtype) ('A', 'B') >>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])]) >>> rfn.get_names_flat(adtype) ('a', 'b', 'ba', 'bb') """ listnames = [] names = adtype.names for name in names: listnames.append(name) current = adtype[name] if current.names is not None: listnames.extend(get_names_flat(current)) return tuple(listnames) def flatten_descr(ndtype): """ Flatten a structured data-type description. Examples -------- >>> from numpy.lib import recfunctions as rfn >>> ndtype = np.dtype([('a', '<i4'), ('b', [('ba', '<f8'), ('bb', '<i4')])]) >>> rfn.flatten_descr(ndtype) (('a', dtype('int32')), ('ba', dtype('float64')), ('bb', dtype('int32'))) """ names = ndtype.names if names is None: return (('', ndtype),) else: descr = [] for field in names: (typ, _) = ndtype.fields[field] if typ.names is not None: descr.extend(flatten_descr(typ)) else: descr.append((field, typ)) return tuple(descr) def _zip_dtype(seqarrays, flatten=False): newdtype = [] if flatten: for a in seqarrays: newdtype.extend(flatten_descr(a.dtype)) else: for a in seqarrays: current = a.dtype if current.names is not None and len(current.names) == 1: # special case - dtypes of 1 field are flattened newdtype.extend(_get_fieldspec(current)) else: newdtype.append(('', current)) return np.dtype(newdtype) def _zip_descr(seqarrays, flatten=False): """ Combine the dtype description of a series of arrays. Parameters ---------- seqarrays : sequence of arrays Sequence of arrays flatten : {boolean}, optional Whether to collapse nested descriptions. """ return _zip_dtype(seqarrays, flatten=flatten).descr def get_fieldstructure(adtype, lastname=None, parents=None,): """ Returns a dictionary with fields indexing lists of their parent fields. This function is used to simplify access to fields nested in other fields. Parameters ---------- adtype : np.dtype Input datatype lastname : optional Last processed field name (used internally during recursion). parents : dictionary Dictionary of parent fields (used interbally during recursion). Examples -------- >>> from numpy.lib import recfunctions as rfn >>> ndtype = np.dtype([('A', int), ... ('B', [('BA', int), ... ('BB', [('BBA', int), ('BBB', int)])])]) >>> rfn.get_fieldstructure(ndtype) ... # XXX: possible regression, order of BBA and BBB is swapped {'A': [], 'B': [], 'BA': ['B'], 'BB': ['B'], 'BBA': ['B', 'BB'], 'BBB': ['B', 'BB']} """ if parents is None: parents = {} names = adtype.names for name in names: current = adtype[name] if current.names is not None: if lastname: parents[name] = [lastname, ] else: parents[name] = [] parents.update(get_fieldstructure(current, name, parents)) else: lastparent = [_ for _ in (parents.get(lastname, []) or [])] if lastparent: lastparent.append(lastname) elif lastname: lastparent = [lastname, ] parents[name] = lastparent or [] return parents def _izip_fields_flat(iterable): """ Returns an iterator of concatenated fields from a sequence of arrays, collapsing any nested structure. """ for element in iterable: if isinstance(element, np.void): yield from _izip_fields_flat(tuple(element)) else: yield element def _izip_fields(iterable): """ Returns an iterator of concatenated fields from a sequence of arrays. """ for element in iterable: if (hasattr(element, '__iter__') and not isinstance(element, str)): yield from _izip_fields(element) elif isinstance(element, np.void) and len(tuple(element)) == 1: # this statement is the same from the previous expression yield from _izip_fields(element) else: yield element def _izip_records(seqarrays, fill_value=None, flatten=True): """ Returns an iterator of concatenated items from a sequence of arrays. Parameters ---------- seqarrays : sequence of arrays Sequence of arrays. fill_value : {None, integer} Value used to pad shorter iterables. flatten : {True, False}, Whether to """ # Should we flatten the items, or just use a nested approach if flatten: zipfunc = _izip_fields_flat else: zipfunc = _izip_fields for tup in itertools.zip_longest(*seqarrays, fillvalue=fill_value): yield tuple(zipfunc(tup)) def _fix_output(output, usemask=True, asrecarray=False): """ Private function: return a recarray, a ndarray, a MaskedArray or a MaskedRecords depending on the input parameters """ if not isinstance(output, MaskedArray): usemask = False if usemask: if asrecarray: output = output.view(MaskedRecords) else: output = ma.filled(output) if asrecarray: output = output.view(recarray) return output def _fix_defaults(output, defaults=None): """ Update the fill_value and masked data of `output` from the default given in a dictionary defaults. """ names = output.dtype.names (data, mask, fill_value) = (output.data, output.mask, output.fill_value) for (k, v) in (defaults or {}).items(): if k in names: fill_value[k] = v data[k][mask[k]] = v return output def _merge_arrays_dispatcher(seqarrays, fill_value=None, flatten=None, usemask=None, asrecarray=None): return seqarrays @array_function_dispatch(_merge_arrays_dispatcher) def merge_arrays(seqarrays, fill_value=-1, flatten=False, usemask=False, asrecarray=False): """ Merge arrays field by field. Parameters ---------- seqarrays : sequence of ndarrays Sequence of arrays fill_value : {float}, optional Filling value used to pad missing data on the shorter arrays. flatten : {False, True}, optional Whether to collapse nested fields. usemask : {False, True}, optional Whether to return a masked array or not. asrecarray : {False, True}, optional Whether to return a recarray (MaskedRecords) or not. Examples -------- >>> from numpy.lib import recfunctions as rfn >>> rfn.merge_arrays((np.array([1, 2]), np.array([10., 20., 30.]))) array([( 1, 10.), ( 2, 20.), (-1, 30.)], dtype=[('f0', '<i8'), ('f1', '<f8')]) >>> rfn.merge_arrays((np.array([1, 2], dtype=np.int64), ... np.array([10., 20., 30.])), usemask=False) array([(1, 10.0), (2, 20.0), (-1, 30.0)], dtype=[('f0', '<i8'), ('f1', '<f8')]) >>> rfn.merge_arrays((np.array([1, 2]).view([('a', np.int64)]), ... np.array([10., 20., 30.])), ... usemask=False, asrecarray=True) rec.array([( 1, 10.), ( 2, 20.), (-1, 30.)], dtype=[('a', '<i8'), ('f1', '<f8')]) Notes ----- * Without a mask, the missing value will be filled with something, depending on what its corresponding type: * ``-1`` for integers * ``-1.0`` for floating point numbers * ``'-'`` for characters * ``'-1'`` for strings * ``True`` for boolean values * XXX: I just obtained these values empirically """ # Only one item in the input sequence? if (len(seqarrays) == 1): seqarrays = np.asanyarray(seqarrays[0]) # Do we have a single ndarray as input? if isinstance(seqarrays, (ndarray, np.void)): seqdtype = seqarrays.dtype # Make sure we have named fields if seqdtype.names is None: seqdtype = np.dtype([('', seqdtype)]) if not flatten or _zip_dtype((seqarrays,), flatten=True) == seqdtype: # Minimal processing needed: just make sure everything's a-ok seqarrays = seqarrays.ravel() # Find what type of array we must return if usemask: if asrecarray: seqtype = MaskedRecords else: seqtype = MaskedArray elif asrecarray: seqtype = recarray else: seqtype = ndarray return seqarrays.view(dtype=seqdtype, type=seqtype) else: seqarrays = (seqarrays,) else: # Make sure we have arrays in the input sequence seqarrays = [np.asanyarray(_m) for _m in seqarrays] # Find the sizes of the inputs and their maximum sizes = tuple(a.size for a in seqarrays) maxlength = max(sizes) # Get the dtype of the output (flattening if needed) newdtype = _zip_dtype(seqarrays, flatten=flatten) # Initialize the sequences for data and mask seqdata = [] seqmask = [] # If we expect some kind of MaskedArray, make a special loop. if usemask: for (a, n) in zip(seqarrays, sizes): nbmissing = (maxlength - n) # Get the data and mask data = a.ravel().__array__() mask = ma.getmaskarray(a).ravel() # Get the filling value (if needed) if nbmissing: fval = _check_fill_value(fill_value, a.dtype) if isinstance(fval, (ndarray, np.void)): if len(fval.dtype) == 1: fval = fval.item()[0] fmsk = True else: fval = np.array(fval, dtype=a.dtype, ndmin=1) fmsk = np.ones((1,), dtype=mask.dtype) else: fval = None fmsk = True # Store an iterator padding the input to the expected length seqdata.append(itertools.chain(data, [fval] * nbmissing)) seqmask.append(itertools.chain(mask, [fmsk] * nbmissing)) # Create an iterator for the data data = tuple(_izip_records(seqdata, flatten=flatten)) output = ma.array(np.fromiter(data, dtype=newdtype, count=maxlength), mask=list(_izip_records(seqmask, flatten=flatten))) if asrecarray: output = output.view(MaskedRecords) else: # Same as before, without the mask we don't need... for (a, n) in zip(seqarrays, sizes): nbmissing = (maxlength - n) data = a.ravel().__array__() if nbmissing: fval = _check_fill_value(fill_value, a.dtype) if isinstance(fval, (ndarray, np.void)): if len(fval.dtype) == 1: fval = fval.item()[0] else: fval = np.array(fval, dtype=a.dtype, ndmin=1) else: fval = None seqdata.append(itertools.chain(data, [fval] * nbmissing)) output = np.fromiter(tuple(_izip_records(seqdata, flatten=flatten)), dtype=newdtype, count=maxlength) if asrecarray: output = output.view(recarray) # And we're done... return output def _drop_fields_dispatcher(base, drop_names, usemask=None, asrecarray=None): return (base,) @array_function_dispatch(_drop_fields_dispatcher) def drop_fields(base, drop_names, usemask=True, asrecarray=False): """ Return a new array with fields in `drop_names` dropped. Nested fields are supported. .. versionchanged:: 1.18.0 `drop_fields` returns an array with 0 fields if all fields are dropped, rather than returning ``None`` as it did previously. Parameters ---------- base : array Input array drop_names : string or sequence String or sequence of strings corresponding to the names of the fields to drop. usemask : {False, True}, optional Whether to return a masked array or not. asrecarray : string or sequence, optional Whether to return a recarray or a mrecarray (`asrecarray=True`) or a plain ndarray or masked array with flexible dtype. The default is False. Examples -------- >>> from numpy.lib import recfunctions as rfn >>> a = np.array([(1, (2, 3.0)), (4, (5, 6.0))], ... dtype=[('a', np.int64), ('b', [('ba', np.double), ('bb', np.int64)])]) >>> rfn.drop_fields(a, 'a') array([((2., 3),), ((5., 6),)], dtype=[('b', [('ba', '<f8'), ('bb', '<i8')])]) >>> rfn.drop_fields(a, 'ba') array([(1, (3,)), (4, (6,))], dtype=[('a', '<i8'), ('b', [('bb', '<i8')])]) >>> rfn.drop_fields(a, ['ba', 'bb']) array([(1,), (4,)], dtype=[('a', '<i8')]) """ if _is_string_like(drop_names): drop_names = [drop_names] else: drop_names = set(drop_names) def _drop_descr(ndtype, drop_names): names = ndtype.names newdtype = [] for name in names: current = ndtype[name] if name in drop_names: continue if current.names is not None: descr = _drop_descr(current, drop_names) if descr: newdtype.append((name, descr)) else: newdtype.append((name, current)) return newdtype newdtype = _drop_descr(base.dtype, drop_names) output = np.empty(base.shape, dtype=newdtype) output = recursive_fill_fields(base, output) return _fix_output(output, usemask=usemask, asrecarray=asrecarray) def _keep_fields(base, keep_names, usemask=True, asrecarray=False): """ Return a new array keeping only the fields in `keep_names`, and preserving the order of those fields. Parameters ---------- base : array Input array keep_names : string or sequence String or sequence of strings corresponding to the names of the fields to keep. Order of the names will be preserved. usemask : {False, True}, optional Whether to return a masked array or not. asrecarray : string or sequence, optional Whether to return a recarray or a mrecarray (`asrecarray=True`) or a plain ndarray or masked array with flexible dtype. The default is False. """ newdtype = [(n, base.dtype[n]) for n in keep_names] output = np.empty(base.shape, dtype=newdtype) output = recursive_fill_fields(base, output) return _fix_output(output, usemask=usemask, asrecarray=asrecarray) def _rec_drop_fields_dispatcher(base, drop_names): return (base,) @array_function_dispatch(_rec_drop_fields_dispatcher) def rec_drop_fields(base, drop_names): """ Returns a new numpy.recarray with fields in `drop_names` dropped. """ return drop_fields(base, drop_names, usemask=False, asrecarray=True) def _rename_fields_dispatcher(base, namemapper): return (base,) @array_function_dispatch(_rename_fields_dispatcher) def rename_fields(base, namemapper): """ Rename the fields from a flexible-datatype ndarray or recarray. Nested fields are supported. Parameters ---------- base : ndarray Input array whose fields must be modified. namemapper : dictionary Dictionary mapping old field names to their new version. Examples -------- >>> from numpy.lib import recfunctions as rfn >>> a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))], ... dtype=[('a', int),('b', [('ba', float), ('bb', (float, 2))])]) >>> rfn.rename_fields(a, {'a':'A', 'bb':'BB'}) array([(1, (2., [ 3., 30.])), (4, (5., [ 6., 60.]))], dtype=[('A', '<i8'), ('b', [('ba', '<f8'), ('BB', '<f8', (2,))])]) """ def _recursive_rename_fields(ndtype, namemapper): newdtype = [] for name in ndtype.names: newname = namemapper.get(name, name) current = ndtype[name] if current.names is not None: newdtype.append( (newname, _recursive_rename_fields(current, namemapper)) ) else: newdtype.append((newname, current)) return newdtype newdtype = _recursive_rename_fields(base.dtype, namemapper) return base.view(newdtype) def _append_fields_dispatcher(base, names, data, dtypes=None, fill_value=None, usemask=None, asrecarray=None): yield base yield from data @array_function_dispatch(_append_fields_dispatcher) def append_fields(base, names, data, dtypes=None, fill_value=-1, usemask=True, asrecarray=False): """ Add new fields to an existing array. The names of the fields are given with the `names` arguments, the corresponding values with the `data` arguments. If a single field is appended, `names`, `data` and `dtypes` do not have to be lists but just values. Parameters ---------- base : array Input array to extend. names : string, sequence String or sequence of strings corresponding to the names of the new fields. data : array or sequence of arrays Array or sequence of arrays storing the fields to add to the base. dtypes : sequence of datatypes, optional Datatype or sequence of datatypes. If None, the datatypes are estimated from the `data`. fill_value : {float}, optional Filling value used to pad missing data on the shorter arrays. usemask : {False, True}, optional Whether to return a masked array or not. asrecarray : {False, True}, optional Whether to return a recarray (MaskedRecords) or not. """ # Check the names if isinstance(names, (tuple, list)): if len(names)!= len(data): msg = "The number of arrays does not match the number of names" raise ValueError(msg) elif isinstance(names, str): names = [names, ] data = [data, ] # if dtypes is None: data = [np.array(a, copy=False, subok=True) for a in data] data = [a.view([(name, a.dtype)]) for (name, a) in zip(names, data)] else: if not isinstance(dtypes, (tuple, list)): dtypes = [dtypes, ] if len(data)!= len(dtypes): if len(dtypes) == 1: dtypes = dtypes * len(data) else: msg = "The dtypes argument must be None, a dtype, or a list." raise ValueError(msg) data = [np.array(a, copy=False, subok=True, dtype=d).view([(n, d)]) for (a, n, d) in zip(data, names, dtypes)] # base = merge_arrays(base, usemask=usemask, fill_value=fill_value) if len(data) > 1: data = merge_arrays(data, flatten=True, usemask=usemask, fill_value=fill_value) else: data = data.pop() # output = ma.masked_all( max(len(base), len(data)), dtype=_get_fieldspec(base.dtype) + _get_fieldspec(data.dtype)) output = recursive_fill_fields(base, output) output = recursive_fill_fields(data, output) # return _fix_output(output, usemask=usemask, asrecarray=asrecarray) def _rec_append_fields_dispatcher(base, names, data, dtypes=None): yield base yield from data @array_function_dispatch(_rec_append_fields_dispatcher) def rec_append_fields(base, names, data, dtypes=None): """ Add new fields to an existing array. The names of the fields are given with the `names` arguments, the corresponding values with the `data` arguments. If a single field is appended, `names`, `data` and `dtypes` do not have to be lists but just values. Parameters ---------- base : array Input array to extend. names : string, sequence String or sequence of strings corresponding to the names of the new fields. data : array or sequence of arrays Array or sequence of arrays storing the fields to add to the base. dtypes : sequence of datatypes, optional Datatype or sequence of datatypes. If None, the datatypes are estimated from the `data`. See Also -------- append_fields Returns ------- appended_array : np.recarray """ return append_fields(base, names, data=data, dtypes=dtypes, asrecarray=True, usemask=False) def _repack_fields_dispatcher(a, align=None, recurse=None): return (a,) @array_function_dispatch(_repack_fields_dispatcher) def repack_fields(a, align=False, recurse=False): """ Re-pack the fields of a structured array or dtype in memory. The memory layout of structured datatypes allows fields at arbitrary byte offsets. This means the fields can be separated by padding bytes, their offsets can be non-monotonically increasing, and they can overlap. This method removes any overlaps and reorders the fields in memory so they have increasing byte offsets, and adds or removes padding bytes depending on the `align` option, which behaves like the `align` option to `numpy.dtype`. If `align=False`, this method produces a "packed" memory layout in which each field starts at the byte the previous field ended, and any padding bytes are removed. If `align=True`, this methods produces an "aligned" memory layout in which each field's offset is a multiple of its alignment, and the total itemsize is a multiple of the largest alignment, by adding padding bytes as needed. Parameters ---------- a : ndarray or dtype array or dtype for which to repack the fields. align : boolean If true, use an "aligned" memory layout, otherwise use a "packed" layout. recurse : boolean If True, also repack nested structures. Returns ------- repacked : ndarray or dtype Copy of `a` with fields repacked, or `a` itself if no repacking was needed. Examples -------- >>> from numpy.lib import recfunctions as rfn >>> def print_offsets(d): ... print("offsets:", [d.fields[name][1] for name in d.names]) ... print("itemsize:", d.itemsize) ... >>> dt = np.dtype('u1, <i8, <f8', align=True) >>> dt dtype({'names': ['f0', 'f1', 'f2'], 'formats': ['u1', '<i8', '<f8'], \ 'offsets': [0, 8, 16], 'itemsize': 24}, align=True) >>> print_offsets(dt) offsets: [0, 8, 16] itemsize: 24 >>> packed_dt = rfn.repack_fields(dt) >>> packed_dt dtype([('f0', 'u1'), ('f1', '<i8'), ('f2', '<f8')]) >>> print_offsets(packed_dt) offsets: [0, 1, 9] itemsize: 17 """ if not isinstance(a, np.dtype): dt = repack_fields(a.dtype, align=align, recurse=recurse) return a.astype(dt, copy=False) if a.names is None: return a fieldinfo = [] for name in a.names: tup = a.fields[name] if recurse: fmt = repack_fields(tup[0], align=align, recurse=True) else: fmt = tup[0] if len(tup) == 3: name = (tup[2], name) fieldinfo.append((name, fmt)) dt = np.dtype(fieldinfo, align=align) return np.dtype((a.type, dt)) def _get_fields_and_offsets(dt, offset=0): """ Returns a flat list of (dtype, count, offset) tuples of all the scalar fields in the dtype "dt", including nested fields, in left to right order. """ # counts up elements in subarrays, including nested subarrays, and returns # base dtype and count def count_elem(dt): count = 1 while dt.shape!= (): for size in dt.shape: count *= size dt = dt.base return dt, count fields = [] for name in dt.names: field = dt.fields[name] f_dt, f_offset = field[0], field[1] f_dt, n = count_elem(f_dt) if f_dt.names is None: fields.append((np.dtype((f_dt, (n,))), n, f_offset + offset)) else: subfields = _get_fields_and_offsets(f_dt, f_offset + offset) size = f_dt.itemsize for i in range(n): if i == 0: # optimization: avoid list comprehension if no subarray fields.extend(subfields) else: fields.extend([(d, c, o + i*size) for d, c, o in subfields]) return fields def _common_stride(offsets, counts, itemsize): """ Returns the stride between the fields, or None if the stride is not constant. The values in "counts" designate the lengths of subarrays. Subarrays are treated as many contiguous fields, with always positive stride. """ if len(offsets) <= 1: return itemsize negative = offsets[1] < offsets[0] # negative stride if negative: # reverse, so offsets will be ascending it = zip(reversed(offsets), reversed(counts)) else: it = zip(offsets, counts) prev_offset = None stride = None for offset, count in it: if count!= 1: # subarray: always c-contiguous if negative: return None # subarrays can never have a negative stride if stride is None: stride = itemsize if stride!= itemsize: return None end_offset = offset + (count - 1) * itemsize else: end_offset = offset if prev_offset is not None: new_stride = offset - prev_offset if stride is None: stride = new_stride if stride!= new_stride: return None prev_offset = end_offset if negative: return -stride return stride def _structured_to_unstructured_dispatcher(arr, dtype=None, copy=None, casting=None): return (arr,) @array_function_dispatch(_structured_to_unstructured_dispatcher) def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'): """ Converts an n-D structured array into an (n+1)-D unstructured array. The new array will have a new last dimension equal in size to the number of field-elements of the input array. If not supplied, the output datatype is determined from the numpy type promotion rules applied to all the field datatypes. Nested fields, as well as each element of any subarray fields, all count as a single field-elements. Parameters ---------- arr : ndarray Structured array or dtype to convert. Cannot contain object datatype. dtype : dtype, optional The dtype of the output unstructured array. copy : bool, optional If true, always return a copy. If false, a view is returned if possible, such as when the `dtype` and strides of the fields are suitable and the array subtype is one of `numpy.ndarray`, `numpy.recarray` or `numpy.memmap`. .. versionchanged:: 1.25.0 A view can now be returned if the fields are separated by a uniform stride. casting : {'no', 'equiv','safe','same_kind', 'unsafe'}, optional See casting argument of `numpy.ndarray.astype`. Controls what kind of data casting may occur. Returns ------- unstructured : ndarray Unstructured array with one more dimension. Examples -------- >>> from numpy.lib import recfunctions as rfn >>> a = np.zeros(4, dtype=[('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)]) >>> a array([(0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.])], dtype=[('a', '<i4'), ('b', [('f0', '<f4'), ('f1', '<u2')]), ('c', '<f4', (2,))]) >>> rfn.structured_to_unstructured(a) array([[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]]) >>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8,11), (10, 11, 12)], ... dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')]) >>> np.mean(rfn.structured_to_unstructured(b[['x', 'z']]), axis=-1) array([ 3., 5.5, 9., 11. ]) """ if arr.dtype.names is None: raise ValueError('arr must be a structured array') fields = _get_fields_and_offsets(arr.dtype) n_fields = len(fields) if n_fields == 0 and dtype is None: raise ValueError("arr has no fields. Unable to guess dtype") elif n_fields == 0: # too many bugs elsewhere for this to work now raise NotImplementedError("arr with no fields is not supported") dts, counts, offsets = zip(*fields) names = ['f{}'.format(n) for n in range(n_fields)] if dtype is None: out_dtype = np.result_type(*[dt.base for dt in dts]) else: out_dtype = np.dtype(dtype) # Use a series of views and casts to convert to an unstructured array: # first view using flattened fields (doesn't work for object arrays) # Note: dts may include a shape for subarrays flattened_fields = np.dtype({'names': names, 'formats': dts, 'offsets': offsets, 'itemsize': arr.dtype.itemsize}) arr = arr.view(flattened_fields) # we only allow a few types to be unstructured by manipulating the # strides, because we know it won't work with, for example, np.matrix nor # np.ma.MaskedArray. can_view = type(arr) in (np.ndarray, np.recarray, np.memmap) if (not copy) and can_view and all(dt.base == out_dtype for dt in dts): # all elements have the right dtype already; if they have a common # stride, we can just return a view common_stride = _common_stride(offsets, counts, out_dtype.itemsize) if common_stride is not None: wrap = arr.__array_wrap__ new_shape = arr.shape + (sum(counts), out_dtype.itemsize) new_strides = arr.strides + (abs(common_stride), 1) arr = arr[..., np.newaxis].view(np.uint8) # view as bytes arr = arr[..., min(offsets):] # remove the leading unused data arr = np.lib.stride_tricks.as_strided(arr, new_shape, new_strides, subok=True) # cast and drop the last dimension again arr = arr.view(out_dtype)[..., 0] if common_stride < 0: arr = arr[..., ::-1] # reverse, if the stride was negative if type(arr) is not type(wrap.__self__): # Some types (e.g. recarray) turn into an ndarray along the # way, so we have to wrap it again in order to match the # behavior with copy=True. arr = wrap(arr) return arr # next cast to a packed format with all fields converted to new dtype packed_fields = np.dtype({'names': names, 'formats': [(out_dtype, dt.shape) for dt in dts]}) arr = arr.astype(packed_fields, copy=copy, casting=casting) # finally is it safe to view the packed fields as the unstructured type return arr.view((out_dtype, (sum(counts),))) def _unstructured_to_structured_dispatcher(arr, dtype=None, names=None, align=None, copy=None, casting=None): return (arr,) @array_function_dispatch(_unstructured_to_structured_dispatcher) def unstructured_to_structured(arr, dtype=None, names=None, align=False, copy=False, casting='unsafe'): """ Converts an n-D unstructured array into an (n-1)-D structured array. The last dimension of the input array is converted into a structure, with number of field-elements equal to the size of the last dimension of the input array. By default all output fields have the input array's dtype, but an output structured dtype with an equal number of fields-elements can be supplied instead. Nested fields, as well as each element of any subarray fields, all count towards the number of field-elements. Parameters ---------- arr : ndarray Unstructured array or dtype to convert. dtype : dtype, optional The structured dtype of the output array names : list of strings, optional If dtype is not supplied, this specifies the field names for the output dtype, in order. The field dtypes will be the same as the input array. align : boolean, optional Whether to create an aligned memory layout. copy : bool, optional See copy argument to `numpy.ndarray.astype`. If true, always return a copy. If false, and `dtype` requirements are satisfied, a view is returned. casting : {'no', 'equiv','safe','same_kind', 'unsafe'}, optional See casting argument of `numpy.ndarray.astype`. Controls what kind of data casting may occur. Returns ------- structured : ndarray Structured array with fewer dimensions. Examples -------- >>> from numpy.lib import recfunctions as rfn >>> dt = np.dtype([('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)]) >>> a = np.arange(20).reshape((4,5)) >>> a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19]]) >>> rfn.unstructured_to_structured(a, dt) array([( 0, ( 1., 2), [ 3., 4.]), ( 5, ( 6., 7), [ 8., 9.]), (10, (11., 12), [13., 14.]), (15, (16., 17), [18., 19.])], dtype=[('a', '<i4'), ('b', [('f0', '<f4'), ('f1', '<u2')]), ('c', '<f4', (2,))]) """ if arr.shape == (): raise ValueError('arr must have at least one dimension') n_elem = arr.shape[-1] if n_elem == 0: # too many bugs elsewhere for this to work now raise NotImplementedError("last axis with size 0 is not supported") if dtype is None: if names is None: names = ['f{}'.format(n) for n in range(n_elem)] out_dtype = np.dtype([(n, arr.dtype) for n in names], align=align) fields = _get_fields_and_offsets(out_dtype) dts, counts, offsets = zip(*fields) else: if names is not None: raise ValueError("don't supply both dtype and names") # if dtype is the args of np.dtype, construct it dtype = np.dtype(dtype) # sanity check of the input dtype fields = _get_fields_and_offsets(dtype) if len(fields) == 0: dts, counts, offsets = [], [], [] else: dts, counts, offsets = zip(*fields) if n_elem!= sum(counts): raise ValueError('The length of the last dimension of arr must ' 'be equal to the number of fields in dtype') out_dtype = dtype if align and not out_dtype.isalignedstruct: raise ValueError("align was True but dtype is not aligned") names = ['f{}'.format(n) for n in range(len(fields))] # Use a series of views and casts to convert to a structured array: # first view as a packed structured array of one dtype packed_fields = np.dtype({'names': names, 'formats': [(arr.dtype, dt.shape) for dt in dts]}) arr = np.ascontiguousarray(arr).view(packed_fields) # next cast to an unpacked but flattened format with varied dtypes flattened_fields = np.dtype({'names': names, 'formats': dts, 'offsets': offsets, 'itemsize': out_dtype.itemsize}) arr = arr.astype(flattened_fields, copy=copy, casting=casting) # finally view as the final nested dtype and remove the last axis return arr.view(out_dtype)[..., 0] def _apply_along_fields_dispatcher(func, arr): return (arr,) @array_function_dispatch(_apply_along_fields_dispatcher) def apply_along_fields(func, arr): """ Apply function 'func' as a reduction across fields of a structured array. This is similar to `numpy.apply_along_axis`, but treats the fields of a structured array as an extra axis. The fields are all first cast to a common type following the type-promotion rules from `numpy.result_type` applied to the field's dtypes. Parameters ---------- func : function Function to apply on the "field" dimension. This function must support an `axis` argument, like `numpy.mean`, `numpy.sum`, etc. arr : ndarray Structured array for which to apply func. Returns ------- out : ndarray Result of the recution operation Examples -------- >>> from numpy.lib import recfunctions as rfn >>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8,11), (10, 11, 12)], ... dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')]) >>> rfn.apply_along_fields(np.mean, b) array([ 2.66666667, 5.33333333, 8.66666667, 11. ]) >>> rfn.apply_along_fields(np.mean, b[['x', 'z']]) array([ 3., 5.5, 9., 11. ]) """ if arr.dtype.names is None: raise ValueError('arr must be a structured array') uarr = structured_to_unstructured(arr) return func(uarr, axis=-1) # works and avoids axis requirement, but very, very slow: #return np.apply_along_axis(func, -1, uarr) def _assign_fields_by_name_dispatcher(dst, src, zero_unassigned=None): return dst, src @array_function_dispatch(_assign_fields_by_name_dispatcher) def assign_fields_by_name(dst, src, zero_unassigned=True): """ Assigns values from one structured array to another by field name. Normally in numpy >= 1.14, assignment of one structured array to another copies fields "by position", meaning that the first field from the src is copied to the first field of the dst, and so on, regardless of field name. This function instead copies "by field name", such that fields in the dst are assigned from the identically named field in the src. This applies recursively for nested structures. This is how structure assignment worked in numpy >= 1.6 to <= 1.13. Parameters ---------- dst : ndarray src : ndarray The source and destination arrays during assignment. zero_unassigned : bool, optional If True, fields in the dst for which there was no matching field in the src are filled with the value 0 (zero). This was the behavior of numpy <= 1.13. If False, those fields are not modified. """ if dst.dtype.names is None: dst[...] = src return for name in dst.dtype.names: if name not in src.dtype.names: if zero_unassigned: dst[name] = 0 else: assign_fields_by_name(dst[name], src[name], zero_unassigned) def _require_fields_dispatcher(array, required_dtype): return (array,) @array_function_dispatch(_require_fields_dispatcher) def require_fields(array, required_dtype): """ Casts a structured array to a new dtype using assignment by field-name. This function assigns from the old to the new array by name, so the value of a field in the output array is the value of the field with the same name in the source array. This has the effect of creating a new ndarray containing only the fields "required" by the required_dtype. If a field name in the required_dtype does not exist in the input array, that field is created and set to 0 in the output array. Parameters ---------- a : ndarray array to cast required_dtype : dtype datatype for output array Returns ------- out : ndarray array with the new dtype, with field values copied from the fields in the input array with the same name Examples -------- >>> from numpy.lib import recfunctions as rfn >>> a = np.ones(4, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')]) >>> rfn.require_fields(a, [('b', 'f4'), ('c', 'u1')]) array([(1., 1), (1., 1), (1., 1), (1., 1)], dtype=[('b', '<f4'), ('c', 'u1')]) >>> rfn.require_fields(a, [('b', 'f4'), ('newf', 'u1')]) array([(1., 0), (1., 0), (1., 0), (1., 0)], dtype=[('b', '<f4'), ('newf', 'u1')]) """ out = np.empty(array.shape, dtype=required_dtype) assign_fields_by_name(out, array) return out def _stack_arrays_dispatcher(arrays, defaults=None, usemask=None, asrecarray=None, autoconvert=None): return arrays @array_function_dispatch(_stack_arrays_dispatcher) def stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False, autoconvert=False): """ Superposes arrays fields by fields Parameters ---------- arrays : array or sequence Sequence of input arrays. defaults : dictionary, optional Dictionary mapping field names to the corresponding default values. usemask : {True, False}, optional Whether to return a MaskedArray (or MaskedRecords is `asrecarray==True`) or a ndarray. asrecarray : {False, True}, optional Whether to return a recarray (or MaskedRecords if `usemask==True`) or just a flexible-type ndarray. autoconvert : {False, True}, optional Whether automatically cast the type of the field to the maximum. Examples -------- >>> from numpy.lib import recfunctions as rfn >>> x = np.array([1, 2,]) >>> rfn.stack_arrays(x) is x True >>> z = np.array([('A', 1), ('B', 2)], dtype=[('A', '|S3'), ('B', float)]) >>> zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)], ... dtype=[('A', '|S3'), ('B', np.double), ('C', np.double)]) >>> test = rfn.stack_arrays((z,zz)) >>> test masked_array(data=[(b'A', 1.0, --), (b'B', 2.0, --), (b'a', 10.0, 100.0), (b'b', 20.0, 200.0), (b'c', 30.0, 300.0)], mask=[(False, False, True), (False, False, True), (False, False, False), (False, False, False), (False, False, False)], fill_value=(b'N/A', 1e+20, 1e+20), dtype=[('A', 'S3'), ('B', '<f8'), ('C', '<f8')]) """ if isinstance(arrays, ndarray): return arrays elif len(arrays) == 1: return arrays[0] seqarrays = [np.asanyarray(a).ravel() for a in arrays] nrecords = [len(a) for a in seqarrays] ndtype = [a.dtype for a in seqarrays] fldnames = [d.names for d in ndtype] # dtype_l = ndtype[0] newdescr = _get_fieldspec(dtype_l) names = [n for n, d in newdescr] for dtype_n in ndtype[1:]: for fname, fdtype in _get_fieldspec(dtype_n): if fname not in names: newdescr.append((fname, fdtype)) names.append(fname) else: nameidx = names.index(fname) _, cdtype = newdescr[nameidx] if autoconvert: newdescr[nameidx] = (fname, max(fdtype, cdtype)) elif fdtype!= cdtype: raise TypeError("Incompatible type '%s' <> '%s'" % (cdtype, fdtype)) # Only one field: use concatenate if len(newdescr) == 1: output = ma.concatenate(seqarrays) else: # output = ma.masked_all((np.sum(nrecords),), newdescr) offset = np.cumsum(np.r_[0, nrecords]) seen = [] for (a, n, i, j) in zip(seqarrays, fldnames, offset[:-1], offset[1:]): names = a.dtype.names if names is None: output['f%i' % len(seen)][i:j] = a else: for name in n: output[name][i:j] = a[name] if name not in seen: seen.append(name) # return _fix_output(_fix_defaults(output, defaults), usemask=usemask, asrecarray=asrecarray) def _find_duplicates_dispatcher( a, key=None, ignoremask=None, return_index=None): return (a,) @array_function_dispatch(_find_duplicates_dispatcher) def find_duplicates(a, key=None, ignoremask=True, return_index=False): """ Find the duplicates in a structured array along a given key Parameters ---------- a : array-like Input array key : {string, None}, optional Name of the fields along which to check the duplicates. If None, the search is performed by records ignoremask : {True, False}, optional Whether masked data should be discarded or considered as duplicates. return_index : {False, True}, optional Whether to return the indices of the duplicated values. Examples -------- >>> from numpy.lib import recfunctions as rfn >>> ndtype = [('a', int)] >>> a = np.ma.array([1, 1, 1, 2, 2, 3, 3], ... mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype) >>> rfn.find_duplicates(a, ignoremask=True, return_index=True) (masked_array(data=[(1,), (1,), (2,), (2,)], mask=[(False,), (False,), (False,), (False,)], fill_value=(999999,), dtype=[('a', '<i8')]), array([0, 1, 3, 4])) """ a = np.asanyarray(a).ravel() # Get a dictionary of fields fields = get_fieldstructure(a.dtype) # Get the sorting data (by selecting the corresponding field) base = a if key: for f in fields[key]: base = base[f] base = base[key] # Get the sorting indices and the sorted data sortidx = base.argsort() sortedbase = base[sortidx] sorteddata = sortedbase.filled() # Compare the sorting data flag = (sorteddata[:-1] == sorteddata[1:]) # If masked data must be ignored, set the flag to false where needed if ignoremask: sortedmask = sortedbase.recordmask flag[sortedmask[1:]] = False flag = np.concatenate(([False], flag)) # We need to take the point on the left as well (else we're missing it) flag[:-1] = flag[:-1] + flag[1:] duplicates = a[sortidx][flag] if return_index: return (duplicates, sortidx[flag]) else: return duplicates def _join_by_dispatcher( key, r1, r2, jointype=None, r1postfix=None, r2postfix=None, defaults=None, usemask=None, asrecarray=None): return (r1, r2) @array_function_dispatch(_join_by_dispatcher) def join_by(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None, usemask=True, asrecarray=False): """ Join arrays `r1` and `r2` on key `key`. The key should be either a string or a sequence of string corresponding to the fields used to join the array. An exception is raised if the `key` field cannot be found in the two input arrays. Neither `r1` nor `r2` should have any duplicates along `key`: the presence of duplicates will make the output quite unreliable. Note that duplicates are not looked for by the algorithm. Parameters ---------- key : {string, sequence} A string or a sequence of strings corresponding to the fields used for comparison. r1, r2 : arrays Structured arrays. jointype : {'inner', 'outer', 'leftouter'}, optional If 'inner', returns the elements common to both r1 and r2. If 'outer', returns the common elements as well as the elements of r1 not in r2 and the elements of not in r2. If 'leftouter', returns the common elements and the elements of r1 not in r2. r1postfix : string, optional String appended to the names of the fields of r1 that are present in r2 but absent of the key. r2postfix : string, optional String appended to the names of the fields of r2 that are present in r1 but absent of the key. defaults : {dictionary}, optional Dictionary mapping field names to the corresponding default values. usemask : {True, False}, optional Whether to return a MaskedArray (or MaskedRecords is `asrecarray==True`) or a ndarray. asrecarray : {False, True}, optional Whether to return a recarray (or MaskedRecords if `usemask==True`) or just a flexible-type ndarray. Notes ----- * The output is sorted along the key. * A temporary array is formed by dropping the fields not in the key for the two arrays and concatenating the result. This array is then sorted, and the common entries selected. The output is constructed by filling the fields with the selected entries. Matching is not preserved if there are some duplicates... """ # Check jointype if jointype not in ('inner', 'outer', 'leftouter'): raise ValueError( "The 'jointype' argument should be in 'inner', " "'outer' or 'leftouter' (got '%s' instead)" % jointype ) # If we have a single key, put it in a tuple if isinstance(key, str): key = (key,) # Check the keys if len(set(key))!= len(key): dup = next(x for n,x in enumerate(key) if x in key[n+1:]) raise ValueError("duplicate join key %r" % dup) for name in key: if name not in r1.dtype.names: raise ValueError('r1 does not have key field %r' % name) if name not in r2.dtype.names: raise ValueError('r2 does not have key field %r' % name) # Make sure we work with ravelled arrays r1 = r1.ravel() r2 = r2.ravel() # Fixme: nb2 below is never used. Commenting out for pyflakes. # (nb1, nb2) = (len(r1), len(r2)) nb1 = len(r1) (r1names, r2names) = (r1.dtype.names, r2.dtype.names) # Check the names for collision collisions = (set(r1names) & set(r2names)) - set(key) if collisions and not (r1postfix or r2postfix): msg = "r1 and r2 contain common names, r1postfix and r2postfix " msg += "can't both be empty" raise ValueError(msg) # Make temporary arrays of just the keys # (use order of keys in `r1` for back-compatibility) key1 = [ n for n in r1names if n in key ] r1k = _keep_fields(r1, key1) r2k = _keep_fields(r2, key1) # Concatenate the two arrays for comparison aux = ma.concatenate((r1k, r2k)) idx_sort = aux.argsort(order=key) aux = aux[idx_sort] # # Get the common keys flag_in = ma.concatenate(([False], aux[1:] == aux[:-1])) flag_in[:-1] = flag_in[1:] + flag_in[:-1] idx_in = idx_sort[flag_in] idx_1 = idx_in[(idx_in < nb1)] idx_2 = idx_in[(idx_in >= nb1)] - nb1 (r1cmn, r2cmn) = (len(idx_1), len(idx_2)) if jointype == 'inner': (r1spc, r2spc) = (0, 0) elif jointype == 'outer': idx_out = idx_sort[~flag_in] idx_1 = np.concatenate((idx_1, idx_out[(idx_out < nb1)])) idx_2 = np.concatenate((idx_2, idx_out[(idx_out >= nb1)] - nb1)) (r1spc, r2spc) = (len(idx_1) - r1cmn, len(idx_2) - r2cmn) elif jointype == 'leftouter': idx_out = idx_sort[~flag_in] idx_1 = np.concatenate((idx_1, idx_out[(idx_out < nb1)])) (r1spc, r2spc) = (len(idx_1) - r1cmn, 0) # Select the entries from each input (s1, s2) = (r1[idx_1], r2[idx_2]) # # Build the new description of the output array....... # Start with the key fields ndtype = _get_fieldspec(r1k.dtype) # Add the fields from r1 for fname, fdtype in _get_fieldspec(r1.dtype): if fname not in key: ndtype.append((fname, fdtype)) # Add the fields from r2 for fname, fdtype in _get_fieldspec(r2.dtype): # Have we seen the current name already? # we need to rebuild this list every time names = list(name for name, dtype in ndtype) try: nameidx = names.index(fname) except ValueError: #... we haven't: just add the description to the current list ndtype.append((fname, fdtype)) else: # collision _, cdtype = ndtype[nameidx] if fname in key: # The current field is part of the key: take the largest dtype ndtype[nameidx] = (fname, max(fdtype, cdtype)) else: # The current field is not part of the key: add the suffixes, # and place the new field adjacent to the old one ndtype[nameidx:nameidx + 1] = [ (fname + r1postfix, cdtype), (fname + r2postfix, fdtype) ] # Rebuild a dtype from the new fields ndtype = np.dtype(ndtype) # Find the largest nb of common fields : # r1cmn and r2cmn should be equal, but... cmn = max(r1cmn, r2cmn) # Construct an empty array output = ma.masked_all((cmn + r1spc + r2spc,), dtype=ndtype) names = output.dtype.names for f in r1names: selected = s1[f] if f not in names or (f in r2names and not r2postfix and f not in key): f += r1postfix current = output[f] current[:r1cmn] = selected[:r1cmn] if jointype in ('outer', 'leftouter'): current[cmn:cmn + r1spc] = selected[r1cmn:] for f in r2names: selected = s2[f] if f not in names or (f in r1names and not r1postfix and f not in key): f += r2postfix current = output[f] current[:r2cmn] = selected[:r2cmn] if (jointype == 'outer') and r2spc: current[-r2spc:] = selected[r2cmn:] # Sort and finalize the output output.sort(order=key) kwargs = dict(usemask=usemask, asrecarray=asrecarray) return _fix_output(_fix_defaults(output, defaults), **kwargs) def _rec_join_dispatcher( key, r1, r2, jointype=None, r1postfix=None, r2postfix=None, defaults=None): return (r1, r2) @array_function_dispatch(_rec_join_dispatcher) def rec_join(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None): """ Join arrays `r1` and `r2` on keys. Alternative to join_by, that always returns a np.recarray. See Also -------- join_by : equivalent function """ kwargs = dict(jointype=jointype, r1postfix=r1postfix, r2postfix=r2postfix, defaults=defaults, usemask=False, asrecarray=True) return join_by(key, r1, r2, **kwargs)

mosaicml__composer

scale_schedule.md

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mosaicml__composer/docs/source/method_cards/scale_schedule.md

# Scale Schedule Scale Schedule changes the number of training steps by a dilation factor and dilating learning rate changes accordingly. Doing so varies the training budget, making it possible to explore tradeoffs between cost (measured in time or money) and the quality of the final model. ## How to Use ### Implementation Details Scale schedule is implemented as part of the {class}~.Trainer via the scale_schedule_ratio argument. The trainer will scale the max_duration by the scale_schedule_ratio, and also adjust non-warmup milestones for the learning rate schedulers. ## Suggested Hyperparameters The default scale schedule ratio is 1.0. For a standard maximum number of epochs (these will differ depending on the task), scaling down the learning rate schedule will lead to a monotonic decrease in accuracy. Increasing the scale schedule ratio will often improve the accuracy up to a plateau, although this leads to longer training time and added cost. ## Techical Details Changing the length of training will affect the final accuracy of the model. For example, training ResNet-50 on ImageNet for the standard schedule in the composer library leads to final validation accuracy of 76.6%, while using scale schedule with a ratio of 0.5 leads to final validation accuracy of 75.6%. Training for longer can lead to diminishing returns or even overfitting and worse validation accuracy. In general, the cost of training is proportional to the length of training when using scale schedule (assuming all other techniques, such as progressive resizing, have their schedules scaled accordingly). `{note} The warmup periods of schedulers are not scaled by the scale schedule ratio.` > As general rule, scale schedule can be applied in conjunction with any method. If other methods also perform actions > according to a schedule, it is important to modify their schedules to coincide with the altered number of epochs. ## Attribution The number of training steps to perform is an important hyperparameter to tune when developing a model. This technique appears implicitly throughout the deep learning literature. One example of a systematic study of this approach is the scan-SGD technique in [_How Important is Importance Sampling for Deep Budgeted Training](https://openreview.net/forum?id=TqQ0oOzJlai) by Eric Arazo, Diego Ortega, Paul Albert, Noel O'Connor, and Kevin McGuinness. Posted to OpenReview in 2020. ## API Reference Trainer attribute: scale_schedule_ratio in {class}composer.Trainer

