%global _empty_manifest_terminate_build 0 Name: python-featuretools Version: 1.24.0 Release: 1 Summary: a framework for automated feature engineering License: BSD 3-clause URL: https://pypi.org/project/featuretools/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/01/c3/a1b500df4cbf1f071458b4d30c962c90a8e1641ede796987d9a5169bfd68/featuretools-1.24.0.tar.gz BuildArch: noarch Requires: python3-cloudpickle Requires: python3-dask[dataframe] Requires: python3-distributed Requires: python3-holidays Requires: python3-numpy Requires: python3-packaging Requires: python3-pandas Requires: python3-psutil Requires: python3-scipy Requires: python3-tqdm Requires: python3-woodwork[dask] Requires: python3-autonormalize Requires: python3-featuretools[autonormalize,nlp,sklearn,spark,sql,tsfresh,updater] Requires: python3-ruff Requires: python3-black[jupyter] Requires: python3-pre-commit Requires: python3-featuretools[docs,spark,test] Requires: python3-ipython Requires: python3-jupyter Requires: python3-jupyter-client Requires: python3-matplotlib Requires: python3-Sphinx Requires: python3-nbsphinx Requires: python3-nbconvert Requires: python3-pydata-sphinx-theme Requires: python3-sphinx-inline-tabs Requires: python3-sphinx-copybutton Requires: python3-myst-parser Requires: python3-nlp-primitives Requires: python3-autonormalize Requires: python3-click Requires: python3-featuretools[sklearn,spark,test] Requires: python3-nlp-primitives[complete] Requires: python3-featuretools-sklearn-transformer Requires: python3-woodwork[spark] Requires: python3-pyspark Requires: python3-numpy Requires: python3-featuretools-sql Requires: python3-boto3 Requires: python3-composeml Requires: python3-graphviz Requires: python3-moto[all] Requires: python3-pip Requires: python3-pyarrow Requires: python3-pympler Requires: python3-pytest Requires: python3-pytest-cov Requires: python3-pytest-xdist Requires: python3-smart-open Requires: python3-urllib3 Requires: python3-pytest-timeout Requires: python3-featuretools-tsfresh-primitives Requires: python3-alteryx-open-src-update-checker %description

Featuretools

"One of the holy grails of machine learning is to automate more and more of the feature engineering process." ― Pedro Domingos, A Few Useful Things to Know about Machine Learning

Tests Documentation Status PyPI Version Anaconda Version StackOverflow PyPI Downloads

