matlok/python-copilot-training-from-many-repos-large

get_object_members

HasTraitsDocumenter

ClassDocumenter

true

self,want_all

Specialized Documenter subclass for traits

["Specialized","Documenter","subclass","for","traits"]

Add traits to members list

["Add","traits","to","members","list"]

check, unknown

def get_object_members(self, want_all): """Add traits to members list""" check, members = super().get_object_members(want_all) get_traits = ( self.object.class_own_traits if self.options.inherited_members else self.object.class_traits ) members_new = OrderedDict() for m in members: members_new[m[0]] = m[1] traits = tuple(get_traits().items()) for name, trait in traits: if name not in members_new: # Don't add a member that would normally be filtered continue # pass # FIXME: Debugging # put help in __doc__ where autodoc will look for it trait.__doc__ = trait.help or extended_trait_info( getattr(self.object, name) ) members_new[name] = trait return check, [kv for kv in members_new.items()]

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51

70

null

autodoc_traits.py

pythreejs/docs/sphinxext/autodoc_traits.py

from collections import OrderedDict from traitlets import TraitType, Undefined, Container, Dict, Any, HasTraits from sphinx.ext.autodoc import ClassDocumenter, AttributeDocumenter

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2

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Use image node_id 2 for calling the HasTraitsDocumenter obj's underlying member method code with example usage: obj.get_object_members(want_all) and returns: check, unknown

173

node_id 2

1,691,041

can_document_member

TraitDocumenter

AttributeDocumenter

true

cls,member,membername,isattr,parent

null

isinstance

def can_document_member(cls, member, membername, isattr, parent): return isinstance(member, TraitType)

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80

81

null

autodoc_traits.py

pythreejs/docs/sphinxext/autodoc_traits.py

from collections import OrderedDict from traitlets import TraitType, Undefined, Container, Dict, Any, HasTraits from sphinx.ext.autodoc import ClassDocumenter, AttributeDocumenter

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Use image node_id 1 for calling the TraitDocumenter obj's underlying member method code with example usage: obj.can_document_member(cls, member, membername, isattr, parent) and returns: isinstance

196

node_id 1

1,691,042

add_directive_header

TraitDocumenter

AttributeDocumenter

true

self,sig

null

super

def add_directive_header(self, sig): default = self.object.default_value if default is Undefined: default_s = "" else: default_s = repr(default) sig = " = {}({})".format( self.object.__class__.__name__, default_s, ) return super().add_directive_header(sig)

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86

96

null

autodoc_traits.py

pythreejs/docs/sphinxext/autodoc_traits.py

from collections import OrderedDict from traitlets import TraitType, Undefined, Container, Dict, Any, HasTraits from sphinx.ext.autodoc import ClassDocumenter, AttributeDocumenter

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Use image node_id 3 for calling the TraitDocumenter obj's underlying member method code with example usage: obj.add_directive_header(sig) and returns: super

156

node_id 3

1,691,044

_design_resample_poly

global

null

false

up,down,window

null

h

def _design_resample_poly(up, down, window): """ Design a prototype FIR low-pass filter using the window method for use in polyphase rational resampling. Parameters ---------- up : int The upsampling factor. down : int The downsampling factor. window : string or tuple Desired window to use to design the low-pass filter. See below for details. Returns ------- h : array The computed FIR filter coefficients. See Also -------- resample_poly : Resample up or down using the polyphase method. Notes ----- The argument `window` specifies the FIR low-pass filter design. The functions `cusignal.get_window` and `cusignal.firwin` are called to generate the appropriate filter coefficients. The returned array of coefficients will always be of data type `complex128` to maintain precision. For use in lower-precision filter operations, this array should be converted to the desired data type before providing it to `cusignal.resample_poly`. """ # Determine our up and down factors # Use a rational approximation to save computation time on really long # signals g_ = gcd(up, down) up //= g_ down //= g_ # Design a linear-phase low-pass FIR filter max_rate = max(up, down) f_c = 1.0 / max_rate # cutoff of FIR filter (rel. to Nyquist) # reasonable cutoff for our sinc-like function half_len = 10 * max_rate h = firwin(2 * half_len + 1, f_c, window=window) return h

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44

96

null

_resample.py

cupy/cupyx/scipy/signal/_resample.py

import operator from math import gcd import cupy from cupyx.scipy.fft import fft, rfft, fftfreq, ifft, irfft, ifftshift from cupyx.scipy.signal._iir_filter_design import cheby1 from cupyx.scipy.signal._fir_filter_design import firwin from cupyx.scipy.signal._iir_filter_conversions import zpk2sos from cupyx.scipy.signal._ltisys import dlti from cupyx.scipy.signal._upfirdn import upfirdn, _output_len from cupyx.scipy.signal._signaltools import sosfiltfilt, filtfilt, sosfilt, lfilter from cupyx.scipy.signal.windows._windows import get_window

15

null

11

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null

Use image node_id 1 for calling a global function with example usage: _design_resample_poly(up, down, window) and returns: h

124

node_id 1

692,581

setup

NdimageInterpolation

Benchmark

true

self,shape,order,mode

null

def setup(self, shape, order, mode): rstate = np.random.RandomState(5) self.x = rstate.standard_normal(shape) self.matrix_2d = np.asarray([[0.8, 0, 1.5], [0, 1.2, -5.0]]) self.matrix_3d = np.asarray( [[0.8, 0, 0, 1.5], [0, 1.2, 0, -5.0], [0, 0, 1, 0]] )

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28

35

null

ndimage_interpolation.py

scipy/benchmarks/benchmarks/ndimage_interpolation.py

import numpy from .common import Benchmark

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Use image node_id 1 for calling the NdimageInterpolation obj's underlying member method code with example usage: obj.setup(shape, order, mode) without return types

163

node_id 1

1,883,756

chrome_command

Converter

ImageConverter

true

self

null

None,None,str,str,str,str+path+str,str

def chrome_command(self) -> str | None: if platform.win32_ver()[0]: if os.system("where chrome") == 0: return "chrome" path = os.path.join( os.environ["PROGRAMW6432"], "Google\\Chrome\\Application\\chrome.exe", ) if os.path.exists(path): return f'"{path}"' return None if os.system("chrome --version") == 0: return "chrome" if platform.mac_ver()[0]: return "'/Applications/Google Chrome.app/Contents/MacOS/Google Chrome'" elif platform.libc_ver()[0]: return "google-chrome" return None

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28

42

null

convert-svg-to-pdf.py

sympy/doc/ext/convert-svg-to-pdf.py

from __future__ import annotations from sphinx.transforms.post_transforms.images import ImageConverter from sphinx.util import logging import os import platform from typing import Any from sphinx.application import Sphinx

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5

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Use image node_id 2 for calling the Converter obj's underlying member method code with example usage: obj.chrome_command() and returns: None, None, str, str, str, str, path, str, str

182

node_id 2

2,029,275

__init__

DropBlock2d

nn

true

self,drop_prob,block_size,gamma_scale,with_noise,inplace,batchwise,fast

DropBlock. See https://arxiv.org/pdf/1810.12890.pdf

["DropBlock",".","See","https",":","\/\/arxiv.org\/pdf\/1810.12890.pdf"]

null

DropBlock2d

def __init__( self, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False, fast: bool = True, ): super(DropBlock2d, self).__init__() self.drop_prob = drop_prob self.gamma_scale = gamma_scale self.block_size = block_size self.with_noise = with_noise self.inplace = inplace self.batchwise = batchwise self.fast = fast

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108

124

null

drop.py

pytorch-image-models/timm/layers/drop.py

import torch import torch.nn import torch.nn.functional

15

2

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2

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Use image node_id 1 to create a new DropBlock2d object from inherited base classes: nn with example: obj = DropBlock2d(drop_prob, block_size, gamma_scale, with_noise, inplace, batchwise, fast)

192

node_id 1

1,692,286

test_symmetric_difference

TestIntervalIndex

null

true

self,closed,sort

null

def test_symmetric_difference(self, closed, sort): index = monotonic_index(0, 11, closed=closed) result = index[1:].symmetric_difference(index[:-1], sort=sort) expected = IntervalIndex([index[0], index[-1]]) if sort in (None, True): tm.assert_index_equal(result, expected) else: tm.assert_index_equal(result.sort_values(), expected) # GH 19101: empty result, same dtype result = index.symmetric_difference(index, sort=sort) expected = empty_index(dtype="int64", closed=closed) if sort in (None, True): tm.assert_index_equal(result, expected) else: tm.assert_index_equal(result.sort_values(), expected) # GH 19101: empty result, different dtypes other = IntervalIndex.from_arrays( index.left.astype("float64"), index.right, closed=closed ) result = index.symmetric_difference(other, sort=sort) expected = empty_index(dtype="float64", closed=closed) tm.assert_index_equal(result, expected)

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151

174

null

test_setops.py

pandas/pandas/tests/indexes/interval/test_setops.py

import numpy import pytest from pandas import Index, IntervalIndex, Timestamp, interval_range import pandas._testing

15

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4

2

0

8

null

Use image node_id 7 for calling the TestIntervalIndex obj's underlying member method code with example usage: obj.test_symmetric_difference(closed, sort) without return types

174

node_id 7

1,514,639

fetch_kddcup99

global

null

false

null

Bunch,data, target

def fetch_kddcup99( *, subset=None, data_home=None, shuffle=False, random_state=None, percent10=True, download_if_missing=True, return_X_y=False, as_frame=False, ): """Load the kddcup99 dataset (classification). Download it if necessary. ================= ==================================== Classes 23 Samples total 4898431 Dimensionality 41 Features discrete (int) or continuous (float) ================= ==================================== Read more in the :ref:`User Guide <kddcup99_dataset>`. .. versionadded:: 0.18 Parameters ---------- subset : {'SA', 'SF', 'http', 'smtp'}, default=None To return the corresponding classical subsets of kddcup 99. If None, return the entire kddcup 99 dataset. data_home : str or path-like, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. .. versionadded:: 0.19 shuffle : bool, default=False Whether to shuffle dataset. random_state : int, RandomState instance or None, default=None Determines random number generation for dataset shuffling and for selection of abnormal samples if `subset='SA'`. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. percent10 : bool, default=True Whether to load only 10 percent of the data. download_if_missing : bool, default=True If False, raise an OSError if the data is not locally available instead of trying to download the data from the source site. return_X_y : bool, default=False If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` object. .. versionadded:: 0.20 as_frame : bool, default=False If `True`, returns a pandas Dataframe for the ``data`` and ``target`` objects in the `Bunch` returned object; `Bunch` return object will also have a ``frame`` member. .. versionadded:: 0.24 Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. data : {ndarray, dataframe} of shape (494021, 41) The data matrix to learn. If `as_frame=True`, `data` will be a pandas DataFrame. target : {ndarray, series} of shape (494021,) The regression target for each sample. If `as_frame=True`, `target` will be a pandas Series. frame : dataframe of shape (494021, 42) Only present when `as_frame=True`. Contains `data` and `target`. DESCR : str The full description of the dataset. feature_names : list The names of the dataset columns target_names: list The names of the target columns (data, target) : tuple if ``return_X_y`` is True A tuple of two ndarray. The first containing a 2D array of shape (n_samples, n_features) with each row representing one sample and each column representing the features. The second ndarray of shape (n_samples,) containing the target samples. .. versionadded:: 0.20 """ data_home = get_data_home(data_home=data_home) kddcup99 = _fetch_brute_kddcup99( data_home=data_home, percent10=percent10, download_if_missing=download_if_missing, ) data = kddcup99.data target = kddcup99.target feature_names = kddcup99.feature_names target_names = kddcup99.target_names if subset == "SA": s = target == b"normal." t = np.logical_not(s) normal_samples = data[s, :] normal_targets = target[s] abnormal_samples = data[t, :] abnormal_targets = target[t] n_samples_abnormal = abnormal_samples.shape[0] # selected abnormal samples: random_state = check_random_state(random_state) r = random_state.randint(0, n_samples_abnormal, 3377) abnormal_samples = abnormal_samples[r] abnormal_targets = abnormal_targets[r] data = np.r_[normal_samples, abnormal_samples] target = np.r_[normal_targets, abnormal_targets] if subset == "SF" or subset == "http" or subset == "smtp": # select all samples with positive logged_in attribute: s = data[:, 11] == 1 data = np.c_[data[s, :11], data[s, 12:]] feature_names = feature_names[:11] + feature_names[12:] target = target[s] data[:, 0] = np.log( (data[:, 0] + 0.1).astype(float, copy=False) ) data[:, 4] = np.log( (data[:, 4] + 0.1).astype(float, copy=False) ) data[:, 5] = np.log( (data[:, 5] + 0.1).astype(float, copy=False) ) if subset == "http": s = data[:, 2] == b"http" data = data[s] target = target[s] data = np.c_[data[:, 0], data[:, 4], data[:, 5]] feature_names = [ feature_names[0], feature_names[4], feature_names[5], ] if subset == "smtp": s = data[:, 2] == b"smtp" data = data[s] target = target[s] data = np.c_[data[:, 0], data[:, 4], data[:, 5]] feature_names = [ feature_names[0], feature_names[4], feature_names[5], ] if subset == "SF": data = np.c_[ data[:, 0], data[:, 2], data[:, 4], data[:, 5] ] feature_names = [ feature_names[0], feature_names[2], feature_names[4], feature_names[5], ] if shuffle: data, target = shuffle_method( data, target, random_state=random_state ) fdescr = load_descr("kddcup99.rst") frame = None if as_frame: frame, data, target = _convert_data_dataframe( "fetch_kddcup99", data, target, feature_names, target_names, ) if return_X_y: return data, target return Bunch( data=data, target=target, frame=frame, target_names=target_names, feature_names=feature_names, DESCR=fdescr, )

63

243

null

_kddcup99.py

catboost/contrib/python/scikit-learn/py3/sklearn/datasets/_kddcup99.py

import errno import logging import os from gzip import GzipFile from os.path import exists, join import joblib import numpy from ..utils import Bunch, check_random_state from ..utils import shuffle from ..utils._param_validation import StrOptions, validate_params from .None import get_data_home from ._base import RemoteFileMetadata, _convert_data_dataframe, _fetch_remote, load_descr

15

null

12

3

null

Use image node_id 1 for calling a global function with example usage: fetch_kddcup99() and returns: Bunch, data, target

120

node_id 1

520,096

__init__

GPT2Decoder

BaseStepDecoder

true

self,gpt2_lm_model

null

GPT2Decoder

def __init__(self, gpt2_lm_model): self._gpt2_lm_model = gpt2_lm_model self._layout = self._gpt2_lm_model._backbone_model.layout

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39

41

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interactive_conditional_gpt2_samples.py

gluon-nlp/scripts/generation/interactive_conditional_gpt2_samples.py

import os import mxnet import argparse from gluonnlp.utils import set_seed from gluonnlp.sequence_sampler import BeamSearchSampler, BaseStepDecoder from gluonnlp.models.gpt2 import GPT2ForLM, list_pretrained_gpt2, get_pretrained_gpt2