# Copyright 2022 MosaicML Composer authors # SPDX-License-Identifier: Apache-2.0 """Stateless learning rate schedulers. Stateless schedulers solve some of the problems associated with PyTorch's built-in schedulers provided in :mod:`torch.optim.lr_scheduler`. The primary design goal of the schedulers provided in this module is to allow schedulers to interface directly with Composer's :mod:`~composer.core.time` abstraction. This means that schedulers can be configured using arbitrary but explicit time units. See :class:`~.ComposerScheduler` for more information on stateless schedulers. """ import inspect import logging import math import textwrap import warnings from typing import TYPE_CHECKING, List, Union from torch.optim.lr_scheduler import LambdaLR from composer.core import PyTorchScheduler, State, Time, TimeUnit if TYPE_CHECKING: from typing import Protocol else: # subclasses of Protocol cannot be instantiated in Python 3.8 Protocol = object log = logging.getLogger(__name__) __all__ = [ 'ComposerScheduler', 'compile_composer_scheduler', 'StepScheduler', 'MultiStepScheduler', 'ConstantScheduler', 'LinearScheduler', 'ExponentialScheduler', 'CosineAnnealingScheduler', 'CosineAnnealingWarmRestartsScheduler', 'PolynomialScheduler', 'MultiStepWithWarmupScheduler', 'ConstantWithWarmupScheduler', 'LinearWithWarmupScheduler', 'CosineAnnealingWithWarmupScheduler', 'PolynomialWithWarmupScheduler' ] class ComposerScheduler(Protocol): r"""Specification for a stateless scheduler function. While this specification is provided as a Python class, an ordinary function can implement this interface as long as it matches the signature of this interface's :meth:`~.ComposerScheduler.__call__` method. For example, a scheduler that halves the learning rate after 10 epochs could be written as: .. code:: python def ten_epoch_decay_scheduler(state: State) -> float: if state.timestamp.epoch < 10: return 1.0 return 0.5 # ten_epoch_decay_scheduler is a valid ComposerScheduler trainer = Trainer( schedulers=[ten_epoch_decay_scheduler], ... ) In order to allow schedulers to be configured, schedulers may also written as callable classes: .. code:: python class VariableEpochDecayScheduler(ComposerScheduler): def __init__(num_epochs: int): self.num_epochs = num_epochs def __call__(state: State) -> float: if state.time.epoch < self.num_epochs: return 1.0 return 0.5 ten_epoch_decay_scheduler = VariableEpochDecayScheduler(num_epochs=10) # ten_epoch_decay_scheduler is also a valid ComposerScheduler trainer = Trainer( schedulers=[ten_epoch_decay_scheduler], ... ) The constructions of ``ten_epoch_decay_scheduler`` in each of the examples above are equivalent. Note that neither scheduler uses the ``scale_schedule_ratio`` parameter. As long as this parameter is not used when initializing :class:`.Trainer`, it is not required that any schedulers implement that parameter. .. automethod:: __call__ """ def __call__(self, state: State, ssr: float = 1.0) -> float: r"""Calculate the current learning rate multiplier :math:`\alpha`. A scheduler function should be a pure function that returns a multiplier to apply to the optimizer's provided learning rate, given the current trainer state, and optionally a "scale schedule ratio" (SSR). A typical implementation will read ``state.timestamp``, and possibly other fields like ``state.max_duration``, to determine the trainer's latest temporal progress. .. note:: All instances of :class:`~.ComposerScheduler` output a `multiplier` for the learning rate, rather than the learning rate directly. By convention, we use the symbol :math:`\alpha` to refer to this multiplier. This means that the learning rate :math:`\eta` at time :math:`t` can be represented as :math:`\eta(t) = \eta_i \times \alpha(t)`, where :math:`\eta_i` represents the learning rate used to initialize the optimizer. .. note:: It is possible to use multiple schedulers, in which case their effects will stack multiplicatively. The ``ssr`` param indicates that the schedule should be "stretched" accordingly. In symbolic terms, where :math:`\alpha_\sigma(t)` represents the scheduler output at time :math:`t` using scale schedule ratio :math:`\sigma`: .. math:: \alpha_{\sigma}(t) = \alpha(t / \sigma) Args: state (State): The current Composer Trainer state. ssr (float): The scale schedule ratio. In general, the learning rate computed by this scheduler at time :math:`t` with an SSR of 1.0 should be the same as that computed by this scheduler at time :math:`t \times s` with an SSR of :math:`s`. Default = ``1.0``. Returns: alpha (float): A multiplier to apply to the optimizer's provided learning rate. """ raise NotImplementedError def _convert_time(time: Union[str, Time[int], Time[float]], state: State, ssr: float = 1.0) -> Time[int]: if isinstance(time, str): time = Time.from_timestring(time) assert state.max_duration is not None,'max_duration should be set whenever schedulers are invoked' if time.unit == TimeUnit.DURATION: if state.max_duration.unit == TimeUnit.EPOCH: if state.dataloader_len is None: raise RuntimeError('Cannot convert time, as state.dataloader_len is None.') return Time(int(time.value * int(state.dataloader_len) * state.max_duration.value), TimeUnit.BATCH) return Time(int(time.value * state.max_duration.value), state.max_duration.unit) elif time.unit == TimeUnit.EPOCH: # Epochs do not provide sufficient granularity for SSR scaling # e.g. if max_duration = 1ep, then any SSR would result in a new duration of 0. # so, convert the time into batches if state.dataloader_len is None: raise RuntimeError('Cannot convert time, as state.dataloader_len is None.') time = Time(value=time.value * int(state.dataloader_len), unit=TimeUnit.BATCH) return Time(value=int(time.value * ssr), unit=time.unit) def compile_composer_scheduler(scheduler: ComposerScheduler, state: State, ssr: float = 1.0) -> PyTorchScheduler: """Converts a stateless scheduler into a PyTorch scheduler object. While the resulting scheduler provides a ``.step()`` interface similar to other PyTorch schedulers, the scheduler is also given a bound reference to the current :class:`~composer.core.State`. This means that any internal state updated by ``.step()`` can be ignored, and the scheduler can instead simply use the bound state to recalculate the current learning rate. Args: scheduler (ComposerScheduler): A stateless scheduler, provided as a :class:`~.ComposerScheduler` object. state (State): The Composer Trainer's state. Returns: compiled_scheduler (PyTorchScheduler): The scheduler, in a form compatible with PyTorch scheduler interfaces. """ optimizers = state.optimizers if len(optimizers)!= 1: raise NotImplementedError('Providing functional schedulers is unsupported with multiple optimizers.') optimizer = optimizers[0] scheduler_sig = inspect.signature(scheduler) def scheduler_fn(epoch: int) -> float: del epoch # unused. Provided by the pytorch LambdaLR # if the ssr is 1.0, don't pass it to the scheduler. This allows users to pass in lambdas that only take # one parameter -- the state if len(scheduler_sig.parameters) == 1: if ssr == 1.0: return scheduler(state) else: raise ValueError( textwrap.dedent(f"""\ Scheduler {scheduler} does not support `scale_schedule_ratio`. To use `scale_schedule_ratio`, the scheduler must take two arguments (state, ssr)""")) return scheduler(state, ssr) lambda_scheduler = LambdaLR(optimizer, scheduler_fn) return lambda_scheduler class StepScheduler(ComposerScheduler): r"""Decays the learning rate discretely at fixed intervals. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.StepLR` from PyTorch. Decays the learning rate by a factor of ``gamma`` periodically, with a frequency determined by ``step_size``. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \gamma ^ {\text{floor}(t / \rho)} Where :math:`\rho` represents the time between changes to the learning rate (the step size), and :math:`\gamma` represents the multiplicative decay factor. Args: step_size (str | Time): Time between changes to the learning rate. gamma (float): Multiplicative decay factor. Default = ``0.1``. """ def __init__(self, step_size: Union[str, Time], gamma: float = 0.1): self.step_size = step_size self.gamma = gamma def __call__(self, state: State, ssr: float = 1.0): step_size = _convert_time(self.step_size, state, ssr=ssr) current_time = state.timestamp.get(step_size.unit) steps = int(current_time / step_size) return self.gamma**steps class MultiStepScheduler(ComposerScheduler): r"""Decays the learning rate discretely at fixed milestones. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.MultiStepLR` from PyTorch. Decays the learning rate by a factor of ``gamma`` whenever a time milestone in ``milestones`` is reached. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \gamma ^ x Where :math:`x` represents the amount of milestones that have been reached, and :math:`\gamma` represents the multiplicative decay factor. Args: milestones (List[str | Time]): Times at which the learning rate should change. gamma (float): Multiplicative decay factor. Default = ``0.1``. """ def __init__(self, milestones: List[Union[str, Time]], gamma: float = 0.1): self.milestones = milestones self.gamma = gamma def __call__(self, state: State, ssr: float = 1.0): milestones = [_convert_time(milestone, state, ssr=ssr) for milestone in self.milestones] factor = 1.0 for milestone in milestones: if state.timestamp >= milestone: factor *= self.gamma return factor class ConstantScheduler(ComposerScheduler): r"""Maintains a fixed learning rate. This scheduler is based on :class:`~torch.optim.lr_scheduler.ConstantLR` from PyTorch. The default settings for this scheduler simply maintain a learning rate factor of 1 for the entire training duration. However, both the factor and the duration of this scheduler can be configured. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} \alpha, & \text{if } t < t_{max} \\ 1.0 & \text{otherwise} \end{cases} Where :math:`\alpha` represents the learning rate multiplier to maintain while this scheduler is active, and :math:`t_{max}` represents the duration of this scheduler. Args: alpha (float): Learning rate multiplier to maintain while this scheduler is active. Default = ``1.0``. t_max (str | Time): Duration of this scheduler. Default = ``"1dur"``. """ def __init__(self, alpha: float = 1.0, t_max: Union[str, Time] = '1dur') -> None: self.alpha = alpha self.t_max = t_max def __call__(self, state: State, ssr: float = 1.0) -> float: t_max = _convert_time(self.t_max, state, ssr=ssr) if state.timestamp < t_max: return self.alpha return 1.0 class LinearScheduler(ComposerScheduler): r"""Adjusts the learning rate linearly. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.LinearLR` from PyTorch. .. warning:: Note that the defaults for this scheduler differ from the defaults for :class:`~torch.optim.lr_scheduler.LinearLR`. The PyTorch scheduler, by default, linearly increases the learning rate multiplier from 1.0 / 3 to 1.0, whereas this implementation, by default, linearly decreases the multiplier rom 1.0 to 0.0. Linearly adjusts the learning rate multiplier from ``alpha_i`` to ``alpha_f`` over ``t_{max}`` time. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \alpha_i + (alpha_f - \alpha_i) \times \tau Given :math:`\tau`, the fraction of time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau = t / t_{max} Where :math:`\alpha_i` represents the initial learning rate multiplier, :math:`\alpha_f` represents the learning rate multiplier to decay to, and :math:`t_{max}` represents the duration of this scheduler. Args: alpha_i (float): Initial learning rate multiplier. Default = ``1.0``. alpha_f (float): Final learning rate multiplier. Default = ``0.0``. t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. """ def __init__(self, alpha_i: float = 1.0, alpha_f: float = 0.0, t_max: Union[str, Time] = '1dur'): self.alpha_i = alpha_i self.alpha_f = alpha_f self.t_max = Time.from_timestring(t_max) if isinstance(t_max, str) else t_max def __call__(self, state: State, ssr: float = 1.0): t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_max.unit) frac_of_total = min(1.0, (current_time / t_max).value) current_factor = self.alpha_i + frac_of_total * (self.alpha_f - self.alpha_i) return current_factor class ExponentialScheduler(ComposerScheduler): r"""Decays the learning rate exponentially. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.ExponentialLR` from PyTorch. Exponentially decays the learning rate such that it decays by a factor of ``gamma`` every ``decay_period`` time. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \gamma ^ {t / \rho} Where :math:`\rho` represents the decay period, and :math:`\gamma` represents the multiplicative decay factor. Args: decay_period (str | Time): Decay period. Default = ``"1ep"``. gamma (float): Multiplicative decay factor. """ def __init__(self, gamma: float, decay_period: Union[str, Time] = '1ep'): self.gamma = gamma self.decay_period = decay_period def __call__(self, state: State, ssr: float = 1.0): decay_period = _convert_time(self.decay_period, state, ssr) current_time_in_decay_units = state.timestamp.get(decay_period.unit) return self.gamma**float(current_time_in_decay_units / decay_period) def _cosine_anneal(x: float, min_y: float = 0.0, max_y: float = 1.0) -> float: """Implements a cosine decay curve. Curve is cos(x) on domain [0, pi], stretched to the domain [0, 1] and range [min_y, max_y]. Additionally, param x is clipped to the interval [0, 1] """ x = min(max(x, 0.0), 1.0) return min_y + (max_y - min_y) * (1 + math.cos(x * math.pi)) / 2 class CosineAnnealingScheduler(ComposerScheduler): r"""Decays the learning rate according to the decreasing part of a cosine curve. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.CosineAnnealingLR` from PyTorch. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \alpha_f + (1 - \alpha_f) \times \frac{1}{2} (1 + \cos(\pi \times \tau)) Given :math:`\tau`, the fraction of time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau = t / t_{max} Where :math:`t_{max}` represents the duration of this scheduler, and :math:`\alpha_f` represents the learning rate multiplier to decay to. Args: t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. alpha_f (float): Learning rate multiplier to decay to. Default = ``0.0``. """ def __init__(self, t_max: Union[str, Time] = '1dur', alpha_f: float = 0.0): self.t_max = t_max self.alpha_f = alpha_f def __call__(self, state: State, ssr: float = 1.0): t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_max.unit) frac_of_total = (current_time / t_max).value return _cosine_anneal(x=frac_of_total, min_y=self.alpha_f) class CosineAnnealingWarmRestartsScheduler(ComposerScheduler): r"""Cyclically decays the learning rate according to the decreasing part of a cosine curve. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.CosineAnnealingWarmRestarts` from PyTorch. This scheduler resembles a regular cosine annealing curve, as seen in :class:`~.CosineAnnealingScheduler`, except that after the curve first completes ``t_0`` time, the curve resets to the start. The durations of subsequent cycles are each multiplied by ``t_mult``. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \alpha_f + (1 - \alpha_f) \times \frac{1}{2}(1 + \cos(\pi \times \tau_i)) Given :math:`\tau_i`, the fraction of time elapsed through the :math:`i^\text{th}` cycle, as: .. math:: \tau_i = (t - \sum_{j=0}^{i-1} t_0 t_{mult}^j) / (t_0 t_{mult}^i) Where :math:`t_0` represents the period of the first cycle, :math:`t_{mult}` represents the multiplier for the duration of successive cycles, and :math:`\alpha_f` represents the learning rate multiplier to decay to. Args: t_0 (str | Time): The period of the first cycle. t_mult (float): The multiplier for the duration of successive cycles. Default = ``1.0``. alpha_f (float): Learning rate multiplier to decay to. Default = ``0.0``. """ def __init__(self, t_0: Union[str, Time], t_mult: float = 1.0, alpha_f: float = 0.0): self.t_0 = t_0 self.t_mult = t_mult self.alpha_f = alpha_f def __call__(self, state: State, ssr: float = 1.0): t_0 = _convert_time(self.t_0, state, ssr=ssr) current_interval_len = t_0 current_interval_end = t_0 while current_interval_end <= state.timestamp.get(current_interval_end.unit): if current_interval_len.value == 0: raise ValueError( 'Interval between restarts for cosine annealing/warm restarts scheduler has decayed to 0.') current_interval_len = Time(value=int(self.t_mult * current_interval_len.value), unit=current_interval_len.unit) current_interval_end += current_interval_len current_interval_start = current_interval_end - current_interval_len frac_of_current_interval = ((state.timestamp.get(t_0.unit) - current_interval_start) / current_interval_len).value return _cosine_anneal(x=frac_of_current_interval, min_y=self.alpha_f) class PolynomialScheduler(ComposerScheduler): r"""Sets the learning rate to be proportional to a power of the fraction of training time left. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \alpha_f + (1 - \alpha_f) \times (1 - \tau) ^ {\kappa} Given :math:`\tau`, the fraction of time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau = t / t_{max} Where :math:`\kappa` represents the exponent to be used for the proportionality relationship, :math:`t_{max}` represents the duration of this scheduler, and :math:`\alpha_f` represents the learning rate multiplier to decay to. Args: power (float): The exponent to be used for the proportionality relationship. t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. alpha_f (float): Learning rate multiplier to decay to. Default = ``0.0``. """ def __init__(self, power: float, t_max: Union[str, Time] = '1dur', alpha_f: float = 0.0): self.t_max = t_max self.power = power self.alpha_f = alpha_f def __call__(self, state: State, ssr: float = 1.0): t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_max.unit) frac_of_total = (current_time / t_max).value coeff = (1 - frac_of_total)**self.power current_factor = self.alpha_f + coeff * (1.0 - self.alpha_f) return current_factor def _raise_if_warmup_and_max_duration_incompatible(t_warmup: Union[str, Time], t_max: Union[str, Time]): if isinstance(t_warmup, str): t_warmup = Time.from_timestring(t_warmup) if isinstance(t_max, str): t_max = Time.from_timestring(t_max) units_same = t_warmup.unit == t_max.unit warmup_is_dur = t_warmup.unit == TimeUnit('dur') batches_vs_epochs = (t_warmup.unit == TimeUnit('ba') and t_max.unit == TimeUnit('ep')) or (t_warmup.unit == TimeUnit('ep') and t_max.unit == TimeUnit('ba')) if not units_same and not warmup_is_dur and not batches_vs_epochs: raise ValueError(f'Cannot use warmup scheduler with max_duration {t_max} and warmup {t_warmup}. ' 't_warmup units must be the same as max_duration units, warmup must be in units "dur", ' 'max_duration must be "ba" and t_warmup "ep", or max_duration must be "ep" and t_warmup "ba".') class MultiStepWithWarmupScheduler(ComposerScheduler): r"""Decays the learning rate discretely at fixed milestones, with an initial warmup. .. seealso:: This scheduler is based on :class:`~.MultiStepScheduler`, with an added warmup. Starts with a linear warmup over ``t_warmup`` time, then decays the learning rate by a factor of ``gamma`` whenever a time milestone in ``milestones`` is reached. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} t / t_{warmup}, & \text{if } t < t_{warmup} \\ \gamma ^ x & \text{otherwise} \end{cases} Where :math:`t_{warmup}` represents the warmup time, :math:`x` represents the amount of milestones that have been reached, and :math:`\gamma` represents the multiplicative decay factor. .. warning:: All milestones should be greater than ``t_warmup``; otherwise, they will have no effect on the computed learning rate multiplier until the warmup has completed. .. warning:: By default, initial warmup time is **not** scaled according to any provided scale schedule ratio. To change this behavior, set ``scale_warmup=True``. Args: t_warmup (str | Time): Warmup time. milestones (List[str | Time]): Times at which the learning rate should change. gamma (float): Multiplicative decay factor. Default = ``0.1``. scale_warmup (float): SSR also scales the warmup period. Default = ``False``. """ def __init__(self, t_warmup: Union[str, Time], milestones: List[Union[str, Time]], gamma: float = 0.1, scale_warmup: bool = False): self.t_warmup = t_warmup self.milestones = milestones self.gamma = gamma self.scale_warmup = scale_warmup self.warmup_scheduler = LinearScheduler(alpha_i=0.0, alpha_f=1.0, t_max=t_warmup) self.step_scheduler = MultiStepScheduler(milestones=milestones, gamma=gamma) def __call__(self, state: State, ssr: float = 1.0): assert state.max_duration is not None,'max_duration should be set whenever schedulers are invoked' _raise_if_warmup_and_max_duration_incompatible(self.t_warmup, state.max_duration) t_warmup = _convert_time(self.t_warmup, state) if t_warmup.value == 0: warnings.warn( textwrap.dedent("""\ The warmup duration is 0. If you specified warmup as a fraction of total training duration, take note that the warmup duration is calculated in the same unit as the trainer's max_duration parameter.""")) if state.timestamp < t_warmup: if self.scale_warmup: return self.warmup_scheduler(state, ssr) return self.warmup_scheduler(state) return self.step_scheduler(state, ssr) class ConstantWithWarmupScheduler(ComposerScheduler): r"""Maintains a fixed learning rate, with an initial warmup. This scheduler is based on :class:`~torch.optim.lr_scheduler.ConstantLR` from PyTorch, with an added warmup. Starts with a linear warmup over ``t_warmup`` time, then simply maintains a learning rate factor of 1 for the entire training duration. However, both the factor and the duration of this scheduler can be configured. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} t / t_{warmup}, & \text{if } t < t_{warmup} \\ \alpha, & \text{if } t < t_{max} \\ 1.0 & \text{otherwise} \end{cases} Where :math:`\alpha` represents the learning rate multiplier to maintain while this scheduler is active, and :math:`t_{max}` represents the duration of this scheduler. .. warning:: By default, initial warmup time is **not** scaled according to any provided scale schedule ratio. To change this behavior, set ``scale_warmup=True``. Args: t_warmup (str | Time): Warmup time. alpha (float): Learning rate multiplier to maintain while this scheduler is active. Default = ``1.0``. t_max (str | Time): Duration of this scheduler. Default = ``"1dur"``. scale_warmup (float): SSR also scales the warmup period. Default = ``False``. """ def __init__(self, t_warmup: Union[str, Time], alpha: float = 1.0, t_max: Union[str, Time] = '1dur', scale_warmup: bool = False) -> None: self.t_warmup = t_warmup self.alpha = alpha self.t_max = t_max self.scale_warmup = scale_warmup self.scheduler = LinearWithWarmupScheduler(t_warmup=t_warmup, alpha_i=alpha, alpha_f=alpha, t_max=t_max, scale_warmup=scale_warmup) def __call__(self, state: State, ssr: float = 1.0) -> float: return self.scheduler(state, ssr) class LinearWithWarmupScheduler(ComposerScheduler): r"""Adjusts the learning rate linearly, with an initial warmup. .. seealso:: This scheduler is based on :class:`~.LinearScheduler`, with an added warmup. Linearly adjusts the learning rate multiplier from ``alpha_i`` to ``alpha_f`` over ``t_{max}`` time. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} t / t_{warmup}, & \text{if } t < t_{warmup} \\ \alpha_i + (alpha_f - \alpha_i) \times \tau_w & \text{otherwise} \end{cases} Given :math:`\tau_w`, the fraction of post-warmup time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau_w = (t - t_{warmup}) / t_{max} Where :math:`t_{warmup}` represents the warmup time, :math:`\alpha_i` represents the initial learning rate multiplier, and :math:`\alpha_f` represents the learning rate multiplier to decay to, and :math:`t_{max}` represents the duration of this scheduler. .. warning:: By default, the initial warmup time is **not** scaled according to any provided scale schedule ratio! However, the duration of the scheduler is still scaled accordingly. To achieve this, after warmup, the scheduler's "slope" will be slightly distorted from what would otherwise be expected. To scale the entire schedule, set ``scale_warmup=True``. Args: t_warmup (str | Time): Warmup time. alpha_i (float): Initial learning rate multiplier. Default = ``1.0``. alpha_f (float): Final learning rate multiplier. Default = ``0.0``. t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. scale_warmup (float): SSR also scales the warmup period. Default = ``False``. """ def __init__(self, t_warmup: Union[str, Time], alpha_i: float = 1.0, alpha_f: float = 0.0, t_max: Union[str, Time] = '1dur', scale_warmup: bool = False): self.t_warmup = t_warmup self.alpha_i = alpha_i self.alpha_f = alpha_f self.t_max = t_max self.scale_warmup = scale_warmup self.warmup_scheduler = LinearScheduler(alpha_i=0.0, alpha_f=alpha_i, t_max=t_warmup) def __call__(self, state: State, ssr: float = 1.0): assert state.max_duration is not None,'max_duration should be set whenever schedulers are invoked' _raise_if_warmup_and_max_duration_incompatible(self.t_warmup, state.max_duration) t_warmup = _convert_time(self.t_warmup, state) if t_warmup.value == 0: warnings.warn( textwrap.dedent("""\ The warmup duration is 0. If you specified warmup as a fraction of total training duration, take note that the warmup duration is calculated in the same unit as the trainer's max_duration parameter.""")) if state.timestamp < t_warmup: if self.scale_warmup: return self.warmup_scheduler(state, ssr) return self.warmup_scheduler(state) t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_warmup.unit) frac_of_total = ((current_time - t_warmup) / (t_max - t_warmup)).value if (t_max > t_warmup) else 0.0 frac_of_total = min(1.0, frac_of_total) current_factor = self.alpha_i + frac_of_total * (self.alpha_f - self.alpha_i) return current_factor class CosineAnnealingWithWarmupScheduler(ComposerScheduler): r"""Decays the learning rate according to the decreasing part of a cosine curve, with an initial warmup. .. seealso:: This scheduler is based on :class:`~.CosineAnnealingScheduler`, with an added warmup. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} t / t_{warmup}, & \text{if } t < t_{warmup} \\ \alpha_f + (1 - \alpha_f) \times \frac{1}{2} (1 + \cos(\pi \times \tau_w)) & \text{otherwise} \end{cases} Given :math:`\tau_w`, the fraction of post-warmup time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau_w = (t - t_{warmup}) / t_{max} Where :math:`t_{warmup}` represents the warmup time, :math:`t_{max}` represents the duration of this scheduler, and :math:`\alpha_f` represents the learning rate multiplier to decay to. .. warning:: By default, initial warmup time is **not** scaled according to any provided scale schedule ratio. To change this behavior, set ``scale_warmup=True``. Args: t_warmup (str | Time): Warmup time. t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. alpha_f (float): Learning rate multiplier to decay to. Default = ``0.0``. scale_warmup (float): SSR also scales the warmup period. Default = ``False``. """ def __init__(self, t_warmup: Union[str, Time], t_max: Union[str, Time] = '1dur', alpha_f: float = 0.0, scale_warmup: bool = False): self.t_warmup = t_warmup self.t_max = t_max self.alpha_f = alpha_f self.scale_warmup = scale_warmup self.warmup_scheduler = LinearScheduler(alpha_i=0.0, alpha_f=1.0, t_max=t_warmup) def __call__(self, state: State, ssr: float = 1.0): assert state.max_duration is not None,'max_duration should be set whenever schedulers are invoked' _raise_if_warmup_and_max_duration_incompatible(self.t_warmup, state.max_duration) t_warmup = _convert_time(self.t_warmup, state) if t_warmup.value == 0: warnings.warn( textwrap.dedent("""\ The warmup duration is 0. If you specified warmup as a fraction of total training duration, take note that the warmup duration is calculated in the same unit as the trainer's max_duration parameter.""")) if state.timestamp < t_warmup: if self.scale_warmup: return self.warmup_scheduler(state, ssr) return self.warmup_scheduler(state) t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_warmup.unit) frac_of_total = ((current_time - t_warmup) / (t_max - t_warmup)).value if (t_max > t_warmup) else 0.0 frac_of_total = min(1.0, frac_of_total) return _cosine_anneal(x=frac_of_total, min_y=self.alpha_f) class PolynomialWithWarmupScheduler(ComposerScheduler): r"""Decays the learning rate according to a power of the fraction of training time left, with an initial warmup. .. seealso:: This scheduler is based on :class:`~.PolynomialScheduler`, with an added warmup. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} t / t_{warmup}, & \text{if } t < t_{warmup} \\ \alpha_f + (1 - \alpha_f) \times (1 - \tau_w) ^ {\kappa} & \text{otherwise} \end{cases} Given :math:`\tau_w`, the fraction of post-warmup time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau_w = (t - t_{warmup}) / t_{max} Where :math:`\kappa` represents the exponent to be used for the proportionality relationship, :math:`t_{warmup}` represents the warmup time, :math:`t_{max}` represents the duration of this scheduler, and :math:`\alpha_f` represents the learning rate multiplier to decay to. .. warning:: By default, initial warmup time is **not** scaled according to any provided scale schedule ratio. To change this behavior, set ``scale_warmup=True``. Args: t_warmup (str | Time): Warmup time. power (float): The exponent to be used for the proportionality relationship. Default = ``2.0``. t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. alpha_f (float): Learning rate multiplier to decay to. Default = ``0.0``. scale_warmup (float): SSR also scales the warmup period. Default = ``False``. """ def __init__(self, t_warmup: Union[str, Time], power: float = 2.0, t_max: Union[str, Time] = '1dur', alpha_f: float = 0.0, scale_warmup: bool = False): self.t_warmup = t_warmup self.power = power self.t_max = t_max self.alpha_f = alpha_f self.scale_warmup = scale_warmup self.warmup_scheduler = LinearScheduler(alpha_i=0.0, alpha_f=1.0, t_max=t_warmup) def __call__(self, state: State, ssr: float = 1.0): assert state.max_duration is not None,'max_duration should be set whenever schedulers are invoked' _raise_if_warmup_and_max_duration_incompatible(self.t_warmup, state.max_duration) t_warmup = _convert_time(self.t_warmup, state) if t_warmup.value == 0: warnings.warn( textwrap.dedent("""\ The warmup duration is 0. If you specified warmup as a fraction of total training duration, take note that the warmup duration is calculated in the same unit as the trainer's max_duration parameter.""")) if state.timestamp < t_warmup: if self.scale_warmup: return self.warmup_scheduler(state, ssr) return self.warmup_scheduler(state) t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_warmup.unit) frac_of_total = ((current_time - t_warmup) / (t_max - t_warmup)).value if (t_max > t_warmup) else 0.0 frac_of_total = min(1.0, frac_of_total) coeff = (1 - frac_of_total)**self.power current_factor = self.alpha_f + coeff * (1.0 - self.alpha_f) return current_factor

mosaicml__composer

schedulers.rst

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mosaicml__composer/docs/source/trainer/schedulers.rst

Schedulers The .Trainer supports both PyTorch torch.optim.lr_scheduler schedulers as well as our own schedulers, which take advantage of the .Time representation. For PyTorch schedulers, we step every epoch by default. To instead step every batch, set step_schedulers_every_batch=True: from composer import Trainer from torch.optim.lr_scheduler import CosineAnnealingLR trainer = Trainer( ..., schedulers=CosineAnnealingLR(optimizer, T_max=2), step_schedulers_every_batch=True, ) Note If setting step_schedulers_every_batch to True, remember to specify the arguments to your pytorch scheduler in units of batches, not epochs. Our experiments have shown better accuracy using stepwise schedulers, so it is the recommended setting in most cases. Composer Schedulers Our schedulers take advantage of our Time</trainer/time> abstraction to provide easier ways to set time. Time parameters can be provided in different units: samples ("sp"), tokens ("tok"), batches ("ba"), epochs ("ep"), and duration ("dur"). See Time</trainer/time>. For example, the below would step the learning rate at 30%, 50%, and 90% of the way through training: from composer import Trainer from composer.optim.scheduler import MultiStepScheduler trainer = Trainer( model=model, train_dataloader=train_dataloader, max_duration='90ep', schedulers=MultiStepScheduler( milestones=['0.3dur', '0.5dur', '0.9dur'], gamma=0.1 )) These schedulers typically read the state.timestamp to determine the trainer's progress and return a learning rate multipler. Inside the Trainer, we convert these to ~torch.optim.lr_scheduler.LambdaLR schedulers. By default, our schedulers are stepped at every batch. Below are the supported schedulers found at composer.optim.scheduler. composer.optim.scheduler StepScheduler MultiStepScheduler MultiStepWithWarmupScheduler ConstantScheduler LinearScheduler LinearWithWarmupScheduler ExponentialScheduler CosineAnnealingScheduler CosineAnnealingWithWarmupScheduler CosineAnnealingWarmRestartsScheduler PolynomialScheduler PolynomialWithWarmupScheduler Note Compared to PyTorch schedulers, .ComposerScheduler need not be provided an optimizer directly. The trainer will handle binding the optimizer when it compiles the scheduler later. Scale Schedule Ratio The Scale Schedule Ratio (SSR) scales the learning rate schedule by some factor and is a powerful way to tradeoff training time and quality. scale_schedule_ratio is an argument to the .Trainer. Scale Schedule changes the training duration by a scaling factor and scales the learning rate scheduler accordingly. This serves to vary the training budget, making it possible to explore tradeoffs between cost (measured in time or money) and model quality. For example, the code below will scale the training time by half (to 10 epochs) and also scale the learning rate schedule. from composer import Trainer from composer.optim.scheduler import MultiStepScheduler trainer = Trainer( ..., max_duration="20ep", schedulers=MultiStepScheduler(milestones=["10ep", "16ep"]), scale_schedule_ratio=0.5, ) # or equivalently, with default SSR=1.0: trainer = Trainer( ..., max_duration="10ep", schedulers=MultiStepScheduler(milestones=["5ep", "8ep"]) ) Importantly, for our schedulers that have warmup, the warmup period is not scaled by default. For example, if we apply scale_schedule_ratio=0.5 to: from composer.optim.scheduler import MultiStepWithWarmupScheduler scheduler = MultiStepWithWarmupScheduler( milestones=["10ep", "20ep"], t_warmup="4ep", ) The resulting scheduler would warmup for 4 epochs and then have step milestones at 5 epochs and 10 epochs. To scale the warmup period as well, set scale_warmup=True. For example: from composer.optim.scheduler import MultiStepWithWarmupScheduler scheduler = MultiStepWithWarmupScheduler( milestones=["10ep", "20ep"], t_warmup="4ep", scale_warmup=True, ) With scale_schedule_ratio=0.5, this scheduler will warmup for 2 epochs, then step on 5 and 10 epochs.

# Copyright 2022 MosaicML Composer authors # SPDX-License-Identifier: Apache-2.0 """Stateless learning rate schedulers. Stateless schedulers solve some of the problems associated with PyTorch's built-in schedulers provided in :mod:`torch.optim.lr_scheduler`. The primary design goal of the schedulers provided in this module is to allow schedulers to interface directly with Composer's :mod:`~composer.core.time` abstraction. This means that schedulers can be configured using arbitrary but explicit time units. See :class:`~.ComposerScheduler` for more information on stateless schedulers. """ import inspect import logging import math import textwrap import warnings from typing import TYPE_CHECKING, List, Union from torch.optim.lr_scheduler import LambdaLR from composer.core import PyTorchScheduler, State, Time, TimeUnit if TYPE_CHECKING: from typing import Protocol else: # subclasses of Protocol cannot be instantiated in Python 3.8 Protocol = object log = logging.getLogger(__name__) __all__ = [ 'ComposerScheduler', 'compile_composer_scheduler', 'StepScheduler', 'MultiStepScheduler', 'ConstantScheduler', 'LinearScheduler', 'ExponentialScheduler', 'CosineAnnealingScheduler', 'CosineAnnealingWarmRestartsScheduler', 'PolynomialScheduler', 'MultiStepWithWarmupScheduler', 'ConstantWithWarmupScheduler', 'LinearWithWarmupScheduler', 'CosineAnnealingWithWarmupScheduler', 'PolynomialWithWarmupScheduler' ] class ComposerScheduler(Protocol): r"""Specification for a stateless scheduler function. While this specification is provided as a Python class, an ordinary function can implement this interface as long as it matches the signature of this interface's :meth:`~.ComposerScheduler.__call__` method. For example, a scheduler that halves the learning rate after 10 epochs could be written as: .. code:: python def ten_epoch_decay_scheduler(state: State) -> float: if state.timestamp.epoch < 10: return 1.0 return 0.5 # ten_epoch_decay_scheduler is a valid ComposerScheduler trainer = Trainer( schedulers=[ten_epoch_decay_scheduler], ... ) In order to allow schedulers to be configured, schedulers may also written as callable classes: .. code:: python class VariableEpochDecayScheduler(ComposerScheduler): def __init__(num_epochs: int): self.num_epochs = num_epochs def __call__(state: State) -> float: if state.time.epoch < self.num_epochs: return 1.0 return 0.5 ten_epoch_decay_scheduler = VariableEpochDecayScheduler(num_epochs=10) # ten_epoch_decay_scheduler is also a valid ComposerScheduler trainer = Trainer( schedulers=[ten_epoch_decay_scheduler], ... ) The constructions of ``ten_epoch_decay_scheduler`` in each of the examples above are equivalent. Note that neither scheduler uses the ``scale_schedule_ratio`` parameter. As long as this parameter is not used when initializing :class:`.Trainer`, it is not required that any schedulers implement that parameter. .. automethod:: __call__ """ def __call__(self, state: State, ssr: float = 1.0) -> float: r"""Calculate the current learning rate multiplier :math:`\alpha`. A scheduler function should be a pure function that returns a multiplier to apply to the optimizer's provided learning rate, given the current trainer state, and optionally a "scale schedule ratio" (SSR). A typical implementation will read ``state.timestamp``, and possibly other fields like ``state.max_duration``, to determine the trainer's latest temporal progress. .. note:: All instances of :class:`~.ComposerScheduler` output a `multiplier` for the learning rate, rather than the learning rate directly. By convention, we use the symbol :math:`\alpha` to refer to this multiplier. This means that the learning rate :math:`\eta` at time :math:`t` can be represented as :math:`\eta(t) = \eta_i \times \alpha(t)`, where :math:`\eta_i` represents the learning rate used to initialize the optimizer. .. note:: It is possible to use multiple schedulers, in which case their effects will stack multiplicatively. The ``ssr`` param indicates that the schedule should be "stretched" accordingly. In symbolic terms, where :math:`\alpha_\sigma(t)` represents the scheduler output at time :math:`t` using scale schedule ratio :math:`\sigma`: .. math:: \alpha_{\sigma}(t) = \alpha(t / \sigma) Args: state (State): The current Composer Trainer state. ssr (float): The scale schedule ratio. In general, the learning rate computed by this scheduler at time :math:`t` with an SSR of 1.0 should be the same as that computed by this scheduler at time :math:`t \times s` with an SSR of :math:`s`. Default = ``1.0``. Returns: alpha (float): A multiplier to apply to the optimizer's provided learning rate. """ raise NotImplementedError def _convert_time(time: Union[str, Time[int], Time[float]], state: State, ssr: float = 1.0) -> Time[int]: if isinstance(time, str): time = Time.from_timestring(time) assert state.max_duration is not None,'max_duration should be set whenever schedulers are invoked' if time.unit == TimeUnit.DURATION: if state.max_duration.unit == TimeUnit.EPOCH: if state.dataloader_len is None: raise RuntimeError('Cannot convert time, as state.dataloader_len is None.') return Time(int(time.value * int(state.dataloader_len) * state.max_duration.value), TimeUnit.BATCH) return Time(int(time.value * state.max_duration.value), state.max_duration.unit) elif time.unit == TimeUnit.EPOCH: # Epochs do not provide sufficient granularity for SSR scaling # e.g. if max_duration = 1ep, then any SSR would result in a new duration of 0. # so, convert the time into batches if state.dataloader_len is None: raise RuntimeError('Cannot convert time, as state.dataloader_len is None.') time = Time(value=time.value * int(state.dataloader_len), unit=TimeUnit.BATCH) return Time(value=int(time.value * ssr), unit=time.unit) def compile_composer_scheduler(scheduler: ComposerScheduler, state: State, ssr: float = 1.0) -> PyTorchScheduler: """Converts a stateless scheduler into a PyTorch scheduler object. While the resulting scheduler provides a ``.step()`` interface similar to other PyTorch schedulers, the scheduler is also given a bound reference to the current :class:`~composer.core.State`. This means that any internal state updated by ``.step()`` can be ignored, and the scheduler can instead simply use the bound state to recalculate the current learning rate. Args: scheduler (ComposerScheduler): A stateless scheduler, provided as a :class:`~.ComposerScheduler` object. state (State): The Composer Trainer's state. Returns: compiled_scheduler (PyTorchScheduler): The scheduler, in a form compatible with PyTorch scheduler interfaces. """ optimizers = state.optimizers if len(optimizers)!= 1: raise NotImplementedError('Providing functional schedulers is unsupported with multiple optimizers.') optimizer = optimizers[0] scheduler_sig = inspect.signature(scheduler) def scheduler_fn(epoch: int) -> float: del epoch # unused. Provided by the pytorch LambdaLR # if the ssr is 1.0, don't pass it to the scheduler. This allows users to pass in lambdas that only take # one parameter -- the state if len(scheduler_sig.parameters) == 1: if ssr == 1.0: return scheduler(state) else: raise ValueError( textwrap.dedent(f"""\ Scheduler {scheduler} does not support `scale_schedule_ratio`. To use `scale_schedule_ratio`, the scheduler must take two arguments (state, ssr)""")) return scheduler(state, ssr) lambda_scheduler = LambdaLR(optimizer, scheduler_fn) return lambda_scheduler class StepScheduler(ComposerScheduler): r"""Decays the learning rate discretely at fixed intervals. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.StepLR` from PyTorch. Decays the learning rate by a factor of ``gamma`` periodically, with a frequency determined by ``step_size``. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \gamma ^ {\text{floor}(t / \rho)} Where :math:`\rho` represents the time between changes to the learning rate (the step size), and :math:`\gamma` represents the multiplicative decay factor. Args: step_size (str | Time): Time between changes to the learning rate. gamma (float): Multiplicative decay factor. Default = ``0.1``. """ def __init__(self, step_size: Union[str, Time], gamma: float = 0.1): self.step_size = step_size self.gamma = gamma def __call__(self, state: State, ssr: float = 1.0): step_size = _convert_time(self.step_size, state, ssr=ssr) current_time = state.timestamp.get(step_size.unit) steps = int(current_time / step_size) return self.gamma**steps class MultiStepScheduler(ComposerScheduler): r"""Decays the learning rate discretely at fixed milestones. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.MultiStepLR` from PyTorch. Decays the learning rate by a factor of ``gamma`` whenever a time milestone in ``milestones`` is reached. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \gamma ^ x Where :math:`x` represents the amount of milestones that have been reached, and :math:`\gamma` represents the multiplicative decay factor. Args: milestones (List[str | Time]): Times at which the learning rate should change. gamma (float): Multiplicative decay factor. Default = ``0.1``. """ def __init__(self, milestones: List[Union[str, Time]], gamma: float = 0.1): self.milestones = milestones self.gamma = gamma def __call__(self, state: State, ssr: float = 1.0): milestones = [_convert_time(milestone, state, ssr=ssr) for milestone in self.milestones] factor = 1.0 for milestone in milestones: if state.timestamp >= milestone: factor *= self.gamma return factor class ConstantScheduler(ComposerScheduler): r"""Maintains a fixed learning rate. This scheduler is based on :class:`~torch.optim.lr_scheduler.ConstantLR` from PyTorch. The default settings for this scheduler simply maintain a learning rate factor of 1 for the entire training duration. However, both the factor and the duration of this scheduler can be configured. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} \alpha, & \text{if } t < t_{max} \\ 1.0 & \text{otherwise} \end{cases} Where :math:`\alpha` represents the learning rate multiplier to maintain while this scheduler is active, and :math:`t_{max}` represents the duration of this scheduler. Args: alpha (float): Learning rate multiplier to maintain while this scheduler is active. Default = ``1.0``. t_max (str | Time): Duration of this scheduler. Default = ``"1dur"``. """ def __init__(self, alpha: float = 1.0, t_max: Union[str, Time] = '1dur') -> None: self.alpha = alpha self.t_max = t_max def __call__(self, state: State, ssr: float = 1.0) -> float: t_max = _convert_time(self.t_max, state, ssr=ssr) if state.timestamp < t_max: return self.alpha return 1.0 class LinearScheduler(ComposerScheduler): r"""Adjusts the learning rate linearly. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.LinearLR` from PyTorch. .. warning:: Note that the defaults for this scheduler differ from the defaults for :class:`~torch.optim.lr_scheduler.LinearLR`. The PyTorch scheduler, by default, linearly increases the learning rate multiplier from 1.0 / 3 to 1.0, whereas this implementation, by default, linearly decreases the multiplier rom 1.0 to 0.0. Linearly adjusts the learning rate multiplier from ``alpha_i`` to ``alpha_f`` over ``t_{max}`` time. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \alpha_i + (alpha_f - \alpha_i) \times \tau Given :math:`\tau`, the fraction of time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau = t / t_{max} Where :math:`\alpha_i` represents the initial learning rate multiplier, :math:`\alpha_f` represents the learning rate multiplier to decay to, and :math:`t_{max}` represents the duration of this scheduler. Args: alpha_i (float): Initial learning rate multiplier. Default = ``1.0``. alpha_f (float): Final learning rate multiplier. Default = ``0.0``. t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. """ def __init__(self, alpha_i: float = 1.0, alpha_f: float = 0.0, t_max: Union[str, Time] = '1dur'): self.alpha_i = alpha_i self.alpha_f = alpha_f self.t_max = Time.from_timestring(t_max) if isinstance(t_max, str) else t_max def __call__(self, state: State, ssr: float = 1.0): t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_max.unit) frac_of_total = min(1.0, (current_time / t_max).value) current_factor = self.alpha_i + frac_of_total * (self.alpha_f - self.alpha_i) return current_factor class ExponentialScheduler(ComposerScheduler): r"""Decays the learning rate exponentially. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.ExponentialLR` from PyTorch. Exponentially decays the learning rate such that it decays by a factor of ``gamma`` every ``decay_period`` time. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \gamma ^ {t / \rho} Where :math:`\rho` represents the decay period, and :math:`\gamma` represents the multiplicative decay factor. Args: decay_period (str | Time): Decay period. Default = ``"1ep"``. gamma (float): Multiplicative decay factor. """ def __init__(self, gamma: float, decay_period: Union[str, Time] = '1ep'): self.gamma = gamma self.decay_period = decay_period def __call__(self, state: State, ssr: float = 1.0): decay_period = _convert_time(self.decay_period, state, ssr) current_time_in_decay_units = state.timestamp.get(decay_period.unit) return self.gamma**float(current_time_in_decay_units / decay_period) def _cosine_anneal(x: float, min_y: float = 0.0, max_y: float = 1.0) -> float: """Implements a cosine decay curve. Curve is cos(x) on domain [0, pi], stretched to the domain [0, 1] and range [min_y, max_y]. Additionally, param x is clipped to the interval [0, 1] """ x = min(max(x, 0.0), 1.0) return min_y + (max_y - min_y) * (1 + math.cos(x * math.pi)) / 2 class CosineAnnealingScheduler(ComposerScheduler): r"""Decays the learning rate according to the decreasing part of a cosine curve. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.CosineAnnealingLR` from PyTorch. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \alpha_f + (1 - \alpha_f) \times \frac{1}{2} (1 + \cos(\pi \times \tau)) Given :math:`\tau`, the fraction of time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau = t / t_{max} Where :math:`t_{max}` represents the duration of this scheduler, and :math:`\alpha_f` represents the learning rate multiplier to decay to. Args: t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. alpha_f (float): Learning rate multiplier to decay to. Default = ``0.0``. """ def __init__(self, t_max: Union[str, Time] = '1dur', alpha_f: float = 0.0): self.t_max = t_max self.alpha_f = alpha_f def __call__(self, state: State, ssr: float = 1.0): t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_max.unit) frac_of_total = (current_time / t_max).value return _cosine_anneal(x=frac_of_total, min_y=self.alpha_f) class CosineAnnealingWarmRestartsScheduler(ComposerScheduler): r"""Cyclically decays the learning rate according to the decreasing part of a cosine curve. .. seealso:: This scheduler is based on :class:`~torch.optim.lr_scheduler.CosineAnnealingWarmRestarts` from PyTorch. This scheduler resembles a regular cosine annealing curve, as seen in :class:`~.CosineAnnealingScheduler`, except that after the curve first completes ``t_0`` time, the curve resets to the start. The durations of subsequent cycles are each multiplied by ``t_mult``. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \alpha_f + (1 - \alpha_f) \times \frac{1}{2}(1 + \cos(\pi \times \tau_i)) Given :math:`\tau_i`, the fraction of time elapsed through the :math:`i^\text{th}` cycle, as: .. math:: \tau_i = (t - \sum_{j=0}^{i-1} t_0 t_{mult}^j) / (t_0 t_{mult}^i) Where :math:`t_0` represents the period of the first cycle, :math:`t_{mult}` represents the multiplier for the duration of successive cycles, and :math:`\alpha_f` represents the learning rate multiplier to decay to. Args: t_0 (str | Time): The period of the first cycle. t_mult (float): The multiplier for the duration of successive cycles. Default = ``1.0``. alpha_f (float): Learning rate multiplier to decay to. Default = ``0.0``. """ def __init__(self, t_0: Union[str, Time], t_mult: float = 1.0, alpha_f: float = 0.0): self.t_0 = t_0 self.t_mult = t_mult self.alpha_f = alpha_f def __call__(self, state: State, ssr: float = 1.0): t_0 = _convert_time(self.t_0, state, ssr=ssr) current_interval_len = t_0 current_interval_end = t_0 while current_interval_end <= state.timestamp.get(current_interval_end.unit): if current_interval_len.value == 0: raise ValueError( 'Interval between restarts for cosine annealing/warm restarts scheduler has decayed to 0.') current_interval_len = Time(value=int(self.t_mult * current_interval_len.value), unit=current_interval_len.unit) current_interval_end += current_interval_len current_interval_start = current_interval_end - current_interval_len frac_of_current_interval = ((state.timestamp.get(t_0.unit) - current_interval_start) / current_interval_len).value return _cosine_anneal(x=frac_of_current_interval, min_y=self.alpha_f) class PolynomialScheduler(ComposerScheduler): r"""Sets the learning rate to be proportional to a power of the fraction of training time left. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \alpha_f + (1 - \alpha_f) \times (1 - \tau) ^ {\kappa} Given :math:`\tau`, the fraction of time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau = t / t_{max} Where :math:`\kappa` represents the exponent to be used for the proportionality relationship, :math:`t_{max}` represents the duration of this scheduler, and :math:`\alpha_f` represents the learning rate multiplier to decay to. Args: power (float): The exponent to be used for the proportionality relationship. t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. alpha_f (float): Learning rate multiplier to decay to. Default = ``0.0``. """ def __init__(self, power: float, t_max: Union[str, Time] = '1dur', alpha_f: float = 0.0): self.t_max = t_max self.power = power self.alpha_f = alpha_f def __call__(self, state: State, ssr: float = 1.0): t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_max.unit) frac_of_total = (current_time / t_max).value coeff = (1 - frac_of_total)**self.power current_factor = self.alpha_f + coeff * (1.0 - self.alpha_f) return current_factor def _raise_if_warmup_and_max_duration_incompatible(t_warmup: Union[str, Time], t_max: Union[str, Time]): if isinstance(t_warmup, str): t_warmup = Time.from_timestring(t_warmup) if isinstance(t_max, str): t_max = Time.from_timestring(t_max) units_same = t_warmup.unit == t_max.unit warmup_is_dur = t_warmup.unit == TimeUnit('dur') batches_vs_epochs = (t_warmup.unit == TimeUnit('ba') and t_max.unit == TimeUnit('ep')) or (t_warmup.unit == TimeUnit('ep') and t_max.unit == TimeUnit('ba')) if not units_same and not warmup_is_dur and not batches_vs_epochs: raise ValueError(f'Cannot use warmup scheduler with max_duration {t_max} and warmup {t_warmup}. ' 't_warmup units must be the same as max_duration units, warmup must be in units "dur", ' 'max_duration must be "ba" and t_warmup "ep", or max_duration must be "ep" and t_warmup "ba".') class MultiStepWithWarmupScheduler(ComposerScheduler): r"""Decays the learning rate discretely at fixed milestones, with an initial warmup. .. seealso:: This scheduler is based on :class:`~.MultiStepScheduler`, with an added warmup. Starts with a linear warmup over ``t_warmup`` time, then decays the learning rate by a factor of ``gamma`` whenever a time milestone in ``milestones`` is reached. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} t / t_{warmup}, & \text{if } t < t_{warmup} \\ \gamma ^ x & \text{otherwise} \end{cases} Where :math:`t_{warmup}` represents the warmup time, :math:`x` represents the amount of milestones that have been reached, and :math:`\gamma` represents the multiplicative decay factor. .. warning:: All milestones should be greater than ``t_warmup``; otherwise, they will have no effect on the computed learning rate multiplier until the warmup has completed. .. warning:: By default, initial warmup time is **not** scaled according to any provided scale schedule ratio. To change this behavior, set ``scale_warmup=True``. Args: t_warmup (str | Time): Warmup time. milestones (List[str | Time]): Times at which the learning rate should change. gamma (float): Multiplicative decay factor. Default = ``0.1``. scale_warmup (float): SSR also scales the warmup period. Default = ``False``. """ def __init__(self, t_warmup: Union[str, Time], milestones: List[Union[str, Time]], gamma: float = 0.1, scale_warmup: bool = False): self.t_warmup = t_warmup self.milestones = milestones self.gamma = gamma self.scale_warmup = scale_warmup self.warmup_scheduler = LinearScheduler(alpha_i=0.0, alpha_f=1.0, t_max=t_warmup) self.step_scheduler = MultiStepScheduler(milestones=milestones, gamma=gamma) def __call__(self, state: State, ssr: float = 1.0): assert state.max_duration is not None,'max_duration should be set whenever schedulers are invoked' _raise_if_warmup_and_max_duration_incompatible(self.t_warmup, state.max_duration) t_warmup = _convert_time(self.t_warmup, state) if t_warmup.value == 0: warnings.warn( textwrap.dedent("""\ The warmup duration is 0. If you specified warmup as a fraction of total training duration, take note that the warmup duration is calculated in the same unit as the trainer's max_duration parameter.""")) if state.timestamp < t_warmup: if self.scale_warmup: return self.warmup_scheduler(state, ssr) return self.warmup_scheduler(state) return self.step_scheduler(state, ssr) class ConstantWithWarmupScheduler(ComposerScheduler): r"""Maintains a fixed learning rate, with an initial warmup. This scheduler is based on :class:`~torch.optim.lr_scheduler.ConstantLR` from PyTorch, with an added warmup. Starts with a linear warmup over ``t_warmup`` time, then simply maintains a learning rate factor of 1 for the entire training duration. However, both the factor and the duration of this scheduler can be configured. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} t / t_{warmup}, & \text{if } t < t_{warmup} \\ \alpha, & \text{if } t < t_{max} \\ 1.0 & \text{otherwise} \end{cases} Where :math:`\alpha` represents the learning rate multiplier to maintain while this scheduler is active, and :math:`t_{max}` represents the duration of this scheduler. .. warning:: By default, initial warmup time is **not** scaled according to any provided scale schedule ratio. To change this behavior, set ``scale_warmup=True``. Args: t_warmup (str | Time): Warmup time. alpha (float): Learning rate multiplier to maintain while this scheduler is active. Default = ``1.0``. t_max (str | Time): Duration of this scheduler. Default = ``"1dur"``. scale_warmup (float): SSR also scales the warmup period. Default = ``False``. """ def __init__(self, t_warmup: Union[str, Time], alpha: float = 1.0, t_max: Union[str, Time] = '1dur', scale_warmup: bool = False) -> None: self.t_warmup = t_warmup self.alpha = alpha self.t_max = t_max self.scale_warmup = scale_warmup self.scheduler = LinearWithWarmupScheduler(t_warmup=t_warmup, alpha_i=alpha, alpha_f=alpha, t_max=t_max, scale_warmup=scale_warmup) def __call__(self, state: State, ssr: float = 1.0) -> float: return self.scheduler(state, ssr) class LinearWithWarmupScheduler(ComposerScheduler): r"""Adjusts the learning rate linearly, with an initial warmup. .. seealso:: This scheduler is based on :class:`~.LinearScheduler`, with an added warmup. Linearly adjusts the learning rate multiplier from ``alpha_i`` to ``alpha_f`` over ``t_{max}`` time. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} t / t_{warmup}, & \text{if } t < t_{warmup} \\ \alpha_i + (alpha_f - \alpha_i) \times \tau_w & \text{otherwise} \end{cases} Given :math:`\tau_w`, the fraction of post-warmup time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau_w = (t - t_{warmup}) / t_{max} Where :math:`t_{warmup}` represents the warmup time, :math:`\alpha_i` represents the initial learning rate multiplier, and :math:`\alpha_f` represents the learning rate multiplier to decay to, and :math:`t_{max}` represents the duration of this scheduler. .. warning:: By default, the initial warmup time is **not** scaled according to any provided scale schedule ratio! However, the duration of the scheduler is still scaled accordingly. To achieve this, after warmup, the scheduler's "slope" will be slightly distorted from what would otherwise be expected. To scale the entire schedule, set ``scale_warmup=True``. Args: t_warmup (str | Time): Warmup time. alpha_i (float): Initial learning rate multiplier. Default = ``1.0``. alpha_f (float): Final learning rate multiplier. Default = ``0.0``. t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. scale_warmup (float): SSR also scales the warmup period. Default = ``False``. """ def __init__(self, t_warmup: Union[str, Time], alpha_i: float = 1.0, alpha_f: float = 0.0, t_max: Union[str, Time] = '1dur', scale_warmup: bool = False): self.t_warmup = t_warmup self.alpha_i = alpha_i self.alpha_f = alpha_f self.t_max = t_max self.scale_warmup = scale_warmup self.warmup_scheduler = LinearScheduler(alpha_i=0.0, alpha_f=alpha_i, t_max=t_warmup) def __call__(self, state: State, ssr: float = 1.0): assert state.max_duration is not None,'max_duration should be set whenever schedulers are invoked' _raise_if_warmup_and_max_duration_incompatible(self.t_warmup, state.max_duration) t_warmup = _convert_time(self.t_warmup, state) if t_warmup.value == 0: warnings.warn( textwrap.dedent("""\ The warmup duration is 0. If you specified warmup as a fraction of total training duration, take note that the warmup duration is calculated in the same unit as the trainer's max_duration parameter.""")) if state.timestamp < t_warmup: if self.scale_warmup: return self.warmup_scheduler(state, ssr) return self.warmup_scheduler(state) t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_warmup.unit) frac_of_total = ((current_time - t_warmup) / (t_max - t_warmup)).value if (t_max > t_warmup) else 0.0 frac_of_total = min(1.0, frac_of_total) current_factor = self.alpha_i + frac_of_total * (self.alpha_f - self.alpha_i) return current_factor class CosineAnnealingWithWarmupScheduler(ComposerScheduler): r"""Decays the learning rate according to the decreasing part of a cosine curve, with an initial warmup. .. seealso:: This scheduler is based on :class:`~.CosineAnnealingScheduler`, with an added warmup. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} t / t_{warmup}, & \text{if } t < t_{warmup} \\ \alpha_f + (1 - \alpha_f) \times \frac{1}{2} (1 + \cos(\pi \times \tau_w)) & \text{otherwise} \end{cases} Given :math:`\tau_w`, the fraction of post-warmup time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau_w = (t - t_{warmup}) / t_{max} Where :math:`t_{warmup}` represents the warmup time, :math:`t_{max}` represents the duration of this scheduler, and :math:`\alpha_f` represents the learning rate multiplier to decay to. .. warning:: By default, initial warmup time is **not** scaled according to any provided scale schedule ratio. To change this behavior, set ``scale_warmup=True``. Args: t_warmup (str | Time): Warmup time. t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. alpha_f (float): Learning rate multiplier to decay to. Default = ``0.0``. scale_warmup (float): SSR also scales the warmup period. Default = ``False``. """ def __init__(self, t_warmup: Union[str, Time], t_max: Union[str, Time] = '1dur', alpha_f: float = 0.0, scale_warmup: bool = False): self.t_warmup = t_warmup self.t_max = t_max self.alpha_f = alpha_f self.scale_warmup = scale_warmup self.warmup_scheduler = LinearScheduler(alpha_i=0.0, alpha_f=1.0, t_max=t_warmup) def __call__(self, state: State, ssr: float = 1.0): assert state.max_duration is not None,'max_duration should be set whenever schedulers are invoked' _raise_if_warmup_and_max_duration_incompatible(self.t_warmup, state.max_duration) t_warmup = _convert_time(self.t_warmup, state) if t_warmup.value == 0: warnings.warn( textwrap.dedent("""\ The warmup duration is 0. If you specified warmup as a fraction of total training duration, take note that the warmup duration is calculated in the same unit as the trainer's max_duration parameter.""")) if state.timestamp < t_warmup: if self.scale_warmup: return self.warmup_scheduler(state, ssr) return self.warmup_scheduler(state) t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_warmup.unit) frac_of_total = ((current_time - t_warmup) / (t_max - t_warmup)).value if (t_max > t_warmup) else 0.0 frac_of_total = min(1.0, frac_of_total) return _cosine_anneal(x=frac_of_total, min_y=self.alpha_f) class PolynomialWithWarmupScheduler(ComposerScheduler): r"""Decays the learning rate according to a power of the fraction of training time left, with an initial warmup. .. seealso:: This scheduler is based on :class:`~.PolynomialScheduler`, with an added warmup. Specifically, the learning rate multiplier :math:`\alpha` can be expressed as: .. math:: \alpha(t) = \begin{cases} t / t_{warmup}, & \text{if } t < t_{warmup} \\ \alpha_f + (1 - \alpha_f) \times (1 - \tau_w) ^ {\kappa} & \text{otherwise} \end{cases} Given :math:`\tau_w`, the fraction of post-warmup time elapsed (clipped to the interval :math:`[0, 1]`), as: .. math:: \tau_w = (t - t_{warmup}) / t_{max} Where :math:`\kappa` represents the exponent to be used for the proportionality relationship, :math:`t_{warmup}` represents the warmup time, :math:`t_{max}` represents the duration of this scheduler, and :math:`\alpha_f` represents the learning rate multiplier to decay to. .. warning:: By default, initial warmup time is **not** scaled according to any provided scale schedule ratio. To change this behavior, set ``scale_warmup=True``. Args: t_warmup (str | Time): Warmup time. power (float): The exponent to be used for the proportionality relationship. Default = ``2.0``. t_max (str | Time): The duration of this scheduler. Default = ``"1dur"``. alpha_f (float): Learning rate multiplier to decay to. Default = ``0.0``. scale_warmup (float): SSR also scales the warmup period. Default = ``False``. """ def __init__(self, t_warmup: Union[str, Time], power: float = 2.0, t_max: Union[str, Time] = '1dur', alpha_f: float = 0.0, scale_warmup: bool = False): self.t_warmup = t_warmup self.power = power self.t_max = t_max self.alpha_f = alpha_f self.scale_warmup = scale_warmup self.warmup_scheduler = LinearScheduler(alpha_i=0.0, alpha_f=1.0, t_max=t_warmup) def __call__(self, state: State, ssr: float = 1.0): assert state.max_duration is not None,'max_duration should be set whenever schedulers are invoked' _raise_if_warmup_and_max_duration_incompatible(self.t_warmup, state.max_duration) t_warmup = _convert_time(self.t_warmup, state) if t_warmup.value == 0: warnings.warn( textwrap.dedent("""\ The warmup duration is 0. If you specified warmup as a fraction of total training duration, take note that the warmup duration is calculated in the same unit as the trainer's max_duration parameter.""")) if state.timestamp < t_warmup: if self.scale_warmup: return self.warmup_scheduler(state, ssr) return self.warmup_scheduler(state) t_max = _convert_time(self.t_max, state, ssr=ssr) current_time = state.timestamp.get(t_warmup.unit) frac_of_total = ((current_time - t_warmup) / (t_max - t_warmup)).value if (t_max > t_warmup) else 0.0 frac_of_total = min(1.0, frac_of_total) coeff = (1 - frac_of_total)**self.power current_factor = self.alpha_f + coeff * (1.0 - self.alpha_f) return current_factor

mitogen-hq__mitogen

getting_started.rst

Tutorial

Generate getting started tutorial

BSD 3-Clause New or Revised License

mitogen-hq__mitogen/docs/getting_started.rst

Getting Started Warning This section is incomplete. Liability Waiver Before proceeding, it is critical you understand what you're involving yourself and possibly your team and its successors with: [image] - Constructing the most fundamental class, Broker <mitogen.master.Broker>, causes a new thread to be spawned, exposing a huge class of difficult to analyse behaviours that Python software generally does not suffer from. While every effort is made to hide this complexity, you should expect threading-related encounters during development, and crucially, years after your program reached production. See troubleshooting for more information. - While high-level abstractions are provided, they are only a convenience, you must still understand how Mitogen works <howitworks> before depending on it. Mitogen interacts with many aspects of the operating system, threading, SSH, sudo, sockets, TTYs, shell, Python runtime, and timing and ordering uncertainty introduced through interaction with the network, GIL and OS scheduling. Knowledge of this domain is typically attained through painful years of failed attempts hacking system-level programs, and learning through continual suffering how to debug the atrocities left behind. If you feel you lack resources or willpower to diagnose problems independently, Mitogen is not appropriate, prefer a higher level solution instead. First Principles Before starting, take a moment to reflect on writing a program that will operate across machines and privilege domains: - As with multithreaded programming, writing a program that spans multiple hosts is exposed to many asynchrony issues. Unlike multithreaded programming, the margin for unexpected failures is much higher, even between only two peers, as communication may be fail at any moment, since that communication depends on reliability of an external network. - Since a multi-host program always spans trust and privilege domains, trust must be taken into consideration in your design from the outset. Mitogen attempts to protect the consuming application by default where possible, however it is paramount that trust considerations are always in mind when exposing any privileged functionality to a potentially untrusted network of peers. A parent must always assume data received from a child is suspect, and must not base privileged control decisions on that data. As a small example, a parent should not form a command to execute in a subprocess using strings received from a child. - As the program spans multiple hosts, its design will benefit from a strict separation of program and data. This entails avoiding some common Python idioms that rely on its ability to manipulate functions and closures as if they were data, such as passing a lambda closed over some program state as a callback parameter. In the general case this is both difficult and unsafe to support in a distributed program, and so (for now at least) it should be assumed this functionality is unlikely to appear in future. Broker And Router [image] mitogen.core Execution starts when your program constructs a Broker and associated Router. The broker is responsible for multiplexing IO to children from a private thread, while in children, it is additionally responsible for ensuring robust destruction if communication with the master is lost. Router is responsible for receiving messages and dispatching them to a callback from the broker thread (registered by add_handler() <mitogen.core.Router.add_handler>), or forwarding them to a Stream <mitogen.core.Stream>. See routing for an in-depth description. Router also doubles as the entry point to Mitogen's public API: >>> import mitogen.master >>> broker = mitogen.master.Broker() >>> router = mitogen.master.Router(broker) >>> try: ... # Your code here. ... pass ... finally: ... broker.shutdown() As Python will not stop if threads still exist after the main thread exits, Broker.shutdown must be called reliably at exit. Helpers are provided by mitogen.utils to ensure Broker is reliably destroyed: def do_mitogen_stuff(router): # Your code here. mitogen.utils.run_with_router(do_mitogen_stuff) If your program cannot live beneath mitogen.utils.run_with_router on the stack, you must arrange for Broker.shutdown to be called anywhere the main thread may exit. Enable Logging Mitogen makes heavy use of the logging package, both for child stdio redirection, and soft errors and warnings that may be generated. You should always configure the logging package in any program that integrates Mitogen. If your program does not otherwise use the logging package, a basic configuration can be performed by calling mitogen.utils.log_to_file: >>> import mitogen.utils # Errors, warnings, and child stdio will be written to stderr. >>> mitogen.utils.log_to_file() Additionally, if your program has logging.DEBUG as the default logging level, you may wish to update its configuration to restrict the mitogen logger to logging.INFO, otherwise vast amounts of output will be generated by default. Logging Environment Variables MITOGEN_LOG_LEVEL Overrides the logging package log level set by any call to mitogen.utils.log_to_file. Defaults to INFO. If set to IO, equivalent to DEBUG but additionally enabled IO logging for any call to mitogen.utils.log_to_file. IO logging produces verbose records of any IO interaction, which is useful for debugging hangs and deadlocks. Logging Records Messages received from a child context via mitogen.master.LogForwarder receive extra attributes: - `mitogen_context`: mitogen.parent.Context referring to the message source. - `mitogen_name`: original logger name in the source context. - `mitogen_msg`: original message in the source context. Creating A Context Contexts are simply external Python programs over which your program has control, and can execute code within. They can be created as subprocesses on the local machine, in another user account via sudo, on a remote machine via ssh, or any recursive combination of the above. Now a Router exists, our first contexts <Context> can be created. To demonstrate basic functionality, we will start with some local() <Router.local> contexts created as subprocesses: >>> local = router.local() >>> local_with_name = router.local(remote_name='i-have-a-name') Examination of the system process list with the pstree utility reveals the resulting process hierarchy: | | \-+= 27660 dmw python | | |--- 27661 dmw mitogen:dmw@Eldil.local:27660 | | \--- 27663 dmw mitogen:i-have-a-name Both contexts are visible as subprocesses of the interactive Python interpreter, with their argv[0] including a description of their identity. To aid systems administrators in identifying errant software running on their machines, the default remote_name includes the location of the program that started the context, however as shown, this can be overridden. Note Presently contexts are constructed in a blocking manner on the thread that invoked the context factory <context-factories>. In a future release, the factory will instead return immediately, and construction will happen asynchronously on the broker thread. Calling A Function mitogen.parent Now that some contexts exist, it is time to execute code in them. Any regular function, static method, or class method reachable directly from module scope may be used, including built-in functions such as time.time. The Context.call method is used to execute a function and block the caller until the return value is available or an exception is raised: >>> import time >>> import os >>> # Returns the current time. >>> print('Time in remote context:', local.call(time.time)) >>> try: ... # Raises OSError. ... local.call(os.chdir, '/nonexistent') ... except mitogen.core.CallError, e: ... print('Call failed:', str(e)) It is a simple wrapper around the more flexible Context.call_async, which immediately returns a Receiver <mitogen.core.Receiver> wired up to receive the return value instead. A receiver may simply be discarded, kept around indefinitely without ever reading its result, or used to wait on the results from several calls. Here get() <mitogen.core.Receiver.get> is called to block the thread until the result arrives: >>> call = local.call_async(time.time) >>> msg = call.get() >>> print(msg.unpickle()) 1507292737.75547 Running User Functions So far we have used the interactive interpreter to call some standard library functions, but since the source code typed at the interpreter cannot be recovered, Mitogen is unable to execute functions defined in this way. We must therefore continue by writing our code as a script: # first-script.py import mitogen.utils def my_first_function(): print('Hello from remote context!') return 123 def main(router): local = router.local() print(local.call(my_first_function)) if __name__ == '__main__': mitogen.utils.log_to_file("mitogen.log") mitogen.utils.run_with_router(main) Let's try running it: $ python first-script.py 19:11:32 I mitogen.ctx.local.32466: stdout: Hello from remote context! 123 Waiting On Multiple Calls Using Context.call_async it is possible to start multiple function calls then sleep waiting for responses as they are available. This makes it trivial to run tasks in parallel across processes (including remote processes) without the need for writing asynchronous code: hostnames = ['host1', 'host2', 'host3', 'host4'] contexts = [router.ssh(hostname=hn) for hn in hostnames] calls = [context.call(my_func) for context in contexts] for msg in mitogen.select.Select(calls): print('Reply from %s: %s' % (recv.context, data)) Running Code That May Hang When executing code that may hang due to, for example, talking to network peers that may become unavailable, it is desirable to be able to recover control in the case a remote call has hung. By specifying the timeout parameter to Receiver.get on the receiver returned by Context.call_async, it becomes possible to wait for a function to complete, but time out if its result does not become available. When a context has become hung like this, it is still possible to gracefully terminate it using the Context.shutdown method. This method sends a shutdown message to the target process, where its IO multiplexer thread can still process it independently of the hung function running on on the target's main thread. Recovering Mitogen Object References In Children @mitogen.core.takes_econtext def func1(a, b, econtext): ... @mitogen.core.takes_router def func2(a, b, router): ... Recursion Let's try something a little more complex: RPC Serialization Rules The following built-in types may be used as parameters or return values in remote procedure calls: - bool - bytes (str on Python 2.x) - dict - int - list - long - tuple - unicode (str on Python 3.x) User-defined types may not be used, except for: - mitogen.core.Blob - mitogen.core.Secret - mitogen.core.CallError - mitogen.core.Context - mitogen.core.Sender Subclasses of built-in types must be undecorated using mitogen.utils.cast. Test Your Design tc qdisc add dev eth0 root netem delay 250ms Troubleshooting Warning This section is incomplete. A typical example is a hang due to your application's main thread exitting perhaps due to an unhandled exception, without first arranging for any Broker <mitogen.master.Broker> to be shut down gracefully. Another example would be your main thread hanging indefinitely because a bug in Mitogen fails to notice an event (such as RPC completion) your thread is waiting for will never complete. Solving this kind of hang is a work in progress. router.enable_debug()