[Featuretools](https://www.featuretools.com) is a python library for automated feature engineering. See the [documentation](https://docs.featuretools.com) for more information. ## Installation Install with pip ``` python -m pip install featuretools ``` or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/featuretools): ``` conda install -c conda-forge featuretools ``` ### Add-ons You can install add-ons individually or all at once by running ``` python -m pip install "featuretools[complete]" ``` **Update checker** - Receive automatic notifications of new Featuretools releases ``` python -m pip install "featuretools[updater]" ``` **NLP Primitives** - Use Natural Language Processing Primitives: ``` python -m pip install "featuretools[nlp]" ``` **TSFresh Primitives** - Use 60+ primitives from [tsfresh](https://tsfresh.readthedocs.io/en/latest/) within Featuretools ``` python -m pip install "featuretools[tsfresh]" ``` **SQL** - Automatic EntitySet generation from relational data stored in a SQL database: ``` python -m pip install "featuretools[sql]" ``` ## Example Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions. ```python >> import featuretools as ft >> es = ft.demo.load_mock_customer(return_entityset=True) >> es.plot() ``` Featuretools can automatically create a single table of features for any "target dataframe" ```python >> feature_matrix, features_defs = ft.dfs(entityset=es, target_dataframe_name="customers") >> feature_matrix.head(5) ``` ``` zip_code COUNT(transactions) COUNT(sessions) SUM(transactions.amount) MODE(sessions.device) MIN(transactions.amount) MAX(transactions.amount) YEAR(join_date) SKEW(transactions.amount) DAY(join_date) ... SUM(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MEAN(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) STD(sessions.MAX(transactions.amount)) NUM_UNIQUE(sessions.DAY(session_start)) MIN(sessions.SKEW(transactions.amount)) customer_id ... 1 60091 131 10 10236.77 desktop 5.60 149.95 2008 0.070041 1 ... 169.77 0.610052 41.95 791.976505 175.939423 9.299023 -0.377150 5.857976 1 -0.395358 2 02139 122 8 9118.81 mobile 5.81 149.15 2008 0.028647 20 ... 114.85 0.492531 42.96 596.243506 230.333502 10.925037 0.962350 7.420480 1 -0.470007 3 02139 78 5 5758.24 desktop 6.78 147.73 2008 0.070814 10 ... 64.98 0.645728 21.77 369.770121 471.048551 9.819148 -0.244976 12.537259 1 -0.630425 4 60091 111 8 8205.28 desktop 5.73 149.56 2008 0.087986 30 ... 83.53 0.516262 17.27 584.673126 322.883448 13.065436 -0.548969 12.738488 1 -0.497169 5 02139 58 4 4571.37 tablet 5.91 148.17 2008 0.085883 19 ... 73.09 0.830112 27.46 313.448942 198.522508 8.950528 0.098885 5.599228 1 -0.396571 [5 rows x 69 columns] ``` We now have a feature vector for each customer that can be used for machine learning. See the [documentation on Deep Feature Synthesis](https://featuretools.alteryx.com/en/stable/getting_started/afe.html) for more examples. Featuretools contains many different types of built-in primitives for creating features. If the primitive you need is not included, Featuretools also allows you to [define your own custom primitives](https://featuretools.alteryx.com/en/stable/getting_started/primitives.html#defining-custom-primitives). ## Demos **Predict Next Purchase** [Repository](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/) | [Notebook](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/Tutorial.ipynb) In this demonstration, we use a multi-table dataset of 3 million online grocery orders from Instacart to predict what a customer will buy next. We show how to generate features with automated feature engineering and build an accurate machine learning pipeline using Featuretools, which can be reused for multiple prediction problems. For more advanced users, we show how to scale that pipeline to a large dataset using Dask. For more examples of how to use Featuretools, check out our [demos](https://www.featuretools.com/demos) page. ## Testing & Development The Featuretools community welcomes pull requests. Instructions for testing and development are available [here.](https://featuretools.alteryx.com/en/stable/install.html#development) ## Support The Featuretools community is happy to provide support to users of Featuretools. Project support can be found in four places depending on the type of question: 1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/featuretools) with the `featuretools` tag. 2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/featuretools/issues). 3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA). 4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com ## Citing Featuretools If you use Featuretools, please consider citing the following paper: James Max Kanter, Kalyan Veeramachaneni. [Deep feature synthesis: Towards automating data science endeavors.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/DSAA_DSM_2015.pdf) *IEEE DSAA 2015*. BibTeX entry: ```bibtex @inproceedings{kanter2015deep, author = {James Max Kanter and Kalyan Veeramachaneni}, title = {Deep feature synthesis: Towards automating data science endeavors}, booktitle = {2015 {IEEE} International Conference on Data Science and Advanced Analytics, DSAA 2015, Paris, France, October 19-21, 2015}, pages = {1--10}, year = {2015}, organization={IEEE} } ``` ## Built at Alteryx **Featuretools** is an open source project maintained by [Alteryx](https://www.alteryx.com). To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch.

Alteryx Open Source

%package -n python3-featuretools Summary: a framework for automated feature engineering Provides: python-featuretools BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-featuretools

Featuretools

"One of the holy grails of machine learning is to automate more and more of the feature engineering process." ― Pedro Domingos, A Few Useful Things to Know about Machine Learning

Tests Documentation Status PyPI Version Anaconda Version StackOverflow PyPI Downloads