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146

node_id 1

1,097,714

sampled_mean

global

null

false

f,lb,ub,npts,map

null

_expectation_given_samples

def sampled_mean(f, lb, ub, npts=10000, map=None): """ use random sampling to calculate the mean of a function Inputs: f -- a function that takes a list and returns a number lb -- a list of lower bounds ub -- a list of upper bounds npts -- the number of points to sample [Default is npts=10000] map -- the mapping function [Default is builtins.map]""" pts = _random_samples(lb, ub, npts) return _expectation_given_samples(f, pts, map)

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samples.py

mystic/mystic/math/samples.py

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null

0

15

null

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144

node_id 4

1,407,024

sampled_variance

global

null

false

f,lb,ub,npts,map

null

_variance_given_samples

def sampled_variance( f, lb, ub, npts=10000, map=None ): # XXX: could be improved """ use random sampling to calculate the variance of a function Inputs: f -- a function that takes a list and returns a number lb -- a list of lower bounds ub -- a list of upper bounds npts -- the number of points to sample [Default is npts=10000] map -- the mapping function [Default is builtins.map]""" pts = _random_samples(lb, ub, npts) return _variance_given_samples(f, pts, map)

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133

145

null

samples.py

mystic/mystic/math/samples.py

15

null

0

15

null

Use image node_id 5 for calling a global function with example usage: sampled_variance(f, lb, ub, npts, map) and returns: _variance_given_samples

145

node_id 5

1,407,025

test_set_incompatible_types

TestIntervalIndex

null

true

self,closed,op_name,sort

null

def test_set_incompatible_types(self, closed, op_name, sort): index = monotonic_index(0, 11, closed=closed) set_op = getattr(index, op_name) # TODO: standardize return type of non-union setops type(self vs other) # non-IntervalIndex if op_name == "difference": expected = index else: expected = getattr(index.astype("O"), op_name)( Index([1, 2, 3]) ) result = set_op(Index([1, 2, 3]), sort=sort) tm.assert_index_equal(result, expected) # mixed closed -> cast to object for other_closed in {"right", "left", "both", "neither"} - { closed }: other = monotonic_index(0, 11, closed=other_closed) expected = getattr(index.astype(object), op_name)( other, sort=sort ) if op_name == "difference": expected = index result = set_op(other, sort=sort) tm.assert_index_equal(result, expected) # GH 19016: incompatible dtypes -> cast to object other = interval_range( Timestamp("20180101"), periods=9, closed=closed ) expected = getattr(index.astype(object), op_name)( other, sort=sort ) if op_name == "difference": expected = index result = set_op(other, sort=sort) tm.assert_index_equal(result, expected)

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180

208

null

test_setops.py

pandas/pandas/tests/indexes/interval/test_setops.py

import numpy import pytest from pandas import Index, IntervalIndex, Timestamp, interval_range import pandas._testing

15

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4

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0

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null

Use image node_id 8 for calling the TestIntervalIndex obj's underlying member method code with example usage: obj.test_set_incompatible_types(closed, op_name, sort) without return types

185

node_id 8

1,514,640

sampled_pof

global

null

false

f,lb,ub,npts,map

null

_pof_given_samples

def sampled_pof(f, lb, ub, npts=10000, map=None): """ use random sampling to calculate probability of failure for a function Inputs: f -- a function that returns True for 'success' and False for 'failure' lb -- a list of lower bounds ub -- a list of upper bounds npts -- the number of points to sample [Default is npts=10000] map -- the mapping function [Default is builtins.map]""" pts = _random_samples(lb, ub, npts) return _pof_given_samples(f, pts, map)

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148

160

null

samples.py

mystic/mystic/math/samples.py

15

null

0

15

null

Use image node_id 6 for calling a global function with example usage: sampled_pof(f, lb, ub, npts, map) and returns: _pof_given_samples

135

node_id 6

1,407,026

__init__

Config

null

true

self,task,out_dir,max_trans,random_seed,fields,flint_model,trans_methods,trans_config,return_unk,sub_methods,sub_config,attack_methods,validate_methods

Hold some config params to control generation and report procedure.

["Hold","some","config","params","to","control","generation","and","report","procedure","."]

:param str task: task name :param string out_dir: out dir for saving generated samples, default current path. :param int max_trans: maximum transformed samples generate by one original sample pre Transformation. :param int random_seed: random number seed to reproduce generation. :param str|list[str] fields: fields on which new samples are generated. ::param str model_file: path to the python file containing the FlintModel instance which named 'model'. :param list trans_methods: indicate what transformations to apply to dataset. :param dict trans_config: parameters for the initialization of the transformation instances. :param bool return_unk: whether apply transformations which may influence label of sample. :param list sub_methods: indicate what subpopulations to apply to dataset. :param dict sub_config: parameters for the initialization of the subpopulation instances. :param str attack_methods: path to the python file containing the Attack instances which named "attacks". :param str|list[str] validate_methods: indicate use which validate methods to calculate confidence of generated samples.

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Config

def __init__( self, task="UT", out_dir=None, max_trans=1, random_seed=1, fields=None, flint_model=None, trans_methods=None, trans_config=None, return_unk=True, sub_methods=None, sub_config=None, attack_methods=None, validate_methods=None, **kwargs, ): """ :param str task: task name :param string out_dir: out dir for saving generated samples, default current path. :param int max_trans: maximum transformed samples generate by one original sample pre Transformation. :param int random_seed: random number seed to reproduce generation. :param str|list[str] fields: fields on which new samples are generated. ::param str model_file: path to the python file containing the FlintModel instance which named 'model'. :param list trans_methods: indicate what transformations to apply to dataset. :param dict trans_config: parameters for the initialization of the transformation instances. :param bool return_unk: whether apply transformations which may influence label of sample. :param list sub_methods: indicate what subpopulations to apply to dataset. :param dict sub_config: parameters for the initialization of the subpopulation instances. :param str attack_methods: path to the python file containing the Attack instances which named "attacks". :param str|list[str] validate_methods: indicate use which validate methods to calculate confidence of generated samples. """ self.task = task self.out_dir = out_dir if out_dir else "." self.max_trans = max_trans self.fields = fields if fields else TRANSFORM_FIELDS[self.task] self.flint_model = flint_model self.random_seed = random_seed if len(task) >= 2 and task[-2:] == "cn": self.trans_methods = self.get_generate_methods( trans_methods, ALLOWED_cn_TRANSFORMATIONS, allow_pipeline=True, ) else: self.trans_methods = self.get_generate_methods( trans_methods, ALLOWED_TRANSFORMATIONS, allow_pipeline=True, ) self.trans_config = trans_config if trans_config else {} # TODO, support the function. default not return origin and return unk self.return_unk = return_unk self.sub_methods = self.get_generate_methods( sub_methods, ALLOWED_SUBPOPULATIONS ) self.sub_config = sub_config if sub_config else {} self.attack_methods = attack_methods self.validate_methods = self.get_generate_methods( validate_methods, ALLOWED_VALIDATORS ) self.check_config()

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24

97

null

config.py

textflint/textflint/input/config/config.py

import os import six import json import copy from ...common.utils.logger import logger from ...common.settings import NLP_TASK_MAP, ALLOWED_TRANSFORMATIONS, TRANSFORM_FIELDS, ALLOWED_SUBPOPULATIONS, ALLOWED_VALIDATORS, ALLOWED_cn_TRANSFORMATIONS

15

1

6

0

8

null

Use image node_id 1 to create a new Config object with example: obj = Config(task, out_dir, max_trans, random_seed, fields, flint_model, trans_methods, trans_config, return_unk, sub_methods, sub_config, attack_methods, validate_methods)

237

node_id 1

2,188,348

check_config

Config

null

true

self

Hold some config params to control generation and report procedure.

["Hold","some","config","params","to","control","generation","and","report","procedure","."]

Check common config params.

["Check","common","config","params","."]

null

def check_config(self): r""" Check common config params. """ if self.task.upper() not in NLP_TASK_MAP: logger.error( "Your task is {0}, just support {1}.".format( self.task, NLP_TASK_MAP.keys() ) ) assert isinstance(self.out_dir, str) assert isinstance(self.max_trans, int) assert isinstance(self.random_seed, int) assert isinstance(self.fields, (str, list)) assert isinstance(self.trans_config, dict) assert isinstance(self.return_unk, bool) assert isinstance(self.sub_config, dict) if self.flint_model: assert os.path.exists(self.flint_model), ( "Please input a exist python file path " "which contains FlintModel instance" ) if self.attack_methods: assert os.path.exists(self.attack_methods), ( "Please input a exist python file path " "which contains Attack instance" ) if self.validate_methods: assert isinstance(self.validate_methods, (str, list))

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99

128

null

config.py

textflint/textflint/input/config/config.py

import os import six import json import copy from ...common.utils.logger import logger from ...common.settings import NLP_TASK_MAP, ALLOWED_TRANSFORMATIONS, TRANSFORM_FIELDS, ALLOWED_SUBPOPULATIONS, ALLOWED_VALIDATORS, ALLOWED_cn_TRANSFORMATIONS

15

1

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0

8

null

Use image node_id 2 for calling the Config obj's underlying member method code with example usage: obj.check_config() without return types

138

node_id 2

2,188,349

get_generate_methods

Config

null

true

self,methods,task_to_methods,allow_pipeline

Hold some config params to control generation and report procedure.

["Hold","some","config","params","to","control","generation","and","report","procedure","."]

Validate transformation or subpopulation methods. Watch out! Some UT transformations/subpopulations may not compatible with your task, please choose your method carefully. :param list methods: transformation or subpopulation need to apply to dataset. If not provide, return default generated methods. :param dict task_to_methods: map allowed methods by task name. :param bool allow_pipeline: whether allow pipeline input :return: list of transformation/subpopulation.

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legal_methods

def get_generate_methods( self, methods, task_to_methods, allow_pipeline=False ): r""" Validate transformation or subpopulation methods. Watch out! Some UT transformations/subpopulations may not compatible with your task, please choose your method carefully. :param list methods: transformation or subpopulation need to apply to dataset. If not provide, return default generated methods. :param dict task_to_methods: map allowed methods by task name. :param bool allow_pipeline: whether allow pipeline input :return: list of transformation/subpopulation. """ allowed_methods = task_to_methods[self.task] legal_methods = [] if methods: for method in methods: if not isinstance(method, (str, list)): raise ValueError( f"Do not support transformation/subpopulation " f"input type {type(method)}" ) if isinstance(method, str): if method not in allowed_methods: logger.warning( "Do not support {0}, skip this " "input method".format(method) ) else: legal_methods.append(method) else: if not allow_pipeline: raise ValueError( f"Do not support pipeline method input {method}" ) allow = True for _method in method: if _method not in allowed_methods: logger.warning( "Do not support {0}, skip " "this method".format(method) ) allow = False if allow: legal_methods.append(method) else: legal_methods = legal_methods + allowed_methods return legal_methods

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135

188

null

config.py

textflint/textflint/input/config/config.py

import os import six import json import copy from ...common.utils.logger import logger from ...common.settings import NLP_TASK_MAP, ALLOWED_TRANSFORMATIONS, TRANSFORM_FIELDS, ALLOWED_SUBPOPULATIONS, ALLOWED_VALIDATORS, ALLOWED_cn_TRANSFORMATIONS

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Use image node_id 3 for calling the Config obj's underlying member method code with example usage: obj.get_generate_methods(methods, task_to_methods, allow_pipeline) and returns: legal_methods

192

node_id 3

2,188,350

from_dict

Config

null

true

cls,json_object

Hold some config params to control generation and report procedure.

["Hold","some","config","params","to","control","generation","and","report","procedure","."]

Constructs a `Config` from a Python dictionary of parameters.

["Constructs","a","`","Config","`","from","a","Python","dictionary","of","parameters","."]

config

def from_dict(cls, json_object): r""" Constructs a `Config` from a Python dictionary of parameters. """ config = cls(task=json_object["task"]) for key, value in six.iteritems(json_object): config.__dict__[key] = value return config

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191

199

null

config.py

textflint/textflint/input/config/config.py

import os import six import json import copy from ...common.utils.logger import logger from ...common.settings import NLP_TASK_MAP, ALLOWED_TRANSFORMATIONS, TRANSFORM_FIELDS, ALLOWED_SUBPOPULATIONS, ALLOWED_VALIDATORS, ALLOWED_cn_TRANSFORMATIONS

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Use image node_id 4 for calling the Config obj's underlying member method code with example usage: obj.from_dict(cls, json_object) and returns: config

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node_id 4

2,188,351

from_json_file

Config

null

true

cls,json_file

Hold some config params to control generation and report procedure.

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Constructs a `Config` from a json file of parameters.

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cls

def from_json_file(cls, json_file): r""" Constructs a `Config` from a json file of parameters. """ with open(json_file, "r", encoding="utf-8") as reader: text = reader.read() return cls.from_dict(json.loads(text))

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202

209

null

config.py

textflint/textflint/input/config/config.py

import os import six import json import copy from ...common.utils.logger import logger from ...common.settings import NLP_TASK_MAP, ALLOWED_TRANSFORMATIONS, TRANSFORM_FIELDS, ALLOWED_SUBPOPULATIONS, ALLOWED_VALIDATORS, ALLOWED_cn_TRANSFORMATIONS

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Use image node_id 5 for calling the Config obj's underlying member method code with example usage: obj.from_json_file(cls, json_file) and returns: cls

150

node_id 5

2,188,352

to_dict

Config

null

true

self

Hold some config params to control generation and report procedure.

["Hold","some","config","params","to","control","generation","and","report","procedure","."]

Serializes this instance to a Python dictionary.

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output

def to_dict(self): r""" Serializes this instance to a Python dictionary. """ output = copy.deepcopy(self.__dict__) return output

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218

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config.py

textflint/textflint/input/config/config.py

import os import six import json import copy from ...common.utils.logger import logger from ...common.settings import NLP_TASK_MAP, ALLOWED_TRANSFORMATIONS, TRANSFORM_FIELDS, ALLOWED_SUBPOPULATIONS, ALLOWED_VALIDATORS, ALLOWED_cn_TRANSFORMATIONS

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monotonic_index

global

null

false

start,end,dtype,closed

null

IntervalIndex

def monotonic_index(start, end, dtype="int64", closed="right"): return IntervalIndex.from_breaks( np.arange(start, end, dtype=dtype), closed=closed )

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14

null

test_setops.py

pandas/pandas/tests/indexes/interval/test_setops.py

import numpy import pytest from pandas import Index, IntervalIndex, Timestamp, interval_range import pandas._testing

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null

4

2

null

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node_id 1

1,514,641

empty_index

global

null

false

dtype,closed

null

IntervalIndex

def empty_index(dtype="int64", closed="right"): return IntervalIndex(np.array([], dtype=dtype), closed=closed)

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18

null

test_setops.py

pandas/pandas/tests/indexes/interval/test_setops.py

import numpy import pytest from pandas import Index, IntervalIndex, Timestamp, interval_range import pandas._testing

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null

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state_batch_axis

GPT2Decoder

BaseStepDecoder

true

self

null

unknown

def state_batch_axis(self): return 2 if self._layout == "NT" else 3

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45

null

interactive_conditional_gpt2_samples.py

gluon-nlp/scripts/generation/interactive_conditional_gpt2_samples.py

import os import mxnet import argparse from gluonnlp.utils import set_seed from gluonnlp.sequence_sampler import BeamSearchSampler, BaseStepDecoder from gluonnlp.models.gpt2 import GPT2ForLM, list_pretrained_gpt2, get_pretrained_gpt2

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1

Use image node_id 2 for calling the GPT2Decoder obj's underlying member method code with example usage: obj.state_batch_axis() and returns: unknown

147

node_id 2

1,097,715

to_json_string

Config

null

true

self

Hold some config params to control generation and report procedure.