# Copyright 2019, David Wilson # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. #!mitogen: minify_safe """ This module defines functionality common to master and parent processes. It is sent to any child context that is due to become a parent, due to recursive connection. """ import codecs import errno import fcntl import getpass import heapq import inspect import logging import os import re import signal import socket import struct import subprocess import sys import termios import textwrap import threading import zlib # Absolute imports for <2.5. select = __import__('select') try: import thread except ImportError: import threading as thread import mitogen.core from mitogen.core import b from mitogen.core import bytes_partition from mitogen.core import IOLOG LOG = logging.getLogger(__name__) # #410: we must avoid the use of socketpairs if SELinux is enabled. try: fp = open('/sys/fs/selinux/enforce', 'rb') try: SELINUX_ENABLED = bool(int(fp.read())) finally: fp.close() except IOError: SELINUX_ENABLED = False try: next except NameError: # Python 2.4/2.5 from mitogen.core import next itervalues = getattr(dict, 'itervalues', dict.values) if mitogen.core.PY3: xrange = range closure_attr = '__closure__' IM_SELF_ATTR = '__self__' else: closure_attr = 'func_closure' IM_SELF_ATTR = 'im_self' try: SC_OPEN_MAX = os.sysconf('SC_OPEN_MAX') except ValueError: SC_OPEN_MAX = 1024 BROKER_SHUTDOWN_MSG = ( 'Connection cancelled because the associated Broker began to shut down.' ) OPENPTY_MSG = ( "Failed to create a PTY: %s. It is likely the maximum number of PTYs has " "been reached. Consider increasing the 'kern.tty.ptmx_max' sysctl on OS " "X, the 'kernel.pty.max' sysctl on Linux, or modifying your configuration " "to avoid PTY use." ) SYS_EXECUTABLE_MSG = ( "The Python sys.executable variable is unset, indicating Python was " "unable to determine its original program name. Unless explicitly " "configured otherwise, child contexts will be started using " "'/usr/bin/python'" ) _sys_executable_warning_logged = False def _ioctl_cast(n): """ Linux ioctl() request parameter is unsigned, whereas on BSD/Darwin it is signed. Until 2.5 Python exclusively implemented the BSD behaviour, preventing use of large unsigned int requests like the TTY layer uses below. So on 2.4, we cast our unsigned to look like signed for Python. """ if sys.version_info < (2, 5): n, = struct.unpack('i', struct.pack('I', n)) return n # If not :data:`None`, called prior to exec() of any new child process. Used by # :func:`mitogen.utils.reset_affinity` to allow the child to be freely # scheduled. _preexec_hook = None # Get PTY number; asm-generic/ioctls.h LINUX_TIOCGPTN = _ioctl_cast(2147767344) # Lock/unlock PTY; asm-generic/ioctls.h LINUX_TIOCSPTLCK = _ioctl_cast(1074025521) IS_LINUX = os.uname()[0] == 'Linux' SIGNAL_BY_NUM = dict( (getattr(signal, name), name) for name in sorted(vars(signal), reverse=True) if name.startswith('SIG') and not name.startswith('SIG_') ) _core_source_lock = threading.Lock() _core_source_partial = None def get_log_level(): return (LOG.getEffectiveLevel() or logging.INFO) def get_sys_executable(): """ Return :data:`sys.executable` if it is set, otherwise return ``"/usr/bin/python"`` and log a warning. """ if sys.executable: return sys.executable global _sys_executable_warning_logged if not _sys_executable_warning_logged: LOG.warn(SYS_EXECUTABLE_MSG) _sys_executable_warning_logged = True return '/usr/bin/python' def _get_core_source(): """ In non-masters, simply fetch the cached mitogen.core source code via the import mechanism. In masters, this function is replaced with a version that performs minification directly. """ return inspect.getsource(mitogen.core) def get_core_source_partial(): """ _get_core_source() is expensive, even with @lru_cache in minify.py, threads can enter it simultaneously causing severe slowdowns. """ global _core_source_partial if _core_source_partial is None: _core_source_lock.acquire() try: if _core_source_partial is None: _core_source_partial = PartialZlib( _get_core_source().encode('utf-8') ) finally: _core_source_lock.release() return _core_source_partial def get_default_remote_name(): """ Return the default name appearing in argv[0] of remote machines. """ s = u'%s@%s:%d' s %= (getpass.getuser(), socket.gethostname(), os.getpid()) # In mixed UNIX/Windows environments, the username may contain slashes. return s.translate({ ord(u'\\'): ord(u'_'), ord(u'/'): ord(u'_') }) def is_immediate_child(msg, stream): """ Handler policy that requires messages to arrive only from immediately connected children. """ return msg.src_id == stream.protocol.remote_id def flags(names): """ Return the result of ORing a set of (space separated) :py:mod:`termios` module constants together. """ return sum(getattr(termios, name, 0) for name in names.split()) def cfmakeraw(tflags): """ Given a list returned by :py:func:`termios.tcgetattr`, return a list modified in a manner similar to the `cfmakeraw()` C library function, but additionally disabling local echo. """ # BSD: github.com/freebsd/freebsd/blob/master/lib/libc/gen/termios.c#L162 # Linux: github.com/lattera/glibc/blob/master/termios/cfmakeraw.c#L20 iflag, oflag, cflag, lflag, ispeed, ospeed, cc = tflags iflag &= ~flags('IMAXBEL IXOFF INPCK BRKINT PARMRK ' 'ISTRIP INLCR ICRNL IXON IGNPAR') iflag &= ~flags('IGNBRK BRKINT PARMRK') oflag &= ~flags('OPOST') lflag &= ~flags('ECHO ECHOE ECHOK ECHONL ICANON ISIG ' 'IEXTEN NOFLSH TOSTOP PENDIN') cflag &= ~flags('CSIZE PARENB') cflag |= flags('CS8 CREAD') return [iflag, oflag, cflag, lflag, ispeed, ospeed, cc] def disable_echo(fd): old = termios.tcgetattr(fd) new = cfmakeraw(old) flags = getattr(termios, 'TCSASOFT', 0) if not mitogen.core.IS_WSL: # issue #319: Windows Subsystem for Linux as of July 2018 throws EINVAL # if TCSAFLUSH is specified. flags |= termios.TCSAFLUSH termios.tcsetattr(fd, flags, new) def create_socketpair(size=None): """ Create a :func:`socket.socketpair` for use as a child's UNIX stdio channels. As socketpairs are bidirectional, they are economical on file descriptor usage as one descriptor can be used for ``stdin`` and ``stdout``. As they are sockets their buffers are tunable, allowing large buffers to improve file transfer throughput and reduce IO loop iterations. """ if size is None: size = mitogen.core.CHUNK_SIZE parentfp, childfp = socket.socketpair() for fp in parentfp, childfp: fp.setsockopt(socket.SOL_SOCKET, socket.SO_SNDBUF, size) return parentfp, childfp def create_best_pipe(escalates_privilege=False): """ By default we prefer to communicate with children over a UNIX socket, as a single file descriptor can represent bidirectional communication, and a cross-platform API exists to align buffer sizes with the needs of the library. SELinux prevents us setting up a privileged process to inherit an AF_UNIX socket, a facility explicitly designed as a better replacement for pipes, because at some point in the mid 90s it might have been commonly possible for AF_INET sockets to end up undesirably connected to a privileged process, so let's make up arbitrary rules breaking all sockets instead. If SELinux is detected, fall back to using pipes. :param bool escalates_privilege: If :data:`True`, the target program may escalate privileges, causing SELinux to disconnect AF_UNIX sockets, so avoid those. :returns: `(parent_rfp, child_wfp, child_rfp, parent_wfp)` """ if (not escalates_privilege) or (not SELINUX_ENABLED): parentfp, childfp = create_socketpair() return parentfp, childfp, childfp, parentfp parent_rfp, child_wfp = mitogen.core.pipe() try: child_rfp, parent_wfp = mitogen.core.pipe() return parent_rfp, child_wfp, child_rfp, parent_wfp except: parent_rfp.close() child_wfp.close() raise def popen(**kwargs): """ Wrap :class:`subprocess.Popen` to ensure any global :data:`_preexec_hook` is invoked in the child. """ real_preexec_fn = kwargs.pop('preexec_fn', None) def preexec_fn(): if _preexec_hook: _preexec_hook() if real_preexec_fn: real_preexec_fn() return subprocess.Popen(preexec_fn=preexec_fn, **kwargs) def create_child(args, merge_stdio=False, stderr_pipe=False, escalates_privilege=False, preexec_fn=None): """ Create a child process whose stdin/stdout is connected to a socket. :param list args: Program argument vector. :param bool merge_stdio: If :data:`True`, arrange for `stderr` to be connected to the `stdout` socketpair, rather than inherited from the parent process. This may be necessary to ensure that no TTY is connected to any stdio handle, for instance when using LXC. :param bool stderr_pipe: If :data:`True` and `merge_stdio` is :data:`False`, arrange for `stderr` to be connected to a separate pipe, to allow any ongoing debug logs generated by e.g. SSH to be output as the session progresses, without interfering with `stdout`. :param bool escalates_privilege: If :data:`True`, the target program may escalate privileges, causing SELinux to disconnect AF_UNIX sockets, so avoid those. :param function preexec_fn: If not :data:`None`, a function to run within the post-fork child before executing the target program. :returns: :class:`Process` instance. """ parent_rfp, child_wfp, child_rfp, parent_wfp = create_best_pipe( escalates_privilege=escalates_privilege ) stderr = None stderr_r = None if merge_stdio: stderr = child_wfp elif stderr_pipe: stderr_r, stderr = mitogen.core.pipe() mitogen.core.set_cloexec(stderr_r.fileno()) try: proc = popen( args=args, stdin=child_rfp, stdout=child_wfp, stderr=stderr, close_fds=True, preexec_fn=preexec_fn, ) except: child_rfp.close() child_wfp.close() parent_rfp.close() parent_wfp.close() if stderr_pipe: stderr.close() stderr_r.close() raise child_rfp.close() child_wfp.close() if stderr_pipe: stderr.close() return PopenProcess( proc=proc, stdin=parent_wfp, stdout=parent_rfp, stderr=stderr_r, ) def _acquire_controlling_tty(): os.setsid() if sys.platform in ('linux', 'linux2'): # On Linux, the controlling tty becomes the first tty opened by a # process lacking any prior tty. os.close(os.open(os.ttyname(2), os.O_RDWR)) if hasattr(termios, 'TIOCSCTTY') and not mitogen.core.IS_WSL: # #550: prehistoric WSL does not like TIOCSCTTY. # On BSD an explicit ioctl is required. For some inexplicable reason, # Python 2.6 on Travis also requires it. fcntl.ioctl(2, termios.TIOCSCTTY) def _linux_broken_devpts_openpty(): """ #462: On broken Linux hosts with mismatched configuration (e.g. old /etc/fstab template installed), /dev/pts may be mounted without the gid= mount option, causing new slave devices to be created with the group ID of the calling process. This upsets glibc, whose openpty() is required by specification to produce a slave owned by a special group ID (which is always the 'tty' group). Glibc attempts to use "pt_chown" to fix ownership. If that fails, it chown()s the PTY directly, which fails due to non-root, causing openpty() to fail with EPERM ("Operation not permitted"). Since we don't need the magical TTY group to run sudo and su, open the PTY ourselves in this case. """ master_fd = None try: # Opening /dev/ptmx causes a PTY pair to be allocated, and the # corresponding slave /dev/pts/* device to be created, owned by UID/GID # matching this process. master_fd = os.open('/dev/ptmx', os.O_RDWR) # Clear the lock bit from the PTY. This a prehistoric feature from a # time when slave device files were persistent. fcntl.ioctl(master_fd, LINUX_TIOCSPTLCK, struct.pack('i', 0)) # Since v4.13 TIOCGPTPEER exists to open the slave in one step, but we # must support older kernels. Ask for the PTY number. pty_num_s = fcntl.ioctl(master_fd, LINUX_TIOCGPTN, struct.pack('i', 0)) pty_num, = struct.unpack('i', pty_num_s) pty_name = '/dev/pts/%d' % (pty_num,) # Now open it with O_NOCTTY to ensure it doesn't change our controlling # TTY. Otherwise when we close the FD we get killed by the kernel, and # the child we spawn that should really attach to it will get EPERM # during _acquire_controlling_tty(). slave_fd = os.open(pty_name, os.O_RDWR|os.O_NOCTTY) return master_fd, slave_fd except OSError: if master_fd is not None: os.close(master_fd) e = sys.exc_info()[1] raise mitogen.core.StreamError(OPENPTY_MSG, e) def openpty(): """ Call :func:`os.openpty`, raising a descriptive error if the call fails. :raises mitogen.core.StreamError: Creating a PTY failed. :returns: `(master_fp, slave_fp)` file-like objects. """ try: master_fd, slave_fd = os.openpty() except OSError: e = sys.exc_info()[1] if not (IS_LINUX and e.args[0] == errno.EPERM): raise mitogen.core.StreamError(OPENPTY_MSG, e) master_fd, slave_fd = _linux_broken_devpts_openpty() master_fp = os.fdopen(master_fd, 'r+b', 0) slave_fp = os.fdopen(slave_fd, 'r+b', 0) disable_echo(master_fd) disable_echo(slave_fd) mitogen.core.set_block(slave_fd) return master_fp, slave_fp def tty_create_child(args): """ Return a file descriptor connected to the master end of a pseudo-terminal, whose slave end is connected to stdin/stdout/stderr of a new child process. The child is created such that the pseudo-terminal becomes its controlling TTY, ensuring access to /dev/tty returns a new file descriptor open on the slave end. :param list args: Program argument vector. :returns: :class:`Process` instance. """ master_fp, slave_fp = openpty() try: proc = popen( args=args, stdin=slave_fp, stdout=slave_fp, stderr=slave_fp, preexec_fn=_acquire_controlling_tty, close_fds=True, ) except: master_fp.close() slave_fp.close() raise slave_fp.close() return PopenProcess( proc=proc, stdin=master_fp, stdout=master_fp, ) def hybrid_tty_create_child(args, escalates_privilege=False): """ Like :func:`tty_create_child`, except attach stdin/stdout to a socketpair like :func:`create_child`, but leave stderr and the controlling TTY attached to a TTY. This permits high throughput communication with programs that are reached via some program that requires a TTY for password input, like many configurations of sudo. The UNIX TTY layer tends to have tiny (no more than 14KiB) buffers, forcing many IO loop iterations when transferring bulk data, causing significant performance loss. :param bool escalates_privilege: If :data:`True`, the target program may escalate privileges, causing SELinux to disconnect AF_UNIX sockets, so avoid those. :param list args: Program argument vector. :returns: :class:`Process` instance. """ master_fp, slave_fp = openpty() try: parent_rfp, child_wfp, child_rfp, parent_wfp = create_best_pipe( escalates_privilege=escalates_privilege, ) try: mitogen.core.set_block(child_rfp) mitogen.core.set_block(child_wfp) proc = popen( args=args, stdin=child_rfp, stdout=child_wfp, stderr=slave_fp, preexec_fn=_acquire_controlling_tty, close_fds=True, ) except: parent_rfp.close() child_wfp.close() parent_wfp.close() child_rfp.close() raise except: master_fp.close() slave_fp.close() raise slave_fp.close() child_rfp.close() child_wfp.close() return PopenProcess( proc=proc, stdin=parent_wfp, stdout=parent_rfp, stderr=master_fp, ) class Timer(object): """ Represents a future event. """ #: Set to :data:`False` if :meth:`cancel` has been called, or immediately #: prior to being executed by :meth:`TimerList.expire`. active = True def __init__(self, when, func): self.when = when self.func = func def __repr__(self): return 'Timer(%r, %r)' % (self.when, self.func) def __eq__(self, other): return self.when == other.when def __lt__(self, other): return self.when < other.when def __le__(self, other): return self.when <= other.when def cancel(self): """ Cancel this event. If it has not yet executed, it will not execute during any subsequent :meth:`TimerList.expire` call. """ self.active = False class TimerList(object): """ Efficiently manage a list of cancellable future events relative to wall clock time. An instance of this class is installed as :attr:`mitogen.master.Broker.timers` by default, and as :attr:`mitogen.core.Broker.timers` in children after a call to :func:`mitogen.parent.upgrade_router`. You can use :class:`TimerList` to cause the broker to wake at arbitrary future moments, useful for implementing timeouts and polling in an asynchronous context. :class:`TimerList` methods can only be called from asynchronous context, for example via :meth:`mitogen.core.Broker.defer`. The broker automatically adjusts its sleep delay according to the installed timer list, and arranges for timers to expire via automatic calls to :meth:`expire`. The main user interface to :class:`TimerList` is :meth:`schedule`. """ _now = mitogen.core.now def __init__(self): self._lst = [] def get_timeout(self): """ Return the floating point seconds until the next event is due. :returns: Floating point delay, or 0.0, or :data:`None` if no events are scheduled. """ while self._lst and not self._lst[0].active: heapq.heappop(self._lst) if self._lst: return max(0, self._lst[0].when - self._now()) def schedule(self, when, func): """ Schedule a future event. :param float when: UNIX time in seconds when event should occur. :param callable func: Callable to invoke on expiry. :returns: A :class:`Timer` instance, exposing :meth:`Timer.cancel`, which may be used to cancel the future invocation. """ timer = Timer(when, func) heapq.heappush(self._lst, timer) return timer def expire(self): """ Invoke callbacks for any events in the past. """ now = self._now() while self._lst and self._lst[0].when <= now: timer = heapq.heappop(self._lst) if timer.active: timer.active = False timer.func() class PartialZlib(object): """ Because the mitogen.core source has a line appended to it during bootstrap, it must be recompressed for each connection. This is not a problem for a small number of connections, but it amounts to 30 seconds CPU time by the time 500 targets are in use. For that reason, build a compressor containing mitogen.core and flush as much of it as possible into an initial buffer. Then to append the custom line, clone the compressor and compress just that line. A full compression costs ~6ms on a modern machine, this method costs ~35 usec. """ def __init__(self, s): self.s = s if sys.version_info > (2, 5): self._compressor = zlib.compressobj(9) self._out = self._compressor.compress(s) self._out += self._compressor.flush(zlib.Z_SYNC_FLUSH) else: self._compressor = None def append(self, s): """ Append the bytestring `s` to the compressor state and return the final compressed output. """ if self._compressor is None: return zlib.compress(self.s + s, 9) else: compressor = self._compressor.copy() out = self._out out += compressor.compress(s) return out + compressor.flush() def _upgrade_broker(broker): """ Extract the poller state from Broker and replace it with the industrial strength poller for this OS. Must run on the Broker thread. """ # This function is deadly! The act of calling start_receive() generates log # messages which must be silenced as the upgrade progresses, otherwise the # poller state will change as it is copied, resulting in write fds that are # lost. (Due to LogHandler->Router->Stream->Protocol->Broker->Poller, where # Stream only calls start_transmit() when transitioning from empty to # non-empty buffer. If the start_transmit() is lost, writes from the child # hang permanently). root = logging.getLogger() old_level = root.level root.setLevel(logging.CRITICAL) try: old = broker.poller new = PREFERRED_POLLER() for fd, data in old.readers: new.start_receive(fd, data) for fd, data in old.writers: new.start_transmit(fd, data) old.close() broker.poller = new finally: root.setLevel(old_level) broker.timers = TimerList() LOG.debug('upgraded %r with %r (new: %d readers, %d writers; ' 'old: %d readers, %d writers)', old, new, len(new.readers), len(new.writers), len(old.readers), len(old.writers)) @mitogen.core.takes_econtext def upgrade_router(econtext): if not isinstance(econtext.router, Router): # TODO econtext.broker.defer(_upgrade_broker, econtext.broker) econtext.router.__class__ = Router # TODO econtext.router.upgrade( importer=econtext.importer, parent=econtext.parent, ) def get_connection_class(name): """ Given the name of a Mitogen connection method, import its implementation module and return its Stream subclass. """ if name == u'local': name = u'parent' module = mitogen.core.import_module(u'mitogen.' + name) return module.Connection @mitogen.core.takes_econtext def _proxy_connect(name, method_name, kwargs, econtext): """ Implements the target portion of Router._proxy_connect() by upgrading the local process to a parent if it was not already, then calling back into Router._connect() using the arguments passed to the parent's Router.connect(). :returns: Dict containing: * ``id``: :data:`None`, or integer new context ID. * ``name``: :data:`None`, or string name attribute of new Context. * ``msg``: :data:`None`, or StreamError exception text. """ upgrade_router(econtext) try: context = econtext.router._connect( klass=get_connection_class(method_name), name=name, **kwargs ) except mitogen.core.StreamError: return { u'id': None, u'name': None, u'msg': 'error occurred on host %s: %s' % ( socket.gethostname(), sys.exc_info()[1], ), } return { u'id': context.context_id, u'name': context.name, u'msg': None, } def returncode_to_str(n): """ Parse and format a :func:`os.waitpid` exit status. """ if n < 0: return 'exited due to signal %d (%s)' % (-n, SIGNAL_BY_NUM.get(-n)) return 'exited with return code %d' % (n,) class EofError(mitogen.core.StreamError): """ Raised by :class:`Connection` when an empty read is detected from the remote process before bootstrap completes. """ # inherits from StreamError to maintain compatibility. pass class CancelledError(mitogen.core.StreamError): """ Raised by :class:`Connection` when :meth:`mitogen.core.Broker.shutdown` is called before bootstrap completes. """ pass class Argv(object): """ Wrapper to defer argv formatting when debug logging is disabled. """ def __init__(self, argv): self.argv = argv must_escape = frozenset('\\$"`!') must_escape_or_space = must_escape | frozenset(' ') def escape(self, x): if not self.must_escape_or_space.intersection(x): return x s = '"' for c in x: if c in self.must_escape: s += '\\' s += c s += '"' return s def __str__(self): return''.join(map(self.escape, self.argv)) class CallSpec(object): """ Wrapper to defer call argument formatting when debug logging is disabled. """ def __init__(self, func, args, kwargs): self.func = func self.args = args self.kwargs = kwargs def _get_name(self): bits = [self.func.__module__] if inspect.ismethod(self.func): im_self = getattr(self.func, IM_SELF_ATTR) bits.append(getattr(im_self, '__name__', None) or getattr(type(im_self), '__name__', None)) bits.append(self.func.__name__) return u'.'.join(bits) def _get_args(self): return u', '.join(repr(a) for a in self.args) def _get_kwargs(self): s = u'' if self.kwargs: s = u', '.join('%s=%r' % (k, v) for k, v in self.kwargs.items()) if self.args: s = u','+ s return s def __repr__(self): return '%s(%s%s)' % ( self._get_name(), self._get_args(), self._get_kwargs(), ) class PollPoller(mitogen.core.Poller): """ Poller based on the POSIX :linux:man2:`poll` interface. Not available on some versions of OS X, otherwise it is the preferred poller for small FD counts, as there is no setup/teardown/configuration system call overhead. """ SUPPORTED = hasattr(select, 'poll') _repr = 'PollPoller()' def __init__(self): super(PollPoller, self).__init__() self._pollobj = select.poll() # TODO: no proof we dont need writemask too _readmask = ( getattr(select, 'POLLIN', 0) | getattr(select, 'POLLHUP', 0) ) def _update(self, fd): mask = (((fd in self._rfds) and self._readmask) | ((fd in self._wfds) and select.POLLOUT)) if mask: self._pollobj.register(fd, mask) else: try: self._pollobj.unregister(fd) except KeyError: pass def _poll(self, timeout): if timeout: timeout *= 1000 events, _ = mitogen.core.io_op(self._pollobj.poll, timeout) for fd, event in events: if event & self._readmask: IOLOG.debug('%r: POLLIN|POLLHUP for %r', self, fd) data, gen = self._rfds.get(fd, (None, None)) if gen and gen < self._generation: yield data if event & select.POLLOUT: IOLOG.debug('%r: POLLOUT for %r', self, fd) data, gen = self._wfds.get(fd, (None, None)) if gen and gen < self._generation: yield data class KqueuePoller(mitogen.core.Poller): """ Poller based on the FreeBSD/Darwin :freebsd:man2:`kqueue` interface. """ SUPPORTED = hasattr(select, 'kqueue') _repr = 'KqueuePoller()' def __init__(self): super(KqueuePoller, self).__init__() self._kqueue = select.kqueue() self._changelist = [] def close(self): super(KqueuePoller, self).close() self._kqueue.close() def _control(self, fd, filters, flags): mitogen.core._vv and IOLOG.debug( '%r._control(%r, %r, %r)', self, fd, filters, flags) # TODO: at shutdown it is currently possible for KQ_EV_ADD/KQ_EV_DEL # pairs to be pending after the associated file descriptor has already # been closed. Fixing this requires maintaining extra state, or perhaps # making fd closure the poller's responsibility. In the meantime, # simply apply changes immediately. # self._changelist.append(select.kevent(fd, filters, flags)) changelist = [select.kevent(fd, filters, flags)] events, _ = mitogen.core.io_op(self._kqueue.control, changelist, 0, 0) assert not events def start_receive(self, fd, data=None): mitogen.core._vv and IOLOG.debug('%r.start_receive(%r, %r)', self, fd, data) if fd not in self._rfds: self._control(fd, select.KQ_FILTER_READ, select.KQ_EV_ADD) self._rfds[fd] = (data or fd, self._generation) def stop_receive(self, fd): mitogen.core._vv and IOLOG.debug('%r.stop_receive(%r)', self, fd) if fd in self._rfds: self._control(fd, select.KQ_FILTER_READ, select.KQ_EV_DELETE) del self._rfds[fd] def start_transmit(self, fd, data=None): mitogen.core._vv and IOLOG.debug('%r.start_transmit(%r, %r)', self, fd, data) if fd not in self._wfds: self._control(fd, select.KQ_FILTER_WRITE, select.KQ_EV_ADD) self._wfds[fd] = (data or fd, self._generation) def stop_transmit(self, fd): mitogen.core._vv and IOLOG.debug('%r.stop_transmit(%r)', self, fd) if fd in self._wfds: self._control(fd, select.KQ_FILTER_WRITE, select.KQ_EV_DELETE) del self._wfds[fd] def _poll(self, timeout): changelist = self._changelist self._changelist = [] events, _ = mitogen.core.io_op(self._kqueue.control, changelist, 32, timeout) for event in events: fd = event.ident if event.flags & select.KQ_EV_ERROR: LOG.debug('ignoring stale event for fd %r: errno=%d: %s', fd, event.data, errno.errorcode.get(event.data)) elif event.filter == select.KQ_FILTER_READ: data, gen = self._rfds.get(fd, (None, None)) # Events can still be read for an already-discarded fd. if gen and gen < self._generation: mitogen.core._vv and IOLOG.debug('%r: POLLIN: %r', self, fd) yield data elif event.filter == select.KQ_FILTER_WRITE and fd in self._wfds: data, gen = self._wfds.get(fd, (None, None)) if gen and gen < self._generation: mitogen.core._vv and IOLOG.debug('%r: POLLOUT: %r', self, fd) yield data class EpollPoller(mitogen.core.Poller): """ Poller based on the Linux :linux:man2:`epoll` interface. """ SUPPORTED = hasattr(select, 'epoll') _repr = 'EpollPoller()' def __init__(self): super(EpollPoller, self).__init__() self._epoll = select.epoll(32) self._registered_fds = set() def close(self): super(EpollPoller, self).close() self._epoll.close() def _control(self, fd): mitogen.core._vv and IOLOG.debug('%r._control(%r)', self, fd) mask = (((fd in self._rfds) and select.EPOLLIN) | ((fd in self._wfds) and select.EPOLLOUT)) if mask: if fd in self._registered_fds: self._epoll.modify(fd, mask) else: self._epoll.register(fd, mask) self._registered_fds.add(fd) elif fd in self._registered_fds: self._epoll.unregister(fd) self._registered_fds.remove(fd) def start_receive(self, fd, data=None): mitogen.core._vv and IOLOG.debug('%r.start_receive(%r, %r)', self, fd, data) self._rfds[fd] = (data or fd, self._generation) self._control(fd) def stop_receive(self, fd): mitogen.core._vv and IOLOG.debug('%r.stop_receive(%r)', self, fd) self._rfds.pop(fd, None) self._control(fd) def start_transmit(self, fd, data=None): mitogen.core._vv and IOLOG.debug('%r.start_transmit(%r, %r)', self, fd, data) self._wfds[fd] = (data or fd, self._generation) self._control(fd) def stop_transmit(self, fd): mitogen.core._vv and IOLOG.debug('%r.stop_transmit(%r)', self, fd) self._wfds.pop(fd, None) self._control(fd) _inmask = (getattr(select, 'EPOLLIN', 0) | getattr(select, 'EPOLLHUP', 0)) def _poll(self, timeout): the_timeout = -1 if timeout is not None: the_timeout = timeout events, _ = mitogen.core.io_op(self._epoll.poll, the_timeout, 32) for fd, event in events: if event & self._inmask: data, gen = self._rfds.get(fd, (None, None)) if gen and gen < self._generation: # Events can still be read for an already-discarded fd. mitogen.core._vv and IOLOG.debug('%r: POLLIN: %r', self, fd) yield data if event & select.EPOLLOUT: data, gen = self._wfds.get(fd, (None, None)) if gen and gen < self._generation: mitogen.core._vv and IOLOG.debug('%r: POLLOUT: %r', self, fd) yield data # 2.4 and 2.5 only had select.select() and select.poll(). for _klass in mitogen.core.Poller, PollPoller, KqueuePoller, EpollPoller: if _klass.SUPPORTED: PREFERRED_POLLER = _klass # For processes that start many threads or connections, it's possible Latch # will also get high-numbered FDs, and so select() becomes useless there too. # So swap in our favourite poller. if PollPoller.SUPPORTED: mitogen.core.Latch.poller_class = PollPoller else: mitogen.core.Latch.poller_class = PREFERRED_POLLER class LineLoggingProtocolMixin(object): def __init__(self, **kwargs): super(LineLoggingProtocolMixin, self).__init__(**kwargs) self.logged_lines = [] self.logged_partial = None def on_line_received(self, line): self.logged_partial = None self.logged_lines.append((mitogen.core.now(), line)) self.logged_lines[:] = self.logged_lines[-100:] return super(LineLoggingProtocolMixin, self).on_line_received(line) def on_partial_line_received(self, line): self.logged_partial = line return super(LineLoggingProtocolMixin, self).on_partial_line_received(line) def on_disconnect(self, broker): if self.logged_partial: self.logged_lines.append((mitogen.core.now(), self.logged_partial)) self.logged_partial = None super(LineLoggingProtocolMixin, self).on_disconnect(broker) def get_history(streams): history = [] for stream in streams: if stream: history.extend(getattr(stream.protocol, 'logged_lines', [])) history.sort() s = b('\n').join(h[1] for h in history) return mitogen.core.to_text(s) class RegexProtocol(LineLoggingProtocolMixin, mitogen.core.DelimitedProtocol): """ Implement a delimited protocol where messages matching a set of regular expressions are dispatched to individual handler methods. Input is dispatches using :attr:`PATTERNS` and :attr:`PARTIAL_PATTERNS`, before falling back to :meth:`on_unrecognized_line_received` and :meth:`on_unrecognized_partial_line_received`. """ #: A sequence of 2-tuples of the form `(compiled pattern, method)` for #: patterns that should be matched against complete (delimited) messages, #: i.e. full lines. PATTERNS = [] #: Like :attr:`PATTERNS`, but patterns that are matched against incomplete #: lines. PARTIAL_PATTERNS = [] def on_line_received(self, line): super(RegexProtocol, self).on_line_received(line) for pattern, func in self.PATTERNS: match = pattern.search(line) if match is not None: return func(self, line, match) return self.on_unrecognized_line_received(line) def on_unrecognized_line_received(self, line): LOG.debug('%s: (unrecognized): %s', self.stream.name, line.decode('utf-8','replace')) def on_partial_line_received(self, line): super(RegexProtocol, self).on_partial_line_received(line) LOG.debug('%s: (partial): %s', self.stream.name, line.decode('utf-8','replace')) for pattern, func in self.PARTIAL_PATTERNS: match = pattern.search(line) if match is not None: return func(self, line, match) return self.on_unrecognized_partial_line_received(line) def on_unrecognized_partial_line_received(self, line): LOG.debug('%s: (unrecognized partial): %s', self.stream.name, line.decode('utf-8','replace')) class BootstrapProtocol(RegexProtocol): """ Respond to stdout of a child during bootstrap. Wait for :attr:`EC0_MARKER` to be written by the first stage to indicate it can receive the bootstrap, then await :attr:`EC1_MARKER` to indicate success, and :class:`MitogenProtocol` can be enabled. """ #: Sentinel value emitted by the first stage to indicate it is ready to #: receive the compressed bootstrap. For :mod:`mitogen.ssh` this must have #: length of at least `max(len('password'), len('debug1:'))` EC0_MARKER = b('MITO000') EC1_MARKER = b('MITO001') EC2_MARKER = b('MITO002') def __init__(self, broker): super(BootstrapProtocol, self).__init__() self._writer = mitogen.core.BufferedWriter(broker, self) def on_transmit(self, broker): self._writer.on_transmit(broker) def _on_ec0_received(self, line, match): LOG.debug('%r: first stage started succcessfully', self) self._writer.write(self.stream.conn.get_preamble()) def _on_ec1_received(self, line, match): LOG.debug('%r: first stage received mitogen.core source', self) def _on_ec2_received(self, line, match): LOG.debug('%r: new child booted successfully', self) self.stream.conn._complete_connection() return False def on_unrecognized_line_received(self, line): LOG.debug('%s: stdout: %s', self.stream.name, line.decode('utf-8','replace')) PATTERNS = [ (re.compile(EC0_MARKER), _on_ec0_received), (re.compile(EC1_MARKER), _on_ec1_received), (re.compile(EC2_MARKER), _on_ec2_received), ] class LogProtocol(LineLoggingProtocolMixin, mitogen.core.DelimitedProtocol): """ For "hybrid TTY/socketpair" mode, after connection setup a spare TTY master FD exists that cannot be closed, and to which SSH or sudo may continue writing log messages. The descriptor cannot be closed since the UNIX TTY layer sends SIGHUP to processes whose controlling TTY is the slave whose master side was closed. LogProtocol takes over this FD and creates log messages for anything written to it. """ def on_line_received(self, line): """ Read a line, decode it as UTF-8, and log it. """ super(LogProtocol, self).on_line_received(line) LOG.info(u'%s: %s', self.stream.name, line.decode('utf-8','replace')) class MitogenProtocol(mitogen.core.MitogenProtocol): """ Extend core.MitogenProtocol to cause SHUTDOWN to be sent to the child during graceful shutdown. """ def on_shutdown(self, broker): """ Respond to the broker's request for the stream to shut down by sending SHUTDOWN to the child. """ LOG.debug('%r: requesting child shutdown', self) self._send( mitogen.core.Message( src_id=mitogen.context_id, dst_id=self.remote_id, handle=mitogen.core.SHUTDOWN, ) ) class Options(object): name = None #: The path to the remote Python interpreter. python_path = get_sys_executable() #: Maximum time to wait for a connection attempt. connect_timeout = 30.0 #: True to cause context to write verbose /tmp/mitogen.<pid>.log. debug = False #: True to cause context to write /tmp/mitogen.stats.<pid>.<thread>.log. profiling = False #: True if unidirectional routing is enabled in the new child. unidirectional = False #: Passed via Router wrapper methods, must eventually be passed to #: ExternalContext.main(). max_message_size = None #: Remote name. remote_name = None #: Derived from :py:attr:`connect_timeout`; absolute floating point #: UNIX timestamp after which the connection attempt should be abandoned. connect_deadline = None def __init__(self, max_message_size, name=None, remote_name=None, python_path=None, debug=False, connect_timeout=None, profiling=False, unidirectional=False, old_router=None): self.name = name self.max_message_size = max_message_size if python_path: self.python_path = python_path if connect_timeout: self.connect_timeout = connect_timeout if remote_name is None: remote_name = get_default_remote_name() if '/' in remote_name or '\\' in remote_name: raise ValueError('remote_name= cannot contain slashes') if remote_name: self.remote_name = mitogen.core.to_text(remote_name) self.debug = debug self.profiling = profiling self.unidirectional = unidirectional self.max_message_size = max_message_size self.connect_deadline = mitogen.core.now() + self.connect_timeout class Connection(object): """ Manage the lifetime of a set of :class:`Streams <Stream>` connecting to a remote Python interpreter, including bootstrap, disconnection, and external tool integration. Base for streams capable of starting children. """ options_class = Options #: The protocol attached to stdio of the child. stream_protocol_class = BootstrapProtocol #: The protocol attached to stderr of the child. diag_protocol_class = LogProtocol #: :class:`Process` proc = None #: :class:`mitogen.core.Stream` with sides connected to stdin/stdout. stdio_stream = None #: If `proc.stderr` is set, referencing either a plain pipe or the #: controlling TTY, this references the corresponding #: :class:`LogProtocol`'s stream, allowing it to be disconnected when this #: stream is disconnected. stderr_stream = None #: Function with the semantics of :func:`create_child` used to create the #: child process. create_child = staticmethod(create_child) #: Dictionary of extra kwargs passed to :attr:`create_child`. create_child_args = {} #: :data:`True` if the remote has indicated that it intends to detach, and #: should not be killed on disconnect. detached = False #: If :data:`True`, indicates the child should not be killed during #: graceful detachment, as it the actual process implementing the child #: context. In all other cases, the subprocess is SSH, sudo, or a similar #: tool that should be reminded to quit during disconnection. child_is_immediate_subprocess = True #: Prefix given to default names generated by :meth:`connect`. name_prefix = u'local' #: :class:`Timer` that runs :meth:`_on_timer_expired` when connection #: timeout occurs. _timer = None #: When disconnection completes, instance of :class:`Reaper` used to wait #: on the exit status of the subprocess. _reaper = None #: On failure, the exception object that should be propagated back to the #: user. exception = None #: Extra text appended to :class:`EofError` if that exception is raised on #: a failed connection attempt. May be used in subclasses to hint at common #: problems with a particular connection method. eof_error_hint = None def __init__(self, options, router): #: :class:`Options` self.options = options self._router = router def __repr__(self): return 'Connection(%r)' % (self.stdio_stream,) # Minimised, gzipped, base64'd and passed to 'python -c'. It forks, dups # file descriptor 0 as 100, creates a pipe, then execs a new interpreter # with a custom argv. # * Optimized for minimum byte count after minification & compression. # * 'CONTEXT_NAME' and 'PREAMBLE_COMPRESSED_LEN' are substituted with # their respective values. # * CONTEXT_NAME must be prefixed with the name of the Python binary in # order to allow virtualenvs to detect their install prefix. # * macOS <= 10.14 (Darwin <= 18) install an unreliable Python version # switcher as /usr/bin/python, which introspects argv0. To workaround # it we redirect attempts to call /usr/bin/python with an explicit # call to /usr/bin/python2.7. macOS 10.15 (Darwin 19) removed it. # * macOS 11.x (Darwin 20, Big Sur) and macOS 12.x (Darwin 21, Montery) # do something slightly different. The Python executable is patched to # perform an extra execvp(). I don't fully understand the details, but # setting PYTHON_LAUNCHED_FROM_WRAPPER=1 avoids it. # * macOS 13.x (Darwin 22?) may remove python 2.x entirely. # # Locals: # R: read side of interpreter stdin. # W: write side of interpreter stdin. # r: read side of core_src FD. # w: write side of core_src FD. # C: the decompressed core source. # Final os.close(2) to avoid --py-debug build from corrupting stream with # "[1234 refs]" during exit. @staticmethod def _first_stage(): R,W=os.pipe() r,w=os.pipe() if os.fork(): os.dup2(0,100) os.dup2(R,0) os.dup2(r,101) os.close(R) os.close(r) os.close(W) os.close(w) if os.uname()[0]=='Darwin'and os.uname()[2][:2]<'19'and sys.executable=='/usr/bin/python':sys.executable='/usr/bin/python2.7' if os.uname()[0]=='Darwin'and os.uname()[2][:2]in'2021'and sys.version[:3]=='2.7':os.environ['PYTHON_LAUNCHED_FROM_WRAPPER']='1' os.environ['ARGV0']=sys.executable os.execl(sys.executable,sys.executable+'(mitogen:CONTEXT_NAME)') os.write(1,'MITO000\n'.encode()) C=_(os.fdopen(0,'rb').read(PREAMBLE_COMPRESSED_LEN),'zip') fp=os.fdopen(W,'wb',0) fp.write(C) fp.close() fp=os.fdopen(w,'wb',0) fp.write(C) fp.close() os.write(1,'MITO001\n'.encode()) os.close(2) def get_python_argv(self): """ Return the initial argument vector elements necessary to invoke Python, by returning a 1-element list containing :attr:`python_path` if it is a string, or simply returning it if it is already a list. This allows emulation of existing tools where the Python invocation may be set to e.g. `['/usr/bin/env', 'python']`. """ if isinstance(self.options.python_path, list): return self.options.python_path return [self.options.python_path] def get_boot_command(self): source = inspect.getsource(self._first_stage) source = textwrap.dedent('\n'.join(source.strip().split('\n')[2:])) source = source.replace(' ','') source = source.replace('CONTEXT_NAME', self.options.remote_name) preamble_compressed = self.get_preamble() source = source.replace('PREAMBLE_COMPRESSED_LEN', str(len(preamble_compressed))) compressed = zlib.compress(source.encode(), 9) encoded = codecs.encode(compressed, 'base64').replace(b('\n'), b('')) # We can't use bytes.decode() in 3.x since it was restricted to always # return unicode, so codecs.decode() is used instead. In 3.x # codecs.decode() requires a bytes object. Since we must be compatible # with 2.4 (no bytes literal), an extra.encode() either returns the # same str (2.x) or an equivalent bytes (3.x). return self.get_python_argv() + [ '-c', 'import codecs,os,sys;_=codecs.decode;' 'exec(_(_("%s".encode(),"base64"),"zip"))' % (encoded.decode(),) ] def get_econtext_config(self): assert self.options.max_message_size is not None parent_ids = mitogen.parent_ids[:] parent_ids.insert(0, mitogen.context_id) return { 'parent_ids': parent_ids, 'context_id': self.context.context_id, 'debug': self.options.debug, 'profiling': self.options.profiling, 'unidirectional': self.options.unidirectional, 'log_level': get_log_level(), 'whitelist': self._router.get_module_whitelist(), 'blacklist': self._router.get_module_blacklist(), 'max_message_size': self.options.max_message_size, 'version': mitogen.__version__, } def get_preamble(self): suffix = ( '\nExternalContext(%r).main()\n' % (self.get_econtext_config(),) ) partial = get_core_source_partial() return partial.append(suffix.encode('utf-8')) def _get_name(self): """ Called by :meth:`connect` after :attr:`pid` is known. Subclasses can override it to specify a default stream name, or set :attr:`name_prefix` to generate a default format. """ return u'%s.%s' % (self.name_prefix, self.proc.pid) def start_child(self): args = self.get_boot_command() LOG.debug('command line for %r: %s', self, Argv(args)) try: return self.create_child(args=args, **self.create_child_args) except OSError: e = sys.exc_info()[1] msg = 'Child start failed: %s. Command was: %s' % (e, Argv(args)) raise mitogen.core.StreamError(msg) def _adorn_eof_error(self, e): """ Subclasses may provide additional information in the case of a failed connection. """ if self.eof_error_hint: e.args = ('%s\n\n%s' % (e.args[0], self.eof_error_hint),) def _complete_connection(self): self._timer.cancel() if not self.exception: mitogen.core.unlisten(self._router.broker,'shutdown', self._on_broker_shutdown) self._router.register(self.context, self.stdio_stream) self.stdio_stream.set_protocol( MitogenProtocol( router=self._router, remote_id=self.context.context_id, ) ) self._router.route_monitor.notice_stream(self.stdio_stream) self.latch.put() def _fail_connection(self, exc): """ Fail the connection attempt. """ LOG.debug('failing connection %s due to %r', self.stdio_stream and self.stdio_stream.name, exc) if self.exception is None: self._adorn_eof_error(exc) self.exception = exc mitogen.core.unlisten(self._router.broker,'shutdown', self._on_broker_shutdown) for stream in self.stdio_stream, self.stderr_stream: if stream and not stream.receive_side.closed: stream.on_disconnect(self._router.broker) self._complete_connection() eof_error_msg = 'EOF on stream; last 100 lines received:\n' def on_stdio_disconnect(self): """ Handle stdio stream disconnection by failing the Connection if the stderr stream has already been closed. Otherwise, wait for it to close (or timeout), to allow buffered diagnostic logs to be consumed. It is normal that when a subprocess aborts, stdio has nothing buffered when it is closed, thus signalling readability, causing an empty read (interpreted as indicating disconnection) on the next loop iteration, even if its stderr pipe has lots of diagnostic logs still buffered in the kernel. Therefore we must wait for both pipes to indicate they are empty before triggering connection failure. """ stderr = self.stderr_stream if stderr is None or stderr.receive_side.closed: self._on_streams_disconnected() def on_stderr_disconnect(self): """ Inverse of :func:`on_stdio_disconnect`. """ if self.stdio_stream.receive_side.closed: self._on_streams_disconnected() def _on_streams_disconnected(self): """ When disconnection has been detected for both streams, cancel the connection timer, mark the connection failed, and reap the child process. Do nothing if the timer has already been cancelled, indicating some existing failure has already been noticed. """ if self._timer.active: self._timer.cancel() self._fail_connection(EofError( self.eof_error_msg + get_history( [self.stdio_stream, self.stderr_stream] ) )) if self._reaper: return self._reaper = Reaper( broker=self._router.broker, proc=self.proc, kill=not ( (self.detached and self.child_is_immediate_subprocess) or # Avoid killing so child has chance to write cProfile data self._router.profiling ), # Don't delay shutdown waiting for a detached child, since the # detached child may expect to live indefinitely after its parent # exited. wait_on_shutdown=(not self.detached), ) self._reaper.reap() def _on_broker_shutdown(self): """ Respond to broker.shutdown() being called by failing the connection attempt. """ self._fail_connection(CancelledError(BROKER_SHUTDOWN_MSG)) def stream_factory(self): return self.stream_protocol_class.build_stream( broker=self._router.broker, ) def stderr_stream_factory(self): return self.diag_protocol_class.build_stream() def _setup_stdio_stream(self): stream = self.stream_factory() stream.conn = self stream.name = self.options.name or self._get_name() stream.accept(self.proc.stdout, self.proc.stdin) mitogen.core.listen(stream, 'disconnect', self.on_stdio_disconnect) self._router.broker.start_receive(stream) return stream def _setup_stderr_stream(self): stream = self.stderr_stream_factory() stream.conn = self stream.name = self.options.name or self._get_name() stream.accept(self.proc.stderr, self.proc.stderr) mitogen.core.listen(stream, 'disconnect', self.on_stderr_disconnect) self._router.broker.start_receive(stream) return stream def _on_timer_expired(self): self._fail_connection( mitogen.core.TimeoutError( 'Failed to setup connection after %.2f seconds', self.options.connect_timeout, ) ) def _async_connect(self): LOG.debug('creating connection to context %d using %s', self.context.context_id, self.__class__.__module__) mitogen.core.listen(self._router.broker,'shutdown', self._on_broker_shutdown) self._timer = self._router.broker.timers.schedule( when=self.options.connect_deadline, func=self._on_timer_expired, ) try: self.proc = self.start_child() except Exception: LOG.debug('failed to start child', exc_info=True) self._fail_connection(sys.exc_info()[1]) return LOG.debug('child for %r started: pid:%r stdin:%r stdout:%r stderr:%r', self, self.proc.pid, self.proc.stdin.fileno(), self.proc.stdout.fileno(), self.proc.stderr and self.proc.stderr.fileno()) self.stdio_stream = self._setup_stdio_stream() if self.context.name is None: self.context.name = self.stdio_stream.name self.proc.name = self.stdio_stream.name if self.proc.stderr: self.stderr_stream = self._setup_stderr_stream() def connect(self, context): self.context = context self.latch = mitogen.core.Latch() self._router.broker.defer(self._async_connect) self.latch.get() if self.exception: raise self.exception class ChildIdAllocator(object): """ Allocate new context IDs from a block of unique context IDs allocated by the master process. """ def __init__(self, router): self.router = router self.lock = threading.Lock() self.it = iter(xrange(0)) def allocate(self): """ Allocate an ID, requesting a fresh block from the master if the existing block is exhausted. :returns: The new context ID. .. warning:: This method is not safe to call from the :class:`Broker` thread, as it may block on IO of its own. """ self.lock.acquire() try: for id_ in self.it: return id_ master = self.router.context_by_id(0) start, end = master.send_await( mitogen.core.Message(dst_id=0, handle=mitogen.core.ALLOCATE_ID) ) self.it = iter(xrange(start, end)) finally: self.lock.release() return self.allocate() class CallChain(object): """ Deliver :data:`mitogen.core.CALL_FUNCTION` messages to a target context, optionally threading related calls so an exception in an earlier call cancels subsequent calls. :param mitogen.core.Context context: Target context. :param bool pipelined: Enable pipelining. :meth:`call`, :meth:`call_no_reply` and :meth:`call_async` normally issue calls and produce responses with no memory of prior exceptions. If a call made with :meth:`call_no_reply` fails, the exception is logged to the target context's logging framework. **Pipelining** When pipelining is enabled, if an exception occurs during a call, subsequent calls made by the same :class:`CallChain` fail with the same exception, including those already in-flight on the network, and no further calls execute until :meth:`reset` is invoked. No exception is logged for calls made with :meth:`call_no_reply`, instead the exception is saved and reported as the result of subsequent :meth:`call` or :meth:`call_async` calls. Sequences of asynchronous calls can be made without wasting network round-trips to discover if prior calls succeed, and chains originating from multiple unrelated source contexts may overlap concurrently at a target context without interference. In this example, 4 calls complete in one round-trip:: chain = mitogen.parent.CallChain(context, pipelined=True) chain.call_no_reply(os.mkdir, '/tmp/foo') # If previous mkdir() failed, this never runs: chain.call_no_reply(os.mkdir, '/tmp/foo/bar') # If either mkdir() failed, this never runs, and the exception is # asynchronously delivered to the receiver. recv = chain.call_async(subprocess.check_output, '/tmp/foo') # If anything so far failed, this never runs, and raises the exception. chain.call(do_something) # If this code was executed, the exception would also be raised. if recv.get().unpickle() == 'baz': pass When pipelining is enabled, :meth:`reset` must be invoked to ensure any exception is discarded, otherwise unbounded memory usage is possible in long-running programs. The context manager protocol is supported to ensure :meth:`reset` is always invoked:: with mitogen.parent.CallChain(context, pipelined=True) as chain: chain.call_no_reply(...) chain.call_no_reply(...) chain.call_no_reply(...) chain.call(...) # chain.reset() automatically invoked. """ def __init__(self, context, pipelined=False): self.context = context if pipelined: self.chain_id = self.make_chain_id() else: self.chain_id = None @classmethod def make_chain_id(cls): return '%s-%s-%x-%x' % ( socket.gethostname(), os.getpid(), thread.get_ident(), int(1e6 * mitogen.core.now()), ) def __repr__(self): return '%s(%s)' % (self.__class__.__name__, self.context) def __enter__(self): return self def __exit__(self, _1, _2, _3): self.reset() def reset(self): """ Instruct the target to forget any related exception. """ if not self.chain_id: return saved, self.chain_id = self.chain_id, None try: self.call_no_reply(mitogen.core.Dispatcher.forget_chain, saved) finally: self.chain_id = saved closures_msg = ( 'Mitogen cannot invoke closures, as doing so would require ' 'serializing arbitrary program state, and no universal ' 'method exists to recover a reference to them.' ) lambda_msg = ( 'Mitogen cannot invoke anonymous functions, as no universal method ' 'exists to recover a reference to an anonymous function.' ) method_msg = ( 'Mitogen cannot invoke instance methods, as doing so would require ' 'serializing arbitrary program state.' ) def make_msg(self, fn, *args, **kwargs): if getattr(fn, closure_attr, None) is not None: raise TypeError(self.closures_msg) if fn.__name__ == '<lambda>': raise TypeError(self.lambda_msg) if inspect.ismethod(fn): im_self = getattr(fn, IM_SELF_ATTR) if not inspect.isclass(im_self): raise TypeError(self.method_msg) klass = mitogen.core.to_text(im_self.__name__) else: klass = None tup = ( self.chain_id, mitogen.core.to_text(fn.__module__), klass, mitogen.core.to_text(fn.__name__), args, mitogen.core.Kwargs(kwargs) ) return mitogen.core.Message.pickled(tup, handle=mitogen.core.CALL_FUNCTION) def call_no_reply(self, fn, *args, **kwargs): """ Like :meth:`call_async`, but do not wait for a return value, and inform the target context no reply is expected. If the call fails and pipelining is disabled, the exception will be logged to the target context's logging framework. """ LOG.debug('starting no-reply function call to %r: %r', self.context.name or self.context.context_id, CallSpec(fn, args, kwargs)) self.context.send(self.make_msg(fn, *args, **kwargs)) def call_async(self, fn, *args, **kwargs): """ Arrange for `fn(*args, **kwargs)` to be invoked on the context's main thread. :param fn: A free function in module scope or a class method of a class directly reachable from module scope: .. code-block:: python # mymodule.py def my_func(): '''A free function reachable as mymodule.my_func''' class MyClass: @classmethod def my_classmethod(cls): '''Reachable as mymodule.MyClass.my_classmethod''' def my_instancemethod(self): '''Unreachable: requires a class instance!''' class MyEmbeddedClass: @classmethod def my_classmethod(cls): '''Not directly reachable from module scope!''' :param tuple args: Function arguments, if any. See :ref:`serialization-rules` for permitted types. :param dict kwargs: Function keyword arguments, if any. See :ref:`serialization-rules` for permitted types. :returns: :class:`mitogen.core.Receiver` configured to receive the result of the invocation: .. code-block:: python recv = context.call_async(os.check_output, 'ls /tmp/') try: # Prints output once it is received. msg = recv.get() print(msg.unpickle()) except mitogen.core.CallError, e: print('Call failed:', str(e)) Asynchronous calls may be dispatched in parallel to multiple contexts and consumed as they complete using :class:`mitogen.select.Select`. """ LOG.debug('starting function call to %s: %r', self.context.name or self.context.context_id, CallSpec(fn, args, kwargs)) return self.context.send_async(self.make_msg(fn, *args, **kwargs)) def call(self, fn, *args, **kwargs): """ Like :meth:`call_async`, but block until the return value is available. Equivalent to:: call_async(fn, *args, **kwargs).get().unpickle() :returns: The function's return value. :raises mitogen.core.CallError: An exception was raised in the remote context during execution. """ receiver = self.call_async(fn, *args, **kwargs) return receiver.get().unpickle(throw_dead=False) class Context(mitogen.core.Context): """ Extend :class:`mitogen.core.Context` with functionality useful to masters, and child contexts who later become parents. Currently when this class is required, the target context's router is upgraded at runtime. """ #: A :class:`CallChain` instance constructed by default, with pipelining #: disabled. :meth:`call`, :meth:`call_async` and :meth:`call_no_reply` use #: this instance. call_chain_class = CallChain via = None def __init__(self, *args, **kwargs): super(Context, self).__init__(*args, **kwargs) self.default_call_chain = self.call_chain_class(self) def __ne__(self, other): return not (self == other) def __eq__(self, other): return ( isinstance(other, mitogen.core.Context) and (other.context_id == self.context_id) and (other.router == self.router) ) def __hash__(self): return hash((self.router, self.context_id)) def call_async(self, fn, *args, **kwargs): """ See :meth:`CallChain.call_async`. """ return self.default_call_chain.call_async(fn, *args, **kwargs) def call(self, fn, *args, **kwargs): """ See :meth:`CallChain.call`. """ return self.default_call_chain.call(fn, *args, **kwargs) def call_no_reply(self, fn, *args, **kwargs): """ See :meth:`CallChain.call_no_reply`. """ self.default_call_chain.call_no_reply(fn, *args, **kwargs) def shutdown(self, wait=False): """ Arrange for the context to receive a ``SHUTDOWN`` message, triggering graceful shutdown. Due to a lack of support for timers, no attempt is made yet to force terminate a hung context using this method. This will be fixed shortly. :param bool wait: If :data:`True`, block the calling thread until the context has completely terminated. :returns: If `wait` is :data:`False`, returns a :class:`mitogen.core.Latch` whose :meth:`get() <mitogen.core.Latch.get>` method returns :data:`None` when shutdown completes. The `timeout` parameter may be used to implement graceful timeouts. """ LOG.debug('%r.shutdown() sending SHUTDOWN', self) latch = mitogen.core.Latch() mitogen.core.listen(self, 'disconnect', lambda: latch.put(None)) self.send( mitogen.core.Message( handle=mitogen.core.SHUTDOWN, ) ) if wait: latch.get() else: return latch class RouteMonitor(object): """ Generate and respond to :data:`mitogen.core.ADD_ROUTE` and :data:`mitogen.core.DEL_ROUTE` messages sent to the local context by maintaining a table of available routes, and propagating messages towards parents and siblings as appropriate. :class:`RouteMonitor` is responsible for generating routing messages for directly attached children. It learns of new children via :meth:`notice_stream` called by :class:`Router`, and subscribes to their ``disconnect`` event to learn when they disappear. In children, constructing this class overwrites the stub :data:`mitogen.core.DEL_ROUTE` handler installed by :class:`mitogen.core.ExternalContext`, which is expected behaviour when a child is beging upgraded in preparation to become a parent of children of its own. By virtue of only being active while responding to messages from a handler, RouteMonitor lives entirely on the broker thread, so its data requires no locking. :param mitogen.master.Router router: Router to install handlers on. :param mitogen.core.Context parent: :data:`None` in the master process, or reference to the parent context we should propagate route updates towards. """ def __init__(self, router, parent=None): self.router = router self.parent = parent self._log = logging.getLogger('mitogen.route_monitor') #: Mapping of Stream instance to integer context IDs reachable via the #: stream; used to cleanup routes during disconnection. self._routes_by_stream = {} self.router.add_handler( fn=self._on_add_route, handle=mitogen.core.ADD_ROUTE, persist=True, policy=is_immediate_child, overwrite=True, ) self.router.add_handler( fn=self._on_del_route, handle=mitogen.core.DEL_ROUTE, persist=True, policy=is_immediate_child, overwrite=True, ) def __repr__(self): return 'RouteMonitor()' def _send_one(self, stream, handle, target_id, name): """ Compose and send an update message on a stream. :param mitogen.core.Stream stream: Stream to send it on. :param int handle: :data:`mitogen.core.ADD_ROUTE` or :data:`mitogen.core.DEL_ROUTE` :param int target_id: ID of the connecting or disconnecting context. :param str name: Context name or :data:`None`. """ if not stream: # We may not have a stream during shutdown. return data = str(target_id) if name: data = '%s:%s' % (target_id, name) stream.protocol.send( mitogen.core.Message( handle=handle, data=data.encode('utf-8'), dst_id=stream.protocol.remote_id, ) ) def _propagate_up(self, handle, target_id, name=None): """ In a non-master context, propagate an update towards the master. :param int handle: :data:`mitogen.core.ADD_ROUTE` or :data:`mitogen.core.DEL_ROUTE` :param int target_id: ID of the connecting or disconnecting context. :param str name: For :data:`mitogen.core.ADD_ROUTE`, the name of the new context assigned by its parent. This is used by parents to assign the :attr:`mitogen.core.Context.name` attribute. """ if self.parent: stream = self.router.stream_by_id(self.parent.context_id) self._send_one(stream, handle, target_id, name) def _propagate_down(self, handle, target_id): """ For DEL_ROUTE, we additionally want to broadcast the message to any stream that has ever communicated with the disconnecting ID, so core.py's :meth:`mitogen.core.Router._on_del_route` can turn the message into a disconnect event. :param int handle: :data:`mitogen.core.ADD_ROUTE` or :data:`mitogen.core.DEL_ROUTE` :param int target_id: ID of the connecting or disconnecting context. """ for stream in self.router.get_streams(): if target_id in stream.protocol.egress_ids and ( (self.parent is None) or (self.parent.context_id!= stream.protocol.remote_id) ): self._send_one(stream, mitogen.core.DEL_ROUTE, target_id, None) def notice_stream(self, stream): """ When this parent is responsible for a new directly connected child stream, we're also responsible for broadcasting :data:`mitogen.core.DEL_ROUTE` upstream when that child disconnects. """ self._routes_by_stream[stream] = set([stream.protocol.remote_id]) self._propagate_up(mitogen.core.ADD_ROUTE, stream.protocol.remote_id, stream.name) mitogen.core.listen( obj=stream, name='disconnect', func=lambda: self._on_stream_disconnect(stream), ) def get_routes(self, stream): """ Return the set of context IDs reachable on a stream. :param mitogen.core.Stream stream: :returns: set([int]) """ return self._routes_by_stream.get(stream) or set() def _on_stream_disconnect(self, stream): """ Respond to disconnection of a local stream by propagating DEL_ROUTE for any contexts we know were attached to it. """ # During a stream crash it is possible for disconnect signal to fire # twice, in which case ignore the second instance. routes = self._routes_by_stream.pop(stream, None) if routes is None: return self._log.debug('stream %s is gone; propagating DEL_ROUTE for %r', stream.name, routes) for target_id in routes: self.router.del_route(target_id) self._propagate_up(mitogen.core.DEL_ROUTE, target_id) self._propagate_down(mitogen.core.DEL_ROUTE, target_id) context = self.router.context_by_id(target_id, create=False) if context: mitogen.core.fire(context, 'disconnect') def _on_add_route(self, msg): """ Respond to :data:`mitogen.core.ADD_ROUTE` by validating the source of the message, updating the local table, and propagating the message upwards. """ if msg.is_dead: return target_id_s, _, target_name = bytes_partition(msg.data, b(':')) target_name = target_name.decode() target_id = int(target_id_s) self.router.context_by_id(target_id).name = target_name stream = self.router.stream_by_id(msg.src_id) current = self.router.stream_by_id(target_id) if current and current.protocol.remote_id!= mitogen.parent_id: self._log.error('Cannot add duplicate route to %r via %r, ' 'already have existing route via %r', target_id, stream, current) return self._log.debug('Adding route to %d via %r', target_id, stream) self._routes_by_stream[stream].add(target_id) self.router.add_route(target_id, stream) self._propagate_up(mitogen.core.ADD_ROUTE, target_id, target_name) def _on_del_route(self, msg): """ Respond to :data:`mitogen.core.DEL_ROUTE` by validating the source of the message, updating the local table, propagating the message upwards, and downwards towards any stream that every had a message forwarded from it towards the disconnecting context. """ if msg.is_dead: return target_id = int(msg.data) registered_stream = self.router.stream_by_id(target_id) if registered_stream is None: return stream = self.router.stream_by_id(msg.src_id) if registered_stream!= stream: self._log.error('received DEL_ROUTE for %d from %r, expected %r', target_id, stream, registered_stream) return context = self.router.context_by_id(target_id, create=False) if context: self._log.debug('firing local disconnect signal for %r', context) mitogen.core.fire(context, 'disconnect') self._log.debug('deleting route to %d via %r', target_id, stream) routes = self._routes_by_stream.get(stream) if routes: routes.discard(target_id) self.router.del_route(target_id) if stream.protocol.remote_id!