[Featuretools](https://www.featuretools.com) is a python library for automated feature engineering. See the [documentation](https://docs.featuretools.com) for more information. ## Installation Install with pip ``` python -m pip install featuretools ``` or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/featuretools): ``` conda install -c conda-forge featuretools ``` ### Add-ons You can install add-ons individually or all at once by running ``` python -m pip install "featuretools[complete]" ``` **Update checker** - Receive automatic notifications of new Featuretools releases ``` python -m pip install "featuretools[updater]" ``` **NLP Primitives** - Use Natural Language Processing Primitives: ``` python -m pip install "featuretools[nlp]" ``` **TSFresh Primitives** - Use 60+ primitives from [tsfresh](https://tsfresh.readthedocs.io/en/latest/) within Featuretools ``` python -m pip install "featuretools[tsfresh]" ``` **SQL** - Automatic EntitySet generation from relational data stored in a SQL database: ``` python -m pip install "featuretools[sql]" ``` ## Example Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions. ```python >> import featuretools as ft >> es = ft.demo.load_mock_customer(return_entityset=True) >> es.plot() ``` Featuretools can automatically create a single table of features for any "target dataframe" ```python >> feature_matrix, features_defs = ft.dfs(entityset=es, target_dataframe_name="customers") >> feature_matrix.head(5) ``` ``` zip_code COUNT(transactions) COUNT(sessions) SUM(transactions.amount) MODE(sessions.device) MIN(transactions.amount) MAX(transactions.amount) YEAR(join_date) SKEW(transactions.amount) DAY(join_date) ... SUM(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MEAN(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) STD(sessions.MAX(transactions.amount)) NUM_UNIQUE(sessions.DAY(session_start)) MIN(sessions.SKEW(transactions.amount)) customer_id ... 1 60091 131 10 10236.77 desktop 5.60 149.95 2008 0.070041 1 ... 169.77 0.610052 41.95 791.976505 175.939423 9.299023 -0.377150 5.857976 1 -0.395358 2 02139 122 8 9118.81 mobile 5.81 149.15 2008 0.028647 20 ... 114.85 0.492531 42.96 596.243506 230.333502 10.925037 0.962350 7.420480 1 -0.470007 3 02139 78 5 5758.24 desktop 6.78 147.73 2008 0.070814 10 ... 64.98 0.645728 21.77 369.770121 471.048551 9.819148 -0.244976 12.537259 1 -0.630425 4 60091 111 8 8205.28 desktop 5.73 149.56 2008 0.087986 30 ... 83.53 0.516262 17.27 584.673126 322.883448 13.065436 -0.548969 12.738488 1 -0.497169 5 02139 58 4 4571.37 tablet 5.91 148.17 2008 0.085883 19 ... 73.09 0.830112 27.46 313.448942 198.522508 8.950528 0.098885 5.599228 1 -0.396571 [5 rows x 69 columns] ``` We now have a feature vector for each customer that can be used for machine learning. See the [documentation on Deep Feature Synthesis](https://featuretools.alteryx.com/en/stable/getting_started/afe.html) for more examples. Featuretools contains many different types of built-in primitives for creating features. If the primitive you need is not included, Featuretools also allows you to [define your own custom primitives](https://featuretools.alteryx.com/en/stable/getting_started/primitives.html#defining-custom-primitives). ## Demos **Predict Next Purchase** [Repository](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/) | [Notebook](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/Tutorial.ipynb) In this demonstration, we use a multi-table dataset of 3 million online grocery orders from Instacart to predict what a customer will buy next. We show how to generate features with automated feature engineering and build an accurate machine learning pipeline using Featuretools, which can be reused for multiple prediction problems. For more advanced users, we show how to scale that pipeline to a large dataset using Dask. For more examples of how to use Featuretools, check out our [demos](https://www.featuretools.com/demos) page. ## Testing & Development The Featuretools community welcomes pull requests. Instructions for testing and development are available [here.](https://featuretools.alteryx.com/en/stable/install.html#development) ## Support The Featuretools community is happy to provide support to users of Featuretools. Project support can be found in four places depending on the type of question: 1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/featuretools) with the `featuretools` tag. 2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/featuretools/issues). 3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA). 4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com ## Citing Featuretools If you use Featuretools, please consider citing the following paper: James Max Kanter, Kalyan Veeramachaneni. [Deep feature synthesis: Towards automating data science endeavors.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/DSAA_DSM_2015.pdf) *IEEE DSAA 2015*. BibTeX entry: ```bibtex @inproceedings{kanter2015deep, author = {James Max Kanter and Kalyan Veeramachaneni}, title = {Deep feature synthesis: Towards automating data science endeavors}, booktitle = {2015 {IEEE} International Conference on Data Science and Advanced Analytics, DSAA 2015, Paris, France, October 19-21, 2015}, pages = {1--10}, year = {2015}, organization={IEEE} } ``` ## Built at Alteryx **Featuretools** is an open source project maintained by [Alteryx](https://www.alteryx.com). To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch.

Alteryx Open Source

%package help Summary: Development documents and examples for featuretools Provides: python3-featuretools-doc %description help

Featuretools

"One of the holy grails of machine learning is to automate more and more of the feature engineering process." ― Pedro Domingos, A Few Useful Things to Know about Machine Learning

Tests Documentation Status PyPI Version Anaconda Version StackOverflow PyPI Downloads