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Serializes this instance to a JSON string.

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json

def to_json_string(self): r""" Serializes this instance to a JSON string. """ return json.dumps( self.to_dict(), indent=2, sort_keys=True, ensure_ascii=False )

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220

231

null

config.py

textflint/textflint/input/config/config.py

import os import six import json import copy from ...common.utils.logger import logger from ...common.settings import NLP_TASK_MAP, ALLOWED_TRANSFORMATIONS, TRANSFORM_FIELDS, ALLOWED_SUBPOPULATIONS, ALLOWED_VALIDATORS, ALLOWED_cn_TRANSFORMATIONS

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137

node_id 7

2,188,354

convert_to_cudf

global

null

false

result

null

df, modularity

def convert_to_cudf( result: cp.ndarray, ) -> Tuple[cudf.DataFrame, float]: """ Creates a cudf DataFrame from cupy arrays from pylibcugraph wrapper """ cupy_vertex, cupy_partition, modularity = result df = cudf.DataFrame() df["vertex"] = cupy_vertex df["partition"] = cupy_partition return df, modularity

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35

44

null

leiden.py

cugraph/python/cugraph/cugraph/dask/community/leiden.py

from __future__ import annotations from dask.distributed import wait, default_client import cugraph.dask.comms.comms import dask_cudf import dask from dask import delayed import cudf from pylibcugraph import ResourceHandle from pylibcugraph import leiden import numpy import cupy from typing import Tuple, TYPE_CHECKING

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null

12

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null

Use image node_id 1 for calling a global function with example usage: convert_to_cudf(result) and returns: df, modularity

122

node_id 1

686,168

set_seed

global

null

false

args

null

def set_seed(args): np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed)

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17

21

null

run_xsum_flexgen.py

H2O/h2o_flexgen/benchmark/xsum/run_xsum_flexgen.py

import argparse import numpy import torch import json import time from tqdm import tqdm import copy from flexgen.flex_opt import Policy, OptLM, ExecutionEnv, CompressionConfig from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig

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115,610

_call_plc_leiden

global

null

false

sID,mg_graph_x,max_iter,resolution,random_state,theta,do_expensive_check

null

pylibcugraph_leiden

def _call_plc_leiden( sID: bytes, mg_graph_x, max_iter: int, resolution: int, random_state: int, theta: int, do_expensive_check: bool, ) -> Tuple[cp.ndarray, cp.ndarray, float]: return pylibcugraph_leiden( resource_handle=ResourceHandle( Comms.get_handle(sID).getHandle() ), random_state=random_state, graph=mg_graph_x, max_level=max_iter, resolution=resolution, theta=theta, do_expensive_check=do_expensive_check, )

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47

64

null

leiden.py

cugraph/python/cugraph/cugraph/dask/community/leiden.py

from __future__ import annotations from dask.distributed import wait, default_client import cugraph.dask.comms.comms import dask_cudf import dask from dask import delayed import cudf from pylibcugraph import ResourceHandle from pylibcugraph import leiden import numpy import cupy from typing import Tuple, TYPE_CHECKING

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null

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null

Use image node_id 2 for calling a global function with example usage: _call_plc_leiden(sID, mg_graph_x, max_iter, resolution, random_state, theta, do_expensive_check) and returns: pylibcugraph_leiden

199

node_id 2

686,169

forward

DropBlock2d

nn

true

self,x

DropBlock. See https://arxiv.org/pdf/1810.12890.pdf

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null

x,drop_block_fast_2d,drop_block_2d

def forward(self, x): if not self.training or not self.drop_prob: return x if self.fast: return drop_block_fast_2d( x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, ) else: return drop_block_2d( x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise, )

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126

134

null

drop.py

pytorch-image-models/timm/layers/drop.py

import torch import torch.nn import torch.nn.functional

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node_id 2

1,692,287

forward

MNISTConvNet

torch.nn

true

self,x

null

F

def forward(self, x): # switched order of pooling and relu compared to the original example # to make it identical to the keras worker # seems to also give better accuracies x = F.max_pool2d(F.relu(self.conv1(x)), 2) if not self.conv2 is None: x = F.max_pool2d(F.relu(self.conv2(x)), 2) if not self.conv3 is None: x = F.max_pool2d(F.relu(self.conv3(x)), 2) x = self.dropout(x) x = x.view(-1, self.conv_output_size) x = F.relu(self.fc1(x)) x = self.dropout(x) x = self.fc2(x) return F.log_softmax(x, dim=1)

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243

262

null

example_5_pytorch_worker.py

HpBandSter/hpbandster/examples/example_5_pytorch_worker.py

import ConfigSpace import ConfigSpace.hyperparameters from hpbandster.core.worker import Worker import logging

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Use image node_id 2 for calling the MNISTConvNet obj's underlying member method code with example usage: obj.forward(x) and returns: F

134

node_id 2

117,777

export

SensitivityAnalysis

null

true

self,filepath

null

Export the results of the sensitivity analysis to a csv file. The firstline of the csv file describe the content structure. The first line is constructed by 'layername' and sparsity list. Each line below records the validation metric returned by val_func when this layer is under different sparsities. Note that, due to the early_stop option, some layers may not have the metrics under all sparsities. layername, 0.25, 0.5, 0.75 conv1, 0.6, 0.55 conv2, 0.61, 0.57, 0.56 Parameters ---------- filepath : str Path of the output file

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def export(self, filepath): """ Export the results of the sensitivity analysis to a csv file. The firstline of the csv file describe the content structure. The first line is constructed by 'layername' and sparsity list. Each line below records the validation metric returned by val_func when this layer is under different sparsities. Note that, due to the early_stop option, some layers may not have the metrics under all sparsities. layername, 0.25, 0.5, 0.75 conv1, 0.6, 0.55 conv2, 0.61, 0.57, 0.56 Parameters ---------- filepath : str Path of the output file """ str_sparsities = [str(x) for x in self.sparsities] header = ["layername"] + str_sparsities with open(filepath, "w") as csvf: csv_w = csv.writer(csvf) csv_w.writerow(header) for layername in self.sensitivities: row = [] row.append(layername) for sparsity in sorted( self.sensitivities[layername].keys() ): row.append(self.sensitivities[layername][sparsity]) csv_w.writerow(row)

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210

238

null

sensitivity_analysis.py

auptimizer/src/aup/compression/torch/utils/sensitivity_analysis.py

import copy import csv import logging from collections import OrderedDict import numpy import torch.nn

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Use image node_id 6 for calling the SensitivityAnalysis obj's underlying member method code with example usage: obj.export(filepath) without return types

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315,482

analysis

SensitivityAnalysis

null

true

self,val_args,val_kwargs,specified_layers

null

This function analyze the sensitivity to pruning for each conv layer in the target model. If start and end are not set, we analyze all the conv layers by default. Users can specify several layers to analyze or parallelize the analysis process easily through the start and end parameter. Parameters ---------- val_args : list args for the val_function val_kwargs : dict kwargs for the val_funtion specified_layers : list list of layer names to analyze sensitivity. If this variable is set, then only analyze the conv layers that specified in the list. User can also use this option to parallelize the sensitivity analysis easily. Returns ------- sensitivities : dict dict object that stores the trajectory of the accuracy/loss when the prune ratio changes

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self

def analysis( self, val_args=None, val_kwargs=None, specified_layers=None ): """ This function analyze the sensitivity to pruning for each conv layer in the target model. If start and end are not set, we analyze all the conv layers by default. Users can specify several layers to analyze or parallelize the analysis process easily through the start and end parameter. Parameters ---------- val_args : list args for the val_function val_kwargs : dict kwargs for the val_funtion specified_layers : list list of layer names to analyze sensitivity. If this variable is set, then only analyze the conv layers that specified in the list. User can also use this option to parallelize the sensitivity analysis easily. Returns ------- sensitivities : dict dict object that stores the trajectory of the accuracy/loss when the prune ratio changes """ if val_args is None: val_args = [] if val_kwargs is None: val_kwargs = {} # Get the original validation metric(accuracy/loss) before pruning # Get the accuracy baseline before starting the analysis. self.ori_metric = self.val_func(*val_args, **val_kwargs) namelist = list(self.target_layer.keys()) if specified_layers is not None: # only analyze several specified conv layers namelist = list( filter(lambda x: x in specified_layers, namelist) ) for name in namelist: self.sensitivities[name] = {} for sparsity in self.sparsities: # here the sparsity is the relative sparsity of the # the remained weights # Calculate the actual prune ratio based on the already pruned ratio real_sparsity = ( 1.0 - self.already_pruned[name] ) * sparsity + self.already_pruned[name] # TODO In current L1/L2 Filter Pruner, the 'op_types' is still necessary # I think the L1/L2 Pruner should specify the op_types automaticlly # according to the op_names cfg = [ { "sparsity": real_sparsity, "op_names": [name], "op_types": ["Conv2d"], } ] pruner = self.Pruner(self.model, cfg) pruner.compress() val_metric = self.val_func(*val_args, **val_kwargs) logger.info( "Layer: %s Sparsity: %.2f Validation Metric: %.4f", name, real_sparsity, val_metric, ) self.sensitivities[name][sparsity] = val_metric pruner._unwrap_model() del pruner # check if the current metric meet the stop condition if self._need_to_stop(self.ori_metric, val_metric): break # reset the weights pruned by the pruner, because the # input sparsities is sorted, so we donnot need to reset # weight of the layer when the sparsity changes, instead, # we only need reset the weight when the pruning layer changes. self.model.load_state_dict(self.ori_state_dict) return self.sensitivities

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138

208

null

sensitivity_analysis.py

auptimizer/src/aup/compression/torch/utils/sensitivity_analysis.py

import copy import csv import logging from collections import OrderedDict import numpy import torch.nn

15

1

6

0

8

null

Use image node_id 5 for calling the SensitivityAnalysis obj's underlying member method code with example usage: obj.analysis(val_args, val_kwargs, specified_layers) and returns: self

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315,481

drop_block_2d

global

null

false

x,drop_prob,block_size,gamma_scale,with_noise,inplace,batchwise

null

x

def drop_block_2d( x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False, ): """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf DropBlock with an experimental gaussian noise option. This layer has been tested on a few training runs with success, but needs further validation and possibly optimization for lower runtime impact. """ B, C, H, W = x.shape total_size = W * H clipped_block_size = min(block_size, min(W, H)) # seed_drop_rate, the gamma parameter gamma = ( gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((W - block_size + 1) * (H - block_size + 1)) ) # Forces the block to be inside the feature map. w_i, h_i = torch.meshgrid( torch.arange(W).to(x.device), torch.arange(H).to(x.device) ) valid_block = ( (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2) ) & ( (h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2) ) valid_block = torch.reshape(valid_block, (1, 1, H, W)).to( dtype=x.dtype ) if batchwise: # one mask for whole batch, quite a bit faster uniform_noise = torch.rand( (1, C, H, W), dtype=x.dtype, device=x.device ) else: uniform_noise = torch.rand_like(x) block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to( dtype=x.dtype ) block_mask = -F.max_pool2d( -block_mask, kernel_size=clipped_block_size, # block_size, stride=1, padding=clipped_block_size // 2, ) if with_noise: normal_noise = ( torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x) ) if inplace: x.mul_(block_mask).add_(normal_noise * (1 - block_mask)) else: x = x * block_mask + normal_noise * (1 - block_mask) else: normalize_scale = ( block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7) ).to(x.dtype) if inplace: x.mul_(block_mask * normalize_scale) else: x = x * block_mask * normalize_scale return x

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22

67

null

drop.py

pytorch-image-models/timm/layers/drop.py

import torch import torch.nn import torch.nn.functional

15

null

3

null

Use image node_id 1 for calling a global function with example usage: drop_block_2d(x, drop_prob, block_size, gamma_scale, with_noise, inplace, batchwise) and returns: x

169

node_id 1

1,692,291

extra_repr

DropPath

nn

true

self

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

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null

str+round

def extra_repr(self): return f"drop_prob={round(self.drop_prob,3):0.3f}"

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168

169

null

drop.py

pytorch-image-models/timm/layers/drop.py

import torch import torch.nn import torch.nn.functional

15

2

3

2

3

1

Use image node_id 3 for calling the DropPath obj's underlying member method code with example usage: obj.extra_repr() and returns: str, round

141

node_id 3

1,692,290

forward

DropPath

nn

true

self,x

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

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null

drop_path

def forward(self, x): return drop_path( x, self.drop_prob, self.training, self.scale_by_keep )

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165

166

null

drop.py

pytorch-image-models/timm/layers/drop.py

import torch import torch.nn import torch.nn.functional

15

2

3

2

3

1

Use image node_id 2 for calling the DropPath obj's underlying member method code with example usage: obj.forward(x) and returns: drop_path

138

node_id 2

1,692,289

__init__

DropPath

nn

true

self,drop_prob,scale_by_keep

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

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null

DropPath

def __init__( self, drop_prob: float = 0.0, scale_by_keep: bool = True ): super(DropPath, self).__init__() self.drop_prob = drop_prob self.scale_by_keep = scale_by_keep

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163

null

drop.py

pytorch-image-models/timm/layers/drop.py

import torch import torch.nn import torch.nn.functional

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3

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Use image node_id 1 to create a new DropPath object from inherited base classes: nn with example: obj = DropPath(drop_prob, scale_by_keep)