= mitogen.parent_id: self._propagate_up(mitogen.core.DEL_ROUTE, target_id) self._propagate_down(mitogen.core.DEL_ROUTE, target_id) class Router(mitogen.core.Router): context_class = Context debug = False profiling = False id_allocator = None responder = None log_forwarder = None route_monitor = None def upgrade(self, importer, parent): LOG.debug('upgrading %r with capabilities to start new children', self) self.id_allocator = ChildIdAllocator(router=self) self.responder = ModuleForwarder( router=self, parent_context=parent, importer=importer, ) self.route_monitor = RouteMonitor(self, parent) self.add_handler( fn=self._on_detaching, handle=mitogen.core.DETACHING, persist=True, ) def _on_detaching(self, msg): if msg.is_dead: return stream = self.stream_by_id(msg.src_id) if stream.protocol.remote_id!= msg.src_id or stream.conn.detached: LOG.warning('bad DETACHING received on %r: %r', stream, msg) return LOG.debug('%r: marking as detached', stream) stream.conn.detached = True msg.reply(None) def get_streams(self): """ Return an atomic snapshot of all streams in existence at time of call. This is safe to call from any thread. """ self._write_lock.acquire() try: return itervalues(self._stream_by_id) finally: self._write_lock.release() def disconnect(self, context): """ Disconnect a context and forget its stream, assuming the context is directly connected. """ stream = self.stream_by_id(context) if stream is None or stream.protocol.remote_id!= context.context_id: return l = mitogen.core.Latch() mitogen.core.listen(stream, 'disconnect', l.put) def disconnect(): LOG.debug('Starting disconnect of %r', stream) stream.on_disconnect(self.broker) self.broker.defer(disconnect) l.get() def add_route(self, target_id, stream): """ Arrange for messages whose `dst_id` is `target_id` to be forwarded on a directly connected :class:`Stream`. Safe to call from any thread. This is called automatically by :class:`RouteMonitor` in response to :data:`mitogen.core.ADD_ROUTE` messages, but remains public while the design has not yet settled, and situations may arise where routing is not fully automatic. :param int target_id: Target context ID to add a route for. :param mitogen.core.Stream stream: Stream over which messages to the target should be routed. """ LOG.debug('%r: adding route to context %r via %r', self, target_id, stream) assert isinstance(target_id, int) assert isinstance(stream, mitogen.core.Stream) self._write_lock.acquire() try: self._stream_by_id[target_id] = stream finally: self._write_lock.release() def del_route(self, target_id): """ Delete any route that exists for `target_id`. It is not an error to delete a route that does not currently exist. Safe to call from any thread. This is called automatically by :class:`RouteMonitor` in response to :data:`mitogen.core.DEL_ROUTE` messages, but remains public while the design has not yet settled, and situations may arise where routing is not fully automatic. :param int target_id: Target context ID to delete route for. """ LOG.debug('%r: deleting route to %r', self, target_id) # DEL_ROUTE may be sent by a parent if it knows this context sent # messages to a peer that has now disconnected, to let us raise # 'disconnect' event on the appropriate Context instance. In that case, # we won't a matching _stream_by_id entry for the disappearing route, # so don't raise an error for a missing key here. self._write_lock.acquire() try: self._stream_by_id.pop(target_id, None) finally: self._write_lock.release() def get_module_blacklist(self): if mitogen.context_id == 0: return self.responder.blacklist return self.importer.master_blacklist def get_module_whitelist(self): if mitogen.context_id == 0: return self.responder.whitelist return self.importer.master_whitelist def allocate_id(self): return self.id_allocator.allocate() connection_timeout_msg = u"Connection timed out." def _connect(self, klass, **kwargs): context_id = self.allocate_id() context = self.context_class(self, context_id) context.name = kwargs.get('name') kwargs['old_router'] = self kwargs['max_message_size'] = self.max_message_size conn = klass(klass.options_class(**kwargs), self) try: conn.connect(context=context) except mitogen.core.TimeoutError: raise mitogen.core.StreamError(self.connection_timeout_msg) return context def connect(self, method_name, name=None, **kwargs): if name: name = mitogen.core.to_text(name) klass = get_connection_class(method_name) kwargs.setdefault(u'debug', self.debug) kwargs.setdefault(u'profiling', self.profiling) kwargs.setdefault(u'unidirectional', self.unidirectional) kwargs.setdefault(u'name', name) via = kwargs.pop(u'via', None) if via is not None: return self.proxy_connect(via, method_name, **mitogen.core.Kwargs(kwargs)) return self._connect(klass, **mitogen.core.Kwargs(kwargs)) def proxy_connect(self, via_context, method_name, name=None, **kwargs): resp = via_context.call(_proxy_connect, name=name, method_name=method_name, kwargs=mitogen.core.Kwargs(kwargs), ) if resp['msg'] is not None: raise mitogen.core.StreamError(resp['msg']) name = u'%s.%s' % (via_context.name, resp['name']) context = self.context_class(self, resp['id'], name=name) context.via = via_context self._write_lock.acquire() try: self._context_by_id[context.context_id] = context finally: self._write_lock.release() return context def buildah(self, **kwargs): return self.connect(u'buildah', **kwargs) def doas(self, **kwargs): return self.connect(u'doas', **kwargs) def docker(self, **kwargs): return self.connect(u'docker', **kwargs) def kubectl(self, **kwargs): return self.connect(u'kubectl', **kwargs) def fork(self, **kwargs): return self.connect(u'fork', **kwargs) def jail(self, **kwargs): return self.connect(u'jail', **kwargs) def local(self, **kwargs): return self.connect(u'local', **kwargs) def lxc(self, **kwargs): return self.connect(u'lxc', **kwargs) def lxd(self, **kwargs): return self.connect(u'lxd', **kwargs) def setns(self, **kwargs): return self.connect(u'setns', **kwargs) def su(self, **kwargs): return self.connect(u'su', **kwargs) def sudo(self, **kwargs): return self.connect(u'sudo', **kwargs) def ssh(self, **kwargs): return self.connect(u'ssh', **kwargs) def podman(self, **kwargs): return self.connect(u'podman', **kwargs) class Reaper(object): """ Asynchronous logic for reaping :class:`Process` objects. This is necessary to prevent uncontrolled buildup of zombie processes in long-lived parents that will eventually reach an OS limit, preventing creation of new threads and processes, and to log the exit status of the child in the case of an error. To avoid modifying process-global state such as with :func:`signal.set_wakeup_fd` or installing a :data:`signal.SIGCHLD` handler that might interfere with the user's ability to use those facilities, Reaper polls for exit with backoff using timers installed on an associated :class:`Broker`. :param mitogen.core.Broker broker: The :class:`Broker` on which to install timers :param mitogen.parent.Process proc: The process to reap. :param bool kill: If :data:`True`, send ``SIGTERM`` and ``SIGKILL`` to the process. :param bool wait_on_shutdown: If :data:`True`, delay :class:`Broker` shutdown if child has not yet exited. If :data:`False` simply forget the child. """ #: :class:`Timer` that invokes :meth:`reap` after some polling delay. _timer = None def __init__(self, broker, proc, kill, wait_on_shutdown): self.broker = broker self.proc = proc self.kill = kill self.wait_on_shutdown = wait_on_shutdown self._tries = 0 def _signal_child(self, signum): # For processes like sudo we cannot actually send sudo a signal, # because it is setuid, so this is best-effort only. LOG.debug('%r: sending %s', self.proc, SIGNAL_BY_NUM[signum]) try: os.kill(self.proc.pid, signum) except OSError: e = sys.exc_info()[1] if e.args[0]!= errno.EPERM: raise def _calc_delay(self, count): """ Calculate a poll delay given `count` attempts have already been made. These constants have no principle, they just produce rapid but still relatively conservative retries. """ delay = 0.05 for _ in xrange(count): delay *= 1.72 return delay def _on_broker_shutdown(self): """ Respond to :class:`Broker` shutdown by cancelling the reap timer if :attr:`Router.await_children_at_shutdown` is disabled. Otherwise shutdown is delayed for up to :attr:`Broker.shutdown_timeout` for subprocesses may have no intention of exiting any time soon. """ if not self.wait_on_shutdown: self._timer.cancel() def _install_timer(self, delay): new = self._timer is None self._timer = self.broker.timers.schedule( when=mitogen.core.now() + delay, func=self.reap, ) if new: mitogen.core.listen(self.broker,'shutdown', self._on_broker_shutdown) def _remove_timer(self): if self._timer and self._timer.active: self._timer.cancel() mitogen.core.unlisten(self.broker,'shutdown', self._on_broker_shutdown) def reap(self): """ Reap the child process during disconnection. """ status = self.proc.poll() if status is not None: LOG.debug('%r: %s', self.proc, returncode_to_str(status)) mitogen.core.fire(self.proc, 'exit') self._remove_timer() return self._tries += 1 if self._tries > 20: LOG.warning('%r: child will not exit, giving up', self) self._remove_timer() return delay = self._calc_delay(self._tries - 1) LOG.debug('%r still running after IO disconnect, recheck in %.03fs', self.proc, delay) self._install_timer(delay) if not self.kill: pass elif self._tries == 2: self._signal_child(signal.SIGTERM) elif self._tries == 6: # roughly 4 seconds self._signal_child(signal.SIGKILL) class Process(object): """ Process objects provide a uniform interface to the :mod:`subprocess` and :mod:`mitogen.fork`. This class is extended by :class:`PopenProcess` and :class:`mitogen.fork.Process`. :param int pid: The process ID. :param file stdin: File object attached to standard input. :param file stdout: File object attached to standard output. :param file stderr: File object attached to standard error, or :data:`None`. """ #: Name of the process used in logs. Set to the stream/context name by #: :class:`Connection`. name = None def __init__(self, pid, stdin, stdout, stderr=None): #: The process ID. self.pid = pid #: File object attached to standard input. self.stdin = stdin #: File object attached to standard output. self.stdout = stdout #: File object attached to standard error. self.stderr = stderr def __repr__(self): return '%s %s pid %d' % ( type(self).__name__, self.name, self.pid, ) def poll(self): """ Fetch the child process exit status, or :data:`None` if it is still running. This should be overridden by subclasses. :returns: Exit status in the style of the :attr:`subprocess.Popen.returncode` attribute, i.e. with signals represented by a negative integer. """ raise NotImplementedError() class PopenProcess(Process): """ :class:`Process` subclass wrapping a :class:`subprocess.Popen` object. :param subprocess.Popen proc: The subprocess. """ def __init__(self, proc, stdin, stdout, stderr=None): super(PopenProcess, self).__init__(proc.pid, stdin, stdout, stderr) #: The subprocess. self.proc = proc def poll(self): return self.proc.poll() class ModuleForwarder(object): """ Respond to :data:`mitogen.core.GET_MODULE` requests in a child by forwarding the request to our parent context, or satisfying the request from our local Importer cache. """ def __init__(self, router, parent_context, importer): self.router = router self.parent_context = parent_context self.importer = importer router.add_handler( fn=self._on_forward_module, handle=mitogen.core.FORWARD_MODULE, persist=True, policy=mitogen.core.has_parent_authority, ) router.add_handler( fn=self._on_get_module, handle=mitogen.core.GET_MODULE, persist=True, policy=is_immediate_child, ) def __repr__(self): return 'ModuleForwarder' def _on_forward_module(self, msg): if msg.is_dead: return context_id_s, _, fullname = bytes_partition(msg.data, b('\x00')) fullname = mitogen.core.to_text(fullname) context_id = int(context_id_s) stream = self.router.stream_by_id(context_id) if stream.protocol.remote_id == mitogen.parent_id: LOG.error('%r: dropping FORWARD_MODULE(%d, %r): no route to child', self, context_id, fullname) return if fullname in stream.protocol.sent_modules: return LOG.debug('%r._on_forward_module() sending %r to %r via %r', self, fullname, context_id, stream.protocol.remote_id) self._send_module_and_related(stream, fullname) if stream.protocol.remote_id!= context_id: stream.protocol._send( mitogen.core.Message( data=msg.data, handle=mitogen.core.FORWARD_MODULE, dst_id=stream.protocol.remote_id, ) ) def _on_get_module(self, msg): if msg.is_dead: return fullname = msg.data.decode('utf-8') LOG.debug('%r: %s requested by context %d', self, fullname, msg.src_id) callback = lambda: self._on_cache_callback(msg, fullname) self.importer._request_module(fullname, callback) def _on_cache_callback(self, msg, fullname): stream = self.router.stream_by_id(msg.src_id) LOG.debug('%r: sending %s to %r', self, fullname, stream) self._send_module_and_related(stream, fullname) def _send_module_and_related(self, stream, fullname): tup = self.importer._cache[fullname] for related in tup[4]: rtup = self.importer._cache.get(related) if rtup: self._send_one_module(stream, rtup) else: LOG.debug('%r: %s not in cache (for %s)', self, related, fullname) self._send_one_module(stream, tup) def _send_one_module(self, stream, tup): if tup[0] not in stream.protocol.sent_modules: stream.protocol.sent_modules.add(tup[0]) self.router._async_route( mitogen.core.Message.pickled( tup, dst_id=stream.protocol.remote_id, handle=mitogen.core.LOAD_MODULE, ) ) # Copyright 2019, David Wilson # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. #!mitogen: minify_safe """ This module implements most package functionality, but remains separate from non-essential code in order to reduce its size, since it is also serves as the bootstrap implementation sent to every new slave context. """ import binascii import collections import encodings.latin_1 import encodings.utf_8 import errno import fcntl import itertools import linecache import logging import os import pickle as py_pickle import pstats import signal import socket import struct import sys import syslog import threading import time import traceback import warnings import weakref import zlib # Python >3.7 deprecated the imp module. warnings.filterwarnings('ignore', message='the imp module is deprecated') import imp # Absolute imports for <2.5. select = __import__('select') try: import cProfile except ImportError: cProfile = None try: import thread except ImportError: import threading as thread try: import cPickle as pickle except ImportError: import pickle try: from cStringIO import StringIO as BytesIO except ImportError: from io import BytesIO try: BaseException except NameError: BaseException = Exception try: ModuleNotFoundError except NameError: ModuleNotFoundError = ImportError # TODO: usage of 'import' after setting __name__, but before fixing up # sys.modules generates a warning. This happens when profiling = True. warnings.filterwarnings('ignore', "Parent module'mitogen' not found while handling absolute import") LOG = logging.getLogger('mitogen') IOLOG = logging.getLogger('mitogen.io') IOLOG.setLevel(logging.INFO) # str.encode() may take import lock. Deadlock possible if broker calls #.encode() on behalf of thread currently waiting for module. LATIN1_CODEC = encodings.latin_1.Codec() _v = False _vv = False GET_MODULE = 100 CALL_FUNCTION = 101 FORWARD_LOG = 102 ADD_ROUTE = 103 DEL_ROUTE = 104 ALLOCATE_ID = 105 SHUTDOWN = 106 LOAD_MODULE = 107 FORWARD_MODULE = 108 DETACHING = 109 CALL_SERVICE = 110 STUB_CALL_SERVICE = 111 #: Special value used to signal disconnection or the inability to route a #: message, when it appears in the `reply_to` field. Usually causes #: :class:`mitogen.core.ChannelError` to be raised when it is received. #: #: It indicates the sender did not know how to process the message, or wishes #: no further messages to be delivered to it. It is used when: #: #: * a remote receiver is disconnected or explicitly closed. #: * a related message could not be delivered due to no route existing for it. #: * a router is being torn down, as a sentinel value to notify #: :meth:`mitogen.core.Router.add_handler` callbacks to clean up. IS_DEAD = 999 try: BaseException except NameError: BaseException = Exception PY24 = sys.version_info < (2, 5) PY3 = sys.version_info > (3,) if PY3: b = str.encode BytesType = bytes UnicodeType = str FsPathTypes = (str,) BufferType = lambda buf, start: memoryview(buf)[start:] long = int else: b = str BytesType = str FsPathTypes = (str, unicode) BufferType = buffer UnicodeType = unicode AnyTextType = (BytesType, UnicodeType) try: next except NameError: next = lambda it: it.next() # #550: prehistoric WSL did not advertise itself in uname output. try: fp = open('/proc/sys/kernel/osrelease') IS_WSL = 'Microsoft' in fp.read() fp.close() except IOError: IS_WSL = False #: Default size for calls to :meth:`Side.read` or :meth:`Side.write`, and the #: size of buffers configured by :func:`mitogen.parent.create_socketpair`. This #: value has many performance implications, 128KiB seems to be a sweet spot. #: #: * When set low, large messages cause many :class:`Broker` IO loop #: iterations, burning CPU and reducing throughput. #: * When set high, excessive RAM is reserved by the OS for socket buffers (2x #: per child), and an identically sized temporary userspace buffer is #: allocated on each read that requires zeroing, and over a particular size #: may require two system calls to allocate/deallocate. #: #: Care must be taken to ensure the underlying kernel object and receiving #: program support the desired size. For example, #: #: * Most UNIXes have TTYs with fixed 2KiB-4KiB buffers, making them unsuitable #: for efficient IO. #: * Different UNIXes have varying presets for pipes, which may not be #: configurable. On recent Linux the default pipe buffer size is 64KiB, but #: under memory pressure may be as low as 4KiB for unprivileged processes. #: * When communication is via an intermediary process, its internal buffers #: effect the speed OS buffers will drain. For example OpenSSH uses 64KiB #: reads. #: #: An ideal :class:`Message` has a size that is a multiple of #: :data:`CHUNK_SIZE` inclusive of headers, to avoid wasting IO loop iterations #: writing small trailer chunks. CHUNK_SIZE = 131072 _tls = threading.local() if __name__ =='mitogen.core': # When loaded using import mechanism, ExternalContext.main() will not have # a chance to set the synthetic mitogen global, so just import it here. import mitogen else: # When loaded as __main__, ensure classes and functions gain a __module__ # attribute consistent with the host process, so that pickling succeeds. __name__ ='mitogen.core' class Error(Exception): """ Base for all exceptions raised by Mitogen. :param str fmt: Exception text, or format string if `args` is non-empty. :param tuple args: Format string arguments. """ def __init__(self, fmt=None, *args): if args: fmt %= args if fmt and not isinstance(fmt, UnicodeType): fmt = fmt.decode('utf-8') Exception.__init__(self, fmt) class LatchError(Error): """ Raised when an attempt is made to use a :class:`mitogen.core.Latch` that has been marked closed. """ pass class Blob(BytesType): """ A serializable bytes subclass whose content is summarized in repr() output, making it suitable for logging binary data. """ def __repr__(self): return '[blob: %d bytes]' % len(self) def __reduce__(self): return (Blob, (BytesType(self),)) class Secret(UnicodeType): """ A serializable unicode subclass whose content is masked in repr() output, making it suitable for logging passwords. """ def __repr__(self): return '[secret]' if not PY3: # TODO: what is this needed for in 2.x? def __str__(self): return UnicodeType(self) def __reduce__(self): return (Secret, (UnicodeType(self),)) class Kwargs(dict): """ A serializable dict subclass that indicates its keys should be coerced to Unicode on Python 3 and bytes on Python<2.6. Python 2 produces keyword argument dicts whose keys are bytes, requiring a helper to ensure compatibility with Python 3 where Unicode is required, whereas Python 3 produces keyword argument dicts whose keys are Unicode, requiring a helper for Python 2.4/2.5, where bytes are required. """ if PY3: def __init__(self, dct): for k, v in dct.items(): if type(k) is bytes: self[k.decode()] = v else: self[k] = v elif sys.version_info < (2, 6, 5): def __init__(self, dct): for k, v in dct.iteritems(): if type(k) is unicode: k, _ = encodings.utf_8.encode(k) self[k] = v def __repr__(self): return 'Kwargs(%s)' % (dict.__repr__(self),) def __reduce__(self): return (Kwargs, (dict(self),)) class CallError(Error): """ Serializable :class:`Error` subclass raised when :meth:`Context.call() <mitogen.parent.Context.call>` fails. A copy of the traceback from the external context is appended to the exception message. """ def __init__(self, fmt=None, *args): if not isinstance(fmt, BaseException): Error.__init__(self, fmt, *args) else: e = fmt cls = e.__class__ fmt = '%s.%s: %s' % (cls.__module__, cls.__name__, e) tb = sys.exc_info()[2] if tb: fmt += '\n' fmt += ''.join(traceback.format_tb(tb)) Error.__init__(self, fmt) def __reduce__(self): return (_unpickle_call_error, (self.args[0],)) def _unpickle_call_error(s): if not (type(s) is UnicodeType and len(s) < 10000): raise TypeError('cannot unpickle CallError: bad input') return CallError(s) class ChannelError(Error): """ Raised when a channel dies or has been closed. """ remote_msg = 'Channel closed by remote end.' local_msg = 'Channel closed by local end.' class StreamError(Error): """ Raised when a stream cannot be established. """ pass class TimeoutError(Error): """ Raised when a timeout occurs on a stream. """ pass def to_text(o): """ Coerce `o` to Unicode by decoding it from UTF-8 if it is an instance of :class:`bytes`, otherwise pass it to the :class:`str` constructor. The returned object is always a plain :class:`str`, any subclass is removed. """ if isinstance(o, BytesType): return o.decode('utf-8') return UnicodeType(o) # Documented in api.rst to work around Sphinx limitation. now = getattr(time,'monotonic', time.time) # Python 2.4 try: any except NameError: def any(it): for elem in it: if elem: return True def _partition(s, sep, find): """ (str|unicode).(partition|rpartition) for Python 2.4/2.5. """ idx = find(sep) if idx!= -1: left = s[0:idx] return left, sep, s[len(left)+len(sep):] def threading__current_thread(): try: return threading.current_thread() # Added in Python 2.6+ except AttributeError: return threading.currentThread() # Deprecated in Python 3.10+ def threading__thread_name(thread): try: return thread.name # Added in Python 2.6+ except AttributeError: return thread.getName() # Deprecated in Python 3.10+ if hasattr(UnicodeType, 'rpartition'): str_partition = UnicodeType.partition str_rpartition = UnicodeType.rpartition bytes_partition = BytesType.partition else: def str_partition(s, sep): return _partition(s, sep, s.find) or (s, u'', u'') def str_rpartition(s, sep): return _partition(s, sep, s.rfind) or (u'', u'', s) def bytes_partition(s, sep): return _partition(s, sep, s.find) or (s, '', '') def _has_parent_authority(context_id): return ( (context_id == mitogen.context_id) or (context_id in mitogen.parent_ids) ) def has_parent_authority(msg, _stream=None): """ Policy function for use with :class:`Receiver` and :meth:`Router.add_handler` that requires incoming messages to originate from a parent context, or on a :class:`Stream` whose :attr:`auth_id <Stream.auth_id>` has been set to that of a parent context or the current context. """ return _has_parent_authority(msg.auth_id) def _signals(obj, signal): return ( obj.__dict__ .setdefault('_signals', {}) .setdefault(signal, []) ) def listen(obj, name, func): """ Arrange for `func()` to be invoked when signal `name` is fired on `obj`. """ _signals(obj, name).append(func) def unlisten(obj, name, func): """ Remove `func()` from the list of functions invoked when signal `name` is fired by `obj`. :raises ValueError: `func()` was not on the list. """ _signals(obj, name).remove(func) def fire(obj, name, *args, **kwargs): """ Arrange for `func(*args, **kwargs)` to be invoked for every function registered for signal `name` on `obj`. """ for func in _signals(obj, name): func(*args, **kwargs) def takes_econtext(func): """ Decorator that marks a function or class method to automatically receive a kwarg named `econtext`, referencing the :class:`mitogen.core.ExternalContext` active in the context in which the function is being invoked in. The decorator is only meaningful when the function is invoked via :data:`CALL_FUNCTION <mitogen.core.CALL_FUNCTION>`. When the function is invoked directly, `econtext` must still be passed to it explicitly. """ func.mitogen_takes_econtext = True return func def takes_router(func): """ Decorator that marks a function or class method to automatically receive a kwarg named `router`, referencing the :class:`mitogen.core.Router` active in the context in which the function is being invoked in. The decorator is only meaningful when the function is invoked via :data:`CALL_FUNCTION <mitogen.core.CALL_FUNCTION>`. When the function is invoked directly, `router` must still be passed to it explicitly. """ func.mitogen_takes_router = True return func def is_blacklisted_import(importer, fullname): """ Return :data:`True` if `fullname` is part of a blacklisted package, or if any packages have been whitelisted and `fullname` is not part of one. NB: - If a package is on both lists, then it is treated as blacklisted. - If any package is whitelisted, then all non-whitelisted packages are treated as blacklisted. """ return ((not any(fullname.startswith(s) for s in importer.whitelist)) or (any(fullname.startswith(s) for s in importer.blacklist))) def set_cloexec(fd): """ Set the file descriptor `fd` to automatically close on :func:`os.execve`. This has no effect on file descriptors inherited across :func:`os.fork`, they must be explicitly closed through some other means, such as :func:`mitogen.fork.on_fork`. """ flags = fcntl.fcntl(fd, fcntl.F_GETFD) assert fd > 2, 'fd %r <= 2' % (fd,) fcntl.fcntl(fd, fcntl.F_SETFD, flags | fcntl.FD_CLOEXEC) def set_nonblock(fd): """ Set the file descriptor `fd` to non-blocking mode. For most underlying file types, this causes :func:`os.read` or :func:`os.write` to raise :class:`OSError` with :data:`errno.EAGAIN` rather than block the thread when the underlying kernel buffer is exhausted. """ flags = fcntl.fcntl(fd, fcntl.F_GETFL) fcntl.fcntl(fd, fcntl.F_SETFL, flags | os.O_NONBLOCK) def set_block(fd): """ Inverse of :func:`set_nonblock`, i.e. cause `fd` to block the thread when the underlying kernel buffer is exhausted. """ flags = fcntl.fcntl(fd, fcntl.F_GETFL) fcntl.fcntl(fd, fcntl.F_SETFL, flags & ~os.O_NONBLOCK) def io_op(func, *args): """ Wrap `func(*args)` that may raise :class:`select.error`, :class:`IOError`, or :class:`OSError`, trapping UNIX error codes relating to disconnection and retry events in various subsystems: * When a signal is delivered to the process on Python 2, system call retry is signalled through :data:`errno.EINTR`. The invocation is automatically restarted. * When performing IO against a TTY, disconnection of the remote end is signalled by :data:`errno.EIO`. * When performing IO against a socket, disconnection of the remote end is signalled by :data:`errno.ECONNRESET`. * When performing IO against a pipe, disconnection of the remote end is signalled by :data:`errno.EPIPE`. :returns: Tuple of `(return_value, disconnect_reason)`, where `return_value` is the return value of `func(*args)`, and `disconnected` is an exception instance when disconnection was detected, otherwise :data:`None`. """ while True: try: return func(*args), None except (select.error, OSError, IOError): e = sys.exc_info()[1] _vv and IOLOG.debug('io_op(%r) -> OSError: %s', func, e) if e.args[0] == errno.EINTR: continue if e.args[0] in (errno.EIO, errno.ECONNRESET, errno.EPIPE): return None, e raise class PidfulStreamHandler(logging.StreamHandler): """ A :class:`logging.StreamHandler` subclass used when :meth:`Router.enable_debug() <mitogen.master.Router.enable_debug>` has been called, or the `debug` parameter was specified during context construction. Verifies the process ID has not changed on each call to :meth:`emit`, reopening the associated log file when a change is detected. This ensures logging to the per-process output files happens correctly even when uncooperative third party components call :func:`os.fork`. """ #: PID that last opened the log file. open_pid = None #: Output path template. template = '/tmp/mitogen.%s.%s.log' def _reopen(self): self.acquire() try: if self.open_pid == os.getpid(): return ts = time.strftime('%Y%m%d_%H%M%S') path = self.template % (os.getpid(), ts) self.stream = open(path, 'w', 1) set_cloexec(self.stream.fileno()) self.stream.write('Parent PID: %s\n' % (os.getppid(),)) self.stream.write('Created by:\n\n%s\n' % ( ''.join(traceback.format_stack()), )) self.open_pid = os.getpid() finally: self.release() def emit(self, record): if self.open_pid!= os.getpid(): self._reopen() logging.StreamHandler.emit(self, record) def enable_debug_logging(): global _v, _vv _v = True _vv = True root = logging.getLogger() root.setLevel(logging.DEBUG) IOLOG.setLevel(logging.DEBUG) handler = PidfulStreamHandler() handler.formatter = logging.Formatter( '%(asctime)s %(levelname).1s %(name)s: %(message)s', '%H:%M:%S' ) root.handlers.insert(0, handler) _profile_hook = lambda name, func, *args: func(*args) _profile_fmt = os.environ.get( 'MITOGEN_PROFILE_FMT', '/tmp/mitogen.stats.%(pid)s.%(identity)s.%(now)s.%(ext)s', ) def _profile_hook(name, func, *args): """ Call `func(*args)` and return its result. This function is replaced by :func:`_real_profile_hook` when :func:`enable_profiling` is called. This interface is obsolete and will be replaced by a signals-based integration later on. """ return func(*args) def _real_profile_hook(name, func, *args): profiler = cProfile.Profile() profiler.enable() try: return func(*args) finally: path = _profile_fmt % { 'now': int(1e6 * now()), 'identity': name, 'pid': os.getpid(), 'ext': '%s' } profiler.dump_stats(path % ('pstats',)) profiler.create_stats() fp = open(path % ('log',), 'w') try: stats = pstats.Stats(profiler, stream=fp) stats.sort_stats('cumulative') stats.print_stats() finally: fp.close() def enable_profiling(econtext=None): global _profile_hook _profile_hook = _real_profile_hook def import_module(modname): """ Import `module` and return the attribute named `attr`. """ return __import__(modname, None, None, ['']) def pipe(): """ Create a UNIX pipe pair using :func:`os.pipe`, wrapping the returned descriptors in Python file objects in order to manage their lifetime and ensure they are closed when their last reference is discarded and they have not been closed explicitly. """ rfd, wfd = os.pipe() return ( os.fdopen(rfd, 'rb', 0), os.fdopen(wfd, 'wb', 0) ) def iter_split(buf, delim, func): """ Invoke `func(s)` for each `delim`-delimited chunk in the potentially large `buf`, avoiding intermediate lists and quadratic string operations. Return the trailing undelimited portion of `buf`, or any unprocessed portion of `buf` after `func(s)` returned :data:`False`. :returns: `(trailer, cont)`, where `cont` is :data:`False` if the last call to `func(s)` returned :data:`False`. """ dlen = len(delim) start = 0 cont = True while cont: nl = buf.find(delim, start) if nl == -1: break cont = not func(buf[start:nl]) is False start = nl + dlen return buf[start:], cont class Py24Pickler(py_pickle.Pickler): """ Exceptions were classic classes until Python 2.5. Sadly for 2.4, cPickle offers little control over how a classic instance is pickled. Therefore 2.4 uses a pure-Python pickler, so CallError can be made to look as it does on newer Pythons. This mess will go away once proper serialization exists. """ @classmethod def dumps(cls, obj, protocol): bio = BytesIO() self = cls(bio, protocol=protocol) self.dump(obj) return bio.getvalue() def save_exc_inst(self, obj): if isinstance(obj, CallError): func, args = obj.__reduce__() self.save(func) self.save(args) self.write(py_pickle.REDUCE) else: py_pickle.Pickler.save_inst(self, obj) if PY24: dispatch = py_pickle.Pickler.dispatch.copy() dispatch[py_pickle.InstanceType] = save_exc_inst if PY3: # In 3.x Unpickler is a class exposing find_class as an overridable, but it # cannot be overridden without subclassing. class _Unpickler(pickle.Unpickler): def find_class(self, module, func): return self.find_global(module, func) pickle__dumps = pickle.dumps elif PY24: # On Python 2.4, we must use a pure-Python pickler. pickle__dumps = Py24Pickler.dumps _Unpickler = pickle.Unpickler else: pickle__dumps = pickle.dumps # In 2.x Unpickler is a function exposing a writeable find_global # attribute. _Unpickler = pickle.Unpickler class Message(object): """ Messages are the fundamental unit of communication, comprising fields from the :ref:`stream-protocol` header, an optional reference to the receiving :class:`mitogen.core.Router` for ingress messages, and helper methods for deserialization and generating replies. """ #: Integer target context ID. :class:`Router` delivers messages locally #: when their :attr:`dst_id` matches :data:`mitogen.context_id`, otherwise #: they are routed up or downstream. dst_id = None #: Integer source context ID. Used as the target of replies if any are #: generated. src_id = None #: Context ID under whose authority the message is acting. See #: :ref:`source-verification`. auth_id = None #: Integer target handle in the destination context. This is one of the #: :ref:`standard-handles`, or a dynamically generated handle used to #: receive a one-time reply, such as the return value of a function call. handle = None #: Integer target handle to direct any reply to this message. Used to #: receive a one-time reply, such as the return value of a function call. #: :data:`IS_DEAD` has a special meaning when it appears in this field. reply_to = None #: Raw message data bytes. data = b('') _unpickled = object() #: The :class:`Router` responsible for routing the message. This is #: :data:`None` for locally originated messages. router = None #: The :class:`Receiver` over which the message was last received. Part of #: the :class:`mitogen.select.Select` interface. Defaults to :data:`None`. receiver = None HEADER_FMT = '>hLLLLLL' HEADER_LEN = struct.calcsize(HEADER_FMT) HEADER_MAGIC = 0x4d49 # 'MI' def __init__(self, **kwargs): """ Construct a message from from the supplied `kwargs`. :attr:`src_id` and :attr:`auth_id` are always set to :data:`mitogen.context_id`. """ self.src_id = mitogen.context_id self.auth_id = mitogen.context_id vars(self).update(kwargs) assert isinstance(self.data, BytesType), 'Message data is not Bytes' def pack(self): return ( struct.pack(self.HEADER_FMT, self.HEADER_MAGIC, self.dst_id, self.src_id, self.auth_id, self.handle, self.reply_to or 0, len(self.data)) + self.data ) def _unpickle_context(self, context_id, name): return _unpickle_context(context_id, name, router=self.router) def _unpickle_sender(self, context_id, dst_handle): return _unpickle_sender(self.router, context_id, dst_handle) def _unpickle_bytes(self, s, encoding): s, n = LATIN1_CODEC.encode(s) return s def _find_global(self, module, func): """ Return the class implementing `module_name.class_name` or raise `StreamError` if the module is not whitelisted. """ if module == __name__: if func == '_unpickle_call_error' or func == 'CallError': return _unpickle_call_error elif func == '_unpickle_sender': return self._unpickle_sender elif func == '_unpickle_context': return self._unpickle_context elif func == 'Blob': return Blob elif func == 'Secret': return Secret elif func == 'Kwargs': return Kwargs elif module == '_codecs' and func == 'encode': return self._unpickle_bytes elif module == '__builtin__' and func == 'bytes': return BytesType raise StreamError('cannot unpickle %r/%r', module, func) @property def is_dead(self): """ :data:`True` if :attr:`reply_to` is set to the magic value :data:`IS_DEAD`, indicating the sender considers the channel dead. Dead messages can be raised in a variety of circumstances, see :data:`IS_DEAD` for more information. """ return self.reply_to == IS_DEAD @classmethod def dead(cls, reason=None, **kwargs): """ Syntax helper to construct a dead message. """ kwargs['data'], _ = encodings.utf_8.encode(reason or u'') return cls(reply_to=IS_DEAD, **kwargs) @classmethod def pickled(cls, obj, **kwargs): """ Construct a pickled message, setting :attr:`data` to the serialization of `obj`, and setting remaining fields using `kwargs`. :returns: The new message. """ self = cls(**kwargs) try: self.data = pickle__dumps(obj, protocol=2) except pickle.PicklingError: e = sys.exc_info()[1] self.data = pickle__dumps(CallError(e), protocol=2) return self def reply(self, msg, router=None, **kwargs): """ Compose a reply to this message and send it using :attr:`router`, or `router` is :attr:`router` is :data:`None`. :param obj: Either a :class:`Message`, or an object to be serialized in order to construct a new message. :param router: Optional router to use if :attr:`router` is :data:`None`. :param kwargs: Optional keyword parameters overriding message fields in the reply. """ if not isinstance(msg, Message): msg = Message.pickled(msg) msg.dst_id = self.src_id msg.handle = self.reply_to vars(msg).update(kwargs) if msg.handle: (self.router or router).route(msg) else: LOG.debug('dropping reply to message with no return address: %r', msg) if PY3: UNPICKLER_KWARGS = {'encoding': 'bytes'} else: UNPICKLER_KWARGS = {} def _throw_dead(self): if len(self.data): raise ChannelError(self.data.decode('utf-8','replace')) elif self.src_id == mitogen.context_id: raise ChannelError(ChannelError.local_msg) else: raise ChannelError(ChannelError.remote_msg) def unpickle(self, throw=True, throw_dead=True): """ Unpickle :attr:`data`, optionally raising any exceptions present. :param bool throw_dead: If :data:`True`, raise exceptions, otherwise it is the caller's responsibility. :raises CallError: The serialized data contained CallError exception. :raises ChannelError: The `is_dead` field was set. """ _vv and IOLOG.debug('%r.unpickle()', self) if throw_dead and self.is_dead: self._throw_dead() obj = self._unpickled if obj is Message._unpickled: fp = BytesIO(self.data) unpickler = _Unpickler(fp, **self.UNPICKLER_KWARGS) unpickler.find_global = self._find_global try: # Must occur off the broker thread. try: obj = unpickler.load() except: LOG.error('raw pickle was: %r', self.data) raise self._unpickled = obj except (TypeError, ValueError): e = sys.exc_info()[1] raise StreamError('invalid message: %s', e) if throw: if isinstance(obj, CallError): raise obj return obj def __repr__(self): return 'Message(%r, %r, %r, %r, %r, %r..%d)' % ( self.dst_id, self.src_id, self.auth_id, self.handle, self.reply_to, (self.data or '')[:50], len(self.data) ) class Sender(object): """ Senders are used to send pickled messages to a handle in another context, it is the inverse of :class:`mitogen.core.Receiver`. Senders may be serialized, making them convenient to wire up data flows. See :meth:`mitogen.core.Receiver.to_sender` for more information. :param mitogen.core.Context context: Context to send messages to. :param int dst_handle: Destination handle to send messages to. """ def __init__(self, context, dst_handle): self.context = context self.dst_handle = dst_handle def send(self, data): """ Send `data` to the remote end. """ _vv and IOLOG.debug('%r.send(%r..)', self, repr(data)[:100]) self.context.send(Message.pickled(data, handle=self.dst_handle)) explicit_close_msg = 'Sender was explicitly closed' def close(self): """ Send a dead message to the remote, causing :meth:`ChannelError` to be raised in any waiting thread. """ _vv and IOLOG.debug('%r.close()', self) self.context.send( Message.dead( reason=self.explicit_close_msg, handle=self.dst_handle ) ) def __repr__(self): return 'Sender(%r, %r)' % (self.context, self.dst_handle) def __reduce__(self): return _unpickle_sender, (self.context.context_id, self.dst_handle) def _unpickle_sender(router, context_id, dst_handle): if not (isinstance(router, Router) and isinstance(context_id, (int, long)) and context_id >= 0 and isinstance(dst_handle, (int, long)) and dst_handle > 0): raise TypeError('cannot unpickle Sender: bad input or missing router') return Sender(Context(router, context_id), dst_handle) class Receiver(object): """ Receivers maintain a thread-safe queue of messages sent to a handle of this context from another context. :param mitogen.core.Router router: Router to register the handler on. :param int handle: If not :data:`None`, an explicit handle to register, otherwise an unused handle is chosen. :param bool persist: If :data:`False`, unregister the handler after one message is received. Single-message receivers are intended for RPC-like transactions, such as in the case of :meth:`mitogen.parent.Context.call_async`. :param mitogen.core.Context respondent: Context this receiver is receiving from. If not :data:`None`, arranges for the receiver to receive a dead message if messages can no longer be routed to the context due to disconnection, and ignores messages that did not originate from the respondent context. """ #: If not :data:`None`, a function invoked as `notify(receiver)` after a #: message has been received. The function is invoked on :class:`Broker` #: thread, therefore it must not block. Used by #: :class:`mitogen.select.Select` to efficiently implement waiting on #: multiple event sources. notify = None raise_channelerror = True def __init__(self, router, handle=None, persist=True, respondent=None, policy=None, overwrite=False): self.router = router #: The handle. self.handle = handle # Avoid __repr__ crash in add_handler() self._latch = Latch() # Must exist prior to.add_handler() self.handle = router.add_handler( fn=self._on_receive, handle=handle, policy=policy, persist=persist, respondent=respondent, overwrite=overwrite, ) def __repr__(self): return 'Receiver(%r, %r)' % (self.router, self.handle) def __enter__(self): return self def __exit__(self, _1, _2, _3): self.close() def to_sender(self): """ Return a :class:`Sender` configured to deliver messages to this receiver. As senders are serializable, this makes it convenient to pass `(context_id, handle)` pairs around:: def deliver_monthly_report(sender): for line in open('monthly_report.txt'): sender.send(line) sender.close() @mitogen.main() def main(router): remote = router.ssh(hostname='mainframe') recv = mitogen.core.Receiver(router) remote.call(deliver_monthly_report, recv.to_sender()) for msg in recv: print(msg) """ return Sender(self.router.myself(), self.handle) def _on_receive(self, msg): """ Callback registered for the handle with :class:`Router`; appends data to the internal queue. """ _vv and IOLOG.debug('%r._on_receive(%r)', self, msg) self._latch.put(msg) if self.notify: self.notify(self) closed_msg = 'the Receiver has been closed' def close(self): """ Unregister the receiver's handle from its associated router, and cause :class:`ChannelError` to be raised in any thread waiting in :meth:`get` on this receiver. """ if self.handle: self.router.del_handler(self.handle) self.handle = None self._latch.close() def size(self): """ Return the number of items currently buffered. As with :class:`Queue.Queue`, `0` may be returned even though a subsequent call to :meth:`get` will succeed, since a message may be posted at any moment between :meth:`size` and :meth:`get`. As with :class:`Queue.Queue`, `>0` may be returned even though a subsequent call to :meth:`get` will block, since another waiting thread may be woken at any moment between :meth:`size` and :meth:`get`. :raises LatchError: The underlying latch has already been marked closed. """ return self._latch.size() def empty(self): """ Return `size() == 0`. .. deprecated:: 0.2.8 Use :meth:`size` instead. :raises LatchError: The latch has already been marked closed. """ return self._latch.empty() def get(self, timeout=None, block=True, throw_dead=True): """ Sleep waiting for a message to arrive on this receiver. :param float timeout: If not :data:`None`, specifies a timeout in seconds. :raises mitogen.core.ChannelError: The remote end indicated the channel should be closed, communication with it was lost, or :meth:`close` was called in the local process. :raises mitogen.core.TimeoutError: Timeout was reached. :returns: :class:`Message` that was received. """ _vv and IOLOG.debug('%r.get(timeout=%r, block=%r)', self, timeout, block) try: msg = self._latch.get(timeout=timeout, block=block) except LatchError: raise ChannelError(self.closed_msg) if msg.is_dead and throw_dead: msg._throw_dead() return msg def __iter__(self): """ Yield consecutive :class:`Message` instances delivered to this receiver until :class:`ChannelError` is raised. """ while True: try: msg = self.get() except ChannelError: return yield msg class Channel(Sender, Receiver): """ A channel inherits from :class:`mitogen.core.Sender` and `mitogen.core.Receiver` to provide bidirectional functionality. .. deprecated:: 0.2.0 This class is incomplete and obsolete, it will be removed in Mitogen 0.3. Channels were an early attempt at syntax sugar. It is always easier to pass around unidirectional pairs of senders/receivers, even though the syntax is baroque: .. literalinclude::../examples/ping_pong.py Since all handles aren't known until after both ends are constructed, for both ends to communicate through a channel, it is necessary for one end to retrieve the handle allocated to the other and reconfigure its own channel to match. Currently this is a manual task. """ def __init__(self, router, context, dst_handle, handle=None): Sender.__init__(self, context, dst_handle) Receiver.__init__(self, router, handle) def close(self): Receiver.close(self) Sender.close(self) def __repr__(self): return 'Channel(%s, %s)' % ( Sender.__repr__(self), Receiver.__repr__(self) ) class Importer(object): """ Import protocol implementation that fetches modules from the parent process. :param context: Context to communicate via. """ # The Mitogen package is handled specially, since the child context must # construct it manually during startup. MITOGEN_PKG_CONTENT = [ 'buildah', 'compat', 'debug', 'doas', 'docker', 'kubectl', 'fakessh', 'fork', 'jail', 'lxc', 'lxd', 'master', 'minify', 'os_fork', 'parent', 'podman', 'select', 'service', 'setns', 'ssh', 'su', 'sudo', 'utils', ] ALWAYS_BLACKLIST = [ # 2.x generates needless imports for 'builtins', while 3.x does the # same for '__builtin__'. The correct one is built-in, the other always # a negative round-trip. 'builtins', '__builtin__', # On some Python releases (e.g. 3.8, 3.9) the subprocess module tries # to import of this Windows-only builtin module. 'msvcrt', # Python 2.x module that was renamed to _thread in 3.x. # This entry avoids a roundtrip on 2.x -> 3.x. 'thread', # org.python.core imported by copy, pickle, xml.sax; breaks Jython, but # very unlikely to trigger a bug report. 'org', ] if PY3: ALWAYS_BLACKLIST += ['cStringIO'] def __init__(self, router, context, core_src, whitelist=(), blacklist=()): self._log = logging.getLogger('mitogen.importer') self._context = context self._present = {'mitogen': self.MITOGEN_PKG_CONTENT} self._lock = threading.Lock() self.whitelist = list(whitelist) or [''] self.blacklist = list(blacklist) + self.ALWAYS_BLACKLIST # Preserve copies of the original server-supplied whitelist/blacklist # for later use by children. self.master_whitelist = self.whitelist[:] self.master_blacklist = self.blacklist[:] # Presence of an entry in this map indicates in-flight GET_MODULE. self._callbacks = {} self._cache = {} if core_src: self._update_linecache('x/mitogen/core.py', core_src) self._cache['mitogen.core'] = ( 'mitogen.core', None, 'x/mitogen/core.py', zlib.compress(core_src, 9), [], ) self._install_handler(router) def _update_linecache(self, path, data): """ The Python 2.4 linecache module, used to fetch source code for tracebacks and :func:`inspect.getsource`, does not support PEP-302, meaning it needs extra help to for Mitogen-loaded modules. Directly populate its cache if a loaded module belongs to the Mitogen package. """ if PY24 and'mitogen' in path: linecache.cache[path] = ( len(data), 0.0, [line+'\n' for line in data.splitlines()], path, ) def _install_handler(self, router): router.add_handler( fn=self._on_load_module, handle=LOAD_MODULE, policy=has_parent_authority, ) def __repr__(self): return 'Importer' def builtin_find_module(self, fullname): # imp.find_module() will always succeed for __main__, because it is a # built-in module. That means it exists on a special linked list deep # within the bowels of the interpreter. We must special case it. if fullname == '__main__': raise ModuleNotFoundError() parent, _, modname = str_rpartition(fullname, '.') if parent: path = sys.modules[parent].__path__ else: path = None fp, pathname, description = imp.find_module(modname, path) if fp: fp.close() def find_module(self, fullname, path=None): """ Return a loader (ourself) or None, for the module with fullname. Implements importlib.abc.MetaPathFinder.find_module(). Deprecrated in Python 3.4+, replaced by find_spec(). Raises ImportWarning in Python 3.10+. fullname A (fully qualified?) module name, e.g. "os.path". path __path__ of parent packge. None for a top level module. """ if hasattr(_tls, 'running'): return None _tls.running = True try: #_v and self._log.debug('Python requested %r', fullname) fullname = to_text(fullname) pkgname, dot, _ = str_rpartition(fullname, '.') pkg = sys.modules.get(pkgname) if pkgname and getattr(pkg, '__loader__', None) is not self: self._log.debug('%s is submodule of a locally loaded package', fullname) return None suffix = fullname[len(pkgname+dot):] if pkgname and suffix not in self._present.get(pkgname, ()): self._log.debug('%s has no submodule %s', pkgname, suffix) return None # #114: explicitly whitelisted prefixes override any # system-installed package. if self.whitelist!= ['']: if any(fullname.startswith(s) for s in self.whitelist): return self try: self.builtin_find_module(fullname) _vv and self._log.debug('%r is available locally', fullname) except ImportError: _vv and self._log.debug('we will try to load %r', fullname) return self finally: del _tls.running blacklisted_msg = ( '%r is present in the Mitogen importer blacklist, therefore this ' 'context will not attempt to request it from the master, as the ' 'request will always be refused.' ) pkg_resources_msg = ( 'pkg_resources is prohibited from importing __main__, as it causes ' 'problems in applications whose main module is not designed to be ' 're-imported by children.' ) absent_msg = ( 'The Mitogen master process was unable to serve %r. It may be a ' 'native Python extension, or it may be missing entirely. Check the ' 'importer debug logs on the master for more information.' ) def _refuse_imports(self, fullname): if is_blacklisted_import(self, fullname): raise ModuleNotFoundError(self.blacklisted_msg % (fullname,)) f = sys._getframe(2) requestee = f.f_globals['__name__'] if fullname == '__main__' and requestee == 'pkg_resources': # Anything that imports pkg_resources will eventually cause # pkg_resources to try and scan __main__ for its __requires__ # attribute (pkg_resources/__init__.py::_build_master()). This # breaks any app that is not expecting its __main__ to suddenly be # sucked over a network and injected into a remote process, like # py.test. raise ModuleNotFoundError(self.pkg_resources_msg) if fullname == 'pbr': # It claims to use pkg_resources to read version information, which # would result in PEP-302 being used, but it actually does direct # filesystem access. So instead smodge the environment to override # any version that was defined. This will probably break something # later. os.environ['PBR_VERSION'] = '0.0.0' def _on_load_module(self, msg): if msg.is_dead: return tup = msg.unpickle() fullname = tup[0] _v and self._log.debug('received %s', fullname) self._lock.acquire() try: self._cache[fullname] = tup if tup[2] is not None and PY24: self._update_linecache( path='master:' + tup[2], data=zlib.decompress(tup[3]) ) callbacks = self._callbacks.pop(fullname, []) finally: self._lock.release() for callback in callbacks: callback() def _request_module(self, fullname, callback): self._lock.acquire() try: present = fullname in self._cache if not present: funcs = self._callbacks.get(fullname) if funcs is not None: _v and self._log.debug('existing request for %s in flight', fullname) funcs.append(callback) else: _v and self._log.debug('sending new %s request to parent', fullname) self._callbacks[fullname] = [callback] self._context.send( Message(data=b(fullname), handle=GET_MODULE) ) finally: self._lock.release() if present: callback() def load_module(self, fullname): """ Return the loaded module specified by fullname. Implements importlib.abc.Loader.load_module(). Deprecated in Python 3.4+, replaced by create_module() & exec_module(). """ fullname = to_text(fullname) _v and self._log.debug('requesting %s', fullname) self._refuse_imports(fullname) event = threading.Event() self._request_module(fullname, event.set) event.wait() ret = self._cache[fullname] if ret[2] is None: raise ModuleNotFoundError(self.absent_msg % (fullname,)) pkg_present = ret[1] mod = sys.modules.setdefault(fullname, imp.new_module(fullname)) mod.__file__ = self.get_filename(fullname) mod.__loader__ = self if pkg_present is not None: # it's a package. mod.__path__ = [] mod.__package__ = fullname self._present[fullname] = pkg_present else: mod.__package__ = str_rpartition(fullname, '.')[0] or None if mod.__package__ and not PY3: # 2.x requires __package__ to be exactly a string. mod.__package__, _ = encodings.utf_8.encode(mod.__package__) source = self.get_source(fullname) try: code = compile(source, mod.__file__, 'exec', 0, 1) except SyntaxError: LOG.exception('while importing %r', fullname) raise if PY3: exec(code, vars(mod)) else: exec('exec code in vars(mod)') # #590: if a module replaces itself in sys.modules during import, below # is necessary. This matches PyImport_ExecCodeModuleEx() return sys.modules.get(fullname, mod) def get_filename(self, fullname): if fullname in self._cache: path = self._cache[fullname][2] if path is None: # If find_loader() returns self but a subsequent master RPC # reveals the module can't be loaded, and so load_module() # throws ImportError, on Python 3.x it is still possible for # the loader to be called to fetch metadata. raise ModuleNotFoundError(self.absent_msg % (fullname,)) return u'master:' + self._cache[fullname][2] def get_source(self, fullname): if fullname in self._cache: compressed = self._cache[fullname][3] if compressed is None: raise ModuleNotFoundError(self.absent_msg % (fullname,)) source = zlib.decompress(self._cache[fullname][3]) if PY3: return to_text(source) return source class LogHandler(logging.Handler): """ A :class:`logging.Handler` subclass that arranges for :data:`FORWARD_LOG` messages to be sent to a parent context in response to logging messages generated by the current context. This is installed by default in child contexts during bootstrap, so that :mod:`logging` events can be viewed and managed centrally in the master process. The handler is initially *corked* after construction, such that it buffers messages until :meth:`uncork` is called. This allows logging to be installed prior to communication with the target being available, and avoids any possible race where early log messages might be dropped. :param mitogen.core.Context context: The context to send log messages towards. At present this is always the master process. """ def __init__(self, context): logging.Handler.__init__(self) self.context = context self.local = threading.local() self._buffer = [] # Private synchronization is needed while corked, to ensure no # concurrent call to _send() exists during uncork(). self._buffer_lock = threading.Lock() def uncork(self): """ #305: during startup :class:`LogHandler` may be installed before it is possible to route messages, therefore messages are buffered until :meth:`uncork` is called by :class:`ExternalContext`. """ self._buffer_lock.acquire() try: self._send = self.context.send for msg in self._buffer: self._send(msg) self._buffer = None finally: self._buffer_lock.release() def _send(self, msg): self._buffer_lock.acquire() try: if self._buffer is None: # uncork() may run concurrent to _send() self._send(msg) else: self._buffer.append(msg) finally: self._buffer_lock.release() def emit(self, rec): """ Send a :data:`FORWARD_LOG` message towards the target context. """ if rec.name =='mitogen.io' or \ getattr(self.local, 'in_emit', False): return self.local.in_emit = True try: msg = self.format(rec) encoded = '%s\x00%s\x00%s' % (rec.name, rec.levelno, msg) if isinstance(encoded, UnicodeType): # Logging package emits both :( encoded = encoded.encode('utf-8') self._send(Message(data=encoded, handle=FORWARD_LOG)) finally: self.local.in_emit = False class Stream(object): """ A :class:`Stream` is one readable and optionally one writeable file descriptor (represented by :class:`Side`) aggregated alongside an associated :class:`Protocol` that knows how to respond to IO readiness events for those descriptors. Streams are registered with :class:`Broker`, and callbacks are invoked on the broker thread in response to IO activity. When registered using :meth:`Broker.start_receive` or :meth:`Broker._start_transmit`, the broker may call any of :meth:`on_receive`, :meth:`on_transmit`, :meth:`on_shutdown` or :meth:`on_disconnect`. It is expected that the :class:`Protocol` associated with a stream will change over its life. For example during connection setup, the initial protocol may be :class:`mitogen.parent.BootstrapProtocol` that knows how to enter SSH and sudo passwords and transmit the :mod:`mitogen.core` source to the target, before handing off to :class:`MitogenProtocol` when the target process is initialized. Streams connecting to children are in turn aggregated by :class:`mitogen.parent.Connection`, which contains additional logic for managing any child process, and a reference to any separate ``stderr`` :class:`Stream` connected to that process. """ #: A :class:`Side` representing the stream's receive file descriptor. receive_side = None #: A :class:`Side` representing the stream's transmit file descriptor. transmit_side = None #: A :class:`Protocol` representing the protocol active on the stream. protocol = None #: In parents, the :class:`mitogen.parent.Connection` instance. conn = None #: The stream name. This is used in the :meth:`__repr__` output in any log #: messages, it may be any descriptive string. name = u'default' def set_protocol(self, protocol): """ Bind a :class:`Protocol` to this stream, by updating :attr:`Protocol.stream` to refer to this stream, and updating this stream's :attr:`Stream.protocol` to the refer to the protocol. Any prior protocol's :attr:`Protocol.stream` is set to :data:`None`. """ if self.protocol: self.protocol.stream = None self.protocol = protocol self.protocol.stream = self def accept(self, rfp, wfp): """ Attach a pair of file objects to :attr:`receive_side` and :attr:`transmit_side`, after wrapping them in :class:`Side` instances. :class:`Side` will call :func:`set_nonblock` and :func:`set_cloexec` on the underlying file descriptors during construction. The same file object may be used for both sides. The default :meth:`on_disconnect` is handles the possibility that only one descriptor may need to be closed. :param file rfp: The file object to receive from. :param file wfp: The file object to transmit to. """ self.receive_side = Side(self, rfp) self.transmit_side = Side(self, wfp) def __repr__(self): return "<Stream %s #%04x>" % (self.name, id(self) & 0xffff,) def on_receive(self, broker): """ Invoked by :class:`Broker` when the stream's :attr:`receive_side` has been marked readable using :meth:`Broker.start_receive` and the broker has detected the associated file descriptor is ready for reading. Subclasses must implement this if they are registered using :meth:`Broker.start_receive`, and the method must invoke :meth:`on_disconnect` if reading produces an empty string. The default implementation reads :attr:`Protocol.read_size` bytes and passes the resulting bytestring to :meth:`Protocol.on_receive`. If the bytestring is 0 bytes, invokes :meth:`on_disconnect` instead. """ buf = self.receive_side.read(self.protocol.read_size) if not buf: LOG.debug('%r: empty read, disconnecting', self.receive_side) return self.on_disconnect(broker) self.protocol.on_receive(broker, buf) def on_transmit(self, broker): """ Invoked by :class:`Broker` when the stream's :attr:`transmit_side` has been marked writeable using :meth:`Broker._start_transmit` and the broker has detected the associated file descriptor is ready for writing. Subclasses must implement they are ever registerd with :meth:`Broker._start_transmit`. The default implementation invokes :meth:`Protocol.on_transmit`. """ self.protocol.on_transmit(broker) def on_shutdown(self, broker): """ Invoked by :meth:`Broker.shutdown` to allow the stream time to gracefully shutdown. The default implementation emits a ``shutdown`` signal before invoking :meth:`on_disconnect`. """ fire(self,'shutdown') self.protocol.on_shutdown(broker) def on_disconnect(self, broker): """ Invoked by :class:`Broker` to force disconnect the stream during shutdown, invoked by the default :meth:`on_shutdown` implementation, and usually invoked by any subclass :meth:`on_receive` implementation in response to a 0-byte read. The base implementation fires a ``disconnect`` event, then closes :attr:`receive_side` and :attr:`transmit_side` after unregistering the stream from the broker. """ fire(self, 'disconnect') self.protocol.on_disconnect(broker) class Protocol(object): """ Implement the program behaviour associated with activity on a :class:`Stream`. The protocol in use may vary over a stream's life, for example to allow :class:`mitogen.parent.BootstrapProtocol` to initialize the connected child before handing it off to :class:`MitogenProtocol`. A stream's active protocol is tracked in the :attr:`Stream.protocol` attribute, and modified via :meth:`Stream.set_protocol`. Protocols do not handle IO, they are entirely reliant on the interface provided by :class:`Stream` and :class:`Side`, allowing the underlying IO implementation to be replaced without modifying behavioural logic. """ stream_class = Stream #: The :class:`Stream` this protocol is currently bound to, or #: :data:`None`. stream = None #: The size of the read buffer used by :class:`Stream` when this is the #: active protocol for the stream. read_size = CHUNK_SIZE @classmethod def build_stream(cls, *args, **kwargs): stream = cls.stream_class() stream.set_protocol(cls(*args, **kwargs)) return stream def __repr__(self): return '%s(%s)' % ( self.__class__.