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| author | CoprDistGit <infra@openeuler.org> | 2023-04-11 00:00:56 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-04-11 00:00:56 +0000 |
| commit | 2ac2bd59b226c9e55c5e0fa54758d957b2610b35 (patch) | |
| tree | 9056e7703dfa5f5068f63c2bbf8fc45eccc56409 | |
| parent | 0563fd799271a6a23508b3dd19fc0de3f19f963f (diff) | |
automatic import of python-feature-engine
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-feature-engine.spec | 803 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 805 insertions, 0 deletions
@@ -0,0 +1 @@ +/feature_engine-1.6.0.tar.gz diff --git a/python-feature-engine.spec b/python-feature-engine.spec new file mode 100644 index 0000000..c83fc42 --- /dev/null +++ b/python-feature-engine.spec @@ -0,0 +1,803 @@ +%global _empty_manifest_terminate_build 0 +Name: python-feature-engine +Version: 1.6.0 +Release: 1 +Summary: Feature engineering package with Scikit-learn's fit transform functionality +License: BSD 3 clause +URL: http://github.com/feature-engine/feature_engine +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5e/71/60f381ce9e68004e96cb325b8c040800b35f3854a95a197412260edee124/feature_engine-1.6.0.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-scikit-learn +Requires: python3-scipy +Requires: python3-statsmodels + +%description +# Feature Engine + +[](https://pypi.org/project/feature-engine/) +[](https://github.com/feature-engine/feature_engine/blob/master/LICENSE.md) +[](https://pypi.org/project/feature-engine) +[](https://anaconda.org/conda-forge/feature_engine) +[](https://app.circleci.com/pipelines/github/feature-engine/feature_engine) +[](https://codecov.io/github/feature-engine/feature_engine) +[](https://feature-engine.readthedocs.io/en/latest/index.html) +[](https://github.com/psf/black) +[](https://github.com/feature-engine/feature_engine/graphs/contributors) +[](https://gitter.im/feature_engine/community) +[](https://pepy.tech/project/feature-engine) +[](https://pepy.tech/project/feature-engine) +[](https://zenodo.org/badge/latestdoi/163630824) +[](https://doi.org/10.21105/joss.03642) +[](https://www.firsttimersonly.com/) +[](https://www.trainindata.com/) + +<div align="center"> + +[](http://feature-engine.readthedocs.io) + +</div> + +Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. +Feature-engine's transformers follow Scikit-learn's functionality with fit() and transform() methods to learn the +transforming parameters from the data and then transform it. + + +## Feature-engine features in the following resources + +* [Feature Engineering for Machine Learning, Online Course](https://www.trainindata.com/p/feature-engineering-for-machine-learning) + +* [Feature Selection for Machine Learning, Online Course](https://www.trainindata.com/p/feature-selection-for-machine-learning) + +* [Feature Engineering for Time Series Forecasting, Online Course](https://www.trainindata.com/p/feature-engineering-for-forecasting) + +* [Python Feature Engineering Cookbook](https://packt.link/0ewSo) + +* [Feature Selection in Machine Learning with Python Book](https://leanpub.com/feature-selection-in-machine-learning) + + +## Blogs about Feature-engine + +* [Feature-engine: A new open-source Python package for feature engineering](https://trainindata.medium.com/feature-engine-a-new-open-source-python-package-for-feature-engineering-29a0ab88ea7c) + +* [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd) + + +## Documentation + +* [Documentation](https://feature-engine.trainindata.com) + + +## Current Feature-engine's transformers include functionality for: + +* Missing Data Imputation +* Categorical Encoding +* Discretisation +* Outlier Capping or Removal +* Variable Transformation +* Variable Creation +* Variable Selection +* Datetime Features +* Time Series +* Preprocessing +* Scikit-learn Wrappers + +### Imputation Methods +* MeanMedianImputer +* RandomSampleImputer +* EndTailImputer +* AddMissingIndicator +* CategoricalImputer +* ArbitraryNumberImputer +* DropMissingData + +### Encoding Methods +* OneHotEncoder +* OrdinalEncoder +* CountFrequencyEncoder +* MeanEncoder +* WoEEncoder +* RareLabelEncoder +* DecisionTreeEncoder +* StringSimilarityEncoder + +### Discretisation methods +* EqualFrequencyDiscretiser +* EqualWidthDiscretiser +* GeometricWidthDiscretiser +* DecisionTreeDiscretiser +* ArbitraryDiscreriser + +### Outlier Handling methods +* Winsorizer +* ArbitraryOutlierCapper +* OutlierTrimmer + +### Variable Transformation methods +* LogTransformer +* LogCpTransformer +* ReciprocalTransformer +* ArcsinTransformer +* PowerTransformer +* BoxCoxTransformer +* YeoJohnsonTransformer + +### Variable Creation: + * MathFeatures + * RelativeFeatures + * CyclicalFeatures + +### Feature Selection: + * DropFeatures + * DropConstantFeatures + * DropDuplicateFeatures + * DropCorrelatedFeatures + * SmartCorrelationSelection + * ShuffleFeaturesSelector + * SelectBySingleFeaturePerformance + * SelectByTargetMeanPerformance + * RecursiveFeatureElimination + * RecursiveFeatureAddition + * DropHighPSIFeatures + * SelectByInformationValue + * ProbeFeatureSelection + +### Datetime + * DatetimeFeatures + * DatetimeSubtraction + +### Time Series + * LagFeatures + * WindowFeatures + * ExpandingWindowFeatures + +### Preprocessing + * MatchCategories + * MatchVariables + +### Wrappers: + * SklearnTransformerWrapper + +## Installation + +From PyPI using pip: + +``` +pip install feature_engine +``` + +From Anaconda: + +``` +conda install -c conda-forge feature_engine +``` + +Or simply clone it: + +``` +git clone https://github.com/feature-engine/feature_engine.git +``` + +## Example Usage + +```python +>>> import pandas as pd +>>> from feature_engine.encoding import RareLabelEncoder + +>>> data = {'var_A': ['A'] * 10 + ['B'] * 10 + ['C'] * 2 + ['D'] * 1} +>>> data = pd.DataFrame(data) +>>> data['var_A'].value_counts() +``` + +``` +Out[1]: +A 10 +B 10 +C 2 +D 1 +Name: var_A, dtype: int64 +``` + +```python +>>> rare_encoder = RareLabelEncoder(tol=0.10, n_categories=3) +>>> data_encoded = rare_encoder.fit_transform(data) +>>> data_encoded['var_A'].value_counts() +``` + +``` +Out[2]: +A 10 +B 10 +Rare 3 +Name: var_A, dtype: int64 +``` + +Find more examples in our [Jupyter Notebook Gallery](https://nbviewer.org/github/feature-engine/feature-engine-examples/tree/main/) +or in the [documentation](https://feature-engine.trainindata.com). + +## Contribute + +Details about how to contribute can be found in the [Contribute Page](https://feature-engine.trainindata.com/en/latest/contribute/index.html) + +Briefly: + +- Fork the repo +- Clone your fork into your local computer: ``git clone https://github.com/<YOURUSERNAME>/feature_engine.git`` +- navigate into the repo folder ``cd feature_engine`` +- Install Feature-engine as a developer: ``pip install -e .`` +- Optional: Create and activate a virtual environment with any tool of choice +- Install Feature-engine dependencies: ``pip install -r requirements.txt`` and ``pip install -r test_requirements.txt`` +- Create a feature branch with a meaningful name for your feature: ``git checkout -b myfeaturebranch`` +- Develop your feature, tests and documentation +- Make sure the tests pass +- Make a PR + +Thank you!! + + +### Documentation + +Feature-engine documentation is built using [Sphinx](https://www.sphinx-doc.org) and is hosted on [Read the Docs](https://readthedocs.org/). + +To build the documentation make sure you have the dependencies installed: from the root directory: ``pip install -r docs/requirements.txt``. + +Now you can build the docs using: ``sphinx-build -b html docs build`` + + +## License + +BSD 3-Clause + +## Sponsor us + +[Sponsor us](https://github.com/sponsors/feature-engine) and support further our +mission to democratize machine learning and programming tools through open-source +software. + + + + +%package -n python3-feature-engine +Summary: Feature engineering package with Scikit-learn's fit transform functionality +Provides: python-feature-engine +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-feature-engine +# Feature Engine + +[](https://pypi.org/project/feature-engine/) +[](https://github.com/feature-engine/feature_engine/blob/master/LICENSE.md) +[](https://pypi.org/project/feature-engine) +[](https://anaconda.org/conda-forge/feature_engine) +[](https://app.circleci.com/pipelines/github/feature-engine/feature_engine) +[](https://codecov.io/github/feature-engine/feature_engine) +[](https://feature-engine.readthedocs.io/en/latest/index.html) +[](https://github.com/psf/black) +[](https://github.com/feature-engine/feature_engine/graphs/contributors) +[](https://gitter.im/feature_engine/community) +[](https://pepy.tech/project/feature-engine) +[](https://pepy.tech/project/feature-engine) +[](https://zenodo.org/badge/latestdoi/163630824) +[](https://doi.org/10.21105/joss.03642) +[](https://www.firsttimersonly.com/) +[](https://www.trainindata.com/) + +<div align="center"> + +[](http://feature-engine.readthedocs.io) + +</div> + +Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. +Feature-engine's transformers follow Scikit-learn's