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node_id 1

1,692,288

make_fitness

global

null

false

null

_Fitness,_Fitness

def make_fitness(*, function, greater_is_better, wrap=True): """Make a fitness measure, a metric scoring the quality of a program's fit. This factory function creates a fitness measure object which measures the quality of a program's fit and thus its likelihood to undergo genetic operations into the next generation. The resulting object is able to be called with NumPy vectorized arguments and return a resulting floating point score quantifying the quality of the program's representation of the true relationship. Parameters ---------- function : callable A function with signature function(y, y_pred, sample_weight) that returns a floating point number. Where `y` is the input target y vector, `y_pred` is the predicted values from the genetic program, and sample_weight is the sample_weight vector. greater_is_better : bool Whether a higher value from `function` indicates a better fit. In general this would be False for metrics indicating the magnitude of the error, and True for metrics indicating the quality of fit. wrap : bool, optional (default=True) When running in parallel, pickling of custom metrics is not supported by Python's default pickler. This option will wrap the function using cloudpickle allowing you to pickle your solution, but the evolution may run slightly more slowly. If you are running single-threaded in an interactive Python session or have no need to save the model, set to `False` for faster runs. """ if not isinstance(greater_is_better, bool): raise ValueError( "greater_is_better must be bool, got %s" % type(greater_is_better) ) if not isinstance(wrap, bool): raise ValueError("wrap must be an bool, got %s" % type(wrap)) if function.__code__.co_argcount != 3: raise ValueError( "function requires 3 arguments (y, y_pred, w)," " got %d." % function.__code__.co_argcount ) if not isinstance( function( np.array([1, 1]), np.array([2, 2]), np.array([1, 1]) ), numbers.Number, ): raise ValueError("function must return a numeric.") if wrap: return _Fitness( function=wrap_non_picklable_objects(function), greater_is_better=greater_is_better, ) return _Fitness( function=function, greater_is_better=greater_is_better )

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52

101

null

fitness.py

gplearn/gplearn/fitness.py

import numbers import numpy from joblib import wrap_non_picklable_objects from scipy.stats import rankdata

15

null

4

7

null

Use image node_id 1 for calling a global function with example usage: make_fitness() and returns: _Fitness, _Fitness

116

node_id 1

1,106,030

_weighted_pearson

global

null

false

y,y_pred,w

null

int,np

def _weighted_pearson(y, y_pred, w): """Calculate the weighted Pearson correlation coefficient.""" with np.errstate(divide="ignore", invalid="ignore"): y_pred_demean = y_pred - np.average(y_pred, weights=w) y_demean = y - np.average(y, weights=w) corr = ( np.sum(w * y_pred_demean * y_demean) / np.sum(w) ) / np.sqrt( ( np.sum(w * y_pred_demean**2) * np.sum(w * y_demean**2) ) / (np.sum(w) ** 2) ) if np.isfinite(corr): return np.abs(corr) return 0.0

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104

115

null

fitness.py

gplearn/gplearn/fitness.py

import numbers import numpy from joblib import wrap_non_picklable_objects from scipy.stats import rankdata

15

null

4

7

null

Use image node_id 2 for calling a global function with example usage: _weighted_pearson(y, y_pred, w) and returns: int, np

122

node_id 2

1,106,031

__init__

PyTorchWorker

Worker

true

self,N_train,N_valid

null

PyTorchWorker

def __init__(self, N_train=8192, N_valid=1024, **kwargs): super().__init__(**kwargs) batch_size = 64 # Load the MNIST Data here train_dataset = torchvision.datasets.MNIST( root="../../data", train=True, transform=transforms.ToTensor(), download=True, ) test_dataset = torchvision.datasets.MNIST( root="../../data", train=False, transform=transforms.ToTensor(), ) train_sampler = torch.utils.data.sampler.SubsetRandomSampler( range(N_train) ) validation_sampler = torch.utils.data.sampler.SubsetRandomSampler( range(N_train, N_train + N_valid) ) self.train_loader = torch.utils.data.DataLoader( dataset=train_dataset, batch_size=batch_size, sampler=train_sampler, ) self.validation_loader = torch.utils.data.DataLoader( dataset=train_dataset, batch_size=1024, sampler=validation_sampler, ) self.test_loader = torch.utils.data.DataLoader( dataset=test_dataset, batch_size=1024, shuffle=False )

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80

96

null

example_5_pytorch_worker.py

HpBandSter/hpbandster/examples/example_5_pytorch_worker.py

import ConfigSpace import ConfigSpace.hyperparameters from hpbandster.core.worker import Worker import logging

15

2

4

0

2

4

1

Use image node_id 1 to create a new PyTorchWorker object from inherited base classes: Worker with example: obj = PyTorchWorker(N_train, N_valid)

144

node_id 1

117,772

compute

PyTorchWorker

Worker

true

self,config,budget,working_directory

null

Simple example for a compute function using a feed forward network. It is trained on the MNIST dataset. The input parameter "config" (dictionary) contains the sampled configurations passed by the bohb optimizer

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dict

def compute(self, config, budget, working_directory, *args, **kwargs): """ Simple example for a compute function using a feed forward network. It is trained on the MNIST dataset. The input parameter "config" (dictionary) contains the sampled configurations passed by the bohb optimizer """ # device = torch.device('cpu') model = MNISTConvNet( num_conv_layers=config["num_conv_layers"], num_filters_1=config["num_filters_1"], num_filters_2=config["num_filters_2"] if "num_filters_2" in config else None, num_filters_3=config["num_filters_3"] if "num_filters_3" in config else None, dropout_rate=config["dropout_rate"], num_fc_units=config["num_fc_units"], kernel_size=3, ) criterion = torch.nn.CrossEntropyLoss() if config["optimizer"] == "Adam": optimizer = torch.optim.Adam( model.parameters(), lr=config["lr"] ) else: optimizer = torch.optim.SGD( model.parameters(), lr=config["lr"], momentum=config["sgd_momentum"], ) for epoch in range(int(budget)): loss = 0 model.train() for i, (x, y) in enumerate(self.train_loader): optimizer.zero_grad() output = model(x) loss = F.nll_loss(output, y) loss.backward() optimizer.step() train_accuracy = self.evaluate_accuracy(model, self.train_loader) validation_accuracy = self.evaluate_accuracy( model, self.validation_loader ) test_accuracy = self.evaluate_accuracy(model, self.test_loader) return { "loss": 1 - validation_accuracy, # remember: HpBandSter always minimizes! "info": { "test accuracy": test_accuracy, "train accuracy": train_accuracy, "validation accuracy": validation_accuracy, "number of parameters": model.number_of_parameters(), }, }

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99

144

null

example_5_pytorch_worker.py

HpBandSter/hpbandster/examples/example_5_pytorch_worker.py

import ConfigSpace import ConfigSpace.hyperparameters from hpbandster.core.worker import Worker import logging

15

2

4

0

2

4

1

Use image node_id 2 for calling the PyTorchWorker obj's underlying member method code with example usage: obj.compute(config, budget, working_directory) and returns: dict

170

node_id 2

117,773

evaluate_accuracy

PyTorchWorker

Worker

true

self,model,data_loader

null

accuracy

def evaluate_accuracy(self, model, data_loader): model.eval() correct = 0 with torch.no_grad(): for x, y in data_loader: output = model(x) # test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.max(1, keepdim=True)[ 1 ] # get the index of the max log-probability correct += pred.eq(y.view_as(pred)).sum().item() # import pdb; pdb.set_trace() accuracy = correct / len(data_loader.sampler) return accuracy

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146

157

null

example_5_pytorch_worker.py

HpBandSter/hpbandster/examples/example_5_pytorch_worker.py

import ConfigSpace import ConfigSpace.hyperparameters from hpbandster.core.worker import Worker import logging

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Use image node_id 3 for calling the PyTorchWorker obj's underlying member method code with example usage: obj.evaluate_accuracy(model, data_loader) and returns: accuracy

169

node_id 3

117,774

get_configspace

PyTorchWorker

Worker

true

null

It builds the configuration space with the needed hyperparameters. It is easily possible to implement different types of hyperparameters. Beside float-hyperparameters on a log scale, it is also able to handle categorical input parameter. :return: ConfigurationsSpace-Object

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cs

def get_configspace(): """ It builds the configuration space with the needed hyperparameters. It is easily possible to implement different types of hyperparameters. Beside float-hyperparameters on a log scale, it is also able to handle categorical input parameter. :return: ConfigurationsSpace-Object """ cs = CS.ConfigurationSpace() lr = CSH.UniformFloatHyperparameter( "lr", lower=1e-6, upper=1e-1, default_value="1e-2", log=True ) # For demonstration purposes, we add different optimizers as categorical hyperparameters. # To show how to use conditional hyperparameters with ConfigSpace, we'll add the optimizers 'Adam' and 'SGD'. # SGD has a different parameter 'momentum'. optimizer = CSH.CategoricalHyperparameter( "optimizer", ["Adam", "SGD"] ) sgd_momentum = CSH.UniformFloatHyperparameter( "sgd_momentum", lower=0.0, upper=0.99, default_value=0.9, log=False, ) cs.add_hyperparameters([lr, optimizer, sgd_momentum]) # The hyperparameter sgd_momentum will be used,if the configuration # contains 'SGD' as optimizer. cond = CS.EqualsCondition(sgd_momentum, optimizer, "SGD") cs.add_condition(cond) num_conv_layers = CSH.UniformIntegerHyperparameter( "num_conv_layers", lower=1, upper=3, default_value=2 ) num_filters_1 = CSH.UniformIntegerHyperparameter( "num_filters_1", lower=4, upper=64, default_value=16, log=True ) num_filters_2 = CSH.UniformIntegerHyperparameter( "num_filters_2", lower=4, upper=64, default_value=16, log=True ) num_filters_3 = CSH.UniformIntegerHyperparameter( "num_filters_3", lower=4, upper=64, default_value=16, log=True ) cs.add_hyperparameters( [num_conv_layers, num_filters_1, num_filters_2, num_filters_3] ) # You can also use inequality conditions: cond = CS.GreaterThanCondition(num_filters_2, num_conv_layers, 1) cs.add_condition(cond) cond = CS.GreaterThanCondition(num_filters_3, num_conv_layers, 2) cs.add_condition(cond) dropout_rate = CSH.UniformFloatHyperparameter( "dropout_rate", lower=0.0, upper=0.9, default_value=0.5, log=False, ) num_fc_units = CSH.UniformIntegerHyperparameter( "num_fc_units", lower=8, upper=256, default_value=32, log=True ) cs.add_hyperparameters([dropout_rate, num_fc_units]) return cs

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161

208

null

example_5_pytorch_worker.py

HpBandSter/hpbandster/examples/example_5_pytorch_worker.py

import ConfigSpace import ConfigSpace.hyperparameters from hpbandster.core.worker import Worker import logging

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Use image node_id 4 for calling the PyTorchWorker obj's underlying member method code with example usage: obj.get_configspace() and returns: cs

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node_id 4

117,775

load_state_dict

SensitivityAnalysis

null

true

self,state_dict

null

Update the weight of the model

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null

def load_state_dict(self, state_dict): """ Update the weight of the model """ self.ori_state_dict = copy.deepcopy(state_dict) self.model.load_state_dict(self.ori_state_dict)

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246

251

null

sensitivity_analysis.py

auptimizer/src/aup/compression/torch/utils/sensitivity_analysis.py

import copy import csv import logging from collections import OrderedDict import numpy import torch.nn

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1

6

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8

null

Use image node_id 8 for calling the SensitivityAnalysis obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types

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315,484

_weighted_spearman

global

null

false

y,y_pred,w

null

_weighted_pearson

def _weighted_spearman(y, y_pred, w): """Calculate the weighted Spearman correlation coefficient.""" y_pred_ranked = np.apply_along_axis(rankdata, 0, y_pred) y_ranked = np.apply_along_axis(rankdata, 0, y) return _weighted_pearson(y_pred_ranked, y_ranked, w)

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118

122

null

fitness.py

gplearn/gplearn/fitness.py

import numbers import numpy from joblib import wrap_non_picklable_objects from scipy.stats import rankdata

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4

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Use image node_id 3 for calling a global function with example usage: _weighted_spearman(y, y_pred, w) and returns: _weighted_pearson

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_mean_absolute_error

global

null

false

y,y_pred,w

null

np

def _mean_absolute_error(y, y_pred, w): """Calculate the mean absolute error.""" return np.average(np.abs(y_pred - y), weights=w)

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fitness.py

gplearn/gplearn/fitness.py

import numbers import numpy from joblib import wrap_non_picklable_objects from scipy.stats import rankdata

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4

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Use image node_id 4 for calling a global function with example usage: _mean_absolute_error(y, y_pred, w) and returns: np

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node_id 4

1,106,033

_mkdirp

global

null

false

d

null

def _mkdirp(d): """Ensure directory d exists (like mkdir -p on Unix) No guarantee that the directory is writable. """ try: os.makedirs(d) except OSError as e: if e.errno != errno.EEXIST: raise

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393

401

null

_kddcup99.py

catboost/contrib/python/scikit-learn/py3/sklearn/datasets/_kddcup99.py

import errno import logging import os from gzip import GzipFile from os.path import exists, join import joblib import numpy from ..utils import Bunch, check_random_state from ..utils import shuffle from ..utils._param_validation import StrOptions, validate_params from .None import get_data_home from ._base import RemoteFileMetadata, _convert_data_dataframe, _fetch_remote, load_descr

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null

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node_id 3

520,098

_fetch_brute_kddcup99

global

null

false

data_home,download_if_missing,percent10

null

Bunch

def _fetch_brute_kddcup99( data_home=None, download_if_missing=True, percent10=True ): """Load the kddcup99 dataset, downloading it if necessary. Parameters ---------- data_home : str, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. download_if_missing : bool, default=True If False, raise an OSError if the data is not locally available instead of trying to download the data from the source site. percent10 : bool, default=True Whether to load only 10 percent of the data. Returns ------- dataset : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. data : ndarray of shape (494021, 41) Each row corresponds to the 41 features in the dataset. target : ndarray of shape (494021,) Each value corresponds to one of the 21 attack types or to the label 'normal.'. feature_names : list The names of the dataset columns target_names: list The names of the target columns DESCR : str Description of the kddcup99 dataset. """ data_home = get_data_home(data_home=data_home) dir_suffix = "-py3" if percent10: kddcup_dir = join(data_home, "kddcup99_10" + dir_suffix) archive = ARCHIVE_10_PERCENT else: kddcup_dir = join(data_home, "kddcup99" + dir_suffix) archive = ARCHIVE samples_path = join(kddcup_dir, "samples") targets_path = join(kddcup_dir, "targets") available = exists(samples_path) dt = [ ("duration", int), ("protocol_type", "S4"), ("service", "S11"), ("flag", "S6"), ("src_bytes", int), ("dst_bytes", int), ("land", int), ("wrong_fragment", int), ("urgent", int), ("hot", int), ("num_failed_logins", int), ("logged_in", int), ("num_compromised", int), ("root_shell", int), ("su_attempted", int), ("num_root", int), ("num_file_creations", int), ("num_shells", int), ("num_access_files", int), ("num_outbound_cmds", int), ("is_host_login", int), ("is_guest_login", int), ("count", int), ("srv_count", int), ("serror_rate", float), ("srv_serror_rate", float), ("rerror_rate", float), ("srv_rerror_rate", float), ("same_srv_rate", float), ("diff_srv_rate", float), ("srv_diff_host_rate", float), ("dst_host_count", int), ("dst_host_srv_count", int), ("dst_host_same_srv_rate", float), ("dst_host_diff_srv_rate", float), ("dst_host_same_src_port_rate", float), ("dst_host_srv_diff_host_rate", float), ("dst_host_serror_rate", float), ("dst_host_srv_serror_rate", float), ("dst_host_rerror_rate", float), ("dst_host_srv_rerror_rate", float), ("labels", "S16"), ] column_names = [c[0] for c in dt] target_names = column_names[-1] feature_names = column_names[:-1] if available: try: X = joblib.load(samples_path) y = joblib.load(targets_path) except Exception as e: raise OSError( "The cache for fetch_kddcup99 is invalid, please delete " f"{str(kddcup_dir)} and run the fetch_kddcup99 again" ) from e elif download_if_missing: _mkdirp(kddcup_dir) logger.info("Downloading %s" % archive.url) _fetch_remote(archive, dirname=kddcup_dir) DT = np.dtype(dt) logger.debug("extracting archive") archive_path = join(kddcup_dir, archive.filename) file_ = GzipFile(filename=archive_path, mode="r") Xy = [] for line in file_.readlines(): line = line.decode() Xy.append(line.replace("\n", "").split(",")) file_.close() logger.debug("extraction done") os.remove(archive_path) Xy = np.asarray(Xy, dtype=object) for j in range(42): Xy[:, j] = Xy[:, j].astype(DT[j]) X = Xy[:, :-1] y = Xy[:, -1] # XXX bug when compress!=0: # (error: 'Incorrect data length while decompressing[...] the file # could be corrupted.') joblib.dump(X, samples_path, compress=0) joblib.dump(y, targets_path, compress=0) else: raise OSError( "Data not found and `download_if_missing` is False" ) return Bunch( data=X, target=y, feature_names=feature_names, target_names=[target_names], )