__name__, self.stream and self.stream.name, ) def on_shutdown(self, broker): _v and LOG.debug('%r: shutting down', self) self.stream.on_disconnect(broker) def on_disconnect(self, broker): # Normally both sides an FD, so it is important that tranmit_side is # deregistered from Poller before closing the receive side, as pollers # like epoll and kqueue unregister all events on FD close, causing # subsequent attempt to unregister the transmit side to fail. LOG.debug('%r: disconnecting', self) broker.stop_receive(self.stream) if self.stream.transmit_side: broker._stop_transmit(self.stream) self.stream.receive_side.close() if self.stream.transmit_side: self.stream.transmit_side.close() class DelimitedProtocol(Protocol): """ Provide a :meth:`Protocol.on_receive` implementation for protocols that are delimited by a fixed string, like text based protocols. Each message is passed to :meth:`on_line_received` as it arrives, with incomplete messages passed to :meth:`on_partial_line_received`. When emulating user input it is often necessary to respond to incomplete lines, such as when a "Password: " prompt is sent. :meth:`on_partial_line_received` may be called repeatedly with an increasingly complete message. When a complete message is finally received, :meth:`on_line_received` will be called once for it before the buffer is discarded. If :func:`on_line_received` returns :data:`False`, remaining data is passed unprocessed to the stream's current protocol's :meth:`on_receive`. This allows switching from line-oriented to binary while the input buffer contains both kinds of data. """ #: The delimiter. Defaults to newline. delimiter = b('\n') _trailer = b('') def on_receive(self, broker, buf): _vv and IOLOG.debug('%r.on_receive()', self) stream = self.stream self._trailer, cont = mitogen.core.iter_split( buf=self._trailer + buf, delim=self.delimiter, func=self.on_line_received, ) if self._trailer: if cont: self.on_partial_line_received(self._trailer) else: assert stream.protocol is not self, \ 'stream protocol is no longer %r' % (self,) stream.protocol.on_receive(broker, self._trailer) def on_line_received(self, line): """ Receive a line from the stream. :param bytes line: The encoded line, excluding the delimiter. :returns: :data:`False` to indicate this invocation modified the stream's active protocol, and any remaining buffered data should be passed to the new protocol's :meth:`on_receive` method. Any other return value is ignored. """ pass def on_partial_line_received(self, line): """ Receive a trailing unterminated partial line from the stream. :param bytes line: The encoded partial line. """ pass class BufferedWriter(object): """ Implement buffered output while avoiding quadratic string operations. This is currently constructed by each protocol, in future it may become fixed for each stream instead. """ def __init__(self, broker, protocol): self._broker = broker self._protocol = protocol self._buf = collections.deque() self._len = 0 def write(self, s): """ Transmit `s` immediately, falling back to enqueuing it and marking the stream writeable if no OS buffer space is available. """ if not self._len: # Modifying epoll/Kqueue state is expensive, as are needless broker # loops. Rather than wait for writeability, just write immediately, # and fall back to the broker loop on error or full buffer. try: n = self._protocol.stream.transmit_side.write(s) if n: if n == len(s): return s = s[n:] except OSError: pass self._broker._start_transmit(self._protocol.stream) self._buf.append(s) self._len += len(s) def on_transmit(self, broker): """ Respond to stream writeability by retrying previously buffered :meth:`write` calls. """ if self._buf: buf = self._buf.popleft() written = self._protocol.stream.transmit_side.write(buf) if not written: _v and LOG.debug('disconnected during write to %r', self) self._protocol.stream.on_disconnect(broker) return elif written!= len(buf): self._buf.appendleft(BufferType(buf, written)) _vv and IOLOG.debug('transmitted %d bytes to %r', written, self) self._len -= written if not self._buf: broker._stop_transmit(self._protocol.stream) class Side(object): """ Represent one side of a :class:`Stream`. This allows unidirectional (e.g. pipe) and bidirectional (e.g. socket) streams to operate identically. Sides are also responsible for tracking the open/closed state of the underlying FD, preventing erroneous duplicate calls to :func:`os.close` due to duplicate :meth:`Stream.on_disconnect` calls, which would otherwise risk silently succeeding by closing an unrelated descriptor. For this reason, it is crucial only one file object exists per unique descriptor. :param mitogen.core.Stream stream: The stream this side is associated with. :param object fp: The file or socket object managing the underlying file descriptor. Any object may be used that supports `fileno()` and `close()` methods. :param bool cloexec: If :data:`True`, the descriptor has its :data:`fcntl.FD_CLOEXEC` flag enabled using :func:`fcntl.fcntl`. :param bool keep_alive: If :data:`True`, the continued existence of this side will extend the shutdown grace period until it has been unregistered from the broker. :param bool blocking: If :data:`False`, the descriptor has its :data:`os.O_NONBLOCK` flag enabled using :func:`fcntl.fcntl`. """ _fork_refs = weakref.WeakValueDictionary() closed = False def __init__(self, stream, fp, cloexec=True, keep_alive=True, blocking=False): #: The :class:`Stream` for which this is a read or write side. self.stream = stream # File or socket object responsible for the lifetime of its underlying # file descriptor. self.fp = fp #: Integer file descriptor to perform IO on, or :data:`None` if #: :meth:`close` has been called. This is saved separately from the #: file object, since :meth:`file.fileno` cannot be called on it after #: it has been closed. self.fd = fp.fileno() #: If :data:`True`, causes presence of this side in #: :class:`Broker`'s active reader set to defer shutdown until the #: side is disconnected. self.keep_alive = keep_alive self._fork_refs[id(self)] = self if cloexec: set_cloexec(self.fd) if not blocking: set_nonblock(self.fd) def __repr__(self): return '<Side of %s fd %s>' % ( self.stream.name or repr(self.stream), self.fd ) @classmethod def _on_fork(cls): while cls._fork_refs: _, side = cls._fork_refs.popitem() _vv and IOLOG.debug('Side._on_fork() closing %r', side) side.close() def close(self): """ Call :meth:`file.close` on :attr:`fp` if it is not :data:`None`, then set it to :data:`None`. """ _vv and IOLOG.debug('%r.close()', self) if not self.closed: self.closed = True self.fp.close() def read(self, n=CHUNK_SIZE): """ Read up to `n` bytes from the file descriptor, wrapping the underlying :func:`os.read` call with :func:`io_op` to trap common disconnection conditions. :meth:`read` always behaves as if it is reading from a regular UNIX file; socket, pipe, and TTY disconnection errors are masked and result in a 0-sized read like a regular file. :returns: Bytes read, or the empty string to indicate disconnection was detected. """ if self.closed: # Refuse to touch the handle after closed, it may have been reused # by another thread. TODO: synchronize read()/write()/close(). return b('') s, disconnected = io_op(os.read, self.fd, n) if disconnected: LOG.debug('%r: disconnected during read: %s', self, disconnected) return b('') return s def write(self, s): """ Write as much of the bytes from `s` as possible to the file descriptor, wrapping the underlying :func:`os.write` call with :func:`io_op` to trap common disconnection conditions. :returns: Number of bytes written, or :data:`None` if disconnection was detected. """ if self.closed: # Don't touch the handle after close, it may be reused elsewhere. return None written, disconnected = io_op(os.write, self.fd, s) if disconnected: LOG.debug('%r: disconnected during write: %s', self, disconnected) return None return written class MitogenProtocol(Protocol): """ :class:`Protocol` implementing mitogen's :ref:`stream protocol <stream-protocol>`. """ #: If not :data:`False`, indicates the stream has :attr:`auth_id` set and #: its value is the same as :data:`mitogen.context_id` or appears in #: :data:`mitogen.parent_ids`. is_privileged = False #: Invoked as `on_message(stream, msg)` each message received from the #: peer. on_message = None def __init__(self, router, remote_id, auth_id=None, local_id=None, parent_ids=None): self._router = router self.remote_id = remote_id #: If not :data:`None`, :class:`Router` stamps this into #: :attr:`Message.auth_id` of every message received on this stream. self.auth_id = auth_id if parent_ids is None: parent_ids = mitogen.parent_ids if local_id is None: local_id = mitogen.context_id self.is_privileged = ( (remote_id in parent_ids) or auth_id in ([local_id] + parent_ids) ) self.sent_modules = set(['mitogen','mitogen.core']) self._input_buf = collections.deque() self._input_buf_len = 0 self._writer = BufferedWriter(router.broker, self) #: Routing records the dst_id of every message arriving from this #: stream. Any arriving DEL_ROUTE is rebroadcast for any such ID. self.egress_ids = set() def on_receive(self, broker, buf): """ Handle the next complete message on the stream. Raise :class:`StreamError` on failure. """ _vv and IOLOG.debug('%r.on_receive()', self) if self._input_buf and self._input_buf_len < 128: self._input_buf[0] += buf else: self._input_buf.append(buf) self._input_buf_len += len(buf) while self._receive_one(broker): pass corrupt_msg = ( '%s: Corruption detected: frame signature incorrect. This likely means' 'some external process is interfering with the connection. Received:' '\n\n' '%r' ) def _receive_one(self, broker): if self._input_buf_len < Message.HEADER_LEN: return False msg = Message() msg.router = self._router (magic, msg.dst_id, msg.src_id, msg.auth_id, msg.handle, msg.reply_to, msg_len) = struct.unpack( Message.HEADER_FMT, self._input_buf[0][:Message.HEADER_LEN], ) if magic!= Message.HEADER_MAGIC: LOG.error(self.corrupt_msg, self.stream.name, self._input_buf[0][:2048]) self.stream.on_disconnect(broker) return False if msg_len > self._router.max_message_size: LOG.error('%r: Maximum message size exceeded (got %d, max %d)', self, msg_len, self._router.max_message_size) self.stream.on_disconnect(broker) return False total_len = msg_len + Message.HEADER_LEN if self._input_buf_len < total_len: _vv and IOLOG.debug( '%r: Input too short (want %d, got %d)', self, msg_len, self._input_buf_len - Message.HEADER_LEN ) return False start = Message.HEADER_LEN prev_start = start remain = total_len bits = [] while remain: buf = self._input_buf.popleft() bit = buf[start:remain] bits.append(bit) remain -= len(bit) + start prev_start = start start = 0 msg.data = b('').join(bits) self._input_buf.appendleft(buf[prev_start+len(bit):]) self._input_buf_len -= total_len self._router._async_route(msg, self.stream) return True def pending_bytes(self): """ Return the number of bytes queued for transmission on this stream. This can be used to limit the amount of data buffered in RAM by an otherwise unlimited consumer. For an accurate result, this method should be called from the Broker thread, for example by using :meth:`Broker.defer_sync`. """ return self._writer._len def on_transmit(self, broker): """ Transmit buffered messages. """ _vv and IOLOG.debug('%r.on_transmit()', self) self._writer.on_transmit(broker) def _send(self, msg): _vv and IOLOG.debug('%r._send(%r)', self, msg) self._writer.write(msg.pack()) def send(self, msg): """ Send `data` to `handle`, and tell the broker we have output. May be called from any thread. """ self._router.broker.defer(self._send, msg) def on_shutdown(self, broker): """ Disable :class:`Protocol` immediate disconnect behaviour. """ _v and LOG.debug('%r: shutting down', self) class Context(object): """ Represent a remote context regardless of the underlying connection method. Context objects are simple facades that emit messages through an associated router, and have :ref:`signals` raised against them in response to various events relating to the context. **Note:** This is the somewhat limited core version, used by child contexts. The master subclass is documented below this one. Contexts maintain no internal state and are thread-safe. Prefer :meth:`Router.context_by_id` over constructing context objects explicitly, as that method is deduplicating, and returns the only context instance :ref:`signals` will be raised on. :param mitogen.core.Router router: Router to emit messages through. :param int context_id: Context ID. :param str name: Context name. """ name = None remote_name = None def __init__(self, router, context_id, name=None): self.router = router self.context_id = context_id if name: self.name = to_text(name) def __reduce__(self): return _unpickle_context, (self.context_id, self.name) def on_disconnect(self): _v and LOG.debug('%r: disconnecting', self) fire(self, 'disconnect') def send_async(self, msg, persist=False): """ Arrange for `msg` to be delivered to this context, with replies directed to a newly constructed receiver. :attr:`dst_id <Message.dst_id>` is set to the target context ID, and :attr:`reply_to <Message.reply_to>` is set to the newly constructed receiver's handle. :param bool persist: If :data:`False`, the handler will be unregistered after a single message has been received. :param mitogen.core.Message msg: The message. :returns: :class:`Receiver` configured to receive any replies sent to the message's `reply_to` handle. """ receiver = Receiver(self.router, persist=persist, respondent=self) msg.dst_id = self.context_id msg.reply_to = receiver.handle _v and LOG.debug('sending message to %r: %r', self, msg) self.send(msg) return receiver def call_service_async(self, service_name, method_name, **kwargs): if isinstance(service_name, BytesType): service_name = service_name.encode('utf-8') elif not isinstance(service_name, UnicodeType): service_name = service_name.name() # Service.name() _v and LOG.debug('calling service %s.%s of %r, args: %r', service_name, method_name, self, kwargs) tup = (service_name, to_text(method_name), Kwargs(kwargs)) msg = Message.pickled(tup, handle=CALL_SERVICE) return self.send_async(msg) def send(self, msg): """ Arrange for `msg` to be delivered to this context. :attr:`dst_id <Message.dst_id>` is set to the target context ID. :param Message msg: Message. """ msg.dst_id = self.context_id self.router.route(msg) def call_service(self, service_name, method_name, **kwargs): recv = self.call_service_async(service_name, method_name, **kwargs) return recv.get().unpickle() def send_await(self, msg, deadline=None): """ Like :meth:`send_async`, but expect a single reply (`persist=False`) delivered within `deadline` seconds. :param mitogen.core.Message msg: The message. :param float deadline: If not :data:`None`, seconds before timing out waiting for a reply. :returns: Deserialized reply. :raises TimeoutError: No message was received and `deadline` passed. """ receiver = self.send_async(msg) response = receiver.get(deadline) data = response.unpickle() _vv and IOLOG.debug('%r._send_await() -> %r', self, data) return data def __repr__(self): return 'Context(%s, %r)' % (self.context_id, self.name) def _unpickle_context(context_id, name, router=None): if not (isinstance(context_id, (int, long)) and context_id >= 0 and ( (name is None) or (isinstance(name, UnicodeType) and len(name) < 100)) ): raise TypeError('cannot unpickle Context: bad input') if isinstance(router, Router): return router.context_by_id(context_id, name=name) return Context(None, context_id, name) # For plain Jane pickle. class Poller(object): """ A poller manages OS file descriptors the user is waiting to become available for IO. The :meth:`poll` method blocks the calling thread until one or more become ready. The default implementation is based on :func:`select.poll`. Each descriptor has an associated `data` element, which is unique for each readiness type, and defaults to being the same as the file descriptor. The :meth:`poll` method yields the data associated with a descriptor, rather than the descriptor itself, allowing concise loops like:: p = Poller() p.start_receive(conn.fd, data=conn.on_read) p.start_transmit(conn.fd, data=conn.on_write) for callback in p.poll(): callback() # invoke appropriate bound instance method Pollers may be modified while :meth:`poll` is yielding results. Removals are processed immediately, causing pending events for the descriptor to be discarded. The :meth:`close` method must be called when a poller is discarded to avoid a resource leak. Pollers may only be used by one thread at a time. """ SUPPORTED = True # This changed from select() to poll() in Mitogen 0.2.4. Since poll() has # no upper FD limit, it is suitable for use with Latch, which must handle # FDs larger than select's limit during many-host runs. We want this # because poll() requires no setup and teardown: just a single system call, # which is important because Latch.get() creates a Poller on each # invocation. In a microbenchmark, poll() vs. epoll_ctl() is 30% faster in # this scenario. If select() must return in future, it is important # Latch.poller_class is set from parent.py to point to the industrial # strength poller for the OS, otherwise Latch will fail randomly. #: Increments on every poll(). Used to version _rfds and _wfds. _generation = 1 def __init__(self): self._rfds = {} self._wfds = {} def __repr__(self): return '%s' % (type(self).__name__,) def _update(self, fd): """ Required by PollPoller subclass. """ pass @property def readers(self): """ Return a list of `(fd, data)` tuples for every FD registered for receive readiness. """ return list((fd, data) for fd, (data, gen) in self._rfds.items()) @property def writers(self): """ Return a list of `(fd, data)` tuples for every FD registered for transmit readiness. """ return list((fd, data) for fd, (data, gen) in self._wfds.items()) def close(self): """ Close any underlying OS resource used by the poller. """ pass def start_receive(self, fd, data=None): """ Cause :meth:`poll` to yield `data` when `fd` is readable. """ self._rfds[fd] = (data or fd, self._generation) self._update(fd) def stop_receive(self, fd): """ Stop yielding readability events for `fd`. Redundant calls to :meth:`stop_receive` are silently ignored, this may change in future. """ self._rfds.pop(fd, None) self._update(fd) def start_transmit(self, fd, data=None): """ Cause :meth:`poll` to yield `data` when `fd` is writeable. """ self._wfds[fd] = (data or fd, self._generation) self._update(fd) def stop_transmit(self, fd): """ Stop yielding writeability events for `fd`. Redundant calls to :meth:`stop_transmit` are silently ignored, this may change in future. """ self._wfds.pop(fd, None) self._update(fd) def _poll(self, timeout): (rfds, wfds, _), _ = io_op(select.select, self._rfds, self._wfds, (), timeout ) for fd in rfds: _vv and IOLOG.debug('%r: POLLIN for %r', self, fd) data, gen = self._rfds.get(fd, (None, None)) if gen and gen < self._generation: yield data for fd in wfds: _vv and IOLOG.debug('%r: POLLOUT for %r', self, fd) data, gen = self._wfds.get(fd, (None, None)) if gen and gen < self._generation: yield data def poll(self, timeout=None): """ Block the calling thread until one or more FDs are ready for IO. :param float timeout: If not :data:`None`, seconds to wait without an event before returning an empty iterable. :returns: Iterable of `data` elements associated with ready FDs. """ _vv and IOLOG.debug('%r.poll(%r)', self, timeout) self._generation += 1 return self._poll(timeout) class Latch(object): """ A latch is a :class:`Queue.Queue`-like object that supports mutation and waiting from multiple threads, however unlike :class:`Queue.Queue`, waiting threads always remain interruptible, so CTRL+C always succeeds, and waits where a timeout is set experience no wake up latency. These properties are not possible in combination using the built-in threading primitives available in Python 2.x. Latches implement queues using the UNIX self-pipe trick, and a per-thread :func:`socket.socketpair` that is lazily created the first time any latch attempts to sleep on a thread, and dynamically associated with the waiting Latch only for duration of the wait. See :ref:`waking-sleeping-threads` for further discussion. """ #: The :class:`Poller` implementation to use for waiting. Since the poller #: will be very short-lived, we prefer :class:`mitogen.parent.PollPoller` #: if it is available, or :class:`mitogen.core.Poller` otherwise, since #: these implementations require no system calls to create, configure or #: destroy. poller_class = Poller #: If not :data:`None`, a function invoked as `notify(latch)` after a #: successful call to :meth:`put`. The function is invoked on the #: :meth:`put` caller's thread, which may be the :class:`Broker` thread, #: therefore it must not block. Used by :class:`mitogen.select.Select` to #: efficiently implement waiting on multiple event sources. notify = None # The _cls_ prefixes here are to make it crystal clear in the code which # state mutation isn't covered by :attr:`_lock`. #: List of reusable :func:`socket.socketpair` tuples. The list is mutated #: from multiple threads, the only safe operations are `append()` and #: `pop()`. _cls_idle_socketpairs = [] #: List of every socket object that must be closed by :meth:`_on_fork`. #: Inherited descriptors cannot be reused, as the duplicated handles #: reference the same underlying kernel object in use by the parent. _cls_all_sockets = [] def __init__(self): self.closed = False self._lock = threading.Lock() #: List of unconsumed enqueued items. self._queue = [] #: List of `(wsock, cookie)` awaiting an element, where `wsock` is the #: socketpair's write side, and `cookie` is the string to write. self._sleeping = [] #: Number of elements of :attr:`_sleeping` that have already been #: woken, and have a corresponding element index from :attr:`_queue` #: assigned to them. self._waking = 0 @classmethod def _on_fork(cls): """ Clean up any files belonging to the parent process after a fork. """ cls._cls_idle_socketpairs = [] while cls._cls_all_sockets: cls._cls_all_sockets.pop().close() def close(self): """ Mark the latch as closed, and cause every sleeping thread to be woken, with :class:`mitogen.core.LatchError` raised in each thread. """ self._lock.acquire() try: self.closed = True while self._waking < len(self._sleeping): wsock, cookie = self._sleeping[self._waking] self._wake(wsock, cookie) self._waking += 1 finally: self._lock.release() def size(self): """ Return the number of items currently buffered. As with :class:`Queue.Queue`, `0` may be returned even though a subsequent call to :meth:`get` will succeed, since a message may be posted at any moment between :meth:`size` and :meth:`get`. As with :class:`Queue.Queue`, `>0` may be returned even though a subsequent call to :meth:`get` will block, since another waiting thread may be woken at any moment between :meth:`size` and :meth:`get`. :raises LatchError: The latch has already been marked closed. """ self._lock.acquire() try: if self.closed: raise LatchError() return len(self._queue) finally: self._lock.release() def empty(self): """ Return `size() == 0`. .. deprecated:: 0.2.8 Use :meth:`size` instead. :raises LatchError: The latch has already been marked closed. """ return self.size() == 0 def _get_socketpair(self): """ Return an unused socketpair, creating one if none exist. """ try: return self._cls_idle_socketpairs.pop() # pop() must be atomic except IndexError: rsock, wsock = socket.socketpair() rsock.setblocking(False) set_cloexec(rsock.fileno()) set_cloexec(wsock.fileno()) self._cls_all_sockets.extend((rsock, wsock)) return rsock, wsock COOKIE_MAGIC, = struct.unpack('L', b('LTCH') * (struct.calcsize('L')//4)) COOKIE_FMT = '>Qqqq' # #545: id() and get_ident() may exceed long on armhfp. COOKIE_SIZE = struct.calcsize(COOKIE_FMT) def _make_cookie(self): """ Return a string encoding the ID of the process, instance and thread. This disambiguates legitimate wake-ups, accidental writes to the FD, and buggy internal FD sharing. """ return struct.pack(self.COOKIE_FMT, self.COOKIE_MAGIC, os.getpid(), id(self), thread.get_ident()) def get(self, timeout=None, block=True): """ Return the next enqueued object, or sleep waiting for one. :param float timeout: If not :data:`None`, specifies a timeout in seconds. :param bool block: If :data:`False`, immediately raise :class:`mitogen.core.TimeoutError` if the latch is empty. :raises mitogen.core.LatchError: :meth:`close` has been called, and the object is no longer valid. :raises mitogen.core.TimeoutError: Timeout was reached. :returns: The de-queued object. """ _vv and IOLOG.debug('%r.get(timeout=%r, block=%r)', self, timeout, block) self._lock.acquire() try: if self.closed: raise LatchError() i = len(self._sleeping) if len(self._queue) > i: _vv and IOLOG.debug('%r.get() -> %r', self, self._queue[i]) return self._queue.pop(i) if not block: raise TimeoutError() rsock, wsock = self._get_socketpair() cookie = self._make_cookie() self._sleeping.append((wsock, cookie)) finally: self._lock.release() poller = self.poller_class() poller.start_receive(rsock.fileno()) try: return self._get_sleep(poller, timeout, block, rsock, wsock, cookie) finally: poller.close() def _get_sleep(self, poller, timeout, block, rsock, wsock, cookie): """ When a result is not immediately available, sleep waiting for :meth:`put` to write a byte to our socket pair. """ _vv and IOLOG.debug( '%r._get_sleep(timeout=%r, block=%r, fd=%d/%d)', self, timeout, block, rsock.fileno(), wsock.fileno() ) e = None try: list(poller.poll(timeout)) except Exception: e = sys.exc_info()[1] self._lock.acquire() try: i = self._sleeping.index((wsock, cookie)) del self._sleeping[i] try: got_cookie = rsock.recv(self.COOKIE_SIZE) except socket.error: e2 = sys.exc_info()[1] if e2.args[0] == errno.EAGAIN: e = TimeoutError() else: e = e2 self._cls_idle_socketpairs.append((rsock, wsock)) if e: raise e assert cookie == got_cookie, ( "Cookie incorrect; got %r, expected %r" % (binascii.hexlify(got_cookie), binascii.hexlify(cookie)) ) assert i < self._waking, ( "Cookie correct, but no queue element assigned." ) self._waking -= 1 if self.closed: raise LatchError() _vv and IOLOG.debug('%r.get() wake -> %r', self, self._queue[i]) return self._queue.pop(i) finally: self._lock.release() def put(self, obj=None): """ Enqueue an object, waking the first thread waiting for a result, if one exists. :param obj: Object to enqueue. Defaults to :data:`None` as a convenience when using :class:`Latch` only for synchronization. :raises mitogen.core.LatchError: :meth:`close` has been called, and the object is no longer valid. """ _vv and IOLOG.debug('%r.put(%r)', self, obj) self._lock.acquire() try: if self.closed: raise LatchError() self._queue.append(obj) wsock = None if self._waking < len(self._sleeping): wsock, cookie = self._sleeping[self._waking] self._waking += 1 _vv and IOLOG.debug('%r.put() -> waking wfd=%r', self, wsock.fileno()) elif self.notify: self.notify(self) finally: self._lock.release() if wsock: self._wake(wsock, cookie) def _wake(self, wsock, cookie): written, disconnected = io_op(os.write, wsock.fileno(), cookie) assert written == len(cookie) and not disconnected def __repr__(self): return 'Latch(%#x, size=%d, t=%r)' % ( id(self), len(self._queue), threading__thread_name(threading__current_thread()), ) class Waker(Protocol): """ :class:`Protocol` implementing the `UNIX self-pipe trick`_. Used to wake :class:`Broker` when another thread needs to modify its state, by enqueing a function call to run on the :class:`Broker` thread. .. _UNIX self-pipe trick: https://cr.yp.to/docs/selfpipe.html """ read_size = 1 broker_ident = None @classmethod def build_stream(cls, broker): stream = super(Waker, cls).build_stream(broker) stream.accept(*pipe()) return stream def __init__(self, broker): self._broker = broker self._deferred = collections.deque() def __repr__(self): return 'Waker(fd=%r/%r)' % ( self.stream.receive_side and self.stream.receive_side.fd, self.stream.transmit_side and self.stream.transmit_side.fd, ) @property def keep_alive(self): """ Prevent immediate Broker shutdown while deferred functions remain. """ return len(self._deferred) def on_receive(self, broker, buf): """ Drain the pipe and fire callbacks. Since :attr:`_deferred` is synchronized, :meth:`defer` and :meth:`on_receive` can conspire to ensure only one byte needs to be pending regardless of queue length. """ _vv and IOLOG.debug('%r.on_receive()', self) while True: try: func, args, kwargs = self._deferred.popleft() except IndexError: return try: func(*args, **kwargs) except Exception: LOG.exception('defer() crashed: %r(*%r, **%r)', func, args, kwargs) broker.shutdown() def _wake(self): """ Wake the multiplexer by writing a byte. If Broker is midway through teardown, the FD may already be closed, so ignore EBADF. """ try: self.stream.transmit_side.write(b(' ')) except OSError: e = sys.exc_info()[1] if e.args[0] not in (errno.EBADF, errno.EWOULDBLOCK): raise broker_shutdown_msg = ( "An attempt was made to enqueue a message with a Broker that has " "already exitted. It is likely your program called Broker.shutdown() " "too early." ) def defer(self, func, *args, **kwargs): """ Arrange for `func()` to execute on the broker thread. This function returns immediately without waiting the result of `func()`. Use :meth:`defer_sync` to block until a result is available. :raises mitogen.core.Error: :meth:`defer` was called after :class:`Broker` has begun shutdown. """ if thread.get_ident() == self.broker_ident: _vv and IOLOG.debug('%r.defer() [immediate]', self) return func(*args, **kwargs) if self._broker._exitted: raise Error(self.broker_shutdown_msg) _vv and IOLOG.debug('%r.defer() [fd=%r]', self, self.stream.transmit_side.fd) self._deferred.append((func, args, kwargs)) self._wake() class IoLoggerProtocol(DelimitedProtocol): """ Attached to one end of a socket pair whose other end overwrites one of the standard ``stdout`` or ``stderr`` file descriptors in a child context. Received data is split up into lines, decoded as UTF-8 and logged to the :mod:`logging` package as either the ``stdout`` or ``stderr`` logger. Logging in child contexts is in turn forwarded to the master process using :class:`LogHandler`. """ @classmethod def build_stream(cls, name, dest_fd): """ Even though the file descriptor `dest_fd` will hold the opposite end of the socket open, we must keep a separate dup() of it (i.e. wsock) in case some code decides to overwrite `dest_fd` later, which would prevent break :meth:`on_shutdown` from calling :meth:`shutdown() <socket.socket.shutdown>` on it. """ rsock, wsock = socket.socketpair() os.dup2(wsock.fileno(), dest_fd) stream = super(IoLoggerProtocol, cls).build_stream(name) stream.name = name stream.accept(rsock, wsock) return stream def __init__(self, name): self._log = logging.getLogger(name) # #453: prevent accidental log initialization in a child creating a # feedback loop. self._log.propagate = False self._log.handlers = logging.getLogger().handlers[:] def on_shutdown(self, broker): """ Shut down the write end of the socket, preventing any further writes to it by this process, or subprocess that inherited it. This allows any remaining kernel-buffered data to be drained during graceful shutdown without the buffer continuously refilling due to some out of control child process. """ _v and LOG.debug('%r: shutting down', self) if not IS_WSL: # #333: WSL generates invalid readiness indication on shutdown(). # This modifies the *kernel object* inherited by children, causing # EPIPE on subsequent writes to any dupped FD in any process. The # read side can then drain completely of prior buffered data. self.stream.transmit_side.fp.shutdown(socket.SHUT_WR) self.stream.transmit_side.close() def on_line_received(self, line): """ Decode the received line as UTF-8 and pass it to the logging framework. """ self._log.info('%s', line.decode('utf-8','replace')) class Router(object): """ Route messages between contexts, and invoke local handlers for messages addressed to this context. :meth:`Router.route() <route>` straddles the :class:`Broker` thread and user threads, it is safe to call anywhere. **Note:** This is the somewhat limited core version of the Router class used by child contexts. The master subclass is documented below this one. """ #: The :class:`mitogen.core.Context` subclass to use when constructing new #: :class:`Context` objects in :meth:`myself` and :meth:`context_by_id`. #: Permits :class:`Router` subclasses to extend the :class:`Context` #: interface, as done in :class:`mitogen.parent.Router`. context_class = Context max_message_size = 128 * 1048576 #: When :data:`True`, permit children to only communicate with the current #: context or a parent of the current context. Routing between siblings or #: children of parents is prohibited, ensuring no communication is possible #: between intentionally partitioned networks, such as when a program #: simultaneously manipulates hosts spread across a corporate and a #: production network, or production networks that are otherwise #: air-gapped. #: #: Sending a prohibited message causes an error to be logged and a dead #: message to be sent in reply to the errant message, if that message has #: ``reply_to`` set. #: #: The value of :data:`unidirectional` becomes the default for the #: :meth:`local() <mitogen.master.Router.local>` `unidirectional` #: parameter. unidirectional = False duplicate_handle_msg = 'cannot register a handle that already exists' refused_msg ='refused by policy' invalid_handle_msg = 'invalid handle' too_large_msg ='message too large (max %d bytes)' respondent_disconnect_msg = 'the respondent Context has disconnected' broker_exit_msg = 'Broker has exitted' no_route_msg = 'no route to %r, my ID is %r' unidirectional_msg = ( 'routing mode prevents forward of message from context %d to ' 'context %d via context %d' ) def __init__(self, broker): self.broker = broker listen(broker, 'exit', self._on_broker_exit) self._setup_logging() self._write_lock = threading.Lock() #: context ID -> Stream; must hold _write_lock to edit or iterate self._stream_by_id = {} #: List of contexts to notify of shutdown; must hold _write_lock self._context_by_id = {} self._last_handle = itertools.count(1000) #: handle -> (persistent?, func(msg)) self._handle_map = {} #: Context -> set { handle,.. } self._handles_by_respondent = {} self.add_handler(self._on_del_route, DEL_ROUTE) def __repr__(self): return 'Router(%r)' % (self.broker,) def _setup_logging(self): """ This is done in the :class:`Router` constructor for historical reasons. It must be called before ExternalContext logs its first messages, but after logging has been setup. It must also be called when any router is constructed for a consumer app. """ # Here seems as good a place as any. global _v, _vv _v = logging.getLogger().level <= logging.DEBUG _vv = IOLOG.level <= logging.DEBUG def _on_del_route(self, msg): """ Stub :data:`DEL_ROUTE` handler; fires 'disconnect' events on the corresponding :attr:`_context_by_id` member. This is replaced by :class:`mitogen.parent.RouteMonitor` in an upgraded context. """ if msg.is_dead: return target_id_s, _, name = bytes_partition(msg.data, b(':')) target_id = int(target_id_s, 10) LOG.error('%r: deleting route to %s (%d)', self, to_text(name), target_id) context = self._context_by_id.get(target_id) if context: fire(context, 'disconnect') else: LOG.debug('DEL_ROUTE for unknown ID %r: %r', target_id, msg) def _on_stream_disconnect(self, stream): notify = [] self._write_lock.acquire() try: for context in list(self._context_by_id.values()): stream_ = self._stream_by_id.get(context.context_id) if stream_ is stream: del self._stream_by_id[context.context_id] notify.append(context) finally: self._write_lock.release() # Happens outside lock as e.g. RouteMonitor wants the same lock. for context in notify: context.on_disconnect() def _on_broker_exit(self): """ Called prior to broker exit, informs callbacks registered with :meth:`add_handler` the connection is dead. """ _v and LOG.debug('%r: broker has exitted', self) while self._handle_map: _, (_, func, _, _) = self._handle_map.popitem() func(Message.dead(self.broker_exit_msg)) def myself(self): """ Return a :class:`Context` referring to the current process. Since :class:`Context` is serializable, this is convenient to use in remote function call parameter lists. """ return self.context_class( router=self, context_id=mitogen.context_id, name='self', ) def context_by_id(self, context_id, via_id=None, create=True, name=None): """ Return or construct a :class:`Context` given its ID. An internal mapping of ID to the canonical :class:`Context` representing that ID, so that :ref:`signals` can be raised. This may be called from any thread, lookup and construction are atomic. :param int context_id: The context ID to look up. :param int via_id: If the :class:`Context` does not already exist, set its :attr:`Context.via` to the :class:`Context` matching this ID. :param bool create: If the :class:`Context` does not already exist, create it. :param str name: If the :class:`Context` does not already exist, set its name. :returns: :class:`Context`, or return :data:`None` if `create` is :data:`False` and no :class:`Context` previously existed. """ context = self._context_by_id.get(context_id) if context: return context if create and via_id is not None: via = self.context_by_id(via_id) else: via = None self._write_lock.acquire() try: context = self._context_by_id.get(context_id) if create and not context: context = self.context_class(self, context_id, name=name) context.via = via self._context_by_id[context_id] = context finally: self._write_lock.release() return context def register(self, context, stream): """ Register a newly constructed context and its associated stream, and add the stream's receive side to the I/O multiplexer. This method remains public while the design has not yet settled. """ _v and LOG.debug('%s: registering %r to stream %r', self, context, stream) self._write_lock.acquire() try: self._stream_by_id[context.context_id] = stream self._context_by_id[context.context_id] = context finally: self._write_lock.release() self.broker.start_receive(stream) listen(stream, 'disconnect', lambda: self._on_stream_disconnect(stream)) def stream_by_id(self, dst_id): """ Return the :class:`Stream` that should be used to communicate with `dst_id`. If a specific route for `dst_id` is not known, a reference to the parent context's stream is returned. If the parent is disconnected, or when running in the master context, return :data:`None` instead. This can be used from any thread, but its output is only meaningful from the context of the :class:`Broker` thread, as disconnection or replacement could happen in parallel on the broker thread at any moment. """ return ( self._stream_by_id.get(dst_id) or self._stream_by_id.get(mitogen.parent_id) ) def del_handler(self, handle): """ Remove the handle registered for `handle` :raises KeyError: The handle wasn't registered. """ _, _, _, respondent = self._handle_map.pop(handle) if respondent: self._handles_by_respondent[respondent].discard(handle) def add_handler(self, fn, handle=None, persist=True, policy=None, respondent=None, overwrite=False): """ Invoke `fn(msg)` on the :class:`Broker` thread for each Message sent to `handle` from this context. Unregister after one invocation if `persist` is :data:`False`. If `handle` is :data:`None`, a new handle is allocated and returned. :param int handle: If not :data:`None`, an explicit handle to register, usually one of the ``mitogen.core.*`` constants. If unspecified, a new unused handle will be allocated. :param bool persist: If :data:`False`, the handler will be unregistered after a single message has been received. :param mitogen.core.Context respondent: Context that messages to this handle are expected to be sent from. If specified, arranges for a dead message to be delivered to `fn` when disconnection of the context is detected. In future `respondent` will likely also be used to prevent other contexts from sending messages to the handle. :param function policy: Function invoked as `policy(msg, stream)` where `msg` is a :class:`mitogen.core.Message` about to be delivered, and `stream` is the :class:`mitogen.core.Stream` on which it was received. The function must return :data:`True`, otherwise an error is logged and delivery is refused. Two built-in policy functions exist: * :func:`has_parent_authority`: requires the message arrived from a parent context, or a context acting with a parent context's authority (``auth_id``). * :func:`mitogen.parent.is_immediate_child`: requires the message arrived from an immediately connected child, for use in messaging patterns where either something becomes buggy or insecure by permitting indirect upstream communication. In case of refusal, and the message's ``reply_to`` field is nonzero, a :class:`mitogen.core.CallError` is delivered to the sender indicating refusal occurred. :param bool overwrite: If :data:`True`, allow existing handles to be silently overwritten. :return: `handle`, or if `handle` was :data:`None`, the newly allocated handle. :raises Error: Attemp to register handle that was already registered. """ handle = handle or next(self._last_handle) _vv and IOLOG.debug('%r.add_handler(%r, %r, %r)', self, fn, handle, persist) if handle in self._handle_map and not overwrite: raise Error(self.duplicate_handle_msg) self._handle_map[handle] = persist, fn, policy, respondent if respondent: if respondent not in self._handles_by_respondent: self._handles_by_respondent[respondent] = set() listen(respondent, 'disconnect', lambda: self._on_respondent_disconnect(respondent)) self._handles_by_respondent[respondent].add(handle) return handle def _on_respondent_disconnect(self, context): for handle in self._handles_by_respondent.pop(context, ()): _, fn, _, _ = self._handle_map[handle] fn(Message.dead(self.respondent_disconnect_msg)) del self._handle_map[handle] def _maybe_send_dead(self, unreachable, msg, reason, *args): """ Send a dead message to either the original sender or the intended recipient of `msg`, if the original sender was expecting a reply (because its `reply_to` was set), otherwise assume the message is a reply of some sort, and send the dead message to the original destination. :param bool unreachable: If :data:`True`, the recipient is known to be dead or routing failed due to a security precaution, so don't attempt to fallback to sending the dead message to the recipient if the original sender did not include a reply address. :param mitogen.core.Message msg: Message that triggered the dead message. :param str reason: Human-readable error reason. :param tuple args: Elements to interpolate with `reason`. """ if args: reason %= args LOG.debug('%r: %r is dead: %r', self, msg, reason) if msg.reply_to and not msg.is_dead: msg.reply(Message.dead(reason=reason), router=self) elif not unreachable: self._async_route( Message.dead( dst_id=msg.dst_id, handle=msg.handle, reason=reason, ) ) def _invoke(self, msg, stream): # IOLOG.debug('%r._invoke(%r)', self, msg) try: persist, fn, policy, respondent = self._handle_map[msg.handle] except KeyError: self._maybe_send_dead(True, msg, reason=self.invalid_handle_msg) return if respondent and not (msg.is_dead or msg.src_id == respondent.context_id): self._maybe_send_dead(True, msg,'reply from unexpected context') return if policy and not policy(msg, stream): self._maybe_send_dead(True, msg, self.refused_msg) return if not persist: self.del_handler(msg.handle) try: fn(msg) except Exception: LOG.exception('%r._invoke(%r): %r crashed', self, msg, fn) def _async_route(self, msg, in_stream=None): """ Arrange for `msg` to be forwarded towards its destination. If its destination is the local context, then arrange for it to be dispatched using the local handlers. This is a lower overhead version of :meth:`route` that may only be called from the :class:`Broker` thread. :param Stream in_stream: If not :data:`None`, the stream the message arrived on. Used for performing source route verification, to ensure sensitive messages such as ``CALL_FUNCTION`` arrive only from trusted contexts. """ _vv and IOLOG.debug('%r._async_route(%r, %r)', self, msg, in_stream) if len(msg.data) > self.max_message_size: self._maybe_send_dead(False, msg, self.too_large_msg % ( self.max_message_size, )) return parent_stream = self._stream_by_id.get(mitogen.parent_id) src_stream = self._stream_by_id.get(msg.src_id, parent_stream) # When the ingress stream is known, verify the message was received on # the same as the stream we would expect to receive messages from the # src_id and auth_id. This is like Reverse Path Filtering in IP, and # ensures messages from a privileged context cannot be spoofed by a # child. if in_stream: auth_stream = self._stream_by_id.get(msg.auth_id, parent_stream) if in_stream!= auth_stream: LOG.error('%r: bad auth_id: got %r via %r, not %r: %r', self, msg.auth_id, in_stream, auth_stream, msg) return if msg.src_id!= msg.auth_id and in_stream!= src_stream: LOG.error('%r: bad src_id: got %r via %r, not %r: %r', self, msg.src_id, in_stream, src_stream, msg) return # If the stream's MitogenProtocol has auth_id set, copy it to the # message. This allows subtrees to become privileged by stamping a # parent's context ID. It is used by mitogen.unix to mark client # streams (like Ansible WorkerProcess) as having the same rights as # the parent. if in_stream.protocol.auth_id is not None: msg.auth_id = in_stream.protocol.auth_id if in_stream.protocol.on_message is not None: in_stream.protocol.on_message(in_stream, msg) # Record the IDs the source ever communicated with. in_stream.protocol.egress_ids.add(msg.dst_id) if msg.dst_id == mitogen.context_id: return self._invoke(msg, in_stream) out_stream = self._stream_by_id.get(msg.dst_id) if (not out_stream) and (parent_stream!= src_stream or not in_stream): # No downstream route exists. The message could be from a child or # ourselves for a parent, in which case we must forward it # upstream, or it could be from a parent for a dead child, in which # case its src_id/auth_id would fail verification if returned to # the parent, so in that case reply with a dead message instead. out_stream = parent_stream if out_stream is None: self._maybe_send_dead(True, msg, self.no_route_msg, msg.dst_id, mitogen.context_id) return if in_stream and self.unidirectional and not \ (in_stream.protocol.is_privileged or out_stream.protocol.is_privileged): self._maybe_send_dead(True, msg, self.unidirectional_msg, in_stream.protocol.remote_id, out_stream.protocol.remote_id, mitogen.context_id) return out_stream.protocol._send(msg) def route(self, msg): """ Arrange for the :class:`Message` `msg` to be delivered to its destination using any relevant downstream context, or if none is found, by forwarding the message upstream towards the master context. If `msg` is destined for the local context, it is dispatched using the handles registered with :meth:`add_handler`. This may be called from any thread. """ self.broker.defer(self._async_route, msg) class NullTimerList(object): def get_timeout(self): return None class Broker(object): """ Responsible for handling I/O multiplexing in a private thread. **Note:** This somewhat limited core version is used by children. The master subclass is documented below. """ poller_class = Poller _waker = None _thread = None # :func:`mitogen.parent._upgrade_broker` replaces this with # :class:`mitogen.parent.TimerList` during upgrade. timers = NullTimerList() #: Seconds grace to allow :class:`streams <Stream>` to shutdown gracefully #: before force-disconnecting them during :meth:`shutdown`. shutdown_timeout = 3.0 def __init__(self, poller_class=None, activate_compat=True): self._alive = True self._exitted = False self._waker = Waker.build_stream(self) #: Arrange for `func(\*args, \**kwargs)` to be executed on the broker #: thread, or immediately if the current thread is the broker thread. #: Safe to call from any thread. self.defer = self._waker.protocol.defer self.poller = self.poller_class() self.poller.start_receive( self._waker.receive_side.fd, (self._waker.receive_side, self._waker.on_receive) ) self._thread = threading.Thread( target=self._broker_main, name='mitogen.broker' ) self._thread.start() if activate_compat: self._py24_25_compat() def _py24_25_compat(self): """ Python 2.4/2.5 have grave difficulties with threads/fork. We mandatorily quiesce all running threads during fork using a monkey-patch there. """ if sys.version_info < (2, 6): # import_module() is used to avoid dep scanner. os_fork = import_module('mitogen.os_fork') os_fork._notice_broker_or_pool(self) def start_receive(self, stream): """ Mark the :attr:`receive_side <Stream.receive_side>` on `stream` as ready for reading. Safe to call from any thread. When the associated file descriptor becomes ready for reading, :meth:`BasicStream.on_receive` will be called. """ _vv and IOLOG.debug('%r.start_receive(%r)', self, stream) side = stream.receive_side assert side and not side.closed self.defer(self.poller.start_receive, side.fd, (side, stream.on_receive)) def stop_receive(self, stream): """ Mark the :attr:`receive_side <Stream.receive_side>` on `stream` as not ready for reading. Safe to call from any thread. """ _vv and IOLOG.debug('%r.stop_receive(%r)', self, stream) self.defer(self.poller.stop_receive, stream.receive_side.fd) def _start_transmit(self, stream): """ Mark the :attr:`transmit_side <Stream.transmit_side>` on `stream` as ready for writing. Must only be called from the Broker thread. When the associated file descriptor becomes ready for writing, :meth:`BasicStream.on_transmit` will be called. """ _vv and IOLOG.debug('%r._start_transmit(%r)', self, stream) side = stream.transmit_side assert side and not side.closed self.poller.start_transmit(side.fd, (side, stream.on_transmit)) def _stop_transmit(self, stream): """ Mark the :attr:`transmit_side <Stream.receive_side>` on `stream` as not ready for writing. """ _vv and IOLOG.debug('%r._stop_transmit(%r)', self, stream) self.poller.stop_transmit(stream.transmit_side.fd) def keep_alive(self): """ Return :data:`True` if any reader's :attr:`Side.keep_alive` attribute is :data:`True`, or any :class:`Context` is still registered that is not the master. Used to delay shutdown while some important work is in progress (e.g. log draining). """ it = (side.keep_alive for (_, (side, _)) in self.poller.readers) return sum(it, 0) > 0 or self.timers.get_timeout() is not None def defer_sync(self, func): """ Arrange for `func()` to execute on :class:`Broker` thread, blocking the current thread until a result or exception is available. :returns: Return value of `func()`. """ latch = Latch() def wrapper(): try: latch.put(func()) except Exception: latch.put(sys.exc_info()[1]) self.defer(wrapper) res = latch.get() if isinstance(res, Exception): raise res return res def _call(self, stream, func): """ Call `func(self)`, catching any exception that might occur, logging it, and force-disconnecting the related `stream`. """ try: func(self) except Exception: LOG.exception('%r crashed', stream) stream.on_disconnect(self) def _loop_once(self, timeout=None): """ Execute a single :class:`Poller` wait, dispatching any IO events that caused the wait to complete. :param float timeout: If not :data:`None`, maximum time in seconds to wait for events. """ _vv and IOLOG.debug('%r._loop_once(%r, %r)', self, timeout, self.poller) timer_to = self.timers.get_timeout() if timeout is None: timeout = timer_to elif timer_to is not None and timer_to < timeout: timeout = timer_to #IOLOG.debug('readers =\n%s', pformat(self.poller.readers)) #IOLOG.debug('writers =\n%s', pformat(self.poller.writers)) for side, func in self.poller.poll(timeout): self._call(side.stream, func) if timer_to is not None: self.timers.expire() def _broker_exit(self): """ Forcefully call :meth:`Stream.on_disconnect` on any streams that failed to shut down gracefully, then discard the :class:`Poller`. """ for _, (side, _) in self.poller.readers + self.poller.writers: LOG.debug('%r: force disconnecting %r', self, side) side.stream.on_disconnect(self) self.poller.close() def _broker_shutdown(self): """ Invoke :meth:`Stream.on_shutdown` for every active stream, then allow up to :attr:`shutdown_timeout` seconds for the streams to unregister themselves, logging an error if any did not unregister during the grace period. """ for _, (side, _) in self.poller.readers + self.poller.writers: self._call(side.stream, side.stream.on_shutdown) deadline = now() + self.shutdown_timeout while self.keep_alive() and now() < deadline: self._loop_once(max(0, deadline - now())) if self.keep_alive(): LOG.error('%r: pending work still existed %d seconds after ' 'shutdown began. This may be due to a timer that is yet ' 'to expire, or a child connection that did not fully ' 'shut down.', self, self.shutdown_timeout) def _do_broker_main(self): """ Broker thread main function. Dispatches IO events until :meth:`shutdown` is called. """ # For Python 2.4, no way to retrieve ident except on thread. self._waker.protocol.broker_ident = thread.get_ident() try: while self._alive: self._loop_once() fire(self, 'before_shutdown') fire(self,'shutdown') self._broker_shutdown() except Exception: e = sys.exc_info()[1] LOG.exception('broker crashed') syslog.syslog(syslog.LOG_ERR, 'broker crashed: %s' % (e,)) syslog.closelog() # prevent test 'fd leak'. self._alive = False # Ensure _alive is consistent on crash. self._exitted = True self._broker_exit() def _broker_main(self): try: _profile_hook('mitogen.broker', self._do_broker_main) finally: # 'finally' to ensure _on_broker_exit() can always SIGTERM. fire(self, 'exit') def shutdown(self): """ Request broker gracefully disconnect streams and stop. Safe to call from any thread. """ _v and LOG.debug('%r: shutting down', self) def _shutdown(): self._alive = False if self._alive and not self._exitted: self.defer(_shutdown) def join(self): """ Wait for the broker to stop, expected to be called after :meth:`shutdown`. """ self._thread.join() def __repr__(self): return 'Broker(%04x)' % (id(self) & 0xffff,) class Dispatcher(object): """ Implementation of the :data:`CALL_FUNCTION` handle for a child context. Listens on the child's main thread for messages sent by :class:`mitogen.parent.CallChain` and dispatches the function calls they describe. If a :class:`mitogen.parent.CallChain` sending a message is in pipelined mode, any exception that occurs is recorded, and causes all subsequent calls with the same `chain_id` to fail with the same exception. """ _service_recv = None def __repr__(self): return 'Dispatcher' def __init__(self, econtext): self.econtext = econtext #: Chain ID -> CallError if prior call failed. self._error_by_chain_id = {} self.recv = Receiver( router=econtext.router, handle=CALL_FUNCTION, policy=has_parent_authority, ) #: The :data:`CALL_SERVICE` :class:`Receiver` that will eventually be #: reused by :class:`mitogen.service.Pool`, should it ever be loaded. #: This is necessary for race-free reception of all service requests #: delivered regardless of whether the stub or real service pool are #: loaded. See #547 for related sorrows. Dispatcher._service_recv = Receiver( router=econtext.router, handle=CALL_SERVICE, policy=has_parent_authority, ) self._service_recv.notify = self._on_call_service listen(econtext.broker,'shutdown', self._on_broker_shutdown) def _on_broker_shutdown(self): if self._service_recv.notify == self._on_call_service: self._service_recv.notify = None self.recv.close() @classmethod @takes_econtext def forget_chain(cls, chain_id, econtext): econtext.dispatcher._error_by_chain_id.pop(chain_id, None) def _parse_request(self, msg): data = msg.unpickle(throw=False) _v and LOG.debug('%r: dispatching %r', self, data) chain_id, modname, klass, func, args, kwargs = data obj = import_module(modname) if klass: obj = getattr(obj, klass) fn = getattr(obj, func) if getattr(fn,'mitogen_takes_econtext', None): kwargs.setdefault('econtext', self.econtext) if getattr(fn,'mitogen_takes_router', None): kwargs.setdefault('router', self.econtext.router) return chain_id, fn, args, kwargs def _dispatch_one(self, msg): try: chain_id, fn, args, kwargs = self._parse_request(msg) except Exception: return None, CallError(sys.exc_info()[1]) if chain_id in self._error_by_chain_id: return chain_id, self._error_by_chain_id[chain_id] try: return chain_id, fn(*args, **kwargs) except Exception: e = CallError(sys.exc_info()[1]) if chain_id is not None: self._error_by_chain_id[chain_id] = e return chain_id, e def _on_call_service(self, recv): """ Notifier for the :data:`CALL_SERVICE` receiver. This is called on the :class:`Broker` thread for any service messages arriving at this context, for as long as no real service pool implementation is loaded. In order to safely bootstrap the service pool implementation a sentinel message is enqueued on the :data:`CALL_FUNCTION` receiver in order to wake the main thread, where the importer can run without any possibility of suffering deadlock due to concurrent uses of the importer. Should the main thread be blocked indefinitely, preventing the import from ever running, if it is blocked waiting on a service call, then it means :mod:`mitogen.service` has already been imported and :func:`mitogen.service.get_or_create_pool` has already run, meaning the service pool is already active and the duplicate initialization was not needed anyway. #547: This trickery is needed to avoid the alternate option of spinning a temporary thread to import the service pool, which could deadlock if a custom import hook executing on the main thread (under the importer lock) would block waiting for some data that was in turn received by a service. Main thread import lock can't be released until service is running, service cannot satisfy request until import lock is released. """ self.recv._on_receive(Message(handle=STUB_CALL_SERVICE)) def _init_service_pool(self): import mitogen.service mitogen.service.get_or_create_pool(router=self.econtext.router) def _dispatch_calls(self): for msg in self.recv: if msg.handle == STUB_CALL_SERVICE: if msg.src_id == mitogen.context_id: self._init_service_pool() continue chain_id, ret = self._dispatch_one(msg) _v and LOG.debug('%r: %r -> %r', self, msg, ret) if msg.reply_to: msg.reply(ret) elif isinstance(ret, CallError) and chain_id is None: LOG.error('No-reply function call failed: %s', ret) def run(self): if self.econtext.config.get('on_start'): self.econtext.config['on_start'](self.econtext) _profile_hook('mitogen.child_main', self._dispatch_calls) class ExternalContext(object): """ External context implementation. This class contains the main program implementation for new children. It is responsible for setting up everything about the process environment, import hooks, standard IO redirection, logging, configuring a :class:`Router` and :class:`Broker`, and finally arranging for :class:`Dispatcher` to take over the main thread after initialization is complete. .. attribute:: broker The :class:`mitogen.core.Broker` instance. .. attribute:: context The :class:`mitogen.core.Context` instance. .. attribute:: channel The :class:`mitogen.core.Channel` over which :data:`CALL_FUNCTION` requests are received. .. attribute:: importer The :class:`mitogen.core.Importer` instance. .. attribute:: stdout_log The :class:`IoLogger` connected to :data:`sys.stdout`. .. attribute:: stderr_log The :class:`IoLogger` connected to :data:`sys.stderr`. """ detached = False def __init__(self, config): self.config = config def _on_broker_exit(self): if not self.config['profiling']: os.kill(os.getpid(), signal.SIGTERM) def _on_shutdown_msg(self, msg): if not msg.is_dead: _v and LOG.debug('shutdown request from context %d', msg.src_id) self.broker.shutdown() def _on_parent_disconnect(self): if self.detached: mitogen.parent_ids = [] mitogen.parent_id = None LOG.info('Detachment complete') else: _v and LOG.debug('parent stream is gone, dying.') self.broker.shutdown() def detach(self): self.detached = True stream = self.router.stream_by_id(mitogen.parent_id) if stream: # not double-detach()'d os.setsid() self.parent.send_await(Message(handle=DETACHING)) LOG.info('Detaching from %r; parent is %s', stream, self.parent) for x in range(20): pending = self.broker.defer_sync(stream.protocol.pending_bytes) if not pending: break time.sleep(0.05) if pending: LOG.error('Stream had %d bytes after 2000ms', pending) self.broker.defer(stream.on_disconnect, self.broker) def _setup_master(self): Router.max_message_size = self.config['max_message_size'] if self.config['profiling']: enable_profiling() self.broker = Broker(activate_compat=False) self.router = Router(self.broker) self.router.debug = self.config.get('debug', False) self.router.unidirectional = self.config['unidirectional'] self.router.add_handler( fn=self._on_shutdown_msg, handle=SHUTDOWN, policy=has_parent_authority, ) self.master = Context(self.router, 0,'master') parent_id = self.config['parent_ids'][0] if parent_id == 0: self.parent = self.master else: self.parent = Context(self.router, parent_id, 'parent') in_fd = self.config.get('in_fd', 100) in_fp = os.fdopen(os.dup(in_fd), 'rb', 0) os.close(in_fd) out_fp = os.fdopen(os.dup(self.config.get('out_fd', 1)), 'wb', 0) self.stream = MitogenProtocol.build_stream( self.router, parent_id, local_id=self.config['context_id'], parent_ids=self.config['parent_ids'] ) self.stream.accept(in_fp, out_fp) self.stream.name = 'parent' self.stream.receive_side.keep_alive = False listen(self.stream, 'disconnect', self._on_parent_disconnect) listen(self.broker, 'exit', self._on_broker_exit) def _reap_first_stage(self): try: os.wait() # Reap first stage. except OSError: pass # No first stage exists (e.g. fakessh) def _setup_logging(self): self.log_handler = LogHandler(self.master) root = logging.getLogger() root.setLevel(self.config['log_level']) root.handlers = [self.log_handler] if self.config['debug']: enable_debug_logging() def _setup_importer(self): importer = self.config.get('importer') if importer: importer._install_handler(self.router) importer._context = self.parent else: core_src_fd = self.config.get('core_src_fd', 101) if core_src_fd: fp = os.fdopen(core_src_fd, 'rb', 0) try: core_src = fp.read() # Strip "ExternalContext.main()" call from last line. core_src = b('\n').join(core_src.splitlines()[:-1]) finally: fp.close() else: core_src = None importer = Importer( self.router, self.parent, core_src, self.config.get('whitelist', ()), self.config.get('blacklist', ()), ) self.importer = importer self.router.importer = importer sys.meta_path.insert(0, self.importer) def _setup_package(self): global mitogen mitogen = imp.new_module('mitogen') mitogen.__package__ ='mitogen' mitogen.__path__ = [] mitogen.__loader__ = self.importer mitogen.main = lambda *args, **kwargs: (lambda func: None) mitogen.core = sys.modules['__main__'] mitogen.core.__file__ = 'x/mitogen/core.py' # For inspect.getsource() mitogen.core.__loader__ = self.importer sys.modules['mitogen'] = mitogen sys.modules['mitogen.core'] = mitogen.core del sys.modules['__main__'] def _setup_globals(self): mitogen.is_master = False mitogen.__version__ = self.config['version'] mitogen.context_id = self.config['context_id'] mitogen.parent_ids = self.config['parent_ids'][:] mitogen.parent_id = mitogen.parent_ids[0] def _nullify_stdio(self): """ Open /dev/null to replace stdio temporarily. In case of odd startup, assume we may be allocated a standard handle. """ for stdfd, mode in ((0, os.O_RDONLY), (1, os.O_RDWR), (2, os.O_RDWR)): fd = os.open('/dev/null', mode) if fd!= stdfd: os.dup2(fd, stdfd) os.close(fd) def _preserve_tty_fp(self): """ #481: when stderr is a TTY due to being started via tty_create_child() or hybrid_tty_create_child(), and some privilege escalation tool like prehistoric versions of sudo exec this process over the top of itself, there is nothing left to keep the slave PTY open after we replace our stdio. Therefore if stderr is a TTY, keep around a permanent dup() to avoid receiving SIGHUP. """ try: if os.isatty(2): self.reserve_tty_fp = os.fdopen(os.dup(2), 'r+b', 0) set_cloexec(self.reserve_tty_fp.fileno()) except OSError: pass def _setup_stdio(self): self._preserve_tty_fp() # When sys.stdout was opened by the runtime, overwriting it will not # close FD 1. However when forking from a child that previously used # fdopen(), overwriting it /will/ close FD 1. So we must swallow the # close before IoLogger overwrites FD 1, otherwise its new FD 1 will be # clobbered. Additionally, stdout must be replaced with /dev/null prior # to stdout.close(), since if block buffering was active in the parent, # any pre-fork buffered data will be flushed on close(), corrupting the # connection to the parent. self._nullify_stdio() sys.stdout.close() self._nullify_stdio() self.loggers = [] for name, fd in (('stdout', 1), ('stderr', 2)): log = IoLoggerProtocol.build_stream(name, fd) self.broker.start_receive(log) self.loggers.append(log) # Reopen with line buffering. sys.stdout = os.fdopen(1, 'w', 1) def main(self): self._setup_master() try: try: self._setup_logging() self._setup_importer() self._reap_first_stage() if self.config.get('setup_package', True): self._setup_package() self._setup_globals() if self.config.get('setup_stdio', True): self._setup_stdio() self.dispatcher = Dispatcher(self) self.router.register(self.parent, self.stream) self.router._setup_logging() _v and LOG.debug('Python version is %s', sys.version) _v and LOG.debug('Parent is context %r (%s); my ID is %r', self.parent.context_id, self.parent.name, mitogen.context_id) _v and LOG.debug('pid:%r ppid:%r uid:%r/%r, gid:%r/%r host:%r', os.getpid(), os.getppid(), os.geteuid(), os.getuid(), os.getegid(), os.getgid(), socket.gethostname()) sys.executable = os.environ.pop('ARGV0', sys.executable) _v and LOG.debug('Recovered sys.executable: %r', sys.executable) if self.config.get('send_ec2', True): self.stream.transmit_side.write(b('MITO002\n')) self.broker._py24_25_compat() self.log_handler.uncork() self.dispatcher.run() _v and LOG.debug('ExternalContext.main() normal exit') except KeyboardInterrupt: LOG.debug('KeyboardInterrupt received, exiting gracefully.') except BaseException: LOG.exception('ExternalContext.main() crashed') raise finally: self.broker.shutdown()