functionality with fit() and transform() methods to learn the +transforming parameters from the data and then transform it. + + +## Feature-engine features in the following resources + +* [Feature Engineering for Machine Learning, Online Course](https://www.trainindata.com/p/feature-engineering-for-machine-learning) + +* [Feature Selection for Machine Learning, Online Course](https://www.trainindata.com/p/feature-selection-for-machine-learning) + +* [Feature Engineering for Time Series Forecasting, Online Course](https://www.trainindata.com/p/feature-engineering-for-forecasting) + +* [Python Feature Engineering Cookbook](https://packt.link/0ewSo) + +* [Feature Selection in Machine Learning with Python Book](https://leanpub.com/feature-selection-in-machine-learning) + + +## Blogs about Feature-engine + +* [Feature-engine: A new open-source Python package for feature engineering](https://trainindata.medium.com/feature-engine-a-new-open-source-python-package-for-feature-engineering-29a0ab88ea7c) + +* [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd) + + +## Documentation + +* [Documentation](https://feature-engine.trainindata.com) + + +## Current Feature-engine's transformers include functionality for: + +* Missing Data Imputation +* Categorical Encoding +* Discretisation +* Outlier Capping or Removal +* Variable Transformation +* Variable Creation +* Variable Selection +* Datetime Features +* Time Series +* Preprocessing +* Scikit-learn Wrappers + +### Imputation Methods +* MeanMedianImputer +* RandomSampleImputer +* EndTailImputer +* AddMissingIndicator +* CategoricalImputer +* ArbitraryNumberImputer +* DropMissingData + +### Encoding Methods +* OneHotEncoder +* OrdinalEncoder +* CountFrequencyEncoder +* MeanEncoder +* WoEEncoder +* RareLabelEncoder +* DecisionTreeEncoder +* StringSimilarityEncoder + +### Discretisation methods +* EqualFrequencyDiscretiser +* EqualWidthDiscretiser +* GeometricWidthDiscretiser +* DecisionTreeDiscretiser +* ArbitraryDiscreriser + +### Outlier Handling methods +* Winsorizer +* ArbitraryOutlierCapper +* OutlierTrimmer + +### Variable Transformation methods +* LogTransformer +* LogCpTransformer +* ReciprocalTransformer +* ArcsinTransformer +* PowerTransformer +* BoxCoxTransformer +* YeoJohnsonTransformer + +### Variable Creation: + * MathFeatures + * RelativeFeatures + * CyclicalFeatures + +### Feature Selection: + * DropFeatures + * DropConstantFeatures + * DropDuplicateFeatures + * DropCorrelatedFeatures + * SmartCorrelationSelection + * ShuffleFeaturesSelector + * SelectBySingleFeaturePerformance + * SelectByTargetMeanPerformance + * RecursiveFeatureElimination + * RecursiveFeatureAddition + * DropHighPSIFeatures + * SelectByInformationValue + * ProbeFeatureSelection + +### Datetime + * DatetimeFeatures + * DatetimeSubtraction + +### Time Series + * LagFeatures + * WindowFeatures + * ExpandingWindowFeatures + +### Preprocessing + * MatchCategories + * MatchVariables + +### Wrappers: + * SklearnTransformerWrapper + +## Installation + +From PyPI using pip: + +``` +pip install feature_engine +``` + +From Anaconda: + +``` +conda install -c conda-forge feature_engine +``` + +Or simply clone it: + +``` +git clone https://github.com/feature-engine/feature_engine.git +``` + +## Example Usage + +```python +>>> import pandas as pd +>>> from feature_engine.encoding import RareLabelEncoder + +>>> data = {'var_A': ['A'] * 10 + ['B'] * 10 + ['C'] * 2 + ['D'] * 1} +>>> data = pd.DataFrame(data) +>>> data['var_A'].value_counts() +``` + +``` +Out[1]: +A 10 +B 10 +C 2 +D 1 +Name: var_A, dtype: int64 +``` + +```python +>>> rare_encoder = RareLabelEncoder(tol=0.10, n_categories=3) +>>> data_encoded = rare_encoder.fit_transform(data) +>>> data_encoded['var_A'].value_counts() +``` + +``` +Out[2]: +A 10 +B 10 +Rare 3 +Name: var_A, dtype: int64 +``` + +Find more examples in our [Jupyter Notebook Gallery](https://nbviewer.org/github/feature-engine/feature-engine-examples/tree/main/) +or in the [documentation](https://feature-engine.trainindata.com). + +## Contribute + +Details about how to contribute can be found in the [Contribute Page](https://feature-engine.trainindata.com/en/latest/contribute/index.html) + +Briefly: + +- Fork the repo +- Clone your fork into your local computer: ``git clone https://github.com/<YOURUSERNAME>/feature_engine.git`` +- navigate into the repo folder ``cd feature_engine`` +- Install Feature-engine as a developer: ``pip install -e .