246

390

null

_kddcup99.py

catboost/contrib/python/scikit-learn/py3/sklearn/datasets/_kddcup99.py

import errno import logging import os from gzip import GzipFile from os.path import exists, join import joblib import numpy from ..utils import Bunch, check_random_state from ..utils import shuffle from ..utils._param_validation import StrOptions, validate_params from .None import get_data_home from ._base import RemoteFileMetadata, _convert_data_dataframe, _fetch_remote, load_descr

15

null

12

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null

Use image node_id 2 for calling a global function with example usage: _fetch_brute_kddcup99(data_home, download_if_missing, percent10) and returns: Bunch

153

node_id 2

520,097

__new__

EdgeTypeStr

str

true

cls

A helper class to construct serializable edge types by merging an edge type tuple into a single string.

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null

str

def __new__(cls, *args: Any) -> "EdgeTypeStr": if isinstance(args[0], (list, tuple)): # Unwrap `EdgeType((src, rel, dst))` and `EdgeTypeStr((src, dst))`: args = tuple(args[0]) if len(args) == 1 and isinstance(args[0], str): arg = args[0] # An edge type string was passed. elif len(args) == 2 and all(isinstance(arg, str) for arg in args): # A `(src, dst)` edge type was passed - add `DEFAULT_REL`: arg = EDGE_TYPE_STR_SPLIT.join( (args[0], DEFAULT_REL, args[1]) ) elif len(args) == 3 and all(isinstance(arg, str) for arg in args): # A `(src, rel, dst)` edge type was passed: arg = EDGE_TYPE_STR_SPLIT.join(args) else: raise ValueError(f"Encountered invalid edge type '{args}'") return str.__new__(cls, arg)

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292

311

null

typing.py

pytorch_geometric/torch_geometric/typing.py

import inspect import os import sys import warnings from typing import Any, Dict, List, Optional, Tuple, Union import numpy import torch from torch import Tensor

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__init__

SensitivityAnalysis

null

true

self,model,val_func,sparsities,prune_type,early_stop_mode,early_stop_value

null

Perform sensitivity analysis for this model. Parameters ---------- model : torch.nn.Module the model to perform sensitivity analysis val_func : function validation function for the model. Due to different models may need different dataset/criterion , therefore the user need to cover this part by themselves. In the val_func, the model should be tested on the validation dateset, and the validation accuracy/loss should be returned as the output of val_func. There are no restrictions on the input parameters of the val_function. User can use the val_args, val_kwargs parameters in analysis to pass all the parameters that val_func needed. sparsities : list The sparsity list provided by users. This parameter is set when the user only wants to test some specific sparsities. In the sparsity list, each element is a sparsity value which means how much weight the pruner should prune. Take [0.25, 0.5, 0.75] for an example, the SensitivityAnalysis will prune 25% 50% 75% weights gradually for each layer. prune_type : str The pruner type used to prune the conv layers, default is 'l1', and 'l2', 'fine-grained' is also supported. early_stop_mode : str If this flag is set, the sensitivity analysis for a conv layer will early stop when the validation metric( for example, accurracy/loss) has alreay meet the threshold. We support four different early stop modes: minimize, maximize, dropped, raised. The default value is None, which means the analysis won't stop until all given sparsities are tested. This option should be used with early_stop_value together. minimize: The analysis stops when the validation metric return by the val_func lower than early_stop_value. maximize: The analysis stops when the validation metric return by the val_func larger than early_stop_value. dropped: The analysis stops when the validation metric has dropped by early_stop_value. raised: The analysis stops when the validation metric has raised by early_stop_value. early_stop_value : float This value is used as the threshold for different earlystop modes. This value is effective only when the early_stop_mode is set.

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SensitivityAnalysis

def __init__( self, model, val_func, sparsities=None, prune_type="l1", early_stop_mode=None, early_stop_value=None, ): """ Perform sensitivity analysis for this model. Parameters ---------- model : torch.nn.Module the model to perform sensitivity analysis val_func : function validation function for the model. Due to different models may need different dataset/criterion , therefore the user need to cover this part by themselves. In the val_func, the model should be tested on the validation dateset, and the validation accuracy/loss should be returned as the output of val_func. There are no restrictions on the input parameters of the val_function. User can use the val_args, val_kwargs parameters in analysis to pass all the parameters that val_func needed. sparsities : list The sparsity list provided by users. This parameter is set when the user only wants to test some specific sparsities. In the sparsity list, each element is a sparsity value which means how much weight the pruner should prune. Take [0.25, 0.5, 0.75] for an example, the SensitivityAnalysis will prune 25% 50% 75% weights gradually for each layer. prune_type : str The pruner type used to prune the conv layers, default is 'l1', and 'l2', 'fine-grained' is also supported. early_stop_mode : str If this flag is set, the sensitivity analysis for a conv layer will early stop when the validation metric( for example, accurracy/loss) has alreay meet the threshold. We support four different early stop modes: minimize, maximize, dropped, raised. The default value is None, which means the analysis won't stop until all given sparsities are tested. This option should be used with early_stop_value together. minimize: The analysis stops when the validation metric return by the val_func lower than early_stop_value. maximize: The analysis stops when the validation metric return by the val_func larger than early_stop_value. dropped: The analysis stops when the validation metric has dropped by early_stop_value. raised: The analysis stops when the validation metric has raised by early_stop_value. early_stop_value : float This value is used as the threshold for different earlystop modes. This value is effective only when the early_stop_mode is set. """ from ..pruning.constants_pruner import PRUNER_DICT self.model = model self.val_func = val_func self.target_layer = OrderedDict() self.ori_state_dict = copy.deepcopy(self.model.state_dict()) self.target_layer = {} self.sensitivities = {} if sparsities is not None: self.sparsities = sorted(sparsities) else: self.sparsities = np.arange(0.1, 1.0, 0.1) self.sparsities = [np.round(x, 2) for x in self.sparsities] self.Pruner = PRUNER_DICT[prune_type] self.early_stop_mode = early_stop_mode self.early_stop_value = early_stop_value self.ori_metric = None # original validation metric for the model # already_pruned is for the iterative sensitivity analysis # For example, sensitivity_pruner iteratively prune the target # model according to the sensitivity. After each round of # pruning, the sensitivity_pruner will test the new sensitivity # for each layer self.already_pruned = {} self.model_parse()

23

91

null

sensitivity_analysis.py

auptimizer/src/aup/compression/torch/utils/sensitivity_analysis.py

import copy import csv import logging from collections import OrderedDict import numpy import torch.nn

15

1

6

0

8

null

Use image node_id 1 to create a new SensitivityAnalysis object with example: obj = SensitivityAnalysis(model, val_func, sparsities, prune_type, early_stop_mode, early_stop_value)

179

node_id 1

315,477

to_tuple

EdgeTypeStr

str

true

self

A helper class to construct serializable edge types by merging an edge type tuple into a single string.

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Returns the original edge type.

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out

def to_tuple(self) -> EdgeType: r"""Returns the original edge type.""" out = tuple(self.split(EDGE_TYPE_STR_SPLIT)) if len(out) != 3: raise ValueError( f"Cannot convert the edge type '{self}' to a " f"tuple since it holds invalid characters" ) return out

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313

319

null

typing.py

pytorch_geometric/torch_geometric/typing.py

import inspect import os import sys import warnings from typing import Any, Dict, List, Optional, Tuple, Union import numpy import torch from torch import Tensor

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Use image node_id 2 for calling the EdgeTypeStr obj's underlying member method code with example usage: obj.to_tuple() and returns: out

135

node_id 2

1,775,554

__init__

MNISTConvNet

torch.nn

true

self,num_conv_layers,num_filters_1,num_filters_2,num_filters_3,dropout_rate,num_fc_units,kernel_size

null

MNISTConvNet

def __init__( self, num_conv_layers, num_filters_1, num_filters_2, num_filters_3, dropout_rate, num_fc_units, kernel_size, ): super().__init__() self.conv1 = nn.Conv2d(1, num_filters_1, kernel_size=kernel_size) self.conv2 = None self.conv3 = None output_size = (28 - kernel_size + 1) // 2 num_output_filters = num_filters_1 if num_conv_layers > 1: self.conv2 = nn.Conv2d( num_filters_1, num_filters_2, kernel_size=kernel_size ) num_output_filters = num_filters_2 output_size = (output_size - kernel_size + 1) // 2 if num_conv_layers > 2: self.conv3 = nn.Conv2d( num_filters_2, num_filters_3, kernel_size=kernel_size ) num_output_filters = num_filters_3 output_size = (output_size - kernel_size + 1) // 2 self.dropout = nn.Dropout(p=dropout_rate) self.conv_output_size = ( num_output_filters * output_size * output_size ) self.fc1 = nn.Linear(self.conv_output_size, num_fc_units) self.fc2 = nn.Linear(num_fc_units, 10)

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214

239

null

example_5_pytorch_worker.py

HpBandSter/hpbandster/examples/example_5_pytorch_worker.py

import ConfigSpace import ConfigSpace.hyperparameters from hpbandster.core.worker import Worker import logging

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2

4

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2

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1

Use image node_id 1 to create a new MNISTConvNet object from inherited base classes: torch.nn with example: obj = MNISTConvNet(num_conv_layers, num_filters_1, num_filters_2, num_filters_3, dropout_rate, num_fc_units, kernel_size)

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node_id 1

117,776

layers_count

SensitivityAnalysis

null

true

self

null

len

def layers_count(self): return len(self.target_layer)

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94

95

null

sensitivity_analysis.py

auptimizer/src/aup/compression/torch/utils/sensitivity_analysis.py

import copy import csv import logging from collections import OrderedDict import numpy import torch.nn

15

1

6

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8

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Use image node_id 2 for calling the SensitivityAnalysis obj's underlying member method code with example usage: obj.layers_count() and returns: len

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node_id 2

315,478

model_parse

SensitivityAnalysis

null

true

self

null

def model_parse(self): for name, submodel in self.model.named_modules(): for op_type in SUPPORTED_OP_TYPE: if isinstance(submodel, op_type): self.target_layer[name] = submodel self.already_pruned[name] = 0

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102

null

sensitivity_analysis.py

auptimizer/src/aup/compression/torch/utils/sensitivity_analysis.py

import copy import csv import logging from collections import OrderedDict import numpy import torch.nn

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Use image node_id 3 for calling the SensitivityAnalysis obj's underlying member method code with example usage: obj.model_parse() without return types

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node_id 3

315,479

_need_to_stop

SensitivityAnalysis

null

true

self,ori_metric,cur_metric

null

Judge if meet the stop conditon(early_stop, min_threshold, max_threshold). Parameters ---------- ori_metric : float original validation metric cur_metric : float current validation metric Returns ------- stop : bool if stop the sensitivity analysis

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False,False,True,True,True,True

def _need_to_stop(self, ori_metric, cur_metric): """ Judge if meet the stop conditon(early_stop, min_threshold, max_threshold). Parameters ---------- ori_metric : float original validation metric cur_metric : float current validation metric Returns ------- stop : bool if stop the sensitivity analysis """ if self.early_stop_mode is None: # early stop mode is not enable return False assert self.early_stop_value is not None if self.early_stop_mode == "minimize": if cur_metric < self.early_stop_value: return True elif self.early_stop_mode == "maximize": if cur_metric > self.early_stop_value: return True elif self.early_stop_mode == "dropped": if cur_metric < ori_metric - self.early_stop_value: return True elif self.early_stop_mode == "raised": if cur_metric > ori_metric + self.early_stop_value: return True return False

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136

null

sensitivity_analysis.py

auptimizer/src/aup/compression/torch/utils/sensitivity_analysis.py

import copy import csv import logging from collections import OrderedDict import numpy import torch.nn

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1

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Use image node_id 4 for calling the SensitivityAnalysis obj's underlying member method code with example usage: obj._need_to_stop(ori_metric, cur_metric) and returns: False, False, True, True, True, True

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node_id 4

315,480

is_available

Converter

ImageConverter

true

self

null

Confirms if converter is available or not.

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True

def is_available(self) -> bool: """Confirms if converter is available or not.""" return True

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null

convert-svg-to-pdf.py

sympy/doc/ext/convert-svg-to-pdf.py

from __future__ import annotations from sphinx.transforms.post_transforms.images import ImageConverter from sphinx.util import logging import os import platform from typing import Any from sphinx.application import Sphinx

15

1

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5

1

Use image node_id 1 for calling the Converter obj's underlying member method code with example usage: obj.is_available() and returns: True

138

node_id 1

2,029,274

reset

COCODetectionMetric

EvalMetric

true

self

Detection metric for COCO bbox task. Parameters ---------- dataset : instance of gluoncv.data.COCODetection The validation dataset. save_prefix : str Prefix for the saved JSON results. use_time : bool Append unique datetime string to created JSON file name if ``True``. cleanup : bool Remove created JSON file if ``True``. score_thresh : float Detection results with confident scores smaller than ``score_thresh`` will be discarded before saving to results. data_shape : tuple of int, default is None If `data_shape` is provided as (height, width), we will rescale bounding boxes when saving the predictions. This is helpful when SSD/YOLO box predictions cannot be rescaled conveniently. Note that the data_shape must be fixed for all validation images. post_affine : a callable function with input signature (orig_w, orig_h, out_w, out_h) If not None, the bounding boxes will be affine transformed rather than simply scaled.

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null

def reset(self): self._current_id = 0 self._results = []

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87

89

null

coco_detection.py

gluon-cv/gluoncv/utils/metrics/coco_detection.py

from __future__ import absolute_import import sys import os from os import path import warnings import numpy import mxnet

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1

7

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Use image node_id 3 for calling the COCODetectionMetric obj's underlying member method code with example usage: obj.reset() without return types

144

node_id 3

1,096,310

delete_pipeline

global

null

false

ctx,engine,pipeline_name,endpoint,iap_client_id,namespace

null

def delete_pipeline( ctx: Context, engine: str, pipeline_name: str, endpoint: str, iap_client_id: str, namespace: str, ) -> None: """Command definition to delete a pipeline.""" click.echo("Deleting pipeline") ctx.flags_dict[labels.ENGINE_FLAG] = engine ctx.flags_dict[labels.PIPELINE_NAME] = pipeline_name ctx.flags_dict[labels.ENDPOINT] = endpoint ctx.flags_dict[labels.IAP_CLIENT_ID] = iap_client_id ctx.flags_dict[labels.NAMESPACE] = namespace handler_factory.create_handler(ctx.flags_dict).delete_pipeline()

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245

254

null

pipeline.py

tfx/tfx/tools/cli/commands/pipeline.py

import sys from typing import Optional import click from tfx.tools.cli import labels from tfx.tools.cli.cli_context import Context from tfx.tools.cli.cli_context import pass_context from tfx.tools.cli.handler import handler_factory

15

null

7

8

null

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170

node_id 5

2,199,077

list_pipelines

global

null

false

ctx,engine,endpoint,iap_client_id,namespace

null

def list_pipelines( ctx: Context, engine: str, endpoint: str, iap_client_id: str, namespace: str, ) -> None: """Command definition to list pipelines.""" click.echo("Listing all pipelines") ctx.flags_dict[labels.ENGINE_FLAG] = engine ctx.flags_dict[labels.ENDPOINT] = endpoint ctx.flags_dict[labels.IAP_CLIENT_ID] = iap_client_id ctx.flags_dict[labels.NAMESPACE] = namespace handler_factory.create_handler(ctx.flags_dict).list_pipelines()

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278

286

null

pipeline.py

tfx/tfx/tools/cli/commands/pipeline.py

import sys from typing import Optional import click from tfx.tools.cli import labels from tfx.tools.cli.cli_context import Context from tfx.tools.cli.cli_context import pass_context from tfx.tools.cli.handler import handler_factory

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null

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Use image node_id 6 for calling a global function with example usage: list_pipelines(ctx, engine, endpoint, iap_client_id, namespace) without return types

154

node_id 6

2,199,078

on_test_end

NotificationCallback

Callback

true

self,logs

Send a notification to a channel at the beginning/ending of the training/testing and at a constant frequency (`alert_frequency`) during the training. Args: notificator (~poutyne.Notificator): The notification channel to send the message. The expected interface need to implement a `send_notification` method to send the message. You can see the `notif <https://notificationdoc.ca/index.html>`_ package which implements some Notificator respecting the interface. alert_frequency (int): The frequency (in epoch), during training, to send an update. By default, 1. experiment_name (Union[str, None]): The name of the experiment to add to the message. By default, None. Example: .. code-block:: python from notif.notificator import SlackNotificator from poutyne.framework.callbacks.notification import NotificationCallback webhook_url = "a_link" slack_notif = SlackNotificator(webhook_url=webhook_url) notif_callback = NotificationCallback(notificator=slack_notif) model = Model(...) model.fit_generator(..., callbacks=[notif_callback])

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Send the message to the channel 'End of the testing' or 'End of the testing for the experiment experiment_name' if an experiment name is given.

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null

def on_test_end(self, logs: Dict) -> None: """ Send the message to the channel 'End of the testing' or 'End of the testing for the experiment experiment_name' if an experiment name is given. """ message = f"Here the test metrics: \n{self._format_logs(logs)}" self.notificator.send_notification( message, subject=f"End of the testing{self.experiment_name_msg}.", )

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120

127

null

notification.py

poutyne/poutyne/framework/callbacks/notification.py

from abc import ABC, abstractmethod from typing import Dict, Union from poutyne.framework.callbacks.callbacks import Callback

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Use image node_id 6 for calling the NotificationCallback obj's underlying member method code with example usage: obj.on_test_end(logs) without return types

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node_id 6

1,602,782

on_test_begin

NotificationCallback

Callback

true

self,logs

Send a notification to a channel at the beginning/ending of the training/testing and at a constant frequency (`alert_frequency`) during the training. Args: notificator (~poutyne.Notificator): The notification channel to send the message. The expected interface need to implement a `send_notification` method to send the message. You can see the `notif <https://notificationdoc.ca/index.html>`_ package which implements some Notificator respecting the interface. alert_frequency (int): The frequency (in epoch), during training, to send an update. By default, 1. experiment_name (Union[str, None]): The name of the experiment to add to the message. By default, None. Example: .. code-block:: python from notif.notificator import SlackNotificator from poutyne.framework.callbacks.notification import NotificationCallback webhook_url = "a_link" slack_notif = SlackNotificator(webhook_url=webhook_url) notif_callback = NotificationCallback(notificator=slack_notif) model = Model(...) model.fit_generator(..., callbacks=[notif_callback])

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Send the message to the channel 'Start of the testing' or 'Start of the testing for the experiment experiment_name' if an experiment name is given.

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null

def on_test_begin(self, logs: Dict) -> None: """ Send the message to the channel 'Start of the testing' or 'Start of the testing for the experiment experiment_name' if an experiment name is given. """ empty_message = "" self.notificator.send_notification( empty_message, subject=f"Start of the testing{self.experiment_name_msg}.", )

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111

118

null

notification.py

poutyne/poutyne/framework/callbacks/notification.py

from abc import ABC, abstractmethod from typing import Dict, Union from poutyne.framework.callbacks.callbacks import Callback

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main

global

null

false

null

def main(): """Convert standard rttm to sample-based result""" args = get_parser().parse_args() # logging info if args.verbose > 1: logging.basicConfig( level=logging.DEBUG, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) elif args.verbose > 0: logging.basicConfig( level=logging.INFO, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) else: logging.basicConfig( level=logging.WARN, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) logging.warning("Skip DEBUG/INFO messages") sampling_rate = humanfriendly.parse_size(args.sampling_rate) convert_rttm_text( args.rttm, args.wavscp, sampling_rate, args.output_path ) logging.info("Successfully finished RTTM converting.")

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111

136

null

convert_rttm.py

espnet/egs2/thchs30/asr1/pyscripts/utils/convert_rttm.py

import argparse import collections.abc import logging import os import re from pathlib import Path from typing import Union import humanfriendly import numpy import soundfile from typeguard import check_argument_types from espnet2.utils.types import str_or_int

15

null

12

3

null

Use image node_id 3 for calling a global function with example usage: main() without return types

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node_id 3

981,194

get_logits

RerankModelPlugin

Plugin

true

self,query,choices

Base class for reranker models

["Base","class","for","reranker","models"]

get search ranking logits for query, choices

["get","search","ranking","logits","for","query",",","choices"]

null

def get_logits(self, query: str, choices: List[str]): """get search ranking logits for query, choices""" raise NotImplementedError()

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74

76

base.py

nboost/nboost/plugins/rerank/base.py

from typing import List, Tuple import time from nboost.plugins import Plugin from nboost.delegates import RequestDelegate, ResponseDelegate from nboost.helpers import calculate_mrr from nboost.database import DatabaseRow from nboost import defaults import numpy

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node_id 4

1,408,510

_restore_configuration

Configurable

object

true

cls,saved

Base class for configurable interfaces. A configurable interface is an (abstract) class whose constructor acts as a factory function for one of its implementation subclasses. The implementation subclass as well as optional keyword arguments to its initializer can be set globally at runtime with `configure`. By using the constructor as the factory method, the interface looks like a normal class, `isinstance` works as usual, etc. This pattern is most useful when the choice of implementation is likely to be a global decision (e.g. when `~select.epoll` is available, always use it instead of `~select.select`), or when a previously-monolithic class has been split into specialized subclasses. Configurable subclasses must define the class methods `configurable_base` and `configurable_default`, and use the instance method `initialize` instead of ``__init__``.

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null

def _restore_configuration(cls, saved): base = cls.configurable_base() base.__impl_class = saved[0] base.__impl_kwargs = saved[1]

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208

211

null

util.py

catboost/contrib/python/pyzmq/py2/zmq/eventloop/minitornado/util.py

from __future__ import absolute_import, division, print_function, with_statement import sys

15

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node_id 8

515,116

forward

MaskL1Loss

nn

true

self,pred,gt,mask

null

loss

def forward(self, pred: paddle.Tensor, gt, mask): loss = (paddle.abs(pred - gt) * mask).sum() / ( mask.sum() + self.eps ) return loss

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95

97

null

basic_loss.py

PaddleOCR/benchmark/PaddleOCR_DBNet/models/losses/basic_loss.py

import paddle import paddle.nn

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__init__

MaskL1Loss

nn

true

self,eps

null

MaskL1Loss

def __init__(self, eps=1e-6): super(MaskL1Loss, self).__init__() self.eps = eps

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93

null

basic_loss.py

PaddleOCR/benchmark/PaddleOCR_DBNet/models/losses/basic_loss.py

import paddle import paddle.nn

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Use image node_id 1 to create a new MaskL1Loss object from inherited base classes: nn with example: obj = MaskL1Loss(eps)

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node_id 1

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import_object

global

null

false

name

null

__import__,getattr

def import_object(name): """Imports an object by name. import_object('x') is equivalent to 'import x'. import_object('x.y.z') is equivalent to 'from x.y import z'. >>> import tornado.escape >>> import_object('tornado.escape') is tornado.escape True >>> import_object('tornado.escape.utf8') is tornado.escape.utf8 True >>> import_object('tornado') is tornado True >>> import_object('tornado.missing_module') Traceback (most recent call last): ... ImportError: No module named missing_module """ if isinstance(name, unicode_type) and str is not unicode_type: # On python 2 a byte string is required. name = name.encode("utf-8") if name.count(".") == 0: return __import__(name, None, None) parts = name.split(".") obj = __import__(".".join(parts[:-1]), None, None, [parts[-1]], 0) try: return getattr(obj, parts[-1]) except AttributeError: raise ImportError("No module named %s" % parts[-1])

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37

66

null

util.py

catboost/contrib/python/pyzmq/py2/zmq/eventloop/minitornado/util.py

from __future__ import absolute_import, division, print_function, with_statement import sys

15

null

2

3

null

Use image node_id 1 for calling a global function with example usage: import_object(name) and returns: __import__, getattr

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node_id 1

515,117

input_expected_output

global

null

false

dask_client,input_combo

null

input_combo

def input_expected_output(dask_client, input_combo): """ This fixture returns the inputs and expected results from the Core number algo. """ core_number = input_combo["core_number"] degree_type = input_combo["degree_type"] input_data_path = input_combo["graph_file"] G = utils.generate_cugraph_graph_from_file( input_data_path, directed=False, edgevals=True ) if core_number: # compute the core_number core_number = cugraph.core_number(G, degree_type=degree_type) else: core_number = None input_combo["core_number"] = core_number input_combo["SGGraph"] = G sg_k_core_graph = cugraph.k_core( G, core_number=core_number, degree_type=degree_type ) sg_k_core_results = sg_k_core_graph.view_edge_list() # FIXME: The result will come asymetric. Symmetrize the results srcCol = sg_k_core_graph.source_columns dstCol = sg_k_core_graph.destination_columns wgtCol = sg_k_core_graph.weight_column sg_k_core_results = ( symmetrize_df(sg_k_core_results, srcCol, dstCol, wgtCol) .sort_values([srcCol, dstCol]) .reset_index(drop=True) ) input_combo["sg_k_core_results"] = sg_k_core_results # Creating an edgelist from a dask cudf dataframe chunksize = dcg.get_chunksize(input_data_path) ddf = dask_cudf.read_csv( input_data_path, chunksize=chunksize, delimiter=" ", names=["src", "dst", "value"], dtype=["int32", "int32", "float32"], ) dg = cugraph.Graph(directed=False) # FIXME: False when renumbering (C++ and python renumbering) dg.from_dask_cudf_edgelist( ddf, source="src", destination="dst", edge_attr="value", renumber=True, ) input_combo["MGGraph"] = dg return input_combo

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59

119

null

test_k_core_mg.py

cugraph/python/cugraph/cugraph/tests/core/test_k_core_mg.py

import gc import pytest import dask_cudf import cugraph import cugraph.dask from cugraph.testing import utils from cudf.testing.testing import assert_frame_equal from cugraph.structure.symmetrize import symmetrize_df from pylibcugraph.testing import gen_fixture_params_product

15

null

9

6

null

Use image node_id 3 for calling a global function with example usage: input_expected_output(dask_client, input_combo) and returns: input_combo

142

node_id 3

686,800

_compute

DiceLoss

nn

true

self,pred,gt,mask,weights

Loss function from https://arxiv.org/abs/1707.03237, where iou computation is introduced heatmap manner to measure the diversity bwtween tow heatmaps.

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null

loss

def _compute(self, pred, gt, mask, weights): if len(pred.shape) == 4: pred = pred[:, 0, :, :] gt = gt[:, 0, :, :] assert pred.shape == gt.shape assert pred.shape == mask.shape if weights is not None: assert weights.shape == mask.shape mask = weights * mask intersection = (pred * gt * mask).sum() union = (pred * mask).sum() + (gt * mask).sum() + self.eps loss = 1 - 2.0 * intersection / union assert loss <= 1 return loss

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73

87

null

basic_loss.py

PaddleOCR/benchmark/PaddleOCR_DBNet/models/losses/basic_loss.py

import paddle import paddle.nn

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Use image node_id 3 for calling the DiceLoss obj's underlying member method code with example usage: obj._compute(pred, gt, mask, weights) and returns: loss

156

node_id 3

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forward

DiceLoss

nn

true

self,pred,gt,mask,weights

Loss function from https://arxiv.org/abs/1707.03237, where iou computation is introduced heatmap manner to measure the diversity bwtween tow heatmaps.

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pred: one or two heatmaps of shape (N, 1, H, W), the losses of tow heatmaps are added together. gt: (N, 1, H, W) mask: (N, H, W)

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self

def forward(self, pred: paddle.Tensor, gt, mask, weights=None): """ pred: one or two heatmaps of shape (N, 1, H, W), the losses of tow heatmaps are added together. gt: (N, 1, H, W) mask: (N, H, W) """ return self._compute(pred, gt, mask, weights)

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64

71

null

basic_loss.py

PaddleOCR/benchmark/PaddleOCR_DBNet/models/losses/basic_loss.py

import paddle import paddle.nn

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Use image node_id 2 for calling the DiceLoss obj's underlying member method code with example usage: obj.forward(pred, gt, mask, weights) and returns: self

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test_buffer

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def test_buffer(df_from_dict): arr = [0, 1, -1] df = df_from_dict({"a": arr}) dfX = df.__dataframe__() colX = dfX.get_column(0) bufX = colX.get_buffers() dataBuf, dataDtype = bufX["data"] assert dataBuf.bufsize > 0 assert dataBuf.ptr != 0 device, _ = dataBuf.__dlpack_device__() # for meanings of dtype[0] see the spec; we cannot import the spec here as this # file is expected to be vendored *anywhere* assert dataDtype[0] == 0 # INT if ( device == 1 ): # CPU-only as we're going to directly read memory here bitwidth = dataDtype[1] ctype = { 8: ctypes.c_int8, 16: ctypes.c_int16, 32: ctypes.c_int32, 64: ctypes.c_int64, }[bitwidth] for idx, truth in enumerate(arr): val = ctype.from_address( dataBuf.ptr + idx * (bitwidth // 8) ).value assert val == truth, f"Buffer at index {idx} mismatch"

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test_spec_conformance.py

pandas/pandas/tests/interchange/test_spec_conformance.py

import ctypes import math import pytest import pandas

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test_get_columns

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df_from_dict

null

def test_get_columns(df_from_dict): df = df_from_dict({"a": [0, 1], "b": [2.5, 3.5]}) dfX = df.__dataframe__() for colX in dfX.get_columns(): assert colX.size() == 2 assert colX.num_chunks() == 1 # for meanings of dtype[0] see the spec; we cannot import the spec here as this # file is expected to be vendored *anywhere* assert dfX.get_column(0).dtype[0] == 0 # INT assert dfX.get_column(1).dtype[0] == 2

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test_spec_conformance.py

pandas/pandas/tests/interchange/test_spec_conformance.py

import ctypes import math import pytest import pandas

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test_column_get_chunks

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def test_column_get_chunks(size, n_chunks, df_from_dict): df = df_from_dict({"x": list(range(size))}) dfX = df.__dataframe__() chunks = list(dfX.get_column(0).get_chunks(n_chunks)) assert len(chunks) == n_chunks assert sum(chunk.size() for chunk in chunks) == size

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test_spec_conformance.py

pandas/pandas/tests/interchange/test_spec_conformance.py

import ctypes import math import pytest import pandas

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test_df_get_chunks

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def test_df_get_chunks(size, n_chunks, df_from_dict): df = df_from_dict({"x": list(range(size))}) dfX = df.__dataframe__() chunks = list(dfX.get_chunks(n_chunks)) assert len(chunks) == n_chunks assert sum(chunk.num_rows() for chunk in chunks) == size

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test_spec_conformance.py

pandas/pandas/tests/interchange/test_spec_conformance.py

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test_dataframe

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def test_dataframe(df_from_dict): df = df_from_dict( { "x": [True, True, False], "y": [1, 2, 0], "z": [9.2, 10.5, 11.8], } ) dfX = df.__dataframe__() assert dfX.num_columns() == 3 assert dfX.num_rows() == 3 assert dfX.num_chunks() == 1 assert list(dfX.column_names()) == ["x", "y", "z"] assert list(dfX.select_columns((0, 2)).column_names()) == list( dfX.select_columns_by_name(("x", "z")).column_names() )

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test_spec_conformance.py

pandas/pandas/tests/interchange/test_spec_conformance.py

import ctypes import math import pytest import pandas

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test_categorical

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df_from_dict

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def test_categorical(df_from_dict): df = df_from_dict( { "weekday": [ "Mon", "Tue", "Mon", "Wed", "Mon", "Thu", "Fri", "Sat", "Sun", ] }, is_categorical=True, ) colX = df.__dataframe__().get_column_by_name("weekday") categorical = colX.describe_categorical assert isinstance(categorical["is_ordered"], bool) assert isinstance(categorical["is_dictionary"], bool)

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test_spec_conformance.py

pandas/pandas/tests/interchange/test_spec_conformance.py

import ctypes import math import pytest import pandas

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test_noncategorical

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df_from_dict

null

def test_noncategorical(df_from_dict): df = df_from_dict({"a": [1, 2, 3]}) dfX = df.__dataframe__() colX = dfX.get_column_by_name("a") with pytest.raises(TypeError, match=".*categorical.*"): colX.describe_categorical

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test_spec_conformance.py

pandas/pandas/tests/interchange/test_spec_conformance.py

import ctypes import math import pytest import pandas

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test_na_float

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df_from_dict

null

def test_na_float(df_from_dict): df = df_from_dict({"a": [1.0, math.nan, 2.0]}) dfX = df.__dataframe__() colX = dfX.get_column_by_name("a") assert colX.null_count == 1 assert isinstance(colX.null_count, int)

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test_spec_conformance.py

pandas/pandas/tests/interchange/test_spec_conformance.py

import ctypes import math import pytest import pandas

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errno_from_exception

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e

null

e,e,None

def errno_from_exception(e): """Provides the errno from an Exception object. There are cases that the errno attribute was not set so we pull the errno out of the args but if someone instantiates an Exception without any args you will get a tuple error. So this function abstracts all that behavior to give you a safe way to get the errno. """ if hasattr(e, "errno"): return e.errno elif e.args: return e.args[0] else: return None

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util.py

catboost/contrib/python/pyzmq/py2/zmq/eventloop/minitornado/util.py

from __future__ import absolute_import, division, print_function, with_statement import sys

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timedelta_to_seconds

global

null

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td

null

unknown

def timedelta_to_seconds(td): """Equivalent to td.total_seconds() (introduced in python 2.7).""" return ( td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6 ) / float(10**6)

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util.py

catboost/contrib/python/pyzmq/py2/zmq/eventloop/minitornado/util.py

from __future__ import absolute_import, division, print_function, with_statement import sys

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reset

DatasetPunctuationErrorRate

null

true

self

Class for computation the total puncutation-related absolute amounts of operations and their rates in pairs of reference and hypothesis strins: - Absolute amounts of correct predictions, deletions, insertions and substitutions for each given punctuation mark - Rates of correct predictions, deletions, insertions and substitutions for each given punctuation mark - Total rates of correct predictions, deletions, insertions and substiturions in pairs of reference and hypothesis strings - Punctuation Error Rate Args to init: references (list[str]) - list of references hypotheses (list[str]) - list of hypotheses punctuation_marks (list[str]) - list of punctuation marks for computing metrics punctuation_mask (str, by default "[PUNCT]") - mask token that will be applied to given punctuation marks while edit distance calculation How to use: 1. Create object of DatasetPunctuationErrorRate class. Example: references = ["Hi, dear! Nice to see you. What's"] hypotheses = ["Hi dear! Nice to see you! What's?"] punctuation_marks = [".", ",", "!", "?"] dper_obj = DatasetPunctuationErrorRate(references, hypotheses, punctuation_marks) 2. To compute punctuation metrics, call the class method "compute()". Example: dper_obj.compute() Result: The following atributes of class object will be updated with calculated metrics values. The values are available with calling the atributes: dper_obj.operation_rates - dict, rates of correctness and errors for each punctuation mark from `preset dper_obj.punctuation_marks` list. dper_obj.substitution_rates - dict, substitution rates between puncutation marks from `preset dper_obj.punctuation_marks` list. dper_obj.correct_rate - float, total rate of correctness between provided pairs of references and hypotheses. dper_obj.deletions_rate - float, total rate of deletions between provided pairs of references and hypotheses. dper_obj.insertions_rate - float, total rate of insertions between provided pairs of references and hypotheses. dper_obj.substitutions_rate - float, total rate of substitutions between provided pairs of references and hypotheses. dper_obj.punct_er - float, total Punctuation Error Rate between provided pairs of references and hypotheses.

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null

def reset(self): self.operation_amounts = [] self.substitution_amounts = [] self.rates = [] self.operation_rates = None self.substitution_rates = None self.correct_rate = None self.deletions_rate = None self.insertions_rate = None self.substitutions_rate = None self.punct_er = None

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punct_er.py

NeMo/nemo/collections/common/metrics/punct_er.py

import re from collections import namedtuple from tqdm import tqdm from nemo.utils import logging

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probs

null

jax

def bernoulli( probs: Union[float, JaxArray], *, logits: Optional[Union[float, JaxArray]] = None, shape: Optional[Union[ivy.NativeArray, Sequence[int]]] = None, device: Optional[jaxlib.xla_extension.Device] = None, dtype: Optional[jnp.dtype] = None, seed: Optional[int] = None, out: Optional[JaxArray] = None, ) -> JaxArray: if seed: rng_input = jax.random.PRNGKey(seed) else: RNG_, rng_input = jax.random.split(_getRNG()) _setRNG(RNG_) if logits is not None: probs = jax.nn.softmax(logits, axis=-1) if hasattr(probs, "shape") and not _check_shapes_broadcastable( shape, probs.shape ): shape = probs.shape return jax.random.bernoulli(rng_input, probs, shape=shape)

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random.py

ivy/ivy/functional/backends/jax/experimental/random.py

from typing import Optional, Union, Sequence import jax.numpy import jax import jaxlib.xla_extension import ivy from ivy.functional.backends.jax import JaxArray from ivy.functional.backends.jax.random import RNG, _setRNG, _getRNG from ivy.functional.ivy.random import _check_bounds_and_get_shape, _check_shapes_broadcastable from ivy.func_wrapper import with_unsupported_dtypes from ..None import backend_version

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input_combo

global

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request

null

parameters

def input_combo(request): """ Simply return the current combination of params as a dictionary for use in tests or other parameterized fixtures. """ parameters = dict( zip( ("graph_file", "core_number", "degree_type"), request.param, ) ) return parameters

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test_k_core_mg.py

cugraph/python/cugraph/cugraph/tests/core/test_k_core_mg.py

import gc import pytest import dask_cudf import cugraph import cugraph.dask from cugraph.testing import utils from cudf.testing.testing import assert_frame_equal from cugraph.structure.symmetrize import symmetrize_df from pylibcugraph.testing import gen_fixture_params_product

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Use image node_id 2 for calling a global function with example usage: input_combo(request) and returns: parameters

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node_id 2

686,799

print

DatasetPunctuationErrorRate

null

true

self

Class for computation the total puncutation-related absolute amounts of operations and their rates in pairs of reference and hypothesis strins: - Absolute amounts of correct predictions, deletions, insertions and substitutions for each given punctuation mark - Rates of correct predictions, deletions, insertions and substitutions for each given punctuation mark - Total rates of correct predictions, deletions, insertions and substiturions in pairs of reference and hypothesis strings - Punctuation Error Rate Args to init: references (list[str]) - list of references hypotheses (list[str]) - list of hypotheses punctuation_marks (list[str]) - list of punctuation marks for computing metrics punctuation_mask (str, by default "[PUNCT]") - mask token that will be applied to given punctuation marks while edit distance calculation How to use: 1. Create object of DatasetPunctuationErrorRate class. Example: references = ["Hi, dear! Nice to see you. What's"] hypotheses = ["Hi dear! Nice to see you! What's?"] punctuation_marks = [".", ",", "!", "?"] dper_obj = DatasetPunctuationErrorRate(references, hypotheses, punctuation_marks) 2. To compute punctuation metrics, call the class method "compute()". Example: dper_obj.compute() Result: The following atributes of class object will be updated with calculated metrics values. The values are available with calling the atributes: dper_obj.operation_rates - dict, rates of correctness and errors for each punctuation mark from `preset dper_obj.punctuation_marks` list. dper_obj.substitution_rates - dict, substitution rates between puncutation marks from `preset dper_obj.punctuation_marks` list. dper_obj.correct_rate - float, total rate of correctness between provided pairs of references and hypotheses. dper_obj.deletions_rate - float, total rate of deletions between provided pairs of references and hypotheses. dper_obj.insertions_rate - float, total rate of insertions between provided pairs of references and hypotheses. dper_obj.substitutions_rate - float, total rate of substitutions between provided pairs of references and hypotheses. dper_obj.punct_er - float, total Punctuation Error Rate between provided pairs of references and hypotheses.

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null

def print(self): logging.info( f"Dataset PER " + str(round(100 * self.punct_er, 2)) + "%" ) if HAVE_TABLUATE_AND_PANDAS: rates_by_pm_df = pd.DataFrame(self.operation_rates) * 100 substitution_rates_by_pm_df = ( pd.DataFrame(self.substitution_rates) * 100 ) logging.info( "Rates of punctuation correctness and errors (%):\n" + tabulate( rates_by_pm_df, headers="keys", tablefmt="psql" ) ) logging.info( "Substitution rates between punctuation marks (%):\n" + tabulate( substitution_rates_by_pm_df, headers="keys", tablefmt="psql", ) ) else: logging.warning( "Some of the modules (pandas or tabulate) can't be imported" ) logging.info( f"Rates of punctuation correctness and errors (in range [0, 1]):\n{self.operation_rates}\n" ) logging.info( f"Substitution rates between punctuation marks (in range [0, 1]):\n{self.substitution_rates}\n" )

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punct_er.py

NeMo/nemo/collections/common/metrics/punct_er.py

import re from collections import namedtuple from tqdm import tqdm from nemo.utils import logging

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Use image node_id 4 for calling the DatasetPunctuationErrorRate obj's underlying member method code with example usage: obj.print() without return types

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sample_batch

EpisodeReplayBuffer

object

true

self,batch_size

null

s_batch, a_batch, r_batch, t_batch, obs_batch, available_actions_batch, filled_batch

def sample_batch(self, batch_size): batch = [] if self.count < batch_size: batch = random.sample(self.buffer, self.count) else: batch = random.sample(self.buffer, batch_size) ( s_batch, a_batch, r_batch, t_batch, obs_batch, available_actions_batch, filled_batch, ) = ([], [], [], [], [], [], []) for episode in batch: ( s, a, r, t, obs, available_actions, filled, ) = episode.get_data() s_batch.append(s) a_batch.append(a) r_batch.append(r) t_batch.append(t) obs_batch.append(obs) available_actions_batch.append(available_actions) filled_batch.append(filled) filled_batch = np.array(filled_batch) r_batch = np.array(r_batch) t_batch = np.array(t_batch) a_batch = np.array(a_batch) obs_batch = np.array(obs_batch) available_actions_batch = np.array(available_actions_batch) return ( s_batch, a_batch, r_batch, t_batch, obs_batch, available_actions_batch, filled_batch, )

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67

94

null

replay_buffer.py

PARL/benchmark/torch/qmix/replay_buffer.py

from collections import deque import numpy import random

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Use image node_id 4 for calling the EpisodeReplayBuffer obj's underlying member method code with example usage: obj.sample_batch(batch_size) and returns: s_batch, a_batch, r_batch, t_batch, obs_batch, available_actions_batch, filled_batch