mitogen-hq__mitogen

howitworks.rst

Tutorial

Generate how Mitogen works tutorial

BSD 3-Clause New or Revised License

mitogen-hq__mitogen/docs/howitworks.rst

How Mitogen Works Some effort is required to accomplish the seemingly magical feat of bootstrapping a remote Python process without any software installed on the remote machine. The steps involved are unlikely to be immediately obvious to the casual reader, and they required several iterations to discover, so we document them thoroughly below. The UNIX First Stage To allow delivery of the bootstrap compressed using zlib, it is necessary for something on the remote to be prepared to decompress the payload and feed it to a Python interpreter[1]. Since we would like to avoid writing an error-prone shell fragment to implement this, and since we must avoid writing to the remote machine's disk in case it is read-only, the Python process started on the remote machine by Mitogen immediately forks in order to implement the decompression. Python Command Line The Python command line sent to the host is a zlib-compressed[2] and base64-encoded copy of the mitogen.master.Stream._first_stage function, which has been carefully optimized to reduce its size. Prior to compression and encoding, CONTEXT_NAME is replaced with the desired context name in the function's source code. python -c 'exec "xxx".decode("base64").decode("zlib")' The command-line arranges for the Python interpreter to decode the base64'd component, decompress it and execute it as Python code. Base64 is used since to protect against any special characters that may be interpreted by the system shell in use. Forking The First Stage The first stage creates a UNIX pipe and saves a copy of the process's real stdin file descriptor (used for communication with the master) so that it can be recovered by the bootstrapped process later. It then forks into a new process. After fork, the parent half overwrites its stdin with the read end of the pipe, and the child half writes the string MITOGEN0\n, then begins reading the zlib-compressed payload supplied on stdin by the master, and writing the decompressed result to the write-end of the UNIX pipe. To allow recovery of stdin for reuse by the bootstrapped process for parent<->child communication, it is necessary for the first stage to avoid closing stdin or reading from it until EOF. Therefore, the master sends the zlib-compressed payload prefixed with an integer size, allowing reading by the first stage of exactly the required bytes. Configuring argv[0] Forking provides an excellent opportunity to tidy up the eventual Python interpreter, in particular, restarting it using a fresh command-line to get rid of the large base64-encoded first stage parameter, and to replace argv[0] with something descriptive. After configuring its stdin to point to the read end of the pipe, the parent half of the fork re-executes Python, with argv[0] taken from the CONTEXT_NAME variable earlier substituted into its source code. As no arguments are provided to this new execution of Python, and since stdin is connected to a pipe (whose write end is connected to the first stage), the Python interpreter begins reading source code to execute from the pipe connected to stdin. Bootstrap Preparation Now we have the mechanism in place to send a zlib-compressed script to the remote Python interpreter, it is time to choose what to send. The script sent is simply the source code for mitogen.core, with a single line suffixed to trigger execution of the mitogen.core.ExternalContext.main function. The encoded arguments to the main function include some additional details, such as the logging package level that was active in the parent process, and whether debugging or profiling are enabled. After the script source code is prepared, it is passed through mitogen.master.minimize_source to strip it of docstrings and comments, while preserving line numbers. This reduces the compressed payload by around 20%. Preserving The mitogen.core Source One final trick is implemented in the first stage: after bootstrapping the new child, it writes a duplicate copy of the mitogen.core source it just used to bootstrap it back into another pipe connected to the child. The child's module importer cache is initialized with a copy of the source, so that subsequent bootstraps of children-of-children do not require the source to be fetched from the master a second time. Signalling Success Once the first stage has signalled MITO000\n, the master knows it is ready to receive the compressed bootstrap. After decompressing and writing the bootstrap source to its parent Python interpreter, the first stage writes the string MITO001\n to stdout before exiting. The master process waits for this string before considering bootstrap successful and the child's stdio ready to receive messages. The signal value is 8 bytes to match the minimum chunk size required to disambiguate between lines containing an interesting token during SSH password authentication, a debug message from the SSH client itself, or a message from the first stage. ExternalContext.main() mitogen.core.ExternalContext.main Generating A Synthetic mitogen Package Since the bootstrap consists of the mitogen.core source code, and this code is loaded by Python by way of its main script (__main__ module), initially the module layout in the child will be incorrect. The first step taken after bootstrap is to rearrange sys.modules slightly so that mitogen.core appears in the correct location, and all classes defined in that module have their __module__ attribute fixed up such that cPickle correctly serializes instance module names. Once a synthetic mitogen package and mitogen.core module have been generated, the bootstrap deletes sys.modules['__main__'], so that any attempt to import it (by cPickle) will cause the import to be satisfied by fetching the master's actual __main__ module. This is necessary to allow master programs to be written as a self-contained Python script. Reaping The First Stage After the bootstrap has called os.dup on the copy of the stdin file descriptor saved by the first stage, it is closed. Additionally, since the first stage was forked prior to re-executing the Python interpreter, it will exist as a zombie process until the parent process reaps it. Therefore the bootstrap must call os.wait soon after startup. Setup Logging The child's logging package root logger is configured to have the same log level as the root logger in the master, and mitogen.core.LogHandler is installed to forward logs to the master context's FORWARD_LOG <mitogen.core.FORWARD_LOG> handle. The log level is copied into the child to avoid generating a potentially large amount of network IO forwarding logs that will simply be filtered away once they reach the master. The Module Importer An instance of mitogen.core.Importer is installed in sys.meta_path, where Python's import statement will execute it before attempting to find a module locally. Standard IO Redirection Two instances of mitogen.core.IoLogger are created, one for stdout and one for stderr. This class creates a UNIX pipe whose read end is added to the IO multiplexer, and whose write end is used to overwrite the handles inherited during process creation. Even without IO redirection, something must replace stdin and stdout, otherwise it is possible for the stream used for communication between parent and child to be accidentally corrupted by subprocesses run by user code. The inherited stdin is replaced by a file descriptor pointing to /dev/null. Finally Python's sys.stdout is reopened to ensure line buffering is active, so that print statements and suchlike promptly appear in the logs. Function Call Dispatch mitogen.core After all initialization is complete, the child's main thread sits in a loop reading from a Channel <mitogen.core.Channel> connected to the CALL_FUNCTION <mitogen.core.CALL_FUNCTION> handle. This handle is written to by call() <mitogen.parent.Context.call> and call_async() <mitogen.parent.Context.call_async>. CALL_FUNCTION <mitogen.core.CALL_FUNCTION> only accepts requests from the context IDs listed in mitogen.parent_ids, forming a chain of trust between the master and any intermediate context leading to the recipient of the message. In combination with source-verification, this is a major contributor to ensuring contexts running on compromised infrastructure cannot trigger code execution in siblings or any parent. Shutdown mitogen.core When a context receives SHUTDOWN <mitogen.core.SHUTDOWN> from its immediate parent, it closes its own CALL_FUNCTION <mitogen.core.CALL_FUNCTION> Channel <mitogen.core.Channel> before sending SHUTDOWN <mitogen.core.SHUTDOWN> to any directly connected children. Closing the channel has the effect of causing ExternalContext._dispatch_calls to exit and begin joining on the broker thread. During shutdown, the master waits up to 5 seconds for children to disconnect gracefully before force disconnecting them, while children will use that time to call socket.shutdown(SHUT_WR) <socket.socket.shutdown> on their IoLogger <mitogen.core.IoLogger> socket's write ends before draining any remaining data buffered on the read ends, and ensuring any deferred broker function callbacks have had a chance to complete, necessary to capture for example forwarding any remaining logging records. An alternative approach is to wait until the IoLogger socket is completely closed, with some hard timeout, but this necessitates greater discipline than is common in infrastructure code (how often have you forgotten to redirect stderr to /dev/null when starting a daemon process?), so needless irritating delays would often be experienced during program termination. If the main thread (responsible for function call dispatch) fails to shut down gracefully, because some user function is hanging, it will still be cleaned up since as the final step in broker shutdown, the broker sends signal.SIGTERM <signal> to its own process. Stream Protocol mitogen.core Once connected, a basic framing protocol is used to communicate between parent and child. Integers use big endian in their encoded form. Field Size Description ---------- ------ --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- magic 2 Integer 0x4d49 (MI), used to detect stream corruption. dst_id 4 Integer target context ID. Router delivers messages locally when their dst_id matches mitogen.context_id, otherwise they are routed up or downstream. src_id 4 Integer source context ID. Used as the target of replies if any are generated. auth_id 4 The context ID under whose authority the message is acting. See source-verification. handle 4 Integer target handle in the destination context. This is one of the standard-handles, or a dynamically generated handle used to receive a one-time reply, such as the return value of a function call. reply_to 4 Integer target handle to direct any reply to this message. Used to receive a one-time reply, such as the return value of a function call, or to signal a special condition for the message. See below <reply_to_values> for special values for this field. length 4 Length of the data part of the message. data n/a Message data, which may be raw or pickled. Standard Handles Masters listen on the following handles: FORWARD_LOG Receives (logger_name, level, msg) 3-tuples and writes them to the master's mitogen.ctx.<context_name> logger. GET_MODULE Receives the name of a module to load fullname, locates the source code for fullname, and routes one or more LOAD_MODULE messages back towards the sender of the GET_MODULE request. If lookup fails, None is sent instead. See import-preloading for a deeper discussion of GET_MODULE/LOAD_MODULE. ALLOCATE_ID Replies to any message sent to it with a newly allocated range of context IDs, to allow children to safely start their own contexts. Presently IDs are allocated in batches of 1000 from a 32 bit range, allowing up to 4.2 million parent contexts to be created and destroyed before the associated Router must be recreated. This is handled by mitogen.master.IdAllocator in the master process, and messages are sent to it from mitogen.parent.ChildIdAllocator in children. Children listen on the following handles: LOAD_MODULE Receives (pkg_present, path, compressed, related) tuples, composed of: - pkg_present: Either None for a plain .py module, or a list of canonical names of submodules existing witin this package. For example, a LOAD_MODULE for the mitogen package would return a list like: ["mitogen.core", "mitogen.fakessh", "mitogen.master", ..]. This list is used by children to avoid generating useless round-trips due to Python 2.x's import statement behavior. - path: Original filesystem where the module was found on the master. - compressed: zlib-compressed module source code. - related: list of canonical module names on which this module appears to depend. Used by children that have ever started any children of their own to preload those children with LOAD_MODULE messages in response to a GET_MODULE request. CALL_FUNCTION Receives (chain_id, mod_name, class_name, func_name, args, kwargs) 6-tuples from mitogen.parent.CallChain, imports mod_name, then attempts to execute class_name.func_name(*args, **kwargs). - `chain_id`: if not None, an identifier unique to the originating mitogen.parent.CallChain. When set, if an exception occurs during a call, future calls with the same ID automatically fail with the same exception without ever executing, and failed calls with no reply_to set are not dumped to the logging framework as they otherwise would. This is used to implement pipelining. When this channel is closed (by way of receiving a dead message), the child's main thread begins graceful shutdown of its own Broker and Router. SHUTDOWN When received from a child's immediate parent, causes the broker thread to enter graceful shutdown, including sending a dead message to the child's main thread, causing it to join on the exit of the broker thread. The final step of a child's broker shutdown process sends signal.SIGTERM <signal> to itself, ensuring the process dies even if the main thread was hung executing user code. Each context is responsible for sending SHUTDOWN to each of its directly connected children in response to the master sending SHUTDOWN to it, and arranging for the connection to its parent to be closed shortly thereafter. Masters, and children that have ever been used to create a descendent child also listen on the following handles: ADD_ROUTE Receives target_id integer from downstream, describing an ID allocated to a recently constructed child. The receiver verifies no existing route exists to target_id before updating its local table to route messages for target_id via the stream from which the ADD_ROUTE message was received. DEL_ROUTE Receives target_id integer from downstream, verifies a route exists to target_id via the stream on which the message was received, removes that route from its local table, triggers the disconnect signal on any mitogen.core.Context instance in the local process, then propagates the message upward towards its own parent. mitogen.core DETACHING Sent to inform a parent that user code has invoked ExternalContext.detach to decouple the lifecycle of a directly connected context and its subtree from the running program. A child usually shuts down immediately if it loses its parent connection, and parents usually terminate any related Python/SSH subprocess on disconnection. Receiving DETACHING informs the parent the connection will soon drop, but the process intends to continue life independently, and to avoid terminating the related subprocess if that subprocess is the child itself. Non-master parents also listen on the following handles: mitogen.core GET_MODULE As with master's GET_MODULE, except this implementation (mitogen.master.ModuleForwarder) serves responses using mitogen.core.Importer's cache before forwarding the request to its parent context. The response is cached by each context in turn before being forwarded on to the child context that originally made the request. In this way, the master need never re-send a module it has already sent to a direct descendant. mitogen.core FORWARD_MODULE Receives (context, fullname) tuples from its parent and arranges for a LOAD_MODULE to be sent towards context for the module fullname and any related modules. The module must already have been delivered to the current context by its parent in a prior LOAD_MODULE message. If the receiver is the immediate parent of context, then only LOAD_MODULE is sent to the child. Otherwise LOAD_MODULE is sent to the next closest parent if the module has not previously been sent on that stream, followed by a copy of the FORWARD_MODULE message. This message is used to recursively preload indirect children with modules, ensuring they are cached and deduplicated at each hop in the chain leading to the target context. Special values for the reply_to field: IS_DEAD Additional handles are created to receive the result of every function call triggered by call_async() <mitogen.parent.Context.call_async>. Use of Pickle The current implementation uses the Python cPickle module, with a restrictive class whitelist to prevent triggering undesirable code execution. The primary reason for using cPickle is that it is computationally efficient, and avoids including a potentially large body of serialization code in the bootstrap. The pickler will instantiate only built-in types and one of 3 constructor functions, to support unpickling CallError <mitogen.core.CallError>, mitogen.core.Sender,and Context <mitogen.core.Context>. The choice of Pickle is one area to be revisited later. All accounts suggest it cannot be used securely, however few of those accounts appear to be expert, and none mention any additional attacks that would not be prevented by using a restrictive class whitelist. The IO Multiplexer Since we must include our IO multiplexer as part of the bootstrap, off-the-shelf implementations are for the most part entirely inappropriate. For example, a minimal copy of Twisted weighs in at around 440KiB and is composed of approximately 115 files. Even if we could arrange for an entire Python package to be transferred during bootstrap, this minimal configuration is massive in comparison to Mitogen's solution, multiplies quickly in the presence of many machines, and would require manually splitting up the parts of Twisted that we would like to use. Message Routing Routing assumes it is impossible to construct a tree such that one of a context's parents will not know the ID of a target the context is attempting to communicate with. When mitogen.core.Router receives a message, it checks the IDs associated with its directly connected streams for a potential route. If any stream matches, either because it directly connects to the target ID, or because the master sent an ADD_ROUTE <mitogen.core.ADD_ROUTE> message associating it, then the message will be forwarded down the tree using that stream. If the message does not match any ADD_ROUTE <mitogen.core.ADD_ROUTE> message or stream, instead it is forwarded upwards to the immediate parent, and recursively by each parent in turn until one is reached that knows how to forward the message down the tree. When a parent establishes a new child, it sends a corresponding ADD_ROUTE <mitogen.core.ADD_ROUTE> message towards its parent, which recursively forwards it up towards the root. Parents keep note of all routes associated with each stream they connect with, and trigger DEL_ROUTE messages propagated upstream for each route associated with that stream if the stream is disconnected for any reason. Example [image] In the diagram, when node12b is creating the sudo:node12b:webapp context, it must send ADD_ROUTE messages to rack12, which will propagate it to dc1, and recursively to bastion, and master; node12b does not require an ADD_ROUTE message since it has a stream directly connected to the new context. Since Mitogen streams are strictly ordered, it is never possible for a parent to receive a message from a newly constructed child before receiving a corresponding ADD_ROUTE sent by the child's parent, describing how to reply to it. When sudo:node12b:webapp wants to send a message to sudo:node22a:webapp, the message will be routed as follows: sudo:node12b:webapp -> node12b -> rack12 -> dc1 -> bastion -> dc2 -> rack22 -> node22a -> sudo:node22a:webapp [image] Disconnect Propagation To ensure timely shutdown when a failure occurs, where some context is awaiting a response from another context that has become disconnected, mitogen.core.Router additionally records the destination context ID of every message received on a particular stream. When DEL_ROUTE is generated locally or received on some other stream, mitogen.parent.RouteMonitor uses this to find every stream that ever communicated with the route that is about to go away, and forwards the message to each found. The recipient DEL_ROUTE handler in turn uses the message to find any mitogen.core.Context in the local process corresponding to the disappearing route, and if found, fires a disconnected event on it. Any interested party, such as mitogen.core.Receiver, may subscribe to the event and use it to abort any threads that were asleep waiting for a reply that will never arrive. Source Verification Before forwarding or dispatching a message it has received, mitogen.core.Router first looks up the corresponding mitogen.core.Stream it would use to send responses towards the context ID listed in the auth_id field, and if the looked up stream does not match the stream on which the message was received, the message is discarded and a warning is logged. This creates a trust chain leading up to the root of the tree, preventing downstream contexts from injecting messages appearing to be from the master or any more trustworthy parent. In this way, privileged functionality such as CALL_FUNCTION <mitogen.core.CALL_FUNCTION> can base trust decisions on the accuracy of auth_id <stream-protocol>. The auth_id field is separate from src_id in order to support granting privilege to contexts that do not follow the tree's natural trust chain. This supports cases where siblings are permitted to execute code on one another, or where isolated processes can connect to a listener and communicate with an already established established tree, such as where a mitogen.unix client receives the same privilege as the process it connects to. Differences Between Master And Child Brokers The main difference between mitogen.core.Broker and mitogen.master.Broker is that when the stream connection to the parent is lost in a child, the broker will trigger its own shutdown. The Module Importer mitogen.core.Importer is still a work in progress, as there are a variety of approaches to implementing it, and the present implementation is not pefectly efficient in every case. It operates by intercepting import statements via sys.meta_path, asking Python if it can satisfy the import by itself, and if not, indicating to Python that it is capable of loading the module. In load_module() <mitogen.core.Importer.load_module> an RPC is started to the parent context, requesting the module source code by way of a GET_MODULE <mitogen.core.GET_MODULE>. If the parent context does not have the module available, it recursively forwards the request upstream, while avoiding duplicate requests for the same module from its own threads and any child contexts. Neutralizing __main__ To avoid accidental execution of the __main__ module's code in a slave context, when serving the source of the main module, Mitogen removes any code occurring after the first conditional that looks like a standard __main__ execution guard: # Code that looks like this is stripped from __main__. if __name__ == '__main__': run_some_code() To further avoid accidental execution, Mitogen will refuse to serve __main__ to children if no execution guard is found, as it is common that no guard is present during early script prototyping. These are hacks, but they are the safest and least annoying found to solve the problem. Avoiding Negative Imports In Python 2.x where relative imports are the default, a large number of import requests will be made for modules that do not exist. For example: # mypkg/__init__.py import sys import os In Python 2.x, Python will first try to load mypkg.sys and mypkg.os, which do not exist, before falling back on sys and os. These negative imports present a challenge, as they introduce a large number of pointless network round-trips. Therefore in addition to the zlib-compressed source, for packages the master sends along a list of child modules known to exist. Before indicating it can satisfy an import request, mitogen.core.Importer first checks to see if the module belongs to a package it has previously imported, and if so, ignores the request if the module does not appear in the enumeration of child modules belonging to the package that was provided by the master. Import Preloading mitogen.core To further avoid round-trips, when a module or package is requested by a child, its bytecode is scanned in the master to find all the module's import statements, and of those, which associated modules appear to have been loaded in the master's sys.modules. The sys.modules check is necessary to handle various kinds of conditional execution, for example, when a module's code guards an import statement based on the active Python runtime version, operating system, or optional third party dependencies. Before replying to a child's request for a module with dependencies: - If the request is for a package, any dependent modules used by the package that appear within the package itself are known to be missing from the child, since the child requested the top-level package module, therefore they are pre-loaded into the child using LOAD_MODULE messages before sending the LOAD_MODULE message for the requested package module itself. In this way, the child will already have dependent modules cached by the time it receives the requested module, avoiding one round-trip for each dependency. For example, when a child requests the django package, and the master determines the django module code in the master has import statements for django.utils, django.utils.lru_cache, and django.utils.version, and that execution of the module code on the master caused those modules to appear in the master's sys.modules, there is high probability execution of the django module code in the child will cause the same modules to be loaded. Since all those modules exist within the django package, and we already know the child lacks that package, it is safe to assume the child will make follow-up requests for those modules too. In the example, 4 round-trips are replaced by 1 round-trip. For any package module ever requested by a child, the parent keeps a note of the name of the package for one final optimization: - If the request is for a sub-module of a package, and it is known the child loaded the package's implementation from the parent, then any dependent modules of the requested module at any nesting level within the package that is known to be missing are sent using LOAD_MODULE messages before sending the LOAD_MODULE message for the requested module, avoiding 1 round-trip for each dependency within the same top-level package. For example, when a child has previously requested the django package module, the parent knows the package was completely absent on the child. Therefore when the child subsequently requests the django.db package module, it is safe to assume the child will generate subsequent GET_MODULE requests for the 2 django.conf, 3 django.core, 2 django.db, 3 django.dispatch, and 7 django.utils indirect dependencies for django.db. In the example, 17 round-trips are replaced by 1 round-trip. The method used to detect import statements is similar to the standard library modulefinder module: rather than analyze module source code, IMPORT_NAME <python:bytecodes> opcodes are extracted from the module's bytecode. This is since clean source analysis methods (ast and compiler) are an order of magnitude slower, and incompatible across major Python versions. Concurrency Duplicate requests must never be issued to the parent, either due to a local import or any GET_MODULE originating from a child. This lets parents assume a module requested once by a downstream connection need never be re-sent, for example, if it appears as a preloading dependency in a subsequent GET_MODULE, or had been requested immediately after being sent as a preloading dependency for an unrelated request by a descendent. Therefore each tree layer must deduplicate GET_MODULE requests, and synchronize their descendents and local threads on corresponding LOAD_MODULE responses from the parent. In each context, pending requests are serialized by a threading.Lock within mitogen.core.Importer, which may only be held for operations that cannot block, since ModuleForwarder <mitogen.master.ModuleForwarder> must acquire it while synchronizing GET_MODULE requests from children on the IO multiplexer thread. Requests From Local Threads When Mitogen begins satisfying an import, it is known the module has never been imported in the local process. Importer <mitogen.core.Importer> executes under the runtime importer lock, ensuring import statements executing in local threads are serialized. Note In Python 2, ImportError is raised when import is attempted while the runtime import lock is held by another thread, therefore imports must be serialized by only attempting them from the main (CALL_FUNCTION) thread. The problem is most likely to manifest in third party libraries that lazily import optional dependencies at runtime from a non-main thread. The workaround is to explicitly import those dependencies from the main thread before initializing the third party library. This was fixed in Python 3.5, but Python 3.x is not yet supported. See Python Issue #9260. While holding its own lock, Importer <mitogen.core.Importer> checks if the source is not yet cached, determines if an in-flight GET_MODULE exists for it, starting one if none exists, adds itself to a list of callbacks fired when a corresponding LOAD_MODULE arrives from the parent, then sleeps waiting for the callback. When the source becomes available, the module is constructed on the calling thread using the best practice documented in PEP 302. Requests From Children As with local imports, when GET_MODULE is received from a child, while holding the Importer <mitogen.core.Importer> lock, ModuleForwarder <mitogen.master.ModuleForwarder> checks if the source is not yet cached, determines if an in-flight GET_MODULE toward the parent exists for it, starting one if none exists, then adds a completion handler to the list of callbacks fired when a corresponding LOAD_MODULE arrives from the parent. When the source becomes available, the completion handler issues corresponding LOAD_MODULE messages toward the child for the requested module after any required for dependencies known to be absent from the child. Since intermediaries do not know a module's dependencies until the module's source arrives, it is not possible to preemptively issue LOAD_MODULE for those dependencies toward a requesting child as they become available from the parent at the intermediary. This creates needless network serialization and latency that should be addressed in a future design. Child Module Enumeration Package children are enumerated using pkgutil.iter_modules. Use Of Threads The package always runs the IO multiplexer in a thread. This is so the multiplexer retains control flow in order to shut down gracefully, say, if the user's code has hung and the master context has disconnected. While it is possible for the IO multiplexer to recover control of a hung function call on UNIX using for example signal.SIGALRM <signal>, this mechanism is not portable to non-UNIX operating systems, and does not work in every case, for example when Python blocks signals during a variety of threading package operations. At some point it is likely Mitogen will be extended to support children running on Windows. When that happens, it would be nice if the process model on Windows and UNIX did not differ, and in fact the code used on both were identical. Waking Sleeping Threads Due to fundamental deficiencies in Python 2's threading implementation, it is not possible to block waiting on synchronization objects sanely. Two major problems exist: - Sleeping with no timeout set causes signals to be blocked, preventing the user from terminating the process using CTRL+C. - Sleeping with a timeout set internally makes use of polling, with an exponential backoff that eventually results in the thread sleeping unconditionally in 50ms increments. . This is a huge source of latency that quickly multiplies. As the UNIX self-pipe trick must already be employed to wake the broker thread from its select loop, Mitogen reuses this technique to wake any thread synchronization primitive exposed by the library, embodied in a queue-like abstraction called a mitogen.core.Latch. Unfortunately it is commonplace for hosts to enforce severe per-process file descriptors limits, so aside from being inefficient, it is impossible in the usual case to create a pair of descriptors for every waitable object, which for example includes the result of every single asynchronous function call. For this reason self-pipes are created on a per-thread basis, with their associated socketpairs <socket.socketpair> kept in thread-local storage. When a latch wishes to sleep its thread, this pair is created on-demand and temporarily associated with it only for the duration of the sleep. Python's garbage collector is relied on to clean up by calling the pair's destructor on thread exit. There does not otherwise seem to be a robust method to trigger cleanup code on arbitrary threads. To summarize, file descriptor usage is bounded by the number of threads rather than the number of waitables, which is a much smaller number, however it also means that Mitogen requires twice as many file descriptors as there are user threads, with a minimum of 4 required in any configuration. Latch Internals mitogen.core Attributes: - lock – threading.Lock. - queue – items waiting to be dequeued. - sleeping – write sides of the socketpairs for each sleeping thread, and threads in the process of waking from sleep. - waking – integer number of sleeping threads in the process of waking up. - closed – boolean defaulting to False. Every time lock is acquired, closed must be tested, and if it is True, LatchError must be thrown. Latch.put() Latch.put operates by: 1. Acquiring lock. 2. Appending the item on to queue. 3. If waking is less than the length of sleeping, write a byte to the socket at sleeping[waking] and increment waking. In this way each thread is woken only once, and receives each element according to when its socket was placed on sleeping. Latch.close() Latch.close acquires lock, sets closed to True, then writes a byte to every sleeping[waking] socket, while incrementing waking, until no more unwoken sockets exist. Per above, on waking from sleep, after removing itself from sleeping, each sleeping thread tests if closed is True, and if so throws LatchError. It is necessary to ensure at most one byte is delivered on each socket, even if the latch is being torn down, as the sockets outlive the scope of a single latch, and must never have extraneous data buffered on them, as this will cause unexpected wakeups if future latches sleep on the same thread. Latch.get() Latch.get is far more intricate, as there are many outcomes to handle. Queue ordering is strictly first-in first-out, and threads always receive items in the order they are requested, as they become available. 1. Non-empty, No Waiters, No sleep On entry lock is taken, and if queue is non-empty, and sleeping is empty, it is safe to return queue's first item without blocking. 2. Non-empty, Waiters Present, Queue > Waiters, No sleep When sleeping is non-empty but there are more items than sleeping threads, it is safe to pop queue[len(sleeping)] without blocking. 3. Non-empty, Waiters Present, Queue <= Waiters In this case sleeping is non-empty and there are no surplus items. It is not safe to pop any item even though we are holding lock, as it would starve waking threads of their position in favour of the calling thread, since scheduling uncertainty exists between a thread waking from select.select and re-acquiring lock. This avoids the need for a retry loop for waking threads, and a thread being continually re-woken to discover queue drained by a thread that never slept. 4. Sleep Since no surplus items existed, the thread adds its socket to sleeping before releasing lock, and sleeping in select.select waiting for timeout, or a write from Latch.put or Latch.close. If select.select throws an exception, the exception must be caught and re-raised only after some of the wake steps below have completed. 5. Wake, Non-empty On wake lock is re-acquired, the socket is removed from sleeping after noting its index, and TimeoutError is thrown if waking indicates Latch.put() nor Latch.close have yet to send a wake byte to that index. The byte is then read off, LatchError is thrown if closed is True, otherwise the queue item corresponding to the thread's index is popped and returned. It is paramount that in every case, if a byte was written to the socket, that the byte is read away. The socket is reused by subsequent latches sleeping on the same thread, and unexpected wakeups are triggered if extraneous data remains buffered on the socket. It is also necessary to favour the synchronized waking variable over the return value of select.select, as scheduling uncertainty introduces a race between the select timing out, and Latch.put() or Latch.close writing a wake byte before Latch.get has re-acquired lock.