`` +- Optional: Create and activate a virtual environment with any tool of choice +- Install Feature-engine dependencies: ``pip install -r requirements.txt`` and ``pip install -r test_requirements.txt`` +- Create a feature branch with a meaningful name for your feature: ``git checkout -b myfeaturebranch`` +- Develop your feature, tests and documentation +- Make sure the tests pass +- Make a PR + +Thank you!! + + +### Documentation + +Feature-engine documentation is built using [Sphinx](https://www.sphinx-doc.org) and is hosted on [Read the Docs](https://readthedocs.org/). + +To build the documentation make sure you have the dependencies installed: from the root directory: ``pip install -r docs/requirements.txt``. + +Now you can build the docs using: ``sphinx-build -b html docs build`` + + +## License + +BSD 3-Clause + +## Sponsor us + +[Sponsor us](https://github.com/sponsors/feature-engine) and support further our +mission to democratize machine learning and programming tools through open-source +software. + + + + +%package help +Summary: Development documents and examples for feature-engine +Provides: python3-feature-engine-doc +%description help +# Feature Engine + +[](https://pypi.org/project/feature-engine/) +[](https://github.com/feature-engine/feature_engine/blob/master/LICENSE.md) +[](https://pypi.org/project/feature-engine) +[](https://anaconda.org/conda-forge/feature_engine) +[](https://app.circleci.com/pipelines/github/feature-engine/feature_engine) +[](https://codecov.io/github/feature-engine/feature_engine) +[](https://feature-engine.readthedocs.io/en/latest/index.html) +[](https://github.com/psf/black) +[](https://github.com/feature-engine/feature_engine/graphs/contributors) +[](https://gitter.im/feature_engine/community) +[](https://pepy.tech/project/feature-engine) +[](https://pepy.tech/project/feature-engine) +[](https://zenodo.org/badge/latestdoi/163630824) +[](https://doi.org/10.21105/joss.03642) +[](https://www.firsttimersonly.com/) +[](https://www.trainindata.com/) + +<div align="center"> + +[](http://feature-engine.readthedocs.io) + +</div> + +Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. +Feature-engine's transformers follow Scikit-learn's functionality with fit() and transform() methods to learn the +transforming parameters from the data and then transform it. + + +## Feature-engine features in the following resources + +* [Feature Engineering for Machine Learning, Online Course](https://www.trainindata.com/p/feature-engineering-for-machine-learning) + +* [Feature Selection for Machine Learning, Online Course](https://www.trainindata.com/p/feature-selection-for-machine-learning) + +* [Feature Engineering for Time Series Forecasting, Online Course](https://www.trainindata.com/p/feature-engineering-for-forecasting) + +* [Python Feature Engineering Cookbook](https://packt.link/0ewSo) + +* [Feature Selection in Machine Learning with Python Book](https://leanpub.com/feature-selection-in-machine-learning) + + +## Blogs about Feature-engine + +* [Feature-engine: A new open-source Python package for feature engineering](https://trainindata.medium.com/feature-engine-a-new-open-source-python-package-for-feature-engineering-29a0ab88ea7c) + +* [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd) + + +## Documentation + +* [Documentation](https://feature-engine.trainindata.com) + + +## Current Feature-engine's transformers include functionality for: + +* Missing Data Imputation +* Categorical Encoding +* Discretisation +* Outlier Capping or Removal +* Variable Transformation +* Variable Creation +* Variable Selection +* Datetime Features +* Time Series +* Preprocessing +* Scikit-learn Wrappers + +### Imputation Methods +* MeanMedianImputer +* RandomSampleImputer +* EndTailImputer +* AddMissingIndicator +* CategoricalImputer +* ArbitraryNumberImputer +* DropMissingData + +### Encoding Methods +* OneHotEncoder +* OrdinalEncoder +* CountFrequencyEncoder +* MeanEncoder +* WoEEncoder +* RareLabelEncoder +* DecisionTreeEncoder +* StringSimilarityEncoder + +### Discretisation methods +* EqualFrequencyDiscretiser +* EqualWidthDiscretiser +* GeometricWidthDiscretiser +* DecisionTreeDiscretiser +* ArbitraryDiscreriser + +### Outlier Handling methods +* Winsorizer +* ArbitraryOutlierCapper +* OutlierTrimmer + +### Variable Transformation methods +* LogTransformer +* LogCpTransformer +* ReciprocalTransformer +* ArcsinTransformer +* PowerTransformer +* BoxCoxTransformer +* YeoJohnsonTransformer + +### Variable Creation: + * MathFeatures + * RelativeFeatures + * CyclicalFeatures + +### Feature Selection: + * DropFeatures + * DropConstantFeatures + * DropDuplicateFeatures + * DropCorrelatedFeