Package: metta-data
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CHICAGO HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.Description: # metta-dataTrain Matrix and Test Matrix Storage[![Build Status](https://travis-ci.org/dssg/metta-data.svg?branch=master)](https://travis-ci.org/dssg/metta-data)[![codecov](https://codecov.io/gh/dssg/metta-data/branch/master/graph/badge.svg)](https://codecov.io/gh/dssg/metta-data)##  DescriptionPython library for storing and recalling meta data, and DataFrames of training andtesting sets.## InstallationTo get the latest stable version:```pip install metta-data```To get the current master branch:```pip install git+git://github.com/dssg/metta-data.git```## How-to`metta` expects you to hand it a dictionary for each dataframe with the following keys:- `feature_start_time` (date.datetime): The earliest time that enters your covariate calculations.- `end_time` (date.dateime): The last time that enters your covariate calculations.- `label_timespan` (str): The length of the labeling window you are using in this matrix eg: '1y', '6m'- `label_name` (str): The outcome variable's column name. This column must be in the last position in your dataframe.- `matrix_id` (str): Human readable id for the dataset### Storing a train and test pair```import mettatrain_config = {'feature_start_time': datetime.date(2012, 12, 20),'end_time': datetime.date(2016, 12, 20),'label_timespan': '3m','label_name': 'inspection_1yr','label_type': 'binary','matrix_id': 'CDPH_2012','feature_names': ['break_last_3yr', 'soil', 'pressure_zone'],'indices': ['entity_id', 'as_of_date'] }test_config = {'feature_start_time': datetime.date(2015, 12, 20),'end_time': datetime.date(2016, 12, 21),'label_timespan': '3m','label_name': 'inspection_1yr','label_type': 'binary''matrix_id': 'CDPH_2015','feature_names': ['break_last_3yr', 'soil', 'pressure_zone'],'inidces': ['entity_id', 'as_of_date'] }metta.archive_train_test(train_config,X_train,test_config,X_test,directory='./old_matrices',format='hd5',overwrite=False)```### Storing a train and multiple test sets```import mettatrain_config = {'feature_start_time': datetime.date(2012, 12, 20),'end_time': datetime.date(2016, 12, 20),'label_timespan': '3m','label_name': 'inspection_1yr','label_type': 'binary','matrix_id': 'CDPH_2012','feature_names': ['break_last_3yr', 'soil', 'pressure_zone'],'indices': ['entity_id', 'as_of_date'] }base_test_config = {'feature_start_time': datetime.date(2015, 12, 20),'end_time': datetime.date(2016, 12, 21),'label_timespan': '3m','label_name': 'inspection_1yr','label_type': 'binary','matrix_id': 'CDPH_2015','feature_names': ['break_last_3yr', 'soil', 'pressure_zone'],'indices': ['entity_id', 'as_of_date']}train_uuid = metta.archive_matrix(train_config, X_train, directory='./matrices')test_uuids = []for years in range(1, 5):test_config = base_test_config.copy()test_config['feature_start_time'] += relativedelta(years=years)test_config['end_time'] += relativedelta(years=years)test_config['matrix_id'] = 'CDPH_{}'.format(test_config['end_time'].year)test_uuids.append(metta.archive_matrix(test_config,df_data,directory='./matrices',overwrite=False,format='csv',train_uuid=train_uuid))```### Uploading to S3```dict_config = yaml.load(open('aws_keys.yaml'))metta.upload_to_s3(access_key_id=dict_config['AWSAccessKey'],secret_access_key=dict_config['AWSSecretKey'],bucket=dict_config['Bucket'],folder=dict_config['Folder'],directory='./old_matrices')```Keywords: mettaPlatform: UNKNOWNClassifier: Development Status :: 2 - Pre-AlphaClassifier: Intended Audience :: DevelopersClassifier: Natural Language :: EnglishClassifier: Programming Language :: Python :: 2Classifier: Programming Language :: Python :: 2.6Classifier: Programming Language :: Python :: 2.7Classifier: Programming Language :: Python :: 3Classifier: Programming Language :: Python :: 3.3Classifier: Programming Language :: Python :: 3.4Classifier: Programming Language :: Python :: 3.5