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count

EpisodeReplayBuffer

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self

null

len

def count(self): return len(self.buffer)

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replay_buffer.py

PARL/benchmark/torch/qmix/replay_buffer.py

from collections import deque import numpy import random

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Use image node_id 3 for calling the EpisodeReplayBuffer obj's underlying member method code with example usage: obj.count() and returns: len

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_format_logs

NotificationCallback

Callback

true

logs

Send a notification to a channel at the beginning/ending of the training/testing and at a constant frequency (`alert_frequency`) during the training. Args: notificator (~poutyne.Notificator): The notification channel to send the message. The expected interface need to implement a `send_notification` method to send the message. You can see the `notif <https://notificationdoc.ca/index.html>`_ package which implements some Notificator respecting the interface. alert_frequency (int): The frequency (in epoch), during training, to send an update. By default, 1. experiment_name (Union[str, None]): The name of the experiment to add to the message. By default, None. Example: .. code-block:: python from notif.notificator import SlackNotificator from poutyne.framework.callbacks.notification import NotificationCallback webhook_url = "a_link" slack_notif = SlackNotificator(webhook_url=webhook_url) notif_callback = NotificationCallback(notificator=slack_notif) model = Model(...) model.fit_generator(..., callbacks=[notif_callback])

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null

str

def _format_logs(logs: Dict) -> str: return " ".join( [f"{key}: {value}\n" for key, value in logs.items()] )

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null

notification.py

poutyne/poutyne/framework/callbacks/notification.py

from abc import ABC, abstractmethod from typing import Dict, Union from poutyne.framework.callbacks.callbacks import Callback

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1,602,783

test_knn_retrieval_non_verbose

global

null

false

null

def test_knn_retrieval_non_verbose(): annoy_index_filepath = "tests/data/.test-annoy-index.index" expected_neighbour_list = np.load("tests/data/test_knn_k3.npy") iris = datasets.load_iris() X = iris.data k = 3 search_k = -1 index = AnnoyKnnMatrix.load( annoy_index_filepath, X.shape, k=k, search_k=search_k, verbose=0, ) neighbour_list = extract_knn(index) assert np.all(expected_neighbour_list == neighbour_list)

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82

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null

test_knn.py

ivis/tests/data/test_knn.py

import tempfile import os import pytest from annoy import AnnoyIndex from scipy.sparse import csr_matrix from sklearn import datasets import numpy from ivis.data.neighbour_retrieval import AnnoyKnnMatrix from ivis.data.neighbour_retrieval.knn import build_annoy_index, extract_knn

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test_knn_matrix_construction_params

global

null

false

annoy_index_file

null

def test_knn_matrix_construction_params(annoy_index_file): # Test too large k raises exception with pytest.raises(Exception): AnnoyKnnMatrix.build( np.zeros(shape=(4, 4)), annoy_index_file, k=4 ) with pytest.raises(Exception): AnnoyKnnMatrix.load(annoy_index_file, (4, 4), k=4) index = AnnoyKnnMatrix.build( np.zeros(shape=(4, 4)), annoy_index_file, k=2 ) loaded_index = AnnoyKnnMatrix.load(annoy_index_file, (4, 4), k=2) for original_row, loaded_row in zip(index, loaded_index): assert original_row == loaded_row

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69

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null

test_knn.py

ivis/tests/data/test_knn.py

import tempfile import os import pytest from annoy import AnnoyIndex from scipy.sparse import csr_matrix from sklearn import datasets import numpy from ivis.data.neighbour_retrieval import AnnoyKnnMatrix from ivis.data.neighbour_retrieval.knn import build_annoy_index, extract_knn

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test_knn_retrieval

global

null

false

null

def test_knn_retrieval(): annoy_index_filepath = "tests/data/.test-annoy-index.index" expected_neighbour_list = np.load("tests/data/test_knn_k3.npy") iris = datasets.load_iris() X = iris.data k = 3 search_k = -1 index = AnnoyKnnMatrix.load( annoy_index_filepath, X.shape, k=k, search_k=search_k ) neighbour_list = extract_knn(index) assert np.all(expected_neighbour_list == neighbour_list)

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test_knn.py

ivis/tests/data/test_knn.py

import tempfile import os import pytest from annoy import AnnoyIndex from scipy.sparse import csr_matrix from sklearn import datasets import numpy from ivis.data.neighbour_retrieval import AnnoyKnnMatrix from ivis.data.neighbour_retrieval.knn import build_annoy_index, extract_knn

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add

EpisodeReplayBuffer

object

true

self,episode_experience

null

def add(self, episode_experience): self.buffer.append(episode_experience)

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null

replay_buffer.py

PARL/benchmark/torch/qmix/replay_buffer.py

from collections import deque import numpy import random

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Use image node_id 2 for calling the EpisodeReplayBuffer obj's underlying member method code with example usage: obj.add(episode_experience) without return types

160

node_id 2

154,121

test_dense_annoy_index

global

null

false

annoy_index_file

null

def test_dense_annoy_index(annoy_index_file): data = np.random.choice([0, 1], size=(10, 5)) index = build_annoy_index(data, annoy_index_file) assert os.path.exists(annoy_index_file) loaded_index = AnnoyIndex(5, metric="angular") loaded_index.load(annoy_index_file) assert index.f == loaded_index.f == 5 assert index.get_n_items() == loaded_index.get_n_items() == 10 assert index.get_nns_by_item( 0, 5 ) == loaded_index.get_nns_by_item(0, 5) index.unload() loaded_index.unload()

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37

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test_knn.py

ivis/tests/data/test_knn.py

import tempfile import os import pytest from annoy import AnnoyIndex from scipy.sparse import csr_matrix from sklearn import datasets import numpy from ivis.data.neighbour_retrieval import AnnoyKnnMatrix from ivis.data.neighbour_retrieval.knn import build_annoy_index, extract_knn

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1,192,741

test_build_sparse_annoy_index

global

null

false

annoy_index_file

null

def test_build_sparse_annoy_index(annoy_index_file): data = np.random.choice([0, 1], size=(10, 5)) sparse_data = csr_matrix(data) index = build_annoy_index(sparse_data, annoy_index_file) assert os.path.exists(annoy_index_file) loaded_index = AnnoyIndex(5, metric="angular") loaded_index.load(annoy_index_file) assert index.f == loaded_index.f == 5 assert index.get_n_items() == loaded_index.get_n_items() == 10 assert index.get_nns_by_item( 0, 5 ) == loaded_index.get_nns_by_item(0, 5) index.unload() loaded_index.unload()

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19

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test_knn.py

ivis/tests/data/test_knn.py

import tempfile import os import pytest from annoy import AnnoyIndex from scipy.sparse import csr_matrix from sklearn import datasets import numpy from ivis.data.neighbour_retrieval import AnnoyKnnMatrix from ivis.data.neighbour_retrieval.knn import build_annoy_index, extract_knn

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node_id 2

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train

DenseRetrievalDataSource

DataSource

true

self

Data source for DPR (https://github.com/facebookresearch/DPR). Expects multiline json for lazy loading and improved memory usage. The original DPR files can be converted to multiline json using `jq -c .[]`

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null

self

def train(self): return self.process_file(self.train_filename, is_train=True)

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62

63

null

dense_retrieval.py

pytext/pytext/data/sources/dense_retrieval.py

import json import random from typing import List, Optional from pytext.data.sources.data_source import DataSource, generator_property from pytext.utils.file_io import PathManager

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1

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Use image node_id 3 for calling the DenseRetrievalDataSource obj's underlying member method code with example usage: obj.train() and returns: self

146

node_id 3

1,680,324

__init__

EpisodeReplayBuffer

object

true

self,max_buffer_size

null

EpisodeReplayBuffer

def __init__(self, max_buffer_size): self.max_buffer_size = max_buffer_size self.buffer = deque(maxlen=max_buffer_size)

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56

58

null

replay_buffer.py

PARL/benchmark/torch/qmix/replay_buffer.py

from collections import deque import numpy import random

15

2

3

0

2

4

1

Use image node_id 1 to create a new EpisodeReplayBuffer object from inherited base classes: object with example: obj = EpisodeReplayBuffer(max_buffer_size)

155

node_id 1

154,120

get_data

EpisodeExperience

object

true

self

null

np, np, np, np, np, np, np

def get_data(self): assert self.count == self.max_len return ( np.array(self.episode_state), np.array(self.episode_actions), np.array(self.episode_reward), np.array(self.episode_terminated), np.array(self.episode_obs), np.array(self.episode_available_actions), np.array(self.episode_filled), )

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47

52

null

replay_buffer.py

PARL/benchmark/torch/qmix/replay_buffer.py

from collections import deque import numpy import random

15

2

3

0

2

4

1

Use image node_id 4 for calling the EpisodeExperience obj's underlying member method code with example usage: obj.get_data() and returns: np, np, np, np, np, np, np

170

node_id 4

154,119

add

EpisodeExperience

object

true

self,state,actions,reward,terminated,obs,available_actions,filled

null

def add( self, state, actions, reward, terminated, obs, available_actions, filled, ): assert self.count < self.max_len self.episode_state.append(state) self.episode_actions.append(actions) self.episode_reward.append(reward) self.episode_terminated.append(terminated) self.episode_obs.append(obs) self.episode_available_actions.append(available_actions) self.episode_filled.append(filled)

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36

45

null

replay_buffer.py

PARL/benchmark/torch/qmix/replay_buffer.py

from collections import deque import numpy import random

15

2

3

0

2

4

1

Use image node_id 3 for calling the EpisodeExperience obj's underlying member method code with example usage: obj.add(state, actions, reward, terminated, obs, available_actions, filled) without return types

206

node_id 3

154,118

count

EpisodeExperience

object

true

self

null

len

def count(self): return len(self.episode_state)

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33

34

null

replay_buffer.py

PARL/benchmark/torch/qmix/replay_buffer.py

from collections import deque import numpy import random

15

2

3

0

2

4

1

Use image node_id 2 for calling the EpisodeExperience obj's underlying member method code with example usage: obj.count() and returns: len

138

node_id 2

154,117

annoy_index_file

global

null

false

null

def annoy_index_file(): with tempfile.TemporaryDirectory() as f: yield os.path.join(f, "annoy.index")

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16

null

test_knn.py

ivis/tests/data/test_knn.py

import tempfile import os import pytest from annoy import AnnoyIndex from scipy.sparse import csr_matrix from sklearn import datasets import numpy from ivis.data.neighbour_retrieval import AnnoyKnnMatrix from ivis.data.neighbour_retrieval.knn import build_annoy_index, extract_knn

15

null

9

6

null

Use image node_id 1 for calling a global function with example usage: annoy_index_file() without return types

109

node_id 1

1,192,739

__init__

EpisodeExperience

object

true

self,episode_len

null

EpisodeExperience

def __init__(self, episode_len): self.max_len = episode_len self.episode_state = [] self.episode_actions = [] self.episode_reward = [] self.episode_terminated = [] self.episode_obs = [] self.episode_available_actions = [] self.episode_filled = []

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null

replay_buffer.py

PARL/benchmark/torch/qmix/replay_buffer.py

from collections import deque import numpy import random

15

2

3

0

2

4

1

Use image node_id 1 to create a new EpisodeExperience object from inherited base classes: object with example: obj = EpisodeExperience(episode_len)

147

node_id 1

154,116

convert_rttm_text

global

null

false

path,wavscp_path,sampling_rate,output_path

null

def convert_rttm_text( path: Union[Path, str], wavscp_path: Union[Path, str], sampling_rate: int, output_path: Union[Path, str], ) -> None: """Convert a RTTM file Note: only support speaker information now """ output_handler = Path( os.path.join(output_path, "espnet_rttm") ).open("w", encoding="utf-8") assert check_argument_types() utt_ids = set() with Path(path).open("r", encoding="utf-8") as f: for linenum, line in enumerate(f, 1): sps = re.split(" +", line.rstrip()) # RTTM format must have exactly 9 fields assert ( len(sps) == 9 ), "{} does not have exactly 9 fields".format(path) ( label_type, utt_id, channel, start, duration, _, _, spk_id, _, ) = sps # Only support speaker label now assert label_type == "SPEAKER" utt_ids.add(utt_id) start = int(np.rint(float(start) * sampling_rate)) end = start + int( np.rint(float(duration) * sampling_rate) ) output_handler.write( "{} {} {} {} {} <NA> <NA> {} <NA>\n".format( label_type, utt_id, channel, start, end, spk_id ) ) with Path(wavscp_path).open("r", encoding="utf-8") as f: for linenum, line in enumerate(f, 1): sps = re.split("[ \t]+", line.rstrip()) utt_id, wav_path = sps assert ( utt_id in utt_ids ), "{} is not in corresponding rttm {}".foramt( utt_id, path ) sf = soundfile.SoundFile(wav_path) assert sf.samplerate == sampling_rate output_handler.write( ( "{} {} <NA> <NA> {} <NA> <NA> <NA> <NA>\n".format( "END", utt_id, sf.frames ) ) ) output_handler.close()

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19

75

null

convert_rttm.py

espnet/egs2/thchs30/asr1/pyscripts/utils/convert_rttm.py

import argparse import collections.abc import logging import os import re from pathlib import Path from typing import Union import humanfriendly import numpy import soundfile from typeguard import check_argument_types from espnet2.utils.types import str_or_int

15

null

12

3

null

Use image node_id 1 for calling a global function with example usage: convert_rttm_text(path, wavscp_path, sampling_rate, output_path) without return types

155

node_id 1

981,192

__init__

DenseRetrievalDataSource

DataSource

true

self,schema,train_filename,test_filename,eval_filename,num_negative_ctxs,use_title,use_cache

Data source for DPR (https://github.com/facebookresearch/DPR). Expects multiline json for lazy loading and improved memory usage. The original DPR files can be converted to multiline json using `jq -c .[]`

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null

DenseRetrievalDataSource

def __init__( self, schema, train_filename=None, test_filename=None, eval_filename=None, num_negative_ctxs=1, use_title=True, use_cache=False, ): super().__init__(schema) self.train_filename = train_filename self.test_filename = test_filename self.eval_filename = eval_filename self.num_negative_ctxs = num_negative_ctxs self.use_title = use_title self.use_cache = use_cache self.cache = {}

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42

59

null

dense_retrieval.py

pytext/pytext/data/sources/dense_retrieval.py

import json import random from typing import List, Optional from pytext.data.sources.data_source import DataSource, generator_property from pytext.utils.file_io import PathManager

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1

5

1

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Use image node_id 2 to create a new DenseRetrievalDataSource object from inherited base classes: DataSource with example: obj = DenseRetrievalDataSource(schema, train_filename, test_filename, eval_filename, num_negative_ctxs, use_title, use_cache)

247

node_id 2

1,680,323