{ "info": { "author": "Feature Labs, Inc.", "author_email": "support@featurelabs.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9" ], "description": "# TSFresh Primitives\n\n

\n \n \"Tests\"\n \n \n \n \n \n \"PyPI\n \n \n \"PyPI\n \n

\n
\n\n### Installation\n\nInstall with pip:\n\n```python\npython -m pip install \"featuretools[tsfresh]\"\n```\n\n## Calculating Features\n\nIn `tsfresh`, this is how you can calculate a feature.\n\n```python\nfrom tsfresh.feature_extraction.feature_calculators import agg_autocorrelation\n\ndata = list(range(10))\nparam = [{'f_agg': 'mean', 'maxlag': 5}]\nagg_autocorrelation(data, param=param)\n```\n\n```bash\n[('f_agg_\"mean\"__maxlag_5', 0.1717171717171717)]\n```\n\nWith tsfresh primtives in `featuretools`, this is how you can calculate the same feature.\n```python\nfrom featuretools.tsfresh import AggAutocorrelation\n\ndata = list(range(10))\nAggAutocorrelation(f_agg='mean', maxlag=5)(data)\n```\n\n```bash\n0.1717171717171717\n```\n\n## Combining Primitives\n\nIn `featuretools`, this is how to combine tsfresh primitives with built-in or other installed primitives.\n```python\nimport featuretools as ft\nfrom featuretools.tsfresh import AggAutocorrelation, Mean\n\nentityset = ft.demo.load_mock_customer(return_entityset=True)\nagg_primitives = [Mean, AggAutocorrelation(f_agg='mean', maxlag=5)]\nfeature_matrix, features = ft.dfs(entityset=entityset, target_dataframe_name='sessions', agg_primitives=agg_primitives)\n\nfeature_matrix[[\n 'MEAN(transactions.amount)',\n 'AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)',\n]].head()\n```\n```\n MEAN(transactions.amount) AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)\nsession_id\n1 76.813125 0.044268\n2 74.696000 -0.053110\n3 88.600000 0.007520\n4 64.557200 -0.034542\n5 70.638182 -0.100571\n```\nNotice that tsfresh primtives are applied across relationships in an entityset generating many features that are otherwise not possible.\n\n```python\nfeature_matrix[['customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)']].head()\n```\n```\n customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)\nsession_id\n1 0.011102\n2 -0.001686\n3 -0.010679\n4 0.011204\n5 -0.010679\n```\n\n## Built at Alteryx Innovation Labs\n\n\n \"Alteryx\n\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/alteryx/featuretools-tsfresh-primitives/", "keywords": "", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "featuretools-tsfresh-primitives", "package_url": "https://pypi.org/project/featuretools-tsfresh-primitives/", "platform": "", "project_url": "https://pypi.org/project/featuretools-tsfresh-primitives/", "project_urls": { "Homepage": "https://github.com/alteryx/featuretools-tsfresh-primitives/" }, "release_url": "https://pypi.org/project/featuretools-tsfresh-primitives/1.0.2/", "requires_dist": [ "tsfresh (>=0.19.0)", "pandas (<2.0.0,>=1.3.0)", "featuretools (>=1.0.0)" ], "requires_python": ">=3.7, <4", "summary": "TSFresh primitives for featuretools", "version": "1.0.2", "yanked": false, 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