[Featuretools](https://www.featuretools.com) is a python library for automated feature engineering. See the [documentation](https://docs.featuretools.com) for more information. ## Installation Install with pip ``` python -m pip install featuretools ``` or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/featuretools): ``` conda install -c conda-forge featuretools ``` ### Add-ons You can install add-ons individually or all at once by running ``` python -m pip install "featuretools[complete]" ``` **Update checker** - Receive automatic notifications of new Featuretools releases ``` python -m pip install "featuretools[updater]" ``` **NLP Primitives** - Use Natural Language Processing Primitives: ``` python -m pip install "featuretools[nlp]" ``` **TSFresh Primitives** - Use 60+ primitives from [tsfresh](https://tsfresh.readthedocs.io/en/latest/) within Featuretools ``` python -m pip install "featuretools[tsfresh]" ``` **SQL** - Automatic EntitySet generation from relational data stored in a SQL database: ``` python -m pip install "featuretools[sql]" ``` ## Example Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions. ```python >> import featuretools as ft >> es = ft.demo.load_mock_customer(return_entityset=True) >> es.plot() ``` Featuretools can automatically create a single table of features for any "target dataframe" ```python >> feature_matrix, features_defs = ft.dfs(entityset=es, target_dataframe_name="customers") >> feature_matrix.head(5) ``` ``` zip_code COUNT(transactions) COUNT(sessions) SUM(transactions.amount) MODE(sessions.device) MIN(transactions.amount) MAX(transactions.amount) YEAR(join_date) SKEW(transactions.amount) DAY(join_date) ... SUM(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MEAN(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) STD(sessions.MAX(transactions.amount)) NUM_UNIQUE(sessions.DAY(session_start)) MIN(sessions.SKEW(transactions.amount)) customer_id ... 1 60091 131 10 10236.77 desktop 5.60 149.95 2008 0.070041 1 ... 169.77 0.610052 41.95 791.976505 175.939423 9.299023 -0.377150 5.857976 1 -0.395358 2 02139 122 8 9118.81 mobile 5.81 149.15 2008 0.028647 20 ... 114.85 0.492531 42.96 596.243506 230.333502 10.925037 0.962350 7.420480 1 -0.470007 3 02139 78 5 5758.24 desktop 6.78 147.73 2008 0.070814 10 ... 64.98 0.645728 21.77 369.770121 471.048551 9.819148 -0.244976 12.537259 1 -0.630425 4 60091 111 8 8205.28 desktop 5.73 149.56 2008 0.087986 30 ... 83.53 0.516262 17.27 584.673126 322.883448 13.065436 -0.548969 12.738488 1 -0.497169 5 02139 58 4 4571.37 tablet 5.91 148.17 2008 0.085883 19 ... 73.09 0.830112 27.46 313.448942 198.522508 8.950528 0.098885 5.599228 1 -0.396571 [5 rows x 69 columns] ``` We now have a feature vector for each customer that can be used for machine learning. See the [documentation on Deep Feature Synthesis](https://featuretools.alteryx.com/en/stable/getting_started/afe.html) for more examples. Featuretools contains many different types of built-in primitives for creating features. If the primitive you need is not included, Featuretools also allows you to [define your own custom primitives](https://featuretools.alteryx.com/en/stable/getting_started/primitives.html#defining-custom-primitives). ## Demos **Predict Next Purchase** [Repository](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/) | [Notebook](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/Tutorial.ipynb) In this demonstration, we use a multi-table dataset of 3 million online grocery orders from Instacart to predict what a customer will buy next. We show how to generate features with automated feature engineering and build an accurate machine learning pipeline using Featuretools, which can be reused for multiple prediction problems. For more advanced users, we show how to scale that pipeline to a large dataset using Dask. For more examples of how to use Featuretools, check out our [demos](https://www.featuretools.com/demos) page. ## Testing & Development The Featuretools community welcomes pull requests. Instructions for testing and development are available [here.](https://featuretools.alteryx.com/en/stable/install.html#development) ## Support The Featuretools community is happy to provide support to users of Featuretools. Project support can be found in four places depending on the type of question: 1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/featuretools) with the `featuretools` tag. 2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/featuretools/issues). 3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA). 4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com ## Citing Featuretools If you use Featuretools, please consider citing the following paper: James Max Kanter, Kalyan Veeramachaneni. [Deep feature synthesis: Towards automating data science endeavors.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/DSAA_DSM_2015.pdf) *IEEE DSAA 2015*. BibTeX entry: ```bibtex @inproceedings{kanter2015deep, author = {James Max Kanter and Kalyan Veeramachaneni}, title = {Deep feature synthesis: Towards automating data science endeavors}, booktitle = {2015 {IEEE} International Conference on Data Science and Advanced Analytics, DSAA 2015, Paris, France, October 19-21, 2015}, pages = {1--10}, year = {2015}, organization={IEEE} } ``` ## Built at Alteryx **Featuretools** is an open source project maintained by [Alteryx](https://www.alteryx.com). To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch.

Alteryx Open Source

%prep %autosetup -n featuretools-1.24.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-featuretools -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Apr 10 2023 Python_Bot - 1.24.0-1 - Package Spec generated