# Copyright 2019, David Wilson # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. #!mitogen: minify_safe """ This module implements most package functionality, but remains separate from non-essential code in order to reduce its size, since it is also serves as the bootstrap implementation sent to every new slave context. """ import binascii import collections import encodings.latin_1 import encodings.utf_8 import errno import fcntl import itertools import linecache import logging import os import pickle as py_pickle import pstats import signal import socket import struct import sys import syslog import threading import time import traceback import warnings import weakref import zlib # Python >3.7 deprecated the imp module. warnings.filterwarnings('ignore', message='the imp module is deprecated') import imp # Absolute imports for <2.5. select = __import__('select') try: import cProfile except ImportError: cProfile = None try: import thread except ImportError: import threading as thread try: import cPickle as pickle except ImportError: import pickle try: from cStringIO import StringIO as BytesIO except ImportError: from io import BytesIO try: BaseException except NameError: BaseException = Exception try: ModuleNotFoundError except NameError: ModuleNotFoundError = ImportError # TODO: usage of 'import' after setting __name__, but before fixing up # sys.modules generates a warning. This happens when profiling = True. warnings.filterwarnings('ignore', "Parent module'mitogen' not found while handling absolute import") LOG = logging.getLogger('mitogen') IOLOG = logging.getLogger('mitogen.io') IOLOG.setLevel(logging.INFO) # str.encode() may take import lock. Deadlock possible if broker calls #.encode() on behalf of thread currently waiting for module. LATIN1_CODEC = encodings.latin_1.Codec() _v = False _vv = False GET_MODULE = 100 CALL_FUNCTION = 101 FORWARD_LOG = 102 ADD_ROUTE = 103 DEL_ROUTE = 104 ALLOCATE_ID = 105 SHUTDOWN = 106 LOAD_MODULE = 107 FORWARD_MODULE = 108 DETACHING = 109 CALL_SERVICE = 110 STUB_CALL_SERVICE = 111 #: Special value used to signal disconnection or the inability to route a #: message, when it appears in the `reply_to` field. Usually causes #: :class:`mitogen.core.ChannelError` to be raised when it is received. #: #: It indicates the sender did not know how to process the message, or wishes #: no further messages to be delivered to it. It is used when: #: #: * a remote receiver is disconnected or explicitly closed. #: * a related message could not be delivered due to no route existing for it. #: * a router is being torn down, as a sentinel value to notify #: :meth:`mitogen.core.Router.add_handler` callbacks to clean up. IS_DEAD = 999 try: BaseException except NameError: BaseException = Exception PY24 = sys.version_info < (2, 5) PY3 = sys.version_info > (3,) if PY3: b = str.encode BytesType = bytes UnicodeType = str FsPathTypes = (str,) BufferType = lambda buf, start: memoryview(buf)[start:] long = int else: b = str BytesType = str FsPathTypes = (str, unicode) BufferType = buffer UnicodeType = unicode AnyTextType = (BytesType, UnicodeType) try: next except NameError: next = lambda it: it.next() # #550: prehistoric WSL did not advertise itself in uname output. try: fp = open('/proc/sys/kernel/osrelease') IS_WSL = 'Microsoft' in fp.read() fp.close() except IOError: IS_WSL = False #: Default size for calls to :meth:`Side.read` or :meth:`Side.write`, and the #: size of buffers configured by :func:`mitogen.parent.create_socketpair`. This #: value has many performance implications, 128KiB seems to be a sweet spot. #: #: * When set low, large messages cause many :class:`Broker` IO loop #: iterations, burning CPU and reducing throughput. #: * When set high, excessive RAM is reserved by the OS for socket buffers (2x #: per child), and an identically sized temporary userspace buffer is #: allocated on each read that requires zeroing, and over a particular size #: may require two system calls to allocate/deallocate. #: #: Care must be taken to ensure the underlying kernel object and receiving #: program support the desired size. For example, #: #: * Most UNIXes have TTYs with fixed 2KiB-4KiB buffers, making them unsuitable #: for efficient IO. #: * Different UNIXes have varying presets for pipes, which may not be #: configurable. On recent Linux the default pipe buffer size is 64KiB, but #: under memory pressure may be as low as 4KiB for unprivileged processes. #: * When communication is via an intermediary process, its internal buffers #: effect the speed OS buffers will drain. For example OpenSSH uses 64KiB #: reads. #: #: An ideal :class:`Message` has a size that is a multiple of #: :data:`CHUNK_SIZE` inclusive of headers, to avoid wasting IO loop iterations #: writing small trailer chunks. CHUNK_SIZE = 131072 _tls = threading.local() if __name__ =='mitogen.core': # When loaded using import mechanism, ExternalContext.main() will not have # a chance to set the synthetic mitogen global, so just import it here. import mitogen else: # When loaded as __main__, ensure classes and functions gain a __module__ # attribute consistent with the host process, so that pickling succeeds. __name__ ='mitogen.core' class Error(Exception): """ Base for all exceptions raised by Mitogen. :param str fmt: Exception text, or format string if `args` is non-empty. :param tuple args: Format string arguments. """ def __init__(self, fmt=None, *args): if args: fmt %= args if fmt and not isinstance(fmt, UnicodeType): fmt = fmt.decode('utf-8') Exception.__init__(self, fmt) class LatchError(Error): """ Raised when an attempt is made to use a :class:`mitogen.core.Latch` that has been marked closed. """ pass class Blob(BytesType): """ A serializable bytes subclass whose content is summarized in repr() output, making it suitable for logging binary data. """ def __repr__(self): return '[blob: %d bytes]' % len(self) def __reduce__(self): return (Blob, (BytesType(self),)) class Secret(UnicodeType): """ A serializable unicode subclass whose content is masked in repr() output, making it suitable for logging passwords. """ def __repr__(self): return '[secret]' if not PY3: # TODO: what is this needed for in 2.x? def __str__(self): return UnicodeType(self) def __reduce__(self): return (Secret, (UnicodeType(self),)) class Kwargs(dict): """ A serializable dict subclass that indicates its keys should be coerced to Unicode on Python 3 and bytes on Python<2.6. Python 2 produces keyword argument dicts whose keys are bytes, requiring a helper to ensure compatibility with Python 3 where Unicode is required, whereas Python 3 produces keyword argument dicts whose keys are Unicode, requiring a helper for Python 2.4/2.5, where bytes are required. """ if PY3: def __init__(self, dct): for k, v in dct.items(): if type(k) is bytes: self[k.decode()] = v else: self[k] = v elif sys.version_info < (2, 6, 5): def __init__(self, dct): for k, v in dct.iteritems(): if type(k) is unicode: k, _ = encodings.utf_8.encode(k) self[k] = v def __repr__(self): return 'Kwargs(%s)' % (dict.__repr__(self),) def __reduce__(self): return (Kwargs, (dict(self),)) class CallError(Error): """ Serializable :class:`Error` subclass raised when :meth:`Context.call() <mitogen.parent.Context.call>` fails. A copy of the traceback from the external context is appended to the exception message. """ def __init__(self, fmt=None, *args): if not isinstance(fmt, BaseException): Error.__init__(self, fmt, *args) else: e = fmt cls = e.__class__ fmt = '%s.%s: %s' % (cls.__module__, cls.__name__, e) tb = sys.exc_info()[2] if tb: fmt += '\n' fmt += ''.join(traceback.format_tb(tb)) Error.__init__(self, fmt) def __reduce__(self): return (_unpickle_call_error, (self.args[0],)) def _unpickle_call_error(s): if not (type(s) is UnicodeType and len(s) < 10000): raise TypeError('cannot unpickle CallError: bad input') return CallError(s) class ChannelError(Error): """ Raised when a channel dies or has been closed. """ remote_msg = 'Channel closed by remote end.' local_msg = 'Channel closed by local end.' class StreamError(Error): """ Raised when a stream cannot be established. """ pass class TimeoutError(Error): """ Raised when a timeout occurs on a stream. """ pass def to_text(o): """ Coerce `o` to Unicode by decoding it from UTF-8 if it is an instance of :class:`bytes`, otherwise pass it to the :class:`str` constructor. The returned object is always a plain :class:`str`, any subclass is removed. """ if isinstance(o, BytesType): return o.decode('utf-8') return UnicodeType(o) # Documented in api.rst to work around Sphinx limitation. now = getattr(time,'monotonic', time.time) # Python 2.4 try: any except NameError: def any(it): for elem in it: if elem: return True def _partition(s, sep, find): """ (str|unicode).(partition|rpartition) for Python 2.4/2.5. """ idx = find(sep) if idx!= -1: left = s[0:idx] return left, sep, s[len(left)+len(sep):] def threading__current_thread(): try: return threading.current_thread() # Added in Python 2.6+ except AttributeError: return threading.currentThread() # Deprecated in Python 3.10+ def threading__thread_name(thread): try: return thread.name # Added in Python 2.6+ except AttributeError: return thread.getName() # Deprecated in Python 3.10+ if hasattr(UnicodeType, 'rpartition'): str_partition = UnicodeType.partition str_rpartition = UnicodeType.rpartition bytes_partition = BytesType.partition else: def str_partition(s, sep): return _partition(s, sep, s.find) or (s, u'', u'') def str_rpartition(s, sep): return _partition(s, sep, s.rfind) or (u'', u'', s) def bytes_partition(s, sep): return _partition(s, sep, s.find) or (s, '', '') def _has_parent_authority(context_id): return ( (context_id == mitogen.context_id) or (context_id in mitogen.parent_ids) ) def has_parent_authority(msg, _stream=None): """ Policy function for use with :class:`Receiver` and :meth:`Router.add_handler` that requires incoming messages to originate from a parent context, or on a :class:`Stream` whose :attr:`auth_id <Stream.auth_id>` has been set to that of a parent context or the current context. """ return _has_parent_authority(msg.auth_id) def _signals(obj, signal): return ( obj.__dict__ .setdefault('_signals', {}) .setdefault(signal, []) ) def listen(obj, name, func): """ Arrange for `func()` to be invoked when signal `name` is fired on `obj`. """ _signals(obj, name).append(func) def unlisten(obj, name, func): """ Remove `func()` from the list of functions invoked when signal `name` is fired by `obj`. :raises ValueError: `func()` was not on the list. """ _signals(obj, name).remove(func) def fire(obj, name, *args, **kwargs): """ Arrange for `func(*args, **kwargs)` to be invoked for every function registered for signal `name` on `obj`. """ for func in _signals(obj, name): func(*args, **kwargs) def takes_econtext(func): """ Decorator that marks a function or class method to automatically receive a kwarg named `econtext`, referencing the :class:`mitogen.core.ExternalContext` active in the context in which the function is being invoked in. The decorator is only meaningful when the function is invoked via :data:`CALL_FUNCTION <mitogen.core.CALL_FUNCTION>`. When the function is invoked directly, `econtext` must still be passed to it explicitly. """ func.mitogen_takes_econtext = True return func def takes_router(func): """ Decorator that marks a function or class method to automatically receive a kwarg named `router`, referencing the :class:`mitogen.core.Router` active in the context in which the function is being invoked in. The decorator is only meaningful when the function is invoked via :data:`CALL_FUNCTION <mitogen.core.CALL_FUNCTION>`. When the function is invoked directly, `router` must still be passed to it explicitly. """ func.mitogen_takes_router = True return func def is_blacklisted_import(importer, fullname): """ Return :data:`True` if `fullname` is part of a blacklisted package, or if any packages have been whitelisted and `fullname` is not part of one. NB: - If a package is on both lists, then it is treated as blacklisted. - If any package is whitelisted, then all non-whitelisted packages are treated as blacklisted. """ return ((not any(fullname.startswith(s) for s in importer.whitelist)) or (any(fullname.startswith(s) for s in importer.blacklist))) def set_cloexec(fd): """ Set the file descriptor `fd` to automatically close on :func:`os.execve`. This has no effect on file descriptors inherited across :func:`os.fork`, they must be explicitly closed through some other means, such as :func:`mitogen.fork.on_fork`. """ flags = fcntl.fcntl(fd, fcntl.F_GETFD) assert fd > 2, 'fd %r <= 2' % (fd,) fcntl.fcntl(fd, fcntl.F_SETFD, flags | fcntl.FD_CLOEXEC) def set_nonblock(fd): """ Set the file descriptor `fd` to non-blocking mode. For most underlying file types, this causes :func:`os.read` or :func:`os.write` to raise :class:`OSError` with :data:`errno.EAGAIN` rather than block the thread when the underlying kernel buffer is exhausted. """ flags = fcntl.fcntl(fd, fcntl.F_GETFL) fcntl.fcntl(fd, fcntl.F_SETFL, flags | os.O_NONBLOCK) def set_block(fd): """ Inverse of :func:`set_nonblock`, i.e. cause `fd` to block the thread when the underlying kernel buffer is exhausted. """ flags = fcntl.fcntl(fd, fcntl.F_GETFL) fcntl.fcntl(fd, fcntl.F_SETFL, flags & ~os.O_NONBLOCK) def io_op(func, *args): """ Wrap `func(*args)` that may raise :class:`select.error`, :class:`IOError`, or :class:`OSError`, trapping UNIX error codes relating to disconnection and retry events in various subsystems: * When a signal is delivered to the process on Python 2, system call retry is signalled through :data:`errno.EINTR`. The invocation is automatically restarted. * When performing IO against a TTY, disconnection of the remote end is signalled by :data:`errno.EIO`. * When performing IO against a socket, disconnection of the remote end is signalled by :data:`errno.ECONNRESET`. * When performing IO against a pipe, disconnection of the remote end is signalled by :data:`errno.EPIPE`. :returns: Tuple of `(return_value, disconnect_reason)`, where `return_value` is the return value of `func(*args)`, and `disconnected` is an exception instance when disconnection was detected, otherwise :data:`None`. """ while True: try: return func(*args), None except (select.error, OSError, IOError): e = sys.exc_info()[1] _vv and IOLOG.debug('io_op(%r) -> OSError: %s', func, e) if e.args[0] == errno.EINTR: continue if e.args[0] in (errno.EIO, errno.ECONNRESET, errno.EPIPE): return None, e raise class PidfulStreamHandler(logging.StreamHandler): """ A :class:`logging.StreamHandler` subclass used when :meth:`Router.enable_debug() <mitogen.master.Router.enable_debug>` has been called, or the `debug` parameter was specified during context construction. Verifies the process ID has not changed on each call to :meth:`emit`, reopening the associated log file when a change is detected. This ensures logging to the per-process output files happens correctly even when uncooperative third party components call :func:`os.fork`. """ #: PID that last opened the log file. open_pid = None #: Output path template. template = '/tmp/mitogen.%s.%s.log' def _reopen(self): self.acquire() try: if self.open_pid == os.getpid(): return ts = time.strftime('%Y%m%d_%H%M%S') path = self.template % (os.getpid(), ts) self.stream = open(path, 'w', 1) set_cloexec(self.stream.fileno()) self.stream.write('Parent PID: %s\n' % (os.getppid(),)) self.stream.write('Created by:\n\n%s\n' % ( ''.join(traceback.format_stack()), )) self.open_pid = os.getpid() finally: self.release() def emit(self, record): if self.open_pid!= os.getpid(): self._reopen() logging.StreamHandler.emit(self, record) def enable_debug_logging(): global _v, _vv _v = True _vv = True root = logging.getLogger() root.setLevel(logging.DEBUG) IOLOG.setLevel(logging.DEBUG) handler = PidfulStreamHandler() handler.formatter = logging.Formatter( '%(asctime)s %(levelname).1s %(name)s: %(message)s', '%H:%M:%S' ) root.handlers.insert(0, handler) _profile_hook = lambda name, func, *args: func(*args) _profile_fmt = os.environ.get( 'MITOGEN_PROFILE_FMT', '/tmp/mitogen.stats.%(pid)s.%(identity)s.%(now)s.%(ext)s', ) def _profile_hook(name, func, *args): """ Call `func(*args)` and return its result. This function is replaced by :func:`_real_profile_hook` when :func:`enable_profiling` is called. This interface is obsolete and will be replaced by a signals-based integration later on. """ return func(*args) def _real_profile_hook(name, func, *args): profiler = cProfile.Profile() profiler.enable() try: return func(*args) finally: path = _profile_fmt % { 'now': int(1e6 * now()), 'identity': name, 'pid': os.getpid(), 'ext': '%s' } profiler.dump_stats(path % ('pstats',)) profiler.create_stats() fp = open(path % ('log',), 'w') try: stats = pstats.Stats(profiler, stream=fp) stats.sort_stats('cumulative') stats.print_stats() finally: fp.close() def enable_profiling(econtext=None): global _profile_hook _profile_hook = _real_profile_hook def import_module(modname): """ Import `module` and return the attribute named `attr`. """ return __import__(modname, None, None, ['']) def pipe(): """ Create a UNIX pipe pair using :func:`os.pipe`, wrapping the returned descriptors in Python file objects in order to manage their lifetime and ensure they are closed when their last reference is discarded and they have not been closed explicitly. """ rfd, wfd = os.pipe() return ( os.fdopen(rfd, 'rb', 0), os.fdopen(wfd, 'wb', 0) ) def iter_split(buf, delim, func): """ Invoke `func(s)` for each `delim`-delimited chunk in the potentially large `buf`, avoiding intermediate lists and quadratic string operations. Return the trailing undelimited portion of `buf`, or any unprocessed portion of `buf` after `func(s)` returned :data:`False`. :returns: `(trailer, cont)`, where `cont` is :data:`False` if the last call to `func(s)` returned :data:`False`. """ dlen = len(delim) start = 0 cont = True while cont: nl = buf.find(delim, start) if nl == -1: break cont = not func(buf[start:nl]) is False start = nl + dlen return buf[start:], cont class Py24Pickler(py_pickle.Pickler): """ Exceptions were classic classes until Python 2.5. Sadly for 2.4, cPickle offers little control over how a classic instance is pickled. Therefore 2.4 uses a pure-Python pickler, so CallError can be made to look as it does on newer Pythons. This mess will go away once proper serialization exists. """ @classmethod def dumps(cls, obj, protocol): bio = BytesIO() self = cls(bio, protocol=protocol) self.dump(obj) return bio.getvalue() def save_exc_inst(self, obj): if isinstance(obj, CallError): func, args = obj.__reduce__() self.save(func) self.save(args) self.write(py_pickle.REDUCE) else: py_pickle.Pickler.save_inst(self, obj) if PY24: dispatch = py_pickle.Pickler.dispatch.copy() dispatch[py_pickle.InstanceType] = save_exc_inst if PY3: # In 3.x Unpickler is a class exposing find_class as an overridable, but it # cannot be overridden without subclassing. class _Unpickler(pickle.Unpickler): def find_class(self, module, func): return self.find_global(module, func) pickle__dumps = pickle.dumps elif PY24: # On Python 2.4, we must use a pure-Python pickler. pickle__dumps = Py24Pickler.dumps _Unpickler = pickle.Unpickler else: pickle__dumps = pickle.dumps # In 2.x Unpickler is a function exposing a writeable find_global # attribute. _Unpickler = pickle.Unpickler class Message(object): """ Messages are the fundamental unit of communication, comprising fields from the :ref:`stream-protocol` header, an optional reference to the receiving :class:`mitogen.core.Router` for ingress messages, and helper methods for deserialization and generating replies. """ #: Integer target context ID. :class:`Router` delivers messages locally #: when their :attr:`dst_id` matches :data:`mitogen.context_id`, otherwise #: they are routed up or downstream. dst_id = None #: Integer source context ID. Used as the target of replies if any are #: generated. src_id = None #: Context ID under whose authority the message is acting. See #: :ref:`source-verification`. auth_id = None #: Integer target handle in the destination context. This is one of the #: :ref:`standard-handles`, or a dynamically generated handle used to #: receive a one-time reply, such as the return value of a function call. handle = None #: Integer target handle to direct any reply to this message. Used to #: receive a one-time reply, such as the return value of a function call. #: :data:`IS_DEAD` has a special meaning when it appears in this field. reply_to = None #: Raw message data bytes. data = b('') _unpickled = object() #: The :class:`Router` responsible for routing the message. This is #: :data:`None` for locally originated messages. router = None #: The :class:`Receiver` over which the message was last received. Part of #: the :class:`mitogen.select.Select` interface. Defaults to :data:`None`. receiver = None HEADER_FMT = '>hLLLLLL' HEADER_LEN = struct.calcsize(HEADER_FMT) HEADER_MAGIC = 0x4d49 # 'MI' def __init__(self, **kwargs): """ Construct a message from from the supplied `kwargs`. :attr:`src_id` and :attr:`auth_id` are always set to :data:`mitogen.context_id`. """ self.src_id = mitogen.context_id self.auth_id = mitogen.context_id vars(self).update(kwargs) assert isinstance(self.data, BytesType), 'Message data is not Bytes' def pack(self): return ( struct.pack(self.HEADER_FMT, self.HEADER_MAGIC, self.dst_id, self.src_id, self.auth_id, self.handle, self.reply_to or 0, len(self.data)) + self.data ) def _unpickle_context(self, context_id, name): return _unpickle_context(context_id, name, router=self.router) def _unpickle_sender(self, context_id, dst_handle): return _unpickle_sender(self.router, context_id, dst_handle) def _unpickle_bytes(self, s, encoding): s, n = LATIN1_CODEC.encode(s) return s def _find_global(self, module, func): """ Return the class implementing `module_name.class_name` or raise `StreamError` if the module is not whitelisted. """ if module == __name__: if func == '_unpickle_call_error' or func == 'CallError': return _unpickle_call_error elif func == '_unpickle_sender': return self._unpickle_sender elif func == '_unpickle_context': return self._unpickle_context elif func == 'Blob': return Blob elif func == 'Secret': return Secret elif func == 'Kwargs': return Kwargs elif module == '_codecs' and func == 'encode': return self._unpickle_bytes elif module == '__builtin__' and func == 'bytes': return BytesType raise StreamError('cannot unpickle %r/%r', module, func) @property def is_dead(self): """ :data:`True` if :attr:`reply_to` is set to the magic value :data:`IS_DEAD`, indicating the sender considers the channel dead. Dead messages can be raised in a variety of circumstances, see :data:`IS_DEAD` for more information. """ return self.reply_to == IS_DEAD @classmethod def dead(cls, reason=None, **kwargs): """ Syntax helper to construct a dead message. """ kwargs['data'], _ = encodings.utf_8.encode(reason or u'') return cls(reply_to=IS_DEAD, **kwargs) @classmethod def pickled(cls, obj, **kwargs): """ Construct a pickled message, setting :attr:`data` to the serialization of `obj`, and setting remaining fields using `kwargs`. :returns: The new message. """ self = cls(**kwargs) try: self.data = pickle__dumps(obj, protocol=2) except pickle.PicklingError: e = sys.exc_info()[1] self.data = pickle__dumps(CallError(e), protocol=2) return self def reply(self, msg, router=None, **kwargs): """ Compose a reply to this message and send it using :attr:`router`, or `router` is :attr:`router` is :data:`None`. :param obj: Either a :class:`Message`, or an object to be serialized in order to construct a new message. :param router: Optional router to use if :attr:`router` is :data:`None`. :param kwargs: Optional keyword parameters overriding message fields in the reply. """ if not isinstance(msg, Message): msg = Message.pickled(msg) msg.dst_id = self.src_id msg.handle = self.reply_to vars(msg).update(kwargs) if msg.handle: (self.router or router).route(msg) else: LOG.debug('dropping reply to message with no return address: %r', msg) if PY3: UNPICKLER_KWARGS = {'encoding': 'bytes'} else: UNPICKLER_KWARGS = {} def _throw_dead(self): if len(self.data): raise ChannelError(self.data.decode('utf-8','replace')) elif self.src_id == mitogen.context_id: raise ChannelError(ChannelError.local_msg) else: raise ChannelError(ChannelError.remote_msg) def unpickle(self, throw=True, throw_dead=True): """ Unpickle :attr:`data`, optionally raising any exceptions present. :param bool throw_dead: If :data:`True`, raise exceptions, otherwise it is the caller's responsibility. :raises CallError: The serialized data contained CallError exception. :raises ChannelError: The `is_dead` field was set. """ _vv and IOLOG.debug('%r.unpickle()', self) if throw_dead and self.is_dead: self._throw_dead() obj = self._unpickled if obj is Message._unpickled: fp = BytesIO(self.data) unpickler = _Unpickler(fp, **self.UNPICKLER_KWARGS) unpickler.find_global = self._find_global try: # Must occur off the broker thread. try: obj = unpickler.load() except: LOG.error('raw pickle was: %r', self.data) raise self._unpickled = obj except (TypeError, ValueError): e = sys.exc_info()[1] raise StreamError('invalid message: %s', e) if throw: if isinstance(obj, CallError): raise obj return obj def __repr__(self): return 'Message(%r, %r, %r, %r, %r, %r..%d)' % ( self.dst_id, self.src_id, self.auth_id, self.handle, self.reply_to, (self.data or '')[:50], len(self.data) ) class Sender(object): """ Senders are used to send pickled messages to a handle in another context, it is the inverse of :class:`mitogen.core.Receiver`. Senders may be serialized, making them convenient to wire up data flows. See :meth:`mitogen.core.Receiver.to_sender` for more information. :param mitogen.core.Context context: Context to send messages to. :param int dst_handle: Destination handle to send messages to. """ def __init__(self, context, dst_handle): self.context = context self.dst_handle = dst_handle def send(self, data): """ Send `data` to the remote end. """ _vv and IOLOG.debug('%r.send(%r..)', self, repr(data)[:100]) self.context.send(Message.pickled(data, handle=self.dst_handle)) explicit_close_msg = 'Sender was explicitly closed' def close(self): """ Send a dead message to the remote, causing :meth:`ChannelError` to be raised in any waiting thread. """ _vv and IOLOG.debug('%r.close()', self) self.context.send( Message.dead( reason=self.explicit_close_msg, handle=self.dst_handle ) ) def __repr__(self): return 'Sender(%r, %r)' % (self.context, self.dst_handle) def __reduce__(self): return _unpickle_sender, (self.context.context_id, self.dst_handle) def _unpickle_sender(router, context_id, dst_handle): if not (isinstance(router, Router) and isinstance(context_id, (int, long)) and context_id >= 0 and isinstance(dst_handle, (int, long)) and dst_handle > 0): raise TypeError('cannot unpickle Sender: bad input or missing router') return Sender(Context(router, context_id), dst_handle) class Receiver(object): """ Receivers maintain a thread-safe queue of messages sent to a handle of this context from another context. :param mitogen.core.Router router: Router to register the handler on. :param int handle: If not :data:`None`, an explicit handle to register, otherwise an unused handle is chosen. :param bool persist: If :data:`False`, unregister the handler after one message is received. Single-message receivers are intended for RPC-like transactions, such as in the case of :meth:`mitogen.parent.Context.call_async`. :param mitogen.core.Context respondent: Context this receiver is receiving from. If not :data:`None`, arranges for the receiver to receive a dead message if messages can no longer be routed to the context due to disconnection, and ignores messages that did not originate from the respondent context. """ #: If not :data:`None`, a function invoked as `notify(receiver)` after a #: message has been received. The function is invoked on :class:`Broker` #: thread, therefore it must not block. Used by #: :class:`mitogen.select.Select` to efficiently implement waiting on #: multiple event sources. notify = None raise_channelerror = True def __init__(self, router, handle=None, persist=True, respondent=None, policy=None, overwrite=False): self.router = router #: The handle. self.handle = handle # Avoid __repr__ crash in add_handler() self._latch = Latch() # Must exist prior to.add_handler() self.handle = router.add_handler( fn=self._on_receive, handle=handle, policy=policy, persist=persist, respondent=respondent, overwrite=overwrite, ) def __repr__(self): return 'Receiver(%r, %r)' % (self.router, self.handle) def __enter__(self): return self def __exit__(self, _1, _2, _3): self.close() def to_sender(self): """ Return a :class:`Sender` configured to deliver messages to this receiver. As senders are serializable, this makes it convenient to pass `(context_id, handle)` pairs around:: def deliver_monthly_report(sender): for line in open('monthly_report.txt'): sender.send(line) sender.close() @mitogen.main() def main(router): remote = router.ssh(hostname='mainframe') recv = mitogen.core.Receiver(router) remote.call(deliver_monthly_report, recv.to_sender()) for msg in recv: print(msg) """ return Sender(self.router.myself(), self.handle) def _on_receive(self, msg): """ Callback registered for the handle with :class:`Router`; appends data to the internal queue. """ _vv and IOLOG.debug('%r._on_receive(%r)', self, msg) self._latch.put(msg) if self.notify: self.notify(self) closed_msg = 'the Receiver has been closed' def close(self): """ Unregister the receiver's handle from its associated router, and cause :class:`ChannelError` to be raised in any thread waiting in :meth:`get` on this receiver. """ if self.handle: self.router.del_handler(self.handle) self.handle = None self._latch.close() def size(self): """ Return the number of items currently buffered. As with :class:`Queue.Queue`, `0` may be returned even though a subsequent call to :meth:`get` will succeed, since a message may be posted at any moment between :meth:`size` and :meth:`get`. As with :class:`Queue.Queue`, `>0` may be returned even though a subsequent call to :meth:`get` will block, since another waiting thread may be woken at any moment between :meth:`size` and :meth:`get`. :raises LatchError: The underlying latch has already been marked closed. """ return self._latch.size() def empty(self): """ Return `size() == 0`. .. deprecated:: 0.2.8 Use :meth:`size` instead. :raises LatchError: The latch has already been marked closed. """ return self._latch.empty() def get(self, timeout=None, block=True, throw_dead=True): """ Sleep waiting for a message to arrive on this receiver. :param float timeout: If not :data:`None`, specifies a timeout in seconds. :raises mitogen.core.ChannelError: The remote end indicated the channel should be closed, communication with it was lost, or :meth:`close` was called in the local process. :raises mitogen.core.TimeoutError: Timeout was reached. :returns: :class:`Message` that was received. """ _vv and IOLOG.debug('%r.get(timeout=%r, block=%r)', self, timeout, block) try: msg = self._latch.get(timeout=timeout, block=block) except LatchError: raise ChannelError(self.closed_msg) if msg.is_dead and throw_dead: msg._throw_dead() return msg def __iter__(self): """ Yield consecutive :class:`Message` instances delivered to this receiver until :class:`ChannelError` is raised. """ while True: try: msg = self.get() except ChannelError: return yield msg class Channel(Sender, Receiver): """ A channel inherits from :class:`mitogen.core.Sender` and `mitogen.core.Receiver` to provide bidirectional functionality. .. deprecated:: 0.2.0 This class is incomplete and obsolete, it will be removed in Mitogen 0.3. Channels were an early attempt at syntax sugar. It is always easier to pass around unidirectional pairs of senders/receivers, even though the syntax is baroque: .. literalinclude::../examples/ping_pong.py Since all handles aren't known until after both ends are constructed, for both ends to communicate through a channel, it is necessary for one end to retrieve the handle allocated to the other and reconfigure its own channel to match. Currently this is a manual task. """ def __init__(self, router, context, dst_handle, handle=None): Sender.__init__(self, context, dst_handle) Receiver.__init__(self, router, handle) def close(self): Receiver.close(self) Sender.close(self) def __repr__(self): return 'Channel(%s, %s)' % ( Sender.__repr__(self), Receiver.__repr__(self) ) class Importer(object): """ Import protocol implementation that fetches modules from the parent process. :param context: Context to communicate via. """ # The Mitogen package is handled specially, since the child context must # construct it manually during startup. MITOGEN_PKG_CONTENT = [ 'buildah', 'compat', 'debug', 'doas', 'docker', 'kubectl', 'fakessh', 'fork', 'jail', 'lxc', 'lxd', 'master', 'minify', 'os_fork', 'parent', 'podman', 'select', 'service', 'setns', 'ssh', 'su', 'sudo', 'utils', ] ALWAYS_BLACKLIST = [ # 2.x generates needless imports for 'builtins', while 3.x does the # same for '__builtin__'. The correct one is built-in, the other always # a negative round-trip. 'builtins', '__builtin__', # On some Python releases (e.g. 3.8, 3.9) the subprocess module tries # to import of this Windows-only builtin module. 'msvcrt', # Python 2.x module that was renamed to _thread in 3.x. # This entry avoids a roundtrip on 2.x -> 3.x. 'thread', # org.python.core imported by copy, pickle, xml.sax; breaks Jython, but # very unlikely to trigger a bug report. 'org', ] if PY3: ALWAYS_BLACKLIST += ['cStringIO'] def __init__(self, router, context, core_src, whitelist=(), blacklist=()): self._log = logging.getLogger('mitogen.importer') self._context = context self._present = {'mitogen': self.MITOGEN_PKG_CONTENT} self._lock = threading.Lock() self.whitelist = list(whitelist) or [''] self.blacklist = list(blacklist) + self.ALWAYS_BLACKLIST # Preserve copies of the original server-supplied whitelist/blacklist # for later use by children. self.master_whitelist = self.whitelist[:] self.master_blacklist = self.blacklist[:] # Presence of an entry in this map indicates in-flight GET_MODULE. self._callbacks = {} self._cache = {} if core_src: self._update_linecache('x/mitogen/core.py', core_src) self._cache['mitogen.core'] = ( 'mitogen.core', None, 'x/mitogen/core.py', zlib.compress(core_src, 9), [], ) self._install_handler(router) def _update_linecache(self, path, data): """ The Python 2.4 linecache module, used to fetch source code for tracebacks and :func:`inspect.getsource`, does not support PEP-302, meaning it needs extra help to for Mitogen-loaded modules. Directly populate its cache if a loaded module belongs to the Mitogen package. """ if PY24 and'mitogen' in path: linecache.cache[path] = ( len(data), 0.0, [line+'\n' for line in data.splitlines()], path, ) def _install_handler(self, router): router.add_handler( fn=self._on_load_module, handle=LOAD_MODULE, policy=has_parent_authority, ) def __repr__(self): return 'Importer' def builtin_find_module(self, fullname): # imp.find_module() will always succeed for __main__, because it is a # built-in module. That means it exists on a special linked list deep # within the bowels of the interpreter. We must special case it. if fullname == '__main__': raise ModuleNotFoundError() parent, _, modname = str_rpartition(fullname, '.') if parent: path = sys.modules[parent].__path__ else: path = None fp, pathname, description = imp.find_module(modname, path) if fp: fp.close() def find_module(self, fullname, path=None): """ Return a loader (ourself) or None, for the module with fullname. Implements importlib.abc.MetaPathFinder.find_module(). Deprecrated in Python 3.4+, replaced by find_spec(). Raises ImportWarning in Python 3.10+. fullname A (fully qualified?) module name, e.g. "os.path". path __path__ of parent packge. None for a top level module. """ if hasattr(_tls, 'running'): return None _tls.running = True try: #_v and self._log.debug('Python requested %r', fullname) fullname = to_text(fullname) pkgname, dot, _ = str_rpartition(fullname, '.') pkg = sys.modules.get(pkgname) if pkgname and getattr(pkg, '__loader__', None) is not self: self._log.debug('%s is submodule of a locally loaded package', fullname) return None suffix = fullname[len(pkgname+dot):] if pkgname and suffix not in self._present.get(pkgname, ()): self._log.debug('%s has no submodule %s', pkgname, suffix) return None # #114: explicitly whitelisted prefixes override any # system-installed package. if self.whitelist!= ['']: if any(fullname.startswith(s) for s in self.whitelist): return self try: self.builtin_find_module(fullname) _vv and self._log.debug('%r is available locally', fullname) except ImportError: _vv and self._log.debug('we will try to load %r', fullname) return self finally: del _tls.running blacklisted_msg = ( '%r is present in the Mitogen importer blacklist, therefore this ' 'context will not attempt to request it from the master, as the ' 'request will always be refused.' ) pkg_resources_msg = ( 'pkg_resources is prohibited from importing __main__, as it causes ' 'problems in applications whose main module is not designed to be ' 're-imported by children.' ) absent_msg = ( 'The Mitogen master process was unable to serve %r. It may be a ' 'native Python extension, or it may be missing entirely. Check the ' 'importer debug logs on the master for more information.' ) def _refuse_imports(self, fullname): if is_blacklisted_import(self, fullname): raise ModuleNotFoundError(self.blacklisted_msg % (fullname,)) f = sys._getframe(2) requestee = f.f_globals['__name__'] if fullname == '__main__' and requestee == 'pkg_resources': # Anything that imports pkg_resources will eventually cause # pkg_resources to try and scan __main__ for its __requires__ # attribute (pkg_resources/__init__.py::_build_master()). This # breaks any app that is not expecting its __main__ to suddenly be # sucked over a network and injected into a remote process, like # py.test. raise ModuleNotFoundError(self.pkg_resources_msg) if fullname == 'pbr': # It claims to use pkg_resources to read version information, which # would result in PEP-302 being used, but it actually does direct # filesystem access. So instead smodge the environment to override # any version that was defined. This will probably break something # later. os.environ['PBR_VERSION'] = '0.0.0' def _on_load_module(self, msg): if msg.is_dead: return tup = msg.unpickle() fullname = tup[0] _v and self._log.debug('received %s', fullname) self._lock.acquire() try: self._cache[fullname] = tup if tup[2] is not None and PY24: self._update_linecache( path='master:' + tup[2], data=zlib.decompress(tup[3]) ) callbacks = self._callbacks.pop(fullname, []) finally: self._lock.release() for callback in callbacks: callback() def _request_module(self, fullname, callback): self._lock.acquire() try: present = fullname in self._cache if not present: funcs = self._callbacks.get(fullname) if funcs is not None: _v and self._log.debug('existing request for %s in flight', fullname) funcs.append(callback) else: _v and self._log.debug('sending new %s request to parent', fullname) self._callbacks[fullname] = [callback] self._context.send( Message(data=b(fullname), handle=GET_MODULE) ) finally: self._lock.release() if present: callback() def load_module(self, fullname): """ Return the loaded module specified by fullname. Implements importlib.abc.Loader.load_module(). Deprecated in Python 3.4+, replaced by create_module() & exec_module(). """ fullname = to_text(fullname) _v and self._log.debug('requesting %s', fullname) self._refuse_imports(fullname) event = threading.Event() self._request_module(fullname, event.set) event.wait() ret = self._cache[fullname] if ret[2] is None: raise ModuleNotFoundError(self.absent_msg % (fullname,)) pkg_present = ret[1] mod = sys.modules.setdefault(fullname, imp.new_module(fullname)) mod.__file__ = self.get_filename(fullname) mod.__loader__ = self if pkg_present is not None: # it's a package. mod.__path__ = [] mod.__package__ = fullname self._present[fullname] = pkg_present else: mod.__package__ = str_rpartition(fullname, '.')[0] or None if mod.__package__ and not PY3: # 2.x requires __package__ to be exactly a string. mod.__package__, _ = encodings.utf_8.encode(mod.__package__) source = self.get_source(fullname) try: code = compile(source, mod.__file__, 'exec', 0, 1) except SyntaxError: LOG.exception('while importing %r', fullname) raise if PY3: exec(code, vars(mod)) else: exec('exec code in vars(mod)') # #590: if a module replaces itself in sys.modules during import, below # is necessary. This matches PyImport_ExecCodeModuleEx() return sys.modules.get(fullname, mod) def get_filename(self, fullname): if fullname in self._cache: path = self._cache[fullname][2] if path is None: # If find_loader() returns self but a subsequent master RPC # reveals the module can't be loaded, and so load_module() # throws ImportError, on Python 3.x it is still possible for # the loader to be called to fetch metadata. raise ModuleNotFoundError(self.absent_msg % (fullname,)) return u'master:' + self._cache[fullname][2] def get_source(self, fullname): if fullname in self._cache: compressed = self._cache[fullname][3] if compressed is None: raise ModuleNotFoundError(self.absent_msg % (fullname,)) source = zlib.decompress(self._cache[fullname][3]) if PY3: return to_text(source) return source class LogHandler(logging.Handler): """ A :class:`logging.Handler` subclass that arranges for :data:`FORWARD_LOG` messages to be sent to a parent context in response to logging messages generated by the current context. This is installed by default in child contexts during bootstrap, so that :mod:`logging` events can be viewed and managed centrally in the master process. The handler is initially *corked* after construction, such that it buffers messages until :meth:`uncork` is called. This allows logging to be installed prior to communication with the target being available, and avoids any possible race where early log messages might be dropped. :param mitogen.core.Context context: The context to send log messages towards. At present this is always the master process. """ def __init__(self, context): logging.Handler.__init__(self) self.context = context self.local = threading.local() self._buffer = [] # Private synchronization is needed while corked, to ensure no # concurrent call to _send() exists during uncork(). self._buffer_lock = threading.Lock() def uncork(self): """ #305: during startup :class:`LogHandler` may be installed before it is possible to route messages, therefore messages are buffered until :meth:`uncork` is called by :class:`ExternalContext`. """ self._buffer_lock.acquire() try: self._send = self.context.send for msg in self._buffer: self._send(msg) self._buffer = None finally: self._buffer_lock.release() def _send(self, msg): self._buffer_lock.acquire() try: if self._buffer is None: # uncork() may run concurrent to _send() self._send(msg) else: self._buffer.append(msg) finally: self._buffer_lock.release() def emit(self, rec): """ Send a :data:`FORWARD_LOG` message towards the target context. """ if rec.name =='mitogen.io' or \ getattr(self.local, 'in_emit', False): return self.local.in_emit = True try: msg = self.format(rec) encoded = '%s\x00%s\x00%s' % (rec.name, rec.levelno, msg) if isinstance(encoded, UnicodeType): # Logging package emits both :( encoded = encoded.encode('utf-8') self._send(Message(data=encoded, handle=FORWARD_LOG)) finally: self.local.in_emit = False class Stream(object): """ A :class:`Stream` is one readable and optionally one writeable file descriptor (represented by :class:`Side`) aggregated alongside an associated :class:`Protocol` that knows how to respond to IO readiness events for those descriptors. Streams are registered with :class:`Broker`, and callbacks are invoked on the broker thread in response to IO activity. When registered using :meth:`Broker.start_receive` or :meth:`Broker._start_transmit`, the broker may call any of :meth:`on_receive`, :meth:`on_transmit`, :meth:`on_shutdown` or :meth:`on_disconnect`. It is expected that the :class:`Protocol` associated with a stream will change over its life. For example during connection setup, the initial protocol may be :class:`mitogen.parent.BootstrapProtocol` that knows how to enter SSH and sudo passwords and transmit the :mod:`mitogen.core` source to the target, before handing off to :class:`MitogenProtocol` when the target process is initialized. Streams connecting to children are in turn aggregated by :class:`mitogen.parent.Connection`, which contains additional logic for managing any child process, and a reference to any separate ``stderr`` :class:`Stream` connected to that process. """ #: A :class:`Side` representing the stream's receive file descriptor. receive_side = None #: A :class:`Side` representing the stream's transmit file descriptor. transmit_side = None #: A :class:`Protocol` representing the protocol active on the stream. protocol = None #: In parents, the :class:`mitogen.parent.Connection` instance. conn = None #: The stream name. This is used in the :meth:`__repr__` output in any log #: messages, it may be any descriptive string. name = u'default' def set_protocol(self, protocol): """ Bind a :class:`Protocol` to this stream, by updating :attr:`Protocol.stream` to refer to this stream, and updating this stream's :attr:`Stream.protocol` to the refer to the protocol. Any prior protocol's :attr:`Protocol.stream` is set to :data:`None`. """ if self.protocol: self.protocol.stream = None self.protocol = protocol self.protocol.stream = self def accept(self, rfp, wfp): """ Attach a pair of file objects to :attr:`receive_side` and :attr:`transmit_side`, after wrapping them in :class:`Side` instances. :class:`Side` will call :func:`set_nonblock` and :func:`set_cloexec` on the underlying file descriptors during construction. The same file object may be used for both sides. The default :meth:`on_disconnect` is handles the possibility that only one descriptor may need to be closed. :param file rfp: The file object to receive from. :param file wfp: The file object to transmit to. """ self.receive_side = Side(self, rfp) self.transmit_side = Side(self, wfp) def __repr__(self): return "<Stream %s #%04x>" % (self.name, id(self) & 0xffff,) def on_receive(self, broker): """ Invoked by :class:`Broker` when the stream's :attr:`receive_side` has been marked readable using :meth:`Broker.start_receive` and the broker has detected the associated file descriptor is ready for reading. Subclasses must implement this if they are registered using :meth:`Broker.start_receive`, and the method must invoke :meth:`on_disconnect` if reading produces an empty string. The default implementation reads :attr:`Protocol.read_size` bytes and passes the resulting bytestring to :meth:`Protocol.on_receive`. If the bytestring is 0 bytes, invokes :meth:`on_disconnect` instead. """ buf = self.receive_side.read(self.protocol.read_size) if not buf: LOG.debug('%r: empty read, disconnecting', self.receive_side) return self.on_disconnect(broker) self.protocol.on_receive(broker, buf) def on_transmit(self, broker): """ Invoked by :class:`Broker` when the stream's :attr:`transmit_side` has been marked writeable using :meth:`Broker._start_transmit` and the broker has detected the associated file descriptor is ready for writing. Subclasses must implement they are ever registerd with :meth:`Broker._start_transmit`. The default implementation invokes :meth:`Protocol.on_transmit`. """ self.protocol.on_transmit(broker) def on_shutdown(self, broker): """ Invoked by :meth:`Broker.shutdown` to allow the stream time to gracefully shutdown. The default implementation emits a ``shutdown`` signal before invoking :meth:`on_disconnect`. """ fire(self,'shutdown') self.protocol.on_shutdown(broker) def on_disconnect(self, broker): """ Invoked by :class:`Broker` to force disconnect the stream during shutdown, invoked by the default :meth:`on_shutdown` implementation, and usually invoked by any subclass :meth:`on_receive` implementation in response to a 0-byte read. The base implementation fires a ``disconnect`` event, then closes :attr:`receive_side` and :attr:`transmit_side` after unregistering the stream from the broker. """ fire(self, 'disconnect') self.protocol.on_disconnect(broker) class Protocol(object): """ Implement the program behaviour associated with activity on a :class:`Stream`. The protocol in use may vary over a stream's life, for example to allow :class:`mitogen.parent.BootstrapProtocol` to initialize the connected child before handing it off to :class:`MitogenProtocol`. A stream's active protocol is tracked in the :attr:`Stream.protocol` attribute, and modified via :meth:`Stream.set_protocol`. Protocols do not handle IO, they are entirely reliant on the interface provided by :class:`Stream` and :class:`Side`, allowing the underlying IO implementation to be replaced without modifying behavioural logic. """ stream_class = Stream #: The :class:`Stream` this protocol is currently bound to, or #: :data:`None`. stream = None #: The size of the read buffer used by :class:`Stream` when this is the #: active protocol for the stream. read_size = CHUNK_SIZE @classmethod def build_stream(cls, *args, **kwargs): stream = cls.stream_class() stream.set_protocol(cls(*args, **kwargs)) return stream def __repr__(self): return '%s(%s)' % ( self.__class__.__name__, self.stream and self.stream.name, ) def on_shutdown(self, broker): _v and LOG.debug('%r: shutting down', self) self.stream.on_disconnect(broker) def on_disconnect(self, broker): # Normally both sides an FD, so it is important that tranmit_side is # deregistered from Poller before closing the receive side, as pollers # like epoll and kqueue unregister all events on FD close, causing # subsequent attempt to unregister the transmit side to fail. LOG.debug('%r: disconnecting', self) broker.stop_receive(self.stream) if self.stream.transmit_side: broker._stop_transmit(self.stream) self.stream.receive_side.close() if self.stream.transmit_side: self.stream.transmit_side.close() class DelimitedProtocol(Protocol): """ Provide a :meth:`Protocol.on_receive` implementation for protocols that are delimited by a fixed string, like text based protocols. Each message is passed to :meth:`on_line_received` as it arrives, with incomplete messages passed to :meth:`on_partial_line_received`. When emulating user input it is often necessary to respond to incomplete lines, such as when a "Password: " prompt is sent. :meth:`on_partial_line_received` may be called repeatedly with an increasingly complete message. When a complete message is finally received, :meth:`on_line_received` will be called once for it before the buffer is discarded. If :func:`on_line_received` returns :data:`False`, remaining data is passed unprocessed to the stream's current protocol's :meth:`on_receive`. This allows switching from line-oriented to binary while the input buffer contains both kinds of data. """ #: The delimiter. Defaults to newline. delimiter = b('\n') _trailer = b('') def on_receive(self, broker, buf): _vv and IOLOG.debug('%r.on_receive()', self) stream = self.stream self._trailer, cont = mitogen.core.iter_split( buf=self._trailer + buf, delim=self.delimiter, func=self.on_line_received, ) if self._trailer: if cont: self.on_partial_line_received(self._trailer) else: assert stream.protocol is not self, \ 'stream protocol is no longer %r' % (self,) stream.protocol.on_receive(broker, self._trailer) def on_line_received(self, line): """ Receive a line from the stream. :param bytes line: The encoded line, excluding the delimiter. :returns: :data:`False` to indicate this invocation modified the stream's active protocol, and any remaining buffered data should be passed to the new protocol's :meth:`on_receive` method. Any other return value is ignored. """ pass def on_partial_line_received(self, line): """ Receive a trailing unterminated partial line from the stream. :param bytes line: The encoded partial line. """ pass class BufferedWriter(object): """ Implement buffered output while avoiding quadratic string operations. This is currently constructed by each protocol, in future it may become fixed for each stream instead. """ def __init__(self, broker, protocol): self._broker = broker self._protocol = protocol self._buf = collections.deque() self._len = 0 def write(self, s): """ Transmit `s` immediately, falling back to enqueuing it and marking the stream writeable if no OS buffer space is available. """ if not self._len: # Modifying epoll/Kqueue state is expensive, as are needless broker # loops. Rather than wait for writeability, just write immediately, # and fall back to the broker loop on error or full buffer. try: n = self._protocol.stream.transmit_side.write(s) if n: if n == len(s): return s = s[n:] except OSError: pass self._broker._start_transmit(self._protocol.stream) self._buf.append(s) self._len += len(s) def on_transmit(self, broker): """ Respond to stream writeability by retrying previously buffered :meth:`write` calls. """ if self._buf: buf = self._buf.popleft() written = self._protocol.stream.transmit_side.write(buf) if not written: _v and LOG.debug('disconnected during write to %r', self) self._protocol.stream.on_disconnect(broker) return elif written!= len(buf): self._buf.appendleft(BufferType(buf, written)) _vv and IOLOG.debug('transmitted %d bytes to %r', written, self) self._len -= written if not self._buf: broker._stop_transmit(self._protocol.stream) class Side(object): """ Represent one side of a :class:`Stream`. This allows unidirectional (e.g. pipe) and bidirectional (e.g. socket) streams to operate identically. Sides are also responsible for tracking the open/closed state of the underlying FD, preventing erroneous duplicate calls to :func:`os.close` due to duplicate :meth:`Stream.on_disconnect` calls, which would otherwise risk silently succeeding by closing an unrelated descriptor. For this reason, it is crucial only one file object exists per unique descriptor. :param mitogen.core.Stream stream: The stream this side is associated with. :param object fp: The file or socket object managing the underlying file descriptor. Any object may be used that supports `fileno()` and `close()` methods. :param bool cloexec: If :data:`True`, the descriptor has its :data:`fcntl.FD_CLOEXEC` flag enabled using :func:`fcntl.fcntl`. :param bool keep_alive: If :data:`True`, the continued existence of this side will extend the shutdown grace period until it has been unregistered from the broker. :param bool blocking: If :data:`False`, the descriptor has its :data:`os.O_NONBLOCK` flag enabled using :func:`fcntl.fcntl`. """ _fork_refs = weakref.WeakValueDictionary() closed = False def __init__(self, stream, fp, cloexec=True, keep_alive=True, blocking=False): #: The :class:`Stream` for which this is a read or write side. self.stream = stream # File or socket object responsible for the lifetime of its underlying # file descriptor. self.fp = fp #: Integer file descriptor to perform IO on, or :data:`None` if #: :meth:`close` has been called. This is saved separately from the #: file object, since :meth:`file.fileno` cannot be called on it after #: it has been closed. self.fd = fp.fileno() #: If :data:`True`, causes presence of this side in #: :class:`Broker`'s active reader set to defer shutdown until the #: side is disconnected. self.keep_alive = keep_alive self._fork_refs[id(self)] = self if cloexec: set_cloexec(self.fd) if not blocking: set_nonblock(self.fd) def __repr__(self): return '<Side of %s fd %s>' % ( self.stream.name or repr(self.stream), self.fd ) @classmethod def _on_fork(cls): while cls._fork_refs: _, side = cls._fork_refs.popitem() _vv and IOLOG.debug('Side._on_fork() closing %r', side) side.close() def close(self): """ Call :meth:`file.close` on :attr:`fp` if it is not :data:`None`, then set it to :data:`None`. """ _vv and IOLOG.debug('%r.close()', self) if not self.closed: self.closed = True self.fp.close() def read(self, n=CHUNK_SIZE): """ Read up to `n` bytes from the file descriptor, wrapping the underlying :func:`os.read` call with :func:`io_op` to trap common disconnection conditions. :meth:`read` always behaves as if it is reading from a regular UNIX file; socket, pipe, and TTY disconnection errors are masked and result in a 0-sized read like a regular file. :returns: Bytes read, or the empty string to indicate disconnection was detected. """ if self.closed: # Refuse to touch the handle after closed, it may have been reused # by another thread. TODO: synchronize read()/write()/close(). return b('') s, disconnected = io_op(os.read, self.fd, n) if disconnected: LOG.debug('%r: disconnected during read: %s', self, disconnected) return b('') return s def write(self, s): """ Write as much of the bytes from `s` as possible to the file descriptor, wrapping the underlying :func:`os.write` call with :func:`io_op` to trap common disconnection conditions. :returns: Number of bytes written, or :data:`None` if disconnection was detected. """ if self.closed: # Don't touch the handle after close, it may be reused elsewhere. return None written, disconnected = io_op(os.write, self.fd, s) if disconnected: LOG.debug('%r: disconnected during write: %s', self, disconnected) return None return written class MitogenProtocol(Protocol): """ :class:`Protocol` implementing mitogen's :ref:`stream protocol <stream-protocol>`. """ #: If not :data:`False`, indicates the stream has :attr:`auth_id` set and #: its value is the same as :data:`mitogen.context_id` or appears in #: :data:`mitogen.parent_ids`. is_privileged = False #: Invoked as `on_message(stream, msg)` each message received from the #: peer. on_message = None def __init__(self, router, remote_id, auth_id=None, local_id=None, parent_ids=None): self._router = router self.remote_id = remote_id #: If not :data:`None`, :class:`Router` stamps this into #: :attr:`Message.auth_id` of every message received on this stream. self.auth_id = auth_id if parent_ids is None: parent_ids = mitogen.parent_ids if local_id is None: local_id = mitogen.context_id self.is_privileged = ( (remote_id in parent_ids) or auth_id in ([local_id] + parent_ids) ) self.sent_modules = set(['mitogen','mitogen.core']) self._input_buf = collections.deque() self._input_buf_len = 0 self._writer = BufferedWriter(router.broker, self) #: Routing records the dst_id of every message arriving from this #: stream. Any arriving DEL_ROUTE is rebroadcast for any such ID. self.egress_ids = set() def on_receive(self, broker, buf): """ Handle the next complete message on the stream. Raise :class:`StreamError` on failure. """ _vv and IOLOG.debug('%r.on_receive()', self) if self._input_buf and self._input_buf_len < 128: self._input_buf[0] += buf else: self._input_buf.append(buf) self._input_buf_len += len(buf) while self._receive_one(broker): pass corrupt_msg = ( '%s: Corruption detected: frame signature incorrect. This likely means' 'some external process is interfering with the connection. Received:' '\n\n' '%r' ) def _receive_one(self, broker): if self._input_buf_len < Message.HEADER_LEN: return False msg = Message() msg.router = self._router (magic, msg.dst_id, msg.src_id, msg.auth_id, msg.handle, msg.reply_to, msg_len) = struct.unpack( Message.HEADER_FMT, self._input_buf[0][:Message.HEADER_LEN], ) if magic!= Message.HEADER_MAGIC: LOG.error(self.corrupt_msg, self.stream.name, self._input_buf[0][:2048]) self.stream.on_disconnect(broker) return False if msg_len > self._router.max_message_size: LOG.error('%r: Maximum message size exceeded (got %d, max %d)', self, msg_len, self._router.max_message_size) self.stream.on_disconnect(broker) return False total_len = msg_len + Message.HEADER_LEN if self._input_buf_len < total_len: _vv and IOLOG.debug( '%r: Input too short (want %d, got %d)', self, msg_len, self._input_buf_len - Message.HEADER_LEN ) return False start = Message.HEADER_LEN prev_start = start remain = total_len bits = [] while remain: buf = self._input_buf.popleft() bit = buf[start:remain] bits.append(bit) remain -= len(bit) + start prev_start = start start = 0 msg.data = b('').join(bits) self._input_buf.appendleft(buf[prev_start+len(bit):]) self._input_buf_len -= total_len self._router._async_route(msg, self.stream) return True def pending_bytes(self): """ Return the number of bytes queued for transmission on this stream. This can be used to limit the amount of data buffered in RAM by an otherwise unlimited consumer. For an accurate result, this method should be called from the Broker thread, for example by using :meth:`Broker.defer_sync`. """ return self._writer._len def on_transmit(self, broker): """ Transmit buffered messages. """ _vv and IOLOG.debug('%r.on_transmit()', self) self._writer.on_transmit(broker) def _send(self, msg): _vv and IOLOG.debug('%r._send(%r)', self, msg) self._writer.write(msg.pack()) def send(self, msg): """ Send `data` to `handle`, and tell the broker we have output. May be called from any thread. """ self._router.broker.defer(self._send, msg) def on_shutdown(self, broker): """ Disable :class:`Protocol` immediate disconnect behaviour. """ _v and LOG.debug('%r: shutting down', self) class Context(object): """ Represent a remote context regardless of the underlying connection method. Context objects are simple facades that emit messages through an associated router, and have :ref:`signals` raised against them in response to various events relating to the context. **Note:** This is the somewhat limited core version, used by child contexts. The master subclass is documented below this one. Contexts maintain no internal state and are thread-safe. Prefer :meth:`Router.context_by_id` over constructing context objects explicitly, as that method is deduplicating, and returns the only context instance :ref:`signals` will be raised on. :param mitogen.core.Router router: Router to emit messages through. :param int context_id: Context ID. :param str name: Context name. """ name = None remote_name = None def __init__(self, router, context_id, name=None): self.router = router self.context_id = context_id if name: self.name = to_text(name) def __reduce__(self): return _unpickle_context, (self.context_id, self.name) def on_disconnect(self): _v and LOG.debug('%r: disconnecting', self) fire(self, 'disconnect') def send_async(self, msg, persist=False): """ Arrange for `msg` to be delivered to this context, with replies directed to a newly constructed receiver. :attr:`dst_id <Message.dst_id>` is set to the target context ID, and :attr:`reply_to <Message.reply_to>` is set to the newly constructed receiver's handle. :param bool persist: If :data:`False`, the handler will be unregistered after a single message has been received. :param mitogen.core.Message msg: The message. :returns: :class:`Receiver` configured to receive any replies sent to the message's `reply_to` handle. """ receiver = Receiver(self.router, persist=persist, respondent=self) msg.dst_id = self.context_id msg.reply_to = receiver.handle _v and LOG.debug('sending message to %r: %r', self, msg) self.send(msg) return receiver def call_service_async(self, service_name, method_name, **kwargs): if isinstance(service_name, BytesType): service_name = service_name.encode('utf-8') elif not isinstance(service_name, UnicodeType): service_name = service_name.name() # Service.name() _v and LOG.debug('calling service %s.%s of %r, args: %r', service_name, method_name, self, kwargs) tup = (service_name, to_text(method_name), Kwargs(kwargs)) msg = Message.pickled(tup, handle=CALL_SERVICE) return self.send_async(msg) def send(self, msg): """ Arrange for `msg` to be delivered to this context. :attr:`dst_id <Message.dst_id>` is set to the target context ID. :param Message msg: Message. """ msg.dst_id = self.context_id self.router.route(msg) def call_service(self, service_name, method_name, **kwargs): recv = self.call_service_async(service_name, method_name, **kwargs) return recv.get().unpickle() def send_await(self, msg, deadline=None): """ Like :meth:`send_async`, but expect a single reply (`persist=False`) delivered within `deadline` seconds. :param mitogen.core.Message msg: The message. :param float deadline: If not :data:`None`, seconds before timing out waiting for a reply. :returns: Deserialized reply. :raises TimeoutError: No message was received and `deadline` passed. """ receiver = self.send_async(msg) response = receiver.get(deadline) data = response.unpickle() _vv and IOLOG.debug('%r._send_await() -> %r', self, data) return data def __repr__(self): return 'Context(%s, %r)' % (self.context_id, self.name) def _unpickle_context(context_id, name, router=None): if not (isinstance(context_id, (int, long)) and context_id >= 0 and ( (name is None) or (isinstance(name, UnicodeType) and len(name) < 100)) ): raise TypeError('cannot unpickle Context: bad input') if isinstance(router, Router): return router.context_by_id(context_id, name=name) return Context(None, context_id, name) # For plain Jane pickle. class Poller(object): """ A poller manages OS file descriptors the user is waiting to become available for IO. The :meth:`poll` method blocks the calling thread until one or more become ready. The default implementation is based on :func:`select.poll`. Each descriptor has an associated `data` element, which is unique for each readiness type, and defaults to being the same as the file descriptor. The :meth:`poll` method yields the data associated with a descriptor, rather than the descriptor itself, allowing concise loops like:: p = Poller() p.start_receive(conn.fd, data=conn.on_read) p.start_transmit(conn.fd, data=conn.on_write) for callback in p.poll(): callback() # invoke appropriate bound instance method Pollers may be modified while :meth:`poll` is yielding results. Removals are processed immediately, causing pending events for the descriptor to be discarded. The :meth:`close` method must be called when a poller is discarded to avoid a resource leak. Pollers may only be used by one thread at a time. """ SUPPORTED = True # This changed from select() to poll() in Mitogen 0.2.4. Since poll() has # no upper FD limit, it is suitable for use with Latch, which must handle # FDs larger than select's limit during many-host runs. We want this # because poll() requires no setup and teardown: just a single system call, # which is important because Latch.get() creates a Poller on each # invocation. In a microbenchmark, poll() vs. epoll_ctl() is 30% faster in # this scenario. If select() must return in future, it is important # Latch.poller_class is set from parent.py to point to the industrial # strength poller for the OS, otherwise Latch will fail randomly. #: Increments on every poll(). Used to version _rfds and _wfds. _generation = 1 def __init__(self): self._rfds = {} self._wfds = {} def __repr__(self): return '%s' % (type(self).__name__,) def _update(self, fd): """ Required by PollPoller subclass. """ pass @property def readers(self): """ Return a list of `(fd, data)` tuples for every FD registered for receive readiness. """ return list((fd, data) for fd, (data, gen) in self._rfds.items()) @property def writers(self): """ Return a list of `(fd, data)` tuples for every FD registered for transmit readiness. """ return list((fd, data) for fd, (data, gen) in self._wfds.items()) def close(self): """ Close any underlying OS resource used by the poller. """ pass def start_receive(self, fd, data=None): """ Cause :meth:`poll` to yield `data` when `fd` is readable. """ self._rfds[fd] = (data or fd, self._generation) self._update(fd) def stop_receive(self, fd): """ Stop yielding readability events for `fd`. Redundant calls to :meth:`stop_receive` are silently ignored, this may change in future. """ self._rfds.pop(fd, None) self._update(fd) def start_transmit(self, fd, data=None): """ Cause :meth:`poll` to yield `data` when `fd` is writeable. """ self._wfds[fd] = (data or fd, self._generation) self._update(fd) def stop_transmit(self, fd): """ Stop yielding writeability events for `fd`. Redundant calls to :meth:`stop_transmit` are silently ignored, this may change in future. """ self._wfds.pop(fd, None) self._update(fd) def _poll(self, timeout): (rfds, wfds, _), _ = io_op(select.select, self._rfds, self._wfds, (), timeout ) for fd in rfds: _vv and IOLOG.debug('%r: POLLIN for %r', self, fd) data, gen = self._rfds.get(fd, (None, None)) if gen and gen < self._generation: yield data for fd in wfds: _vv and IOLOG.debug('%r: POLLOUT for %r', self, fd) data, gen = self._wfds.get(fd, (None, None)) if gen and gen < self._generation: yield data def poll(self, timeout=None): """ Block the calling thread until one or more FDs are ready for IO. :param float timeout: If not :data:`None`, seconds to wait without an event before returning an empty iterable. :returns: Iterable of `data` elements associated with ready FDs. """ _vv and IOLOG.debug('%r.poll(%r)', self, timeout) self._generation += 1 return self._poll(timeout) class Latch(object): """ A latch is a :class:`Queue.Queue`-like object that supports mutation and waiting from multiple threads, however unlike :class:`Queue.Queue`, waiting threads always remain interruptible, so CTRL+C always succeeds, and waits where a timeout is set experience no wake up latency. These properties are not possible in combination using the built-in threading primitives available in Python 2.x. Latches implement queues using the UNIX self-pipe trick, and a per-thread :func:`socket.socketpair` that is lazily created the first time any latch attempts to sleep on a thread, and dynamically associated with the waiting Latch only for duration of the wait. See :ref:`waking-sleeping-threads` for further discussion. """ #: The :class:`Poller` implementation to use for waiting. Since the poller #: will be very short-lived, we prefer :class:`mitogen.parent.PollPoller` #: if it is available, or :class:`mitogen.core.Poller` otherwise, since #: these implementations require no system calls to create, configure or #: destroy. poller_class = Poller #: If not :data:`None`, a function invoked as `notify(latch)` after a #: successful call to :meth:`put`. The function is invoked on the #: :meth:`put` caller's thread, which may be the :class:`Broker` thread, #: therefore it must not block. Used by :class:`mitogen.select.Select` to #: efficiently implement waiting on multiple event sources. notify = None # The _cls_ prefixes here are to make it crystal clear in the code which # state mutation isn't covered by :attr:`_lock`. #: List of reusable :func:`socket.socketpair` tuples. The list is mutated #: from multiple threads, the only safe operations are `append()` and #: `pop()`. _cls_idle_socketpairs = [] #: List of every socket object that must be closed by :meth:`_on_fork`. #: Inherited descriptors cannot be reused, as the duplicated handles #: reference the same underlying kernel object in use by the parent. _cls_all_sockets = [] def __init__(self): self.closed = False self._lock = threading.Lock() #: List of unconsumed enqueued items. self._queue = [] #: List of `(wsock, cookie)` awaiting an element, where `wsock` is the #: socketpair's write side, and `cookie` is the string to write. self._sleeping = [] #: Number of elements of :attr:`_sleeping` that have already been #: woken, and have a corresponding element index from :attr:`_queue` #: assigned to them. self._waking = 0 @classmethod def _on_fork(cls): """ Clean up any files belonging to the parent process after a fork. """ cls._cls_idle_socketpairs = [] while cls._cls_all_sockets: cls._cls_all_sockets.pop().close() def close(self): """ Mark the latch as closed, and cause every sleeping thread to be woken, with :class:`mitogen.core.LatchError` raised in each thread. """ self._lock.acquire() try: self.closed = True while self._waking < len(self._sleeping): wsock, cookie = self._sleeping[self._waking] self._wake(wsock, cookie) self._waking += 1 finally: self._lock.release() def size(self): """ Return the number of items currently buffered. As with :class:`Queue.Queue`, `0` may be returned even though a subsequent call to :meth:`get` will succeed, since a message may be posted at any moment between :meth:`size` and :meth:`get`. As with :class:`Queue.Queue`, `>0` may be returned even though a subsequent call to :meth:`get` will block, since another waiting thread may be woken at any moment between :meth:`size` and :meth:`get`. :raises LatchError: The latch has already been marked closed. """ self._lock.acquire() try: if self.closed: raise LatchError() return len(self._queue) finally: self._lock.release() def empty(self): """ Return `size() == 0`. .. deprecated:: 0.2.8 Use :meth:`size` instead. :raises LatchError: The latch has already been marked closed. """ return self.size() == 0 def _get_socketpair(self): """ Return an unused socketpair, creating one if none exist. """ try: return self._cls_idle_socketpairs.pop() # pop() must be atomic except IndexError: rsock, wsock = socket.socketpair() rsock.setblocking(False) set_cloexec(rsock.fileno()) set_cloexec(wsock.fileno()) self._cls_all_sockets.extend((rsock, wsock)) return rsock, wsock COOKIE_MAGIC, = struct.unpack('L', b('LTCH') * (struct.calcsize('L')//4)) COOKIE_FMT = '>Qqqq' # #545: id() and get_ident() may exceed long on armhfp. COOKIE_SIZE = struct.calcsize(COOKIE_FMT) def _make_cookie(self): """ Return a string encoding the ID of the process, instance and thread. This disambiguates legitimate wake-ups, accidental writes to the FD, and buggy internal FD sharing. """ return struct.pack(self.COOKIE_FMT, self.COOKIE_MAGIC, os.getpid(), id(self), thread.get_ident()) def get(self, timeout=None, block=True): """ Return the next enqueued object, or sleep waiting for one. :param float timeout: If not :data:`None`, specifies a timeout in seconds. :param bool block: If :data:`False`, immediately raise :class:`mitogen.core.TimeoutError` if the latch is empty. :raises mitogen.core.LatchError: :meth:`close` has been called, and the object is no longer valid. :raises mitogen.core.TimeoutError: Timeout was reached. :returns: The de-queued object. """ _vv and IOLOG.debug('%r.get(timeout=%r, block=%r)', self, timeout, block) self._lock.acquire() try: if self.closed: raise LatchError() i = len(self._sleeping) if len(self._queue) > i: _vv and IOLOG.debug('%r.get() -> %r', self, self._queue[i]) return self._queue.pop(i) if not block: raise TimeoutError() rsock, wsock = self._get_socketpair() cookie = self._make_cookie() self._sleeping.append((wsock, cookie)) finally: self._lock.release() poller = self.poller_class() poller.start_receive(rsock.fileno()) try: return self._get_sleep(poller, timeout, block, rsock, wsock, cookie) finally: poller.close() def _get_sleep(self, poller, timeout, block, rsock, wsock, cookie): """ When a result is not immediately available, sleep waiting for :meth:`put` to write a byte to our socket pair. """ _vv and IOLOG.debug( '%r._get_sleep(timeout=%r, block=%r, fd=%d/%d)', self, timeout, block, rsock.fileno(), wsock.fileno() ) e = None try: list(poller.poll(timeout)) except Exception: e = sys.exc_info()[1] self._lock.acquire() try: i = self._sleeping.index((wsock, cookie)) del self._sleeping[i] try: got_cookie = rsock.recv(self.COOKIE_SIZE) except socket.error: e2 = sys.exc_info()[1] if e2.args[0] == errno.EAGAIN: e = TimeoutError() else: e = e2 self._cls_idle_socketpairs.append((rsock, wsock)) if e: raise e assert cookie == got_cookie, ( "Cookie incorrect; got %r, expected %r" % (binascii.hexlify(got_cookie), binascii.hexlify(cookie)) ) assert i < self._waking, ( "Cookie correct, but no queue element assigned." ) self._waking -= 1 if self.closed: raise LatchError() _vv and IOLOG.debug('%r.get() wake -> %r', self, self._queue[i]) return self._queue.pop(i) finally: self._lock.release() def put(self, obj=None): """ Enqueue an object, waking the first thread waiting for a result, if one exists. :param obj: Object to enqueue. Defaults to :data:`None` as a convenience when using :class:`Latch` only for synchronization. :raises mitogen.core.LatchError: :meth:`close` has been called, and the object is no longer valid. """ _vv and IOLOG.debug('%r.put(%r)', self, obj) self._lock.acquire() try: if self.closed: raise LatchError() self._queue.append(obj) wsock = None if self._waking < len(self._sleeping): wsock, cookie = self._sleeping[self._waking] self._waking += 1 _vv and IOLOG.debug('%r.put() -> waking wfd=%r', self, wsock.fileno()) elif self.notify: self.notify(self) finally: self._lock.release() if wsock: self._wake(wsock, cookie) def _wake(self, wsock, cookie): written, disconnected = io_op(os.write, wsock.fileno(), cookie) assert written == len(cookie) and not disconnected def __repr__(self): return 'Latch(%#x, size=%d, t=%r)' % ( id(self), len(self._queue), threading__thread_name(threading__current_thread()), ) class Waker(Protocol): """ :class:`Protocol` implementing the `UNIX self-pipe trick`_. Used to wake :class:`Broker` when another thread needs to modify its state, by enqueing a function call to run on the :class:`Broker` thread. .. _UNIX self-pipe trick: https://cr.yp.to/docs/selfpipe.html """ read_size = 1 broker_ident = None @classmethod def build_stream(cls, broker): stream = super(Waker, cls).build_stream(broker) stream.accept(*pipe()) return stream def __init__(self, broker): self._broker = broker self._deferred = collections.deque() def __repr__(self): return 'Waker(fd=%r/%r)' % ( self.stream.receive_side and self.stream.receive_side.fd, self.stream.transmit_side and self.stream.transmit_side.fd, ) @property def keep_alive(self): """ Prevent immediate Broker shutdown while deferred functions remain. """ return len(self._deferred) def on_receive(self, broker, buf): """ Drain the pipe and fire callbacks. Since :attr:`_deferred` is synchronized, :meth:`defer` and :meth:`on_receive` can conspire to ensure only one byte needs to be pending regardless of queue length. """ _vv and IOLOG.debug('%r.on_receive()', self) while True: try: func, args, kwargs = self._deferred.popleft() except IndexError: return try: func(*args, **kwargs) except Exception: LOG.exception('defer() crashed: %r(*%r, **%r)', func, args, kwargs) broker.shutdown() def _wake(self): """ Wake the multiplexer by writing a byte. If Broker is midway through teardown, the FD may already be closed, so ignore EBADF. """ try: self.stream.transmit_side.write(b(' ')) except OSError: e = sys.exc_info()[1] if e.args[0] not in (errno.EBADF, errno.EWOULDBLOCK): raise broker_shutdown_msg = ( "An attempt was made to enqueue a message with a Broker that has " "already exitted. It is likely your program called Broker.shutdown() " "too early." ) def defer(self, func, *args, **kwargs): """ Arrange for `func()` to execute on the broker thread. This function returns immediately without waiting the result of `func()`. Use :meth:`defer_sync` to block until a result is available. :raises mitogen.core.Error: :meth:`defer` was called after :class:`Broker` has begun shutdown. """ if thread.get_ident() == self.broker_ident: _vv and IOLOG.debug('%r.defer() [immediate]', self) return func(*args, **kwargs) if self._broker._exitted: raise Error(self.broker_shutdown_msg) _vv and IOLOG.debug('%r.defer() [fd=%r]', self, self.stream.transmit_side.fd) self._deferred.append((func, args, kwargs)) self._wake() class IoLoggerProtocol(DelimitedProtocol): """ Attached to one end of a socket pair whose other end overwrites one of the standard ``stdout`` or ``stderr`` file descriptors in a child context. Received data is split up into lines, decoded as UTF-8 and logged to the :mod:`logging` package as either the ``stdout`` or ``stderr`` logger. Logging in child contexts is in turn forwarded to the master process using :class:`LogHandler`. """ @classmethod def build_stream(cls, name, dest_fd): """ Even though the file descriptor `dest_fd` will hold the opposite end of the socket open, we must keep a separate dup() of it (i.e. wsock) in case some code decides to overwrite `dest_fd` later, which would prevent break :meth:`on_shutdown` from calling :meth:`shutdown() <socket.socket.shutdown>` on it. """ rsock, wsock = socket.socketpair() os.dup2(wsock.fileno(), dest_fd) stream = super(IoLoggerProtocol, cls).build_stream(name) stream.name = name stream.accept(rsock, wsock) return stream def __init__(self, name): self._log = logging.getLogger(name) # #453: prevent accidental log initialization in a child creating a # feedback loop. self._log.propagate = False self._log.handlers = logging.getLogger().handlers[:] def on_shutdown(self, broker): """ Shut down the write end of the socket, preventing any further writes to it by this process, or subprocess that inherited it. This allows any remaining kernel-buffered data to be drained during graceful shutdown without the buffer continuously refilling due to some out of control child process. """ _v and LOG.debug('%r: shutting down', self) if not IS_WSL: # #333: WSL generates invalid readiness indication on shutdown(). # This modifies the *kernel object* inherited by children, causing # EPIPE on subsequent writes to any dupped FD in any process. The # read side can then drain completely of prior buffered data. self.stream.transmit_side.fp.shutdown(socket.SHUT_WR) self.stream.transmit_side.close() def on_line_received(self, line): """ Decode the received line as UTF-8 and pass it to the logging framework. """ self._log.info('%s', line.decode('utf-8','replace')) class Router(object): """ Route messages between contexts, and invoke local handlers for messages addressed to this context. :meth:`Router.route() <route>` straddles the :class:`Broker` thread and user threads, it is safe to call anywhere. **Note:** This is the somewhat limited core version of the Router class used by child contexts. The master subclass is documented below this one. """ #: The :class:`mitogen.core.Context` subclass to use when constructing new #: :class:`Context` objects in :meth:`myself` and :meth:`context_by_id`. #: Permits :class:`Router` subclasses to extend the :class:`Context` #: interface, as done in :class:`mitogen.parent.Router`. context_class = Context max_message_size = 128 * 1048576 #: When :data:`True`, permit children to only communicate with the current #: context or a parent of the current context. Routing between siblings or #: children of parents is prohibited, ensuring no communication is possible #: between intentionally partitioned networks, such as when a program #: simultaneously manipulates hosts spread across a corporate and a #: production network, or production networks that are otherwise #: air-gapped. #: #: Sending a prohibited message causes an error to be logged and a dead #: message to be sent in reply to the errant message, if that message has #: ``reply_to`` set. #: #: The value of :data:`unidirectional` becomes the default for the #: :meth:`local() <mitogen.master.Router.local>` `unidirectional` #: parameter. unidirectional = False duplicate_handle_msg = 'cannot register a handle that already exists' refused_msg ='refused by policy' invalid_handle_msg = 'invalid handle' too_large_msg ='message too large (max %d bytes)' respondent_disconnect_msg = 'the respondent Context has disconnected' broker_exit_msg = 'Broker has exitted' no_route_msg = 'no route to %r, my ID is %r' unidirectional_msg = ( 'routing mode prevents forward of message from context %d to ' 'context %d via context %d' ) def __init__(self, broker): self.broker = broker listen(broker, 'exit', self._on_broker_exit) self._setup_logging() self._write_lock = threading.Lock() #: context ID -> Stream; must hold _write_lock to edit or iterate self._stream_by_id = {} #: List of contexts to notify of shutdown; must hold _write_lock self._context_by_id = {} self._last_handle = itertools.count(1000) #: handle -> (persistent?, func(msg)) self._handle_map = {} #: Context -> set { handle,.. } self._handles_by_respondent = {} self.add_handler(self._on_del_route, DEL_ROUTE) def __repr__(self): return 'Router(%r)' % (self.broker,) def _setup_logging(self): """ This is done in the :class:`Router` constructor for historical reasons. It must be called before ExternalContext logs its first messages, but after logging has been setup. It must also be called when any router is constructed for a consumer app. """ # Here seems as good a place as any. global _v, _vv _v = logging.getLogger().level <= logging.DEBUG _vv = IOLOG.level <= logging.DEBUG def _on_del_route(self, msg): """ Stub :data:`DEL_ROUTE` handler; fires 'disconnect' events on the corresponding :attr:`_context_by_id` member. This is replaced by :class:`mitogen.parent.RouteMonitor` in an upgraded context. """ if msg.is_dead: return target_id_s, _, name = bytes_partition(msg.data, b(':')) target_id = int(target_id_s, 10) LOG.error('%r: deleting route to %s (%d)', self, to_text(name), target_id) context = self._context_by_id.get(target_id) if context: fire(context, 'disconnect') else: LOG.debug('DEL_ROUTE for unknown ID %r: %r', target_id, msg) def _on_stream_disconnect(self, stream): notify = [] self._write_lock.acquire() try: for context in list(self._context_by_id.values()): stream_ = self._stream_by_id.get(context.context_id) if stream_ is stream: del self._stream_by_id[context.context_id] notify.append(context) finally: self._write_lock.release() # Happens outside lock as e.g. RouteMonitor wants the same lock. for context in notify: context.on_disconnect() def _on_broker_exit(self): """ Called prior to broker exit, informs callbacks registered with :meth:`add_handler` the connection is dead. """ _v and LOG.debug('%r: broker has exitted', self) while self._handle_map: _, (_, func, _, _) = self._handle_map.popitem() func(Message.dead(self.broker_exit_msg)) def myself(self): """ Return a :class:`Context` referring to the current process. Since :class:`Context` is serializable, this is convenient to use in remote function call parameter lists. """ return self.context_class( router=self, context_id=mitogen.context_id, name='self', ) def context_by_id(self, context_id, via_id=None, create=True, name=None): """ Return or construct a :class:`Context` given its ID. An internal mapping of ID to the canonical :class:`Context` representing that ID, so that :ref:`signals` can be raised. This may be called from any thread, lookup and construction are atomic. :param int context_id: The context ID to look up. :param int via_id: If the :class:`Context` does not already exist, set its :attr:`Context.via` to the :class:`Context` matching this ID. :param bool create: If the :class:`Context` does not already exist, create it. :param str name: If the :class:`Context` does not already exist, set its name. :returns: :class:`Context`, or return :data:`None` if `create` is :data:`False` and no :class:`Context` previously existed. """ context = self._context_by_id.get(context_id) if context: return context if create and via_id is not None: via = self.context_by_id(via_id) else: via = None self._write_lock.acquire() try: context = self._context_by_id.get(context_id) if create and not context: context = self.context_class(self, context_id, name=name) context.via = via self._context_by_id[context_id] = context finally: self._write_lock.release() return context def register(self, context, stream): """ Register a newly constructed context and its associated stream, and add the stream's receive side to the I/O multiplexer. This method remains public while the design has not yet settled. """ _v and LOG.debug('%s: registering %r to stream %r', self, context, stream) self._write_lock.acquire() try: self._stream_by_id[context.context_id] = stream self._context_by_id[context.context_id] = context finally: self._write_lock.release() self.broker.start_receive(stream) listen(stream, 'disconnect', lambda: self._on_stream_disconnect(stream)) def stream_by_id(self, dst_id): """ Return the :class:`Stream` that should be used to communicate with `dst_id`. If a specific route for `dst_id` is not known, a reference to the parent context's stream is returned. If the parent is disconnected, or when running in the master context, return :data:`None` instead. This can be used from any thread, but its output is only meaningful from the context of the :class:`Broker` thread, as disconnection or replacement could happen in parallel on the broker thread at any moment. """ return ( self._stream_by_id.get(dst_id) or self._stream_by_id.get(mitogen.parent_id) ) def del_handler(self, handle): """ Remove the handle registered for `handle` :raises KeyError: The handle wasn't registered. """ _, _, _, respondent = self._handle_map.pop(handle) if respondent: self._handles_by_respondent[respondent].discard(handle) def add_handler(self, fn, handle=None, persist=True, policy=None, respondent=None, overwrite=False): """ Invoke `fn(msg)` on the :class:`Broker` thread for each Message sent to `handle` from this context. Unregister after one invocation if `persist` is :data:`False`. If `handle` is :data:`None`, a new handle is allocated and returned. :param int handle: If not :data:`None`, an explicit handle to register, usually one of the ``mitogen.core.*`` constants. If unspecified, a new unused handle will be allocated. :param bool persist: If :data:`False`, the handler will be unregistered after a single message has been received. :param mitogen.core.Context respondent: Context that messages to this handle are expected to be sent from. If specified, arranges for a dead message to be delivered to `fn` when disconnection of the context is detected. In future `respondent` will likely also be used to prevent other contexts from sending messages to the handle. :param function policy: Function invoked as `policy(msg, stream)` where `msg` is a :class:`mitogen.core.Message` about to be delivered, and `stream` is the :class:`mitogen.core.Stream` on which it was received. The function must return :data:`True`, otherwise an error is logged and delivery is refused. Two built-in policy functions exist: * :func:`has_parent_authority`: requires the message arrived from a parent context, or a context acting with a parent context's authority (``auth_id``). * :func:`mitogen.parent.is_immediate_child`: requires the message arrived from an immediately connected child, for use in messaging patterns where either something becomes buggy or insecure by permitting indirect upstream communication. In case of refusal, and the message's ``reply_to`` field is nonzero, a :class:`mitogen.core.CallError` is delivered to the sender indicating refusal occurred. :param bool overwrite: If :data:`True`, allow existing handles to be silently overwritten. :return: `handle`, or if `handle` was :data:`None`, the newly allocated handle. :raises Error: Attemp to register handle that was already registered. """ handle = handle or next(self._last_handle) _vv and IOLOG.debug('%r.add_handler(%r, %r, %r)', self, fn, handle, persist) if handle in self._handle_map and not overwrite: raise Error(self.duplicate_handle_msg) self._handle_map[handle] = persist, fn, policy, respondent if respondent: if respondent not in self._handles_by_respondent: self._handles_by_respondent[respondent] = set() listen(respondent, 'disconnect', lambda: self._on_respondent_disconnect(respondent)) self._handles_by_respondent[respondent].add(handle) return handle def _on_respondent_disconnect(self, context): for handle in self._handles_by_respondent.pop(context, ()): _, fn, _, _ = self._handle_map[handle] fn(Message.dead(self.respondent_disconnect_msg)) del self._handle_map[handle] def _maybe_send_dead(self, unreachable, msg, reason, *args): """ Send a dead message to either the original sender or the intended recipient of `msg`, if the original sender was expecting a reply (because its `reply_to` was set), otherwise assume the message is a reply of some sort, and send the dead message to the original destination. :param bool unreachable: If :data:`True`, the recipient is known to be dead or routing failed due to a security precaution, so don't attempt to fallback to sending the dead message to the recipient if the original sender did not include a reply address. :param mitogen.core.Message msg: Message that triggered the dead message. :param str reason: Human-readable error reason. :param tuple args: Elements to interpolate with `reason`. """ if args: reason %= args LOG.debug('%r: %r is dead: %r', self, msg, reason) if msg.reply_to and not msg.is_dead: msg.reply(Message.dead(reason=reason), router=self) elif not unreachable: self._async_route( Message.dead( dst_id=msg.dst_id, handle=msg.handle, reason=reason, ) ) def _invoke(self, msg, stream): # IOLOG.debug('%r._invoke(%r)', self, msg) try: persist, fn, policy, respondent = self._handle_map[msg.handle] except KeyError: self._maybe_send_dead(True, msg, reason=self.invalid_handle_msg) return if respondent and not (msg.is_dead or msg.src_id == respondent.context_id): self._maybe_send_dead(True, msg,'reply from unexpected context') return if policy and not policy(msg, stream): self._maybe_send_dead(True, msg, self.refused_msg) return if not persist: self.del_handler(msg.handle) try: fn(msg) except Exception: LOG.exception('%r._invoke(%r): %r crashed', self, msg, fn) def _async_route(self, msg, in_stream=None): """ Arrange for `msg` to be forwarded towards its destination. If its destination is the local context, then arrange for it to be dispatched using the local handlers. This is a lower overhead version of :meth:`route` that may only be called from the :class:`Broker` thread. :param Stream in_stream: If not :data:`None`, the stream the message arrived on. Used for performing source route verification, to ensure sensitive messages such as ``CALL_FUNCTION`` arrive only from trusted contexts. """ _vv and IOLOG.debug('%r._async_route(%r, %r)', self, msg, in_stream) if len(msg.data) > self.max_message_size: self._maybe_send_dead(False, msg, self.too_large_msg % ( self.max_message_size, )) return parent_stream = self._stream_by_id.get(mitogen.parent_id) src_stream = self._stream_by_id.get(msg.src_id, parent_stream) # When the ingress stream is known, verify the message was received on # the same as the stream we would expect to receive messages from the # src_id and auth_id. This is like Reverse Path Filtering in IP, and # ensures messages from a privileged context cannot be spoofed by a # child. if in_stream: auth_stream = self._stream_by_id.get(msg.auth_id, parent_stream) if in_stream!= auth_stream: LOG.error('%r: bad auth_id: got %r via %r, not %r: %r', self, msg.auth_id, in_stream, auth_stream, msg) return if msg.src_id!= msg.auth_id and in_stream!= src_stream: LOG.error('%r: bad src_id: got %r via %r, not %r: %r', self, msg.src_id, in_stream, src_stream, msg) return # If the stream's MitogenProtocol has auth_id set, copy it to the # message. This allows subtrees to become privileged by stamping a # parent's context ID. It is used by mitogen.unix to mark client # streams (like Ansible WorkerProcess) as having the same rights as # the parent. if in_stream.protocol.auth_id is not None: msg.auth_id = in_stream.protocol.auth_id if in_stream.protocol.on_message is not None: in_stream.protocol.on_message(in_stream, msg) # Record the IDs the source ever communicated with. in_stream.protocol.egress_ids.add(msg.dst_id) if msg.dst_id == mitogen.context_id: return self._invoke(msg, in_stream) out_stream = self._stream_by_id.get(msg.dst_id) if (not out_stream) and (parent_stream!= src_stream or not in_stream): # No downstream route exists. The message could be from a child or # ourselves for a parent, in which case we must forward it # upstream, or it could be from a parent for a dead child, in which # case its src_id/auth_id would fail verification if returned to # the parent, so in that case reply with a dead message instead. out_stream = parent_stream if out_stream is None: self._maybe_send_dead(True, msg, self.no_route_msg, msg.dst_id, mitogen.context_id) return if in_stream and self.unidirectional and not \ (in_stream.protocol.is_privileged or out_stream.protocol.is_privileged): self._maybe_send_dead(True, msg, self.unidirectional_msg, in_stream.protocol.remote_id, out_stream.protocol.remote_id, mitogen.context_id) return out_stream.protocol._send(msg) def route(self, msg): """ Arrange for the :class:`Message` `msg` to be delivered to its destination using any relevant downstream context, or if none is found, by forwarding the message upstream towards the master context. If `msg` is destined for the local context, it is dispatched using the handles registered with :meth:`add_handler`. This may be called from any thread. """ self.broker.defer(self._async_route, msg) class NullTimerList(object): def get_timeout(self): return None class Broker(object): """ Responsible for handling I/O multiplexing in a private thread. **Note:** This somewhat limited core version is used by children. The master subclass is documented below. """ poller_class = Poller _waker = None _thread = None # :func:`mitogen.parent._upgrade_broker` replaces this with # :class:`mitogen.parent.TimerList` during upgrade. timers = NullTimerList() #: Seconds grace to allow :class:`streams <Stream>` to shutdown gracefully #: before force-disconnecting them during :meth:`shutdown`. shutdown_timeout = 3.0 def __init__(self, poller_class=None, activate_compat=True): self._alive = True self._exitted = False self._waker = Waker.build_stream(self) #: Arrange for `func(\*args, \**kwargs)` to be executed on the broker #: thread, or immediately if the current thread is the broker thread. #: Safe to call from any thread. self.defer = self._waker.protocol.defer self.poller = self.poller_class() self.poller.start_receive( self._waker.receive_side.fd, (self._waker.receive_side, self._waker.on_receive) ) self._thread = threading.Thread( target=self._broker_main, name='mitogen.broker' ) self._thread.start() if activate_compat: self._py24_25_compat() def _py24_25_compat(self): """ Python 2.4/2.5 have grave difficulties with threads/fork. We mandatorily quiesce all running threads during fork using a monkey-patch there. """ if sys.version_info < (2, 6): # import_module() is used to avoid dep scanner. os_fork = import_module('mitogen.os_fork') os_fork._notice_broker_or_pool(self) def start_receive(self, stream): """ Mark the :attr:`receive_side <Stream.receive_side>` on `stream` as ready for reading. Safe to call from any thread. When the associated file descriptor becomes ready for reading, :meth:`BasicStream.on_receive` will be called. """ _vv and IOLOG.debug('%r.start_receive(%r)', self, stream) side = stream.receive_side assert side and not side.closed self.defer(self.poller.start_receive, side.fd, (side, stream.on_receive)) def stop_receive(self, stream): """ Mark the :attr:`receive_side <Stream.receive_side>` on `stream` as not ready for reading. Safe to call from any thread. """ _vv and IOLOG.debug('%r.stop_receive(%r)', self, stream) self.defer(self.poller.stop_receive, stream.receive_side.fd) def _start_transmit(self, stream): """ Mark the :attr:`transmit_side <Stream.transmit_side>` on `stream` as ready for writing. Must only be called from the Broker thread. When the associated file descriptor becomes ready for writing, :meth:`BasicStream.on_transmit` will be called. """ _vv and IOLOG.debug('%r._start_transmit(%r)', self, stream) side = stream.transmit_side assert side and not side.closed self.poller.start_transmit(side.fd, (side, stream.on_transmit)) def _stop_transmit(self, stream): """ Mark the :attr:`transmit_side <Stream.receive_side>` on `stream` as not ready for writing. """ _vv and IOLOG.debug('%r._stop_transmit(%r)', self, stream) self.poller.stop_transmit(stream.transmit_side.fd) def keep_alive(self): """ Return :data:`True` if any reader's :attr:`Side.keep_alive` attribute is :data:`True`, or any :class:`Context` is still registered that is not the master. Used to delay shutdown while some important work is in progress (e.g. log draining). """ it = (side.keep_alive for (_, (side, _)) in self.poller.readers) return sum(it, 0) > 0 or self.timers.get_timeout() is not None def defer_sync(self, func): """ Arrange for `func()` to execute on :class:`Broker` thread, blocking the current thread until a result or exception is available. :returns: Return value of `func()`. """ latch = Latch() def wrapper(): try: latch.put(func()) except Exception: latch.put(sys.exc_info()[1]) self.defer(wrapper) res = latch.get() if isinstance(res, Exception): raise res return res def _call(self, stream, func): """ Call `func(self)`, catching any exception that might occur, logging it, and force-disconnecting the related `stream`. """ try: func(self) except Exception: LOG.exception('%r crashed', stream) stream.on_disconnect(self) def _loop_once(self, timeout=None): """ Execute a single :class:`Poller` wait, dispatching any IO events that caused the wait to complete. :param float timeout: If not :data:`None`, maximum time in seconds to wait for events. """ _vv and IOLOG.debug('%r._loop_once(%r, %r)', self, timeout, self.poller) timer_to = self.timers.get_timeout() if timeout is None: timeout = timer_to elif timer_to is not None and timer_to < timeout: timeout = timer_to #IOLOG.debug('readers =\n%s', pformat(self.poller.readers)) #IOLOG.debug('writers =\n%s', pformat(self.poller.writers)) for side, func in self.poller.poll(timeout): self._call(side.stream, func) if timer_to is not None: self.timers.expire() def _broker_exit(self): """ Forcefully call :meth:`Stream.on_disconnect` on any streams that failed to shut down gracefully, then discard the :class:`Poller`. """ for _, (side, _) in self.poller.readers + self.poller.writers: LOG.debug('%r: force disconnecting %r', self, side) side.stream.on_disconnect(self) self.poller.close() def _broker_shutdown(self): """ Invoke :meth:`Stream.on_shutdown` for every active stream, then allow up to :attr:`shutdown_timeout` seconds for the streams to unregister themselves, logging an error if any did not unregister during the grace period. """ for _, (side, _) in self.poller.readers + self.poller.writers: self._call(side.stream, side.stream.on_shutdown) deadline = now() + self.shutdown_timeout while self.keep_alive() and now() < deadline: self._loop_once(max(0, deadline - now())) if self.keep_alive(): LOG.error('%r: pending work still existed %d seconds after ' 'shutdown began. This may be due to a timer that is yet ' 'to expire, or a child connection that did not fully ' 'shut down.', self, self.shutdown_timeout) def _do_broker_main(self): """ Broker thread main function. Dispatches IO events until :meth:`shutdown` is called. """ # For Python 2.4, no way to retrieve ident except on thread. self._waker.protocol.broker_ident = thread.get_ident() try: while self._alive: self._loop_once() fire(self, 'before_shutdown') fire(self,'shutdown') self._broker_shutdown() except Exception: e = sys.exc_info()[1] LOG.exception('broker crashed') syslog.syslog(syslog.LOG_ERR, 'broker crashed: %s' % (e,)) syslog.closelog() # prevent test 'fd leak'. self._alive = False # Ensure _alive is consistent on crash. self._exitted = True self._broker_exit() def _broker_main(self): try: _profile_hook('mitogen.broker', self._do_broker_main) finally: # 'finally' to ensure _on_broker_exit() can always SIGTERM. fire(self, 'exit') def shutdown(self): """ Request broker gracefully disconnect streams and stop. Safe to call from any thread. """ _v and LOG.debug('%r: shutting down', self) def _shutdown(): self._alive = False if self._alive and not self._exitted: self.defer(_shutdown) def join(self): """ Wait for the broker to stop, expected to be called after :meth:`shutdown`. """ self._thread.join() def __repr__(self): return 'Broker(%04x)' % (id(self) & 0xffff,) class Dispatcher(object): """ Implementation of the :data:`CALL_FUNCTION` handle for a child context. Listens on the child's main thread for messages sent by :class:`mitogen.parent.CallChain` and dispatches the function calls they describe. If a :class:`mitogen.parent.CallChain` sending a message is in pipelined mode, any exception that occurs is recorded, and causes all subsequent calls with the same `chain_id` to fail with the same exception. """ _service_recv = None def __repr__(self): return 'Dispatcher' def __init__(self, econtext): self.econtext = econtext #: Chain ID -> CallError if prior call failed. self._error_by_chain_id = {} self.recv = Receiver( router=econtext.router, handle=CALL_FUNCTION, policy=has_parent_authority, ) #: The :data:`CALL_SERVICE` :class:`Receiver` that will eventually be #: reused by :class:`mitogen.service.Pool`, should it ever be loaded. #: This is necessary for race-free reception of all service requests #: delivered regardless of whether the stub or real service pool are #: loaded. See #547 for related sorrows. Dispatcher._service_recv = Receiver( router=econtext.router, handle=CALL_SERVICE, policy=has_parent_authority, ) self._service_recv.notify = self._on_call_service listen(econtext.broker,'shutdown', self._on_broker_shutdown) def _on_broker_shutdown(self): if self._service_recv.notify == self._on_call_service: self._service_recv.notify = None self.recv.close() @classmethod @takes_econtext def forget_chain(cls, chain_id, econtext): econtext.dispatcher._error_by_chain_id.pop(chain_id, None) def _parse_request(self, msg): data = msg.unpickle(throw=False) _v and LOG.debug('%r: dispatching %r', self, data) chain_id, modname, klass, func, args, kwargs = data obj = import_module(modname) if klass: obj = getattr(obj, klass) fn = getattr(obj, func) if getattr(fn,'mitogen_takes_econtext', None): kwargs.setdefault('econtext', self.econtext) if getattr(fn,'mitogen_takes_router', None): kwargs.setdefault('router', self.econtext.router) return chain_id, fn, args, kwargs def _dispatch_one(self, msg): try: chain_id, fn, args, kwargs = self._parse_request(msg) except Exception: return None, CallError(sys.exc_info()[1]) if chain_id in self._error_by_chain_id: return chain_id, self._error_by_chain_id[chain_id] try: return chain_id, fn(*args, **kwargs) except Exception: e = CallError(sys.exc_info()[1]) if chain_id is not None: self._error_by_chain_id[chain_id] = e return chain_id, e def _on_call_service(self, recv): """ Notifier for the :data:`CALL_SERVICE` receiver. This is called on the :class:`Broker` thread for any service messages arriving at this context, for as long as no real service pool implementation is loaded. In order to safely bootstrap the service pool implementation a sentinel message is enqueued on the :data:`CALL_FUNCTION` receiver in order to wake the main thread, where the importer can run without any possibility of suffering deadlock due to concurrent uses of the importer. Should the main thread be blocked indefinitely, preventing the import from ever running, if it is blocked waiting on a service call, then it means :mod:`mitogen.service` has already been imported and :func:`mitogen.service.get_or_create_pool` has already run, meaning the service pool is already active and the duplicate initialization was not needed anyway. #547: This trickery is needed to avoid the alternate option of spinning a temporary thread to import the service pool, which could deadlock if a custom import hook executing on the main thread (under the importer lock) would block waiting for some data that was in turn received by a service. Main thread import lock can't be released until service is running, service cannot satisfy request until import lock is released. """ self.recv._on_receive(Message(handle=STUB_CALL_SERVICE)) def _init_service_pool(self): import mitogen.service mitogen.service.get_or_create_pool(router=self.econtext.router) def _dispatch_calls(self): for msg in self.recv: if msg.handle == STUB_CALL_SERVICE: if msg.src_id == mitogen.context_id: self._init_service_pool() continue chain_id, ret = self._dispatch_one(msg) _v and LOG.debug('%r: %r -> %r', self, msg, ret) if msg.reply_to: msg.reply(ret) elif isinstance(ret, CallError) and chain_id is None: LOG.error('No-reply function call failed: %s', ret) def run(self): if self.econtext.config.get('on_start'): self.econtext.config['on_start'](self.econtext) _profile_hook('mitogen.child_main', self._dispatch_calls) class ExternalContext(object): """ External context implementation. This class contains the main program implementation for new children. It is responsible for setting up everything about the process environment, import hooks, standard IO redirection, logging, configuring a :class:`Router` and :class:`Broker`, and finally arranging for :class:`Dispatcher` to take over the main thread after initialization is complete. .. attribute:: broker The :class:`mitogen.core.Broker` instance. .. attribute:: context The :class:`mitogen.core.Context` instance. .. attribute:: channel The :class:`mitogen.core.Channel` over which :data:`CALL_FUNCTION` requests are received. .. attribute:: importer The :class:`mitogen.core.Importer` instance. .. attribute:: stdout_log The :class:`IoLogger` connected to :data:`sys.stdout`. .. attribute:: stderr_log The :class:`IoLogger` connected to :data:`sys.stderr`. """ detached = False def __init__(self, config): self.config = config def _on_broker_exit(self): if not self.config['profiling']: os.kill(os.getpid(), signal.SIGTERM) def _on_shutdown_msg(self, msg): if not msg.is_dead: _v and LOG.debug('shutdown request from context %d', msg.src_id) self.broker.shutdown() def _on_parent_disconnect(self): if self.detached: mitogen.parent_ids = [] mitogen.parent_id = None LOG.info('Detachment complete') else: _v and LOG.debug('parent stream is gone, dying.') self.broker.shutdown() def detach(self): self.detached = True stream = self.router.stream_by_id(mitogen.parent_id) if stream: # not double-detach()'d os.setsid() self.parent.send_await(Message(handle=DETACHING)) LOG.info('Detaching from %r; parent is %s', stream, self.parent) for x in range(20): pending = self.broker.defer_sync(stream.protocol.pending_bytes) if not pending: break time.sleep(0.05) if pending: LOG.error('Stream had %d bytes after 2000ms', pending) self.broker.defer(stream.on_disconnect, self.broker) def _setup_master(self): Router.max_message_size = self.config['max_message_size'] if self.config['profiling']: enable_profiling() self.broker = Broker(activate_compat=False) self.router = Router(self.broker) self.router.debug = self.config.get('debug', False) self.router.unidirectional = self.config['unidirectional'] self.router.add_handler( fn=self._on_shutdown_msg, handle=SHUTDOWN, policy=has_parent_authority, ) self.master = Context(self.router, 0,'master') parent_id = self.config['parent_ids'][0] if parent_id == 0: self.parent = self.master else: self.parent = Context(self.router, parent_id, 'parent') in_fd = self.config.get('in_fd', 100) in_fp = os.fdopen(os.dup(in_fd), 'rb', 0) os.close(in_fd) out_fp = os.fdopen(os.dup(self.config.get('out_fd', 1)), 'wb', 0) self.stream = MitogenProtocol.build_stream( self.router, parent_id, local_id=self.config['context_id'], parent_ids=self.config['parent_ids'] ) self.stream.accept(in_fp, out_fp) self.stream.name = 'parent' self.stream.receive_side.keep_alive = False listen(self.stream, 'disconnect', self._on_parent_disconnect) listen(self.broker, 'exit', self._on_broker_exit) def _reap_first_stage(self): try: os.wait() # Reap first stage. except OSError: pass # No first stage exists (e.g. fakessh) def _setup_logging(self): self.log_handler = LogHandler(self.master) root = logging.getLogger() root.setLevel(self.config['log_level']) root.handlers = [self.log_handler] if self.config['debug']: enable_debug_logging() def _setup_importer(self): importer = self.config.get('importer') if importer: importer._install_handler(self.router) importer._context = self.parent else: core_src_fd = self.config.get('core_src_fd', 101) if core_src_fd: fp = os.fdopen(core_src_fd, 'rb', 0) try: core_src = fp.read() # Strip "ExternalContext.main()" call from last line. core_src = b('\n').join(core_src.splitlines()[:-1]) finally: fp.close() else: core_src = None importer = Importer( self.router, self.parent, core_src, self.config.get('whitelist', ()), self.config.get('blacklist', ()), ) self.importer = importer self.router.importer = importer sys.meta_path.insert(0, self.importer) def _setup_package(self): global mitogen mitogen = imp.new_module('mitogen') mitogen.__package__ ='mitogen' mitogen.__path__ = [] mitogen.__loader__ = self.importer mitogen.main = lambda *args, **kwargs: (lambda func: None) mitogen.core = sys.modules['__main__'] mitogen.core.__file__ = 'x/mitogen/core.py' # For inspect.getsource() mitogen.core.__loader__ = self.importer sys.modules['mitogen'] = mitogen sys.modules['mitogen.core'] = mitogen.core del sys.modules['__main__'] def _setup_globals(self): mitogen.is_master = False mitogen.__version__ = self.config['version'] mitogen.context_id = self.config['context_id'] mitogen.parent_ids = self.config['parent_ids'][:] mitogen.parent_id = mitogen.parent_ids[0] def _nullify_stdio(self): """ Open /dev/null to replace stdio temporarily. In case of odd startup, assume we may be allocated a standard handle. """ for stdfd, mode in ((0, os.O_RDONLY), (1, os.O_RDWR), (2, os.O_RDWR)): fd = os.open('/dev/null', mode) if fd!= stdfd: os.dup2(fd, stdfd) os.close(fd) def _preserve_tty_fp(self): """ #481: when stderr is a TTY due to being started via tty_create_child() or hybrid_tty_create_child(), and some privilege escalation tool like prehistoric versions of sudo exec this process over the top of itself, there is nothing left to keep the slave PTY open after we replace our stdio. Therefore if stderr is a TTY, keep around a permanent dup() to avoid receiving SIGHUP. """ try: if os.isatty(2): self.reserve_tty_fp = os.fdopen(os.dup(2), 'r+b', 0) set_cloexec(self.reserve_tty_fp.fileno()) except OSError: pass def _setup_stdio(self): self._preserve_tty_fp() # When sys.stdout was opened by the runtime, overwriting it will not # close FD 1. However when forking from a child that previously used # fdopen(), overwriting it /will/ close FD 1. So we must swallow the # close before IoLogger overwrites FD 1, otherwise its new FD 1 will be # clobbered. Additionally, stdout must be replaced with /dev/null prior # to stdout.close(), since if block buffering was active in the parent, # any pre-fork buffered data will be flushed on close(), corrupting the # connection to the parent. self._nullify_stdio() sys.stdout.close() self._nullify_stdio() self.loggers = [] for name, fd in (('stdout', 1), ('stderr', 2)): log = IoLoggerProtocol.build_stream(name, fd) self.broker.start_receive(log) self.loggers.append(log) # Reopen with line buffering. sys.stdout = os.fdopen(1, 'w', 1) def main(self): self._setup_master() try: try: self._setup_logging() self._setup_importer() self._reap_first_stage() if self.config.get('setup_package', True): self._setup_package() self._setup_globals() if self.config.get('setup_stdio', True): self._setup_stdio() self.dispatcher = Dispatcher(self) self.router.register(self.parent, self.stream) self.router._setup_logging() _v and LOG.debug('Python version is %s', sys.version) _v and LOG.debug('Parent is context %r (%s); my ID is %r', self.parent.context_id, self.parent.name, mitogen.context_id) _v and LOG.debug('pid:%r ppid:%r uid:%r/%r, gid:%r/%r host:%r', os.getpid(), os.getppid(), os.geteuid(), os.getuid(), os.getegid(), os.getgid(), socket.gethostname()) sys.executable = os.environ.pop('ARGV0', sys.executable) _v and LOG.debug('Recovered sys.executable: %r', sys.executable) if self.config.get('send_ec2', True): self.stream.transmit_side.write(b('MITO002\n')) self.broker._py24_25_compat() self.log_handler.uncork() self.dispatcher.run() _v and LOG.debug('ExternalContext.main() normal exit') except KeyboardInterrupt: LOG.debug('KeyboardInterrupt received, exiting gracefully.') except BaseException: LOG.exception('ExternalContext.main() crashed') raise finally: self.broker.shutdown()

ethereum__web3.py

contracts.rst

Module doc

Generate documentation for this code

MIT License

ethereum__web3.py/docs/contracts.rst

Contracts Contract Factories The Contract class is not intended to be used or instantiated directly. Instead you should use the web3.eth.contract(...) method to generate the contract factory classes for your contracts. Contract Factories provide an interface for deploying and interacting with Ethereum smart contracts. Properties Each Contract Factory exposes the following properties. The hexidecimal encoded 20 byte address of the contract. May be None if not provided during factory creation. The contract ABI array. The contract bytecode string. May be None if not provided during factory creation. The runtime part of the contract bytecode string. May be None if not provided during factory creation. The runtime part of the contract bytecode string. May be None if not provided during factory creation. Methods Each Contract Factory exposes the following methods. Construct and send a transaction to deploy the contract. If provided transaction should be a dictionary conforming to the web3.eth.sendTransaction(transaction) method. This value may not contain the keys data or to. If the contract takes constructor arguments they should be provided as a list via the arguments parameter. If a gas value is not provided, then the gas value for the deployment transaction will be created using the web3.eth.estimateGas() method. Returns the transaction hash for the deploy transaction. Execute the specified function by sending a new public transaction. This is executed in two steps. The first portion of this function call transact(transaction) takes a single parameter which should be a python dictionary conforming to the same format as the web3.eth.sendTransaction(transaction) method. This dictionary may not contain the keys data or to. The second portion of the function call myMethod(*args, **kwargs) selects the appropriate contract function based on the name and provided argument. Arguments can be provided as positional arguments, keyword arguments, or a mix of the two. Returns the transaction hash. >>> token_contract.transact().transfer(web3.eth.accounts[1], 12345) "0x4e3a3754410177e6937ef1f84bba68ea139e8d1a2258c5f85db9f1cd715a1bdd" Call a contract function, executing the transaction locally using the eth_call API. This will not create a new public transaction. This method behaves the same as the :py:method::Contract.transact method, with transaction details being passed into the first portion of the function call, and function arguments being passed into the second portion. Returns the return value of the executed function. >>> my_contract.call().multiply7(3) 21 >>> token_contract.call({'from': web3.eth.coinbase}).myBalance() 12345 # the token balance for `web3.eth.coinbase` >>> token_contract.call({'from': web3.eth.accounts[1]}).myBalance() 54321 # the token balance for the account `web3.eth.accounts[1]` Call a contract function, executing the transaction locally using the eth_call API. This will not create a new public transaction. This method behaves the same as the :py:method::Contract.transact method, with transaction details being passed into the first portion of the function call, and function arguments being passed into the second portion. Returns the amount of gas consumed which can be used as a gas estimate for executing this transaction publicly. >>> my_contract.estimateGas().multiply7(3) 42650 Events Creates a new web3.utils.filters.LogFilter instance. The event_name parameter should be the name of the contract event you want to filter on. If provided, filter_params should be a dictionary specifying additional filters for log entries. The following keys are supported. - filters: dictionary - (optional) Dictionary keys should be argument names for the Event arguments. Dictionary values should be the value you want to filter on, or a list of values to be filtered on. Lists of values will match log entries who's argument matches any value in the list. - fromBlock: integer/tag - (optional, default: "latest") Integer block number, or "latest" for the last mined block or "pending", "earliest" for not yet mined transactions. - toBlock: integer/tag - (optional, default: "latest") Integer block number, or "latest" for the last mined block or "pending", "earliest" for not yet mined transactions. - address: string or list of strings, each 20 Bytes -(optional) Contract address or a list of addresses from which logs should originate. - topics: list of 32 byte strings or null - (optional) Array of topics that should be used for filtering. Topics are order-dependent. This parameter can also be a list of topic lists in which case filtering will match any of the provided topic arrays. The event topic for the event specified by event_name will be added to the filter_params['topics'] list. If the Contract.address attribute for this contract is non-null, the contract address will be added to the filter_params. If provided, the *callbacks parameter should be callables which accept a single Event Log object. When callbacks are provided, the filter will be started. Otherwise the filter will be returned without starting it. The Event Log Object is a python dictionary with the following keys: - args: Dictionary - The arguments coming from the event. - event: String - The event name. - logIndex: Number - integer of the log index position in the block. - transactionIndex: Number - integer of the transactions index position log was created from. - transactionHash: String, 32 Bytes - hash of the transactions this log was created from. - address: String, 32 Bytes - address from which this log originated. - blockHash: String, 32 Bytes - hash of the block where this log was in. null when its pending. - blockNumber: Number - the block number where this log was in. null when its pending. >>> transfer_filter = my_token_contract.on('Transfer', {'filters': {'_from': '0xdc3a9db694bcdd55ebae4a89b22ac6d12b3f0c24'}}) >>> transfer_filter.get() [...] # array of Event Log Objects that match the filter. >>> transfer_filter.watch(my_callback) # now `my_callback` will be called each time a new matching event log # is encountered. Creates a new web3.utils.filters.PastLogFilter instance which will match historical event logs. All parameters behave the same as the :py:method::Contract.on method. >>> transfer_filter = my_token_contract.pastEvents('Transfer', {'filters': {'_from': '0xdc3a9db694bcdd55ebae4a89b22ac6d12b3f0c24'}}) >>> transfer_filter.get() [...] # array of Event Log Objects that match the filter for all historical events.

"""Interaction with smart contracts over Web3 connector. """ import functools from eth_abi import ( encode_abi, decode_abi, ) from eth_abi.exceptions import ( EncodingError, DecodingError, ) from web3.exceptions import ( BadFunctionCallOutput, ) from web3.utils.encoding import ( encode_hex, ) from web3.utils.exception import ( raise_from, ) from web3.utils.formatting import ( add_0x_prefix, remove_0x_prefix, ) from web3.utils.string import ( force_bytes, coerce_return_to_text, force_obj_to_bytes, ) from web3.utils.functional import ( compose, ) from web3.utils.abi import ( filter_by_type, filter_by_name, filter_by_argument_count, filter_by_argument_name, filter_by_encodability, get_abi_input_types, get_abi_output_types, get_constructor_abi, function_abi_to_4byte_selector, merge_args_and_kwargs, normalize_return_type, check_if_arguments_can_be_encoded, ) from web3.utils.decorators import ( combomethod, ) from web3.utils.events import ( get_event_data, ) from web3.utils.filters import ( construct_event_filter_params, PastLogFilter, ) class Contract(object): """Base class for Contract proxy classes. First you need to create your Contract classes using :func:`construct_contract_factory` that takes compiled Solidity contract ABI definitions as input. The created class object will be a subclass of this base class. After you have your Contract proxy class created you can interact with smart contracts * Create a Contract proxy object for an existing deployed smart contract by its address using :meth:`__init__` * Deploy a new smart contract using :py:meth:`Contract.deploy` """ # set during class construction web3 = None # class properties (overridable at instance level) _abi = None _code = None _code_runtime = None _source = None # instance level properties address = None def __init__(self, abi=None, address=None, code=None, code_runtime=None, source=None): """Create a new smart contract proxy object. :param address: Contract address as 0x hex string :param abi: Override class level definition :param code: Override class level definition :param code_runtime: Override class level definition :param source: Override class level definition """ if self.web3 is None: raise AttributeError( 'The `Contract` class has not been initialized. Please use the ' '`web3.contract` interface to create your contract class.' ) if abi is not None: self._abi = abi if code is not None: self._code = code if code_runtime is not None: self._code_runtime = code_runtime if source is not None: self._source = source self.address = address @property def abi(self): if self._abi is not None: return self._abi # TODO: abi can be derived from the contract source. raise AttributeError("No contract abi was specified for thes contract") @property def code(self): if self._code is not None: return self._code # TODO: code can be derived from the contract source. raise AttributeError("No contract code was specified for thes contract") @property def code_runtime(self): if self._code_runtime is not None: return self._code_runtime # TODO: runtime can be derived from the contract source. raise AttributeError( "No contract code_runtime was specified for thes contract" ) @property def source(self): if self._source is not None: return self._source raise AttributeError("No contract source was specified for thes contract") @classmethod def deploy(cls, transaction=None, args=None, kwargs=None): """ Deploys the contract on a blockchain. Example: .. code-block:: python >>> MyContract.deploy( transaction={ 'from': web3.eth.accounts[1], 'value': 12345, }, args=('DGD', 18), ) '0x5c504ed432cb51138bcf09aa5e8a410dd4a1e204ef84bfed1be16dfba1b22060' :param transaction: Transaction parameters for the deployment transaction as a dict :param args: The contract constructor arguments as positional arguments :param kwargs: The contract constructor arguments as keyword arguments :return: hexidecimal transaction hash of the deployment transaction """ if transaction is None: deploy_transaction = {} else: deploy_transaction = dict(**transaction) if not cls.code: raise ValueError( "Cannot deploy a contract that does not have 'code' associated " "with it" ) if 'data' in deploy_transaction: raise ValueError( "Cannot specify `data` for contract deployment" ) if 'to' in deploy_transaction: raise ValueError( "Cannot specify `to` for contract deployment" ) deploy_transaction['data'] = cls._encode_constructor_data(args, kwargs) # TODO: handle asynchronous contract creation txn_hash = cls.web3.eth.sendTransaction(deploy_transaction) return txn_hash # # Public API # @classmethod @coerce_return_to_text def encodeABI(cls, fn_name, args=None, kwargs=None, data=None): """ encodes the arguments using the Ethereum ABI for the contract function that matches the given name and arguments.. :param data: defaults to function selector """ fn_abi, fn_selector, fn_arguments = cls._get_function_info( fn_name, args, kwargs, ) if data is None: data = fn_selector return cls._encode_abi(fn_abi, fn_arguments, data) @combomethod def on(self, event_name, filter_params=None, *callbacks): """ register a callback to be triggered on the appropriate events. """ if filter_params is None: filter_params = {} argument_filters = filter_params.pop('filter', {}) argument_filter_names = list(argument_filters.keys()) event_abi = self._find_matching_event_abi( event_name, argument_filter_names, ) data_filter_set, event_filter_params = construct_event_filter_params( event_abi, contract_address=self.address, argument_filters=argument_filters, **filter_params ) log_data_extract_fn = functools.partial(get_event_data, event_abi) log_filter = self.web3.eth.filter(event_filter_params) log_filter.set_data_filters(data_filter_set) log_filter.log_entry_formatter = log_data_extract_fn log_filter.filter_params = event_filter_params if callbacks: log_filter.watch(*callbacks) return log_filter @combomethod def pastEvents(self, event_name, filter_params=None, *callbacks): """ register a callback to be triggered on all past events. """ if filter_params is None: filter_params = {} event_filter_params = {} event_filter_params.update(filter_params) event_filter_params.setdefault('fromBlock', 'earliest') event_filter_params.setdefault('toBlock', self.web3.eth.blockNumber) log_filter = self.on( event_name, filter_params=event_filter_params, ) past_log_filter = PastLogFilter( web3=log_filter.web3, filter_id=log_filter.filter_id, log_entry_formatter=log_filter.log_entry_formatter, data_filter_set=log_filter.data_filter_set, ) past_log_filter.filter_params = log_filter.filter_params if callbacks: past_log_filter.watch(*callbacks) return past_log_filter @combomethod def estimateGas(self, transaction=None): """ Estimate the gas for a call """ if transaction is None: estimate_transaction = {} else: estimate_transaction = dict(**transaction) if 'data' in estimate_transaction: raise ValueError("Cannot set data in call transaction") if 'to' in estimate_transaction: raise ValueError("Cannot set to in call transaction") if self.address: estimate_transaction.setdefault('to', self.address) estimate_transaction.setdefault('from', self.web3.eth.defaultAccount) if 'to' not in estimate_transaction: if isinstance(self, type): raise ValueError( "When using `Contract.estimateGas` from a contract factory " "you must provide a `to` address with the transaction" ) else: raise ValueError( "Please ensure that this contract instance has an address." ) contract = self class Caller(object): def __getattr__(self, function_name): callable_fn = functools.partial( estimate_gas_for_function, contract, function_name, estimate_transaction, ) return callable_fn return Caller() @combomethod def call(self, transaction=None): """ Execute a contract function call using the `eth_call` interface. This method prepares a ``Caller`` object that exposes the contract functions and publib variables as callable Python functions. Reading a public ``owner`` address variable example: .. code-block:: python ContractFactory = construct_contract_factory( web3=web3, abi=wallet_contract_definition["abi"] ) # Not a real contract address contract = contract_class("0x2f70d3d26829e412a602e83fe8eebf80255aeea5") # Read "owner" public variable addr = contract.call().owner() :param transaction: Dictionary of transaction info for web3 interface :return: ``Caller`` object that has contract public functions and variables exposed as Python methods """ if transaction is None: call_transaction = {} else: call_transaction = dict(**transaction) if 'data' in call_transaction: raise ValueError("Cannot set data in call transaction") if self.address: call_transaction.setdefault('to', self.address) call_transaction.setdefault('from', self.web3.eth.defaultAccount) if 'to' not in call_transaction: if isinstance(self, type): raise ValueError( "When using `Contract.call` from a contract factory you " "must provide a `to` address with the transaction" ) else: raise ValueError( "Please ensure that this contract instance has an address." ) contract = self class Caller(object): def __getattr__(self, function_name): callable_fn = functools.partial( call_contract_function, contract, function_name, call_transaction, ) return callable_fn return Caller() @combomethod def transact(self, transaction=None): """ Execute a contract function call using the `eth_sendTransaction` interface. You should specify the account that pays the gas for this transaction in `transaction`. If no account is specified the coinbase account of web3 interface is used. Example: .. code-block:: python # Assume we have a Wallet contract with the following methods. # * Wallet.deposit() # deposits to `msg.sender` # * Wallet.deposit(address to) # deposits to the account indicated # by the `to` parameter. # * Wallet.withdraw(address amount) >>> wallet = Wallet(address='0xdc3a9db694bcdd55ebae4a89b22ac6d12b3f0c24') # Deposit to the `web3.eth.coinbase` account. >>> wallet.transact({'value': 12345}).deposit() '0x5c504ed432cb51138bcf09aa5e8a410dd4a1e204ef84bfed1be16dfba1b22060' # Deposit to some other account using funds from `web3.eth.coinbase`. >>> wallet.transact({'value': 54321}).deposit(web3.eth.accounts[1]) '0xe122ba26d25a93911e241232d3ba7c76f5a6bfe9f8038b66b198977115fb1ddf' # Withdraw 12345 wei. >>> wallet.transact().withdraw(12345) The new public transaction will be created. Transaction receipt will be available once the transaction has been mined. :param transaction: Dictionary of transaction info for web3 interface. Variables include ``from``, ``gas``, ``value``, ``gasPrice``. :return: ``Transactor`` object that has contract public functions exposed as Python methods. Calling these methods will execute a transaction against the contract. """ if transaction is None: transact_transaction = {} else: transact_transaction = dict(**transaction) if 'data' in transact_transaction: raise ValueError("Cannot set data in call transaction") if self.address is not None: transact_transaction.setdefault('to', self.address) transact_transaction.setdefault('from', self.web3.eth.defaultAccount) if 'to' not in transact_transaction: if isinstance(self, type): raise ValueError( "When using `Contract.transact` from a contract factory you " "must provide a `to` address with the transaction" ) else: raise ValueError( "Please ensure that this contract instance has an address." ) contract = self class Transactor(object): def __getattr__(self, function_name): callable_fn = functools.partial( transact_with_contract_function, contract, function_name, transact_transaction, ) return callable_fn return Transactor() # # Private Helpers # @classmethod def _find_matching_fn_abi(cls, fn_name=None, args=None, kwargs=None): filters = [] if fn_name: filters.append(functools.partial(filter_by_name, fn_name)) if args is not None or kwargs is not None: if args is None: args = tuple() if kwargs is None: kwargs = {} num_arguments = len(args) + len(kwargs) filters.extend([ functools.partial(filter_by_argument_count, num_arguments), functools.partial(filter_by_encodability, args, kwargs), ]) function_candidates = filter_by_type('function', cls.abi) for filter_fn in filters: function_candidates = filter_fn(function_candidates) if len(function_candidates) == 1: return function_candidates[0] elif not function_candidates: break if not function_candidates: raise ValueError("No matching functions found") else: raise ValueError("Multiple functions found") @classmethod def _find_matching_event_abi(cls, event_name=None, argument_names=None): filters = [ functools.partial(filter_by_type, 'event'), ] if event_name is not None: filters.append(functools.partial(filter_by_name, event_name)) if argument_names is not None: filters.append( functools.partial(filter_by_argument_name, argument_names) ) filter_fn = compose(*filters) event_abi_candidates = filter_fn(cls.abi) if len(event_abi_candidates) == 1: return event_abi_candidates[0] elif not event_abi_candidates: raise ValueError("No matching functions found") else: raise ValueError("Multiple functions found") @classmethod def _get_function_info(cls, fn_name, args=None, kwargs=None): if args is None: args = tuple() if kwargs is None: kwargs = {} fn_abi = cls._find_matching_fn_abi(fn_name, args, kwargs) fn_selector = function_abi_to_4byte_selector(fn_abi) fn_arguments = merge_args_and_kwargs(fn_abi, args, kwargs) return fn_abi, fn_selector, fn_arguments @combomethod def _prepare_transaction(cls, fn_name, fn_args=None, fn_kwargs=None, transaction=None): """ Returns a dictionary of the transaction that could be used to call this """ if transaction is None: prepared_transaction = {} else: prepared_transaction = dict(**transaction) if 'data' in prepared_transaction: raise ValueError("Transaction parameter may not contain a 'data' key") if cls.address: prepared_transaction.setdefault('to', cls.address) prepared_transaction['data'] = cls._encode_transaction_data( fn_name, fn_args, fn_kwargs, ) return prepared_transaction @classmethod def _encode_abi(cls, abi, arguments, data=None): argument_types = get_abi_input_types(abi) if not check_if_arguments_can_be_encoded(abi, arguments, {}): raise TypeError( "One or more arguments could not be encoded to the necessary " "ABI type. Expected types are: {0}".format( ', '.join(argument_types), ) ) try: encoded_arguments = encode_abi( argument_types, force_obj_to_bytes(arguments), ) except EncodingError as e: raise TypeError( "One or more arguments could not be encoded to the necessary " "ABI type: {0}".format(str(e)) ) if data: return add_0x_prefix( force_bytes(remove_0x_prefix(data)) + force_bytes(remove_0x_prefix(encode_hex(encoded_arguments))) ) else: return encode_hex(encoded_arguments) @classmethod @coerce_return_to_text def _encode_transaction_data(cls, fn_name, args=None, kwargs=None): fn_abi, fn_selector, fn_arguments = cls._get_function_info( fn_name, args, kwargs, ) return add_0x_prefix(cls._encode_abi(fn_abi, fn_arguments, fn_selector)) @classmethod @coerce_return_to_text def _encode_constructor_data(cls, args=None, kwargs=None): constructor_abi = get_constructor_abi(cls.abi) if constructor_abi: if args is None: args = tuple() if kwargs is None: kwargs = {} arguments = merge_args_and_kwargs(constructor_abi, args, kwargs) deploy_data = add_0x_prefix( cls._encode_abi(constructor_abi, arguments, data=cls.code) ) else: deploy_data = add_0x_prefix(cls.code) return deploy_data @coerce_return_to_text def call_contract_function(contract, function_name, transaction, *args, **kwargs): """ Helper function for interacting with a contract function using the `eth_call` API. """ call_transaction = contract._prepare_transaction( fn_name=function_name, fn_args=args, fn_kwargs=kwargs, transaction=transaction, ) return_data = contract.web3.eth.call(call_transaction) function_abi = contract._find_matching_fn_abi(function_name, args, kwargs) output_types = get_abi_output_types(function_abi) try: output_data = decode_abi(output_types, return_data) except DecodingError as e: # Provide a more helpful error message than the one provided by # eth-abi-utils msg = ( "Could not decode contract function call {} return data {} for " "output_types {}".format( function_name, return_data, output_types ) ) raise_from(BadFunctionCallOutput(msg), e) normalized_data = [ normalize_return_type(data_type, data_value) for data_type, data_value in zip(output_types, output_data) ] if len(normalized_data) == 1: return normalized_data[0] else: return normalized_data def transact_with_contract_function(contract=None, function_name=None, transaction=None, *args, **kwargs): """ Helper function for interacting with a contract function by sending a transaction. """ transact_transaction = contract._prepare_transaction( fn_name=function_name, fn_args=args, fn_kwargs=kwargs, transaction=transaction, ) txn_hash = contract.web3.eth.sendTransaction(transact_transaction) return txn_hash def estimate_gas_for_function(contract=None, function_name=None, transaction=None, *args, **kwargs): """Estimates gas cost a function call would take. Don't call this directly, instead use :meth:`Contract.estimateGas` on your contract instance. """ estimate_transaction = contract._prepare_transaction( fn_name=function_name, fn_args=args, fn_kwargs=kwargs, transaction=transaction, ) gas_estimate = contract.web3.eth.estimateGas(estimate_transaction) return gas_estimate def construct_contract_factory(web3, abi, code=None, code_runtime=None, source=None, contract_name='Contract', base_contract_factory_class=Contract): """Creates a new Contract class. Contract lass is a Python proxy class to interact with smart contracts. ``abi`` and other contract definition fields are coming from ``solc`` compiler or ``build/contracts.json`` in the case of Populus framework. After contract has been instiated you can interact with it using :meth:`transact_with_contract_function` and :meth:`call_contract_function`. Example: .. code-block:: python # Assume we have a Token contract token_contract_data = { 'abi': [...], 'code': '0x...', 'code_runtime': '0x...', 'source': 'contract Token {.....}', } # contract_factory is a python class that can be used to interact with # or deploy the "Token" contract. token_contract_factory = construct_contract_factory( web3=web3, abi=token_contract_data["abi"], code=token_contract_data["code"], code_runtime=token_contract_data["code_runtime"], source=token_contract_data["source"], ) # Create Contract instance to interact with a deployed smart contract. token_contract = token_contract_factory( address=address, abi=token_contract_data["abi"], code=token_contract_data["code"], code_runtime=token_contract_data["code_runtime"], source=token_contract_data["source"]) :param web3: Web3 connection :param abi: As given by solc compiler :param code: As given by solc compiler :param code_runtime: As given by solc compiler :param source: As given by solc compiler :return: Contract class (not instance) """ _dict = { 'web3': web3, 'abi': abi, 'code': code, 'code_runtime': code_runtime, 'source': source, } return type(contract_name, (base_contract_factory_class,), _dict)

ethereum__web3.py

filters.rst

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ethereum__web3.py/docs/filters.rst

Filtering The web3.eth.filter method can be used to setup filter for: - Pending Transactions - New Blocks - Event Logs Filter API The :py:class::Filter object is a subclass of the :py:class::gevent.Greenlet object. It exposes these additional properties and methods. The filter_id for this filter as returned by the eth_newFilter RPC method when this filter was created. A list of callbacks that this filter will call with new entries. Boolean as to whether this filter is currently polling. Boolean as to whether this filter has been stopped. Will be set to None if the filter has not yet been started. Hook for subclasses to modify the format of the log entries this filter returns, or passes to it's callback functions. By default this returns the entry parameter umodified. Hook for subclasses to add additional programatic filtering. The default implementation always returns True. Registers the provided callbacks to be called with each new entry this filter encounters and starts the filter polling for changes. Can only be called once on each filter. Cannot be called on a filter that has already been started. Stops the filter from polling and uninstalls the filter. Blocks until all events that are currently being processed have been processed. Block and Transaction Filters You can setup a filter for new blocks using web3.eth.filter('latest') which will return a new :py:class::BlockFilter object. >>> def new_block_callback(block_hash): ... sys.stdout.write("New Block: {0}".format(block_hash)) ... >>> new_block_filter = web3.eth.filter('latest') >>> new_block_filter.watch(new_block_filter) # each time the client receieves a new block the `new_block_callback` # function will be called with the block hash. You can setup a filter for new blocks using web3.eth.filter('pending') which will return a new :py:class::BlockFilter object. >>> def new_transaction_callback(transaction_hash): ... sys.stdout.write("New Block: {0}".format(transaction_hash)) ... >>> new_transaction_filter = web3.eth.filter('pending') >>> new_transaction_filter.watch(new_transaction_callback) # each time the client receieves a unmined transaction the # `new_transaction_filter` function will be called with the transaction # hash. Event Log Filters The :py:class::LogFilter class is used for all filters pertaining to even logs. It exposes the following additional methods. Synchronously retrieve the event logs for this filter. If only_changes is True then logs will be retrieved using the web3.eth.getFilterChanges which returns only new entries since the last poll. If only_changes is False then the logs will be retrieved using the web3.eth.getFilterLogs which returns all logs that match the given filter. This method will raise a ValueError if called on a filter that is currently polling. The :py:class::LogFilter class is returned from the :py:method::web3.contract.Contract.on and will be configured to extract the event data from the event logs. The :py:class::PastLogFilter is a subclass of :py:class::LogFilter that is configured specially to return historical event logs. It conforms to the same API as the LogFilter class. Shh Filter The :py:class:: ShhFilter class is used for filtering Shh messages. You can setup a callback function for Whipser messages matching the topics subscribed using web3.shh.filter(filter_params),which will return a :py:class::ShhFilter object >>>def filter_callback(new_message): ... sys.stdout.write("New Shh Message: {0}".format(new_message)) ... >>>shh_filter = web3.shh.filter({"topics":[web3.fromAscii("topic_to_subscribe")]}) >>>shh_filter.watch(filter_callback) #each time client recieves a Shh messages matching the topics subscibed, #filter_callback is called

import re import random import gevent from.types import ( is_string, is_array, ) from.events import ( construct_event_topic_set, construct_event_data_set, ) def construct_event_filter_params(event_abi, contract_address=None, argument_filters=None, topics=None, fromBlock=None, toBlock=None, address=None): filter_params = {} if topics is None: topic_set = construct_event_topic_set(event_abi, argument_filters) else: topic_set = [topics] + construct_event_topic_set(event_abi, argument_filters) if len(topic_set) == 1 and is_array(topic_set[0]): filter_params['topics'] = topic_set[0] else: filter_params['topics'] = topic_set if address and contract_address: if is_array(address): filter_params['address'] = address + [contract_address] elif is_string(address): filter_params['address'] = [address, contract_address] else: raise ValueError( "Unsupported type for `address` parameter: {0}".format(type(address)) ) elif address: filter_params['address'] = address elif contract_address: filter_params['address'] = contract_address if fromBlock is not None: filter_params['fromBlock'] = fromBlock if toBlock is not None: filter_params['toBlock'] = toBlock data_filters_set = construct_event_data_set(event_abi, argument_filters) return data_filters_set, filter_params class Filter(gevent.Greenlet): callbacks = None running = None stopped = False poll_interval = None def __init__(self, web3, filter_id): self.web3 = web3 self.filter_id = filter_id self.callbacks = [] gevent.Greenlet.__init__(self) def __str__(self): return "Filter for {0}".format(self.filter_id) def _run(self): if self.stopped: raise ValueError("Cannot restart a Filter") self.running = True while self.running: changes = self.web3.eth.getFilterChanges(self.filter_id) if changes: for entry in changes: for callback_fn in self.callbacks: if self.is_valid_entry(entry): callback_fn(self.format_entry(entry)) if self.poll_interval is None: gevent.sleep(random.random()) else: gevent.sleep(self.poll_interval) def format_entry(self, entry): """ Hook for subclasses to change the format of the value that is passed into the callback functions. """ return entry def is_valid_entry(self, entry): """ Hook for subclasses to implement additional filtering layers. """ return True def watch(self, *callbacks): if self.stopped: raise ValueError("Cannot watch on a filter that has been stopped") self.callbacks.extend(callbacks) if not self.running: self.start() gevent.sleep(0) def stop_watching(self, timeout=0): self.running = False self.stopped = True self.web3.eth.uninstallFilter(self.filter_id) self.join(timeout) stopWatching = stop_watching class BlockFilter(Filter): pass class TransactionFilter(Filter): pass ZERO_32BYTES = '[a-f0-9]{64}' def construct_data_filter_regex(data_filter_set): return re.compile(( '^' + '|'.join(( '0x' + ''.join( (ZERO_32BYTES if v is None else v[2:] for v in data_filter) ) for data_filter in data_filter_set )) + '$' )) class LogFilter(Filter): data_filter_set = None data_filter_set_regex = None log_entry_formatter = None def __init__(self, *args, **kwargs): self.log_entry_formatter = kwargs.pop( 'log_entry_formatter', self.log_entry_formatter, ) if 'data_filter_set' in kwargs: self.set_data_filters(kwargs.pop('data_filter_set')) super(LogFilter, self).__init__(*args, **kwargs) def get(self, only_changes=True): if self.running: raise ValueError( "Cannot call `get` on a filter object which is actively watching" ) if only_changes: log_entries = self.web3.eth.getFilterChanges(self.filter_id) else: log_entries = self.web3.eth.getFilterLogs(self.filter_id) if log_entries is None: log_entries = [] formatted_log_entries = [ self.format_entry(log_entry) for log_entry in log_entries ] return formatted_log_entries def format_entry(self, entry): if self.log_entry_formatter: return self.log_entry_formatter(entry) return entry def set_data_filters(self, data_filter_set): self.data_filter_set = data_filter_set if any(data_filter_set): self.data_filter_set_regex = construct_data_filter_regex( data_filter_set, ) def is_valid_entry(self, entry): if not self.data_filter_set_regex: return True return bool(self.data_filter_set_regex.match(entry['data'])) class PastLogFilter(LogFilter): def _run(self): if self.stopped: raise ValueError("Cannot restart a Filter") self.running = True previous_logs = self.web3.eth.getFilterLogs(self.filter_id) if previous_logs: for entry in previous_logs: for callback_fn in self.callbacks: if self.is_valid_entry(entry): callback_fn(self.format_entry(entry)) self.running = False class ShhFilter(Filter): def _run(self): if self.stopped: raise ValueError("Cannot restart a filter") self.running = True while self.running: changes = self.web3.shh.getFilterChanges(self.filter_id) if changes: for entry in changes: for callback_fn in self.callbacks: if self.is_valid_entry(entry): callback_fn(self.format_entry(entry)) if self.poll_interval is None: gevent.sleep(random.random()) else: gevent.sleep(self.poll_interval) def stop_watching(self, timeout=0): self.running = False self.stopped = True self.web3.shh.uninstallFilter(self.filter_id) self.join(timeout) stopWatching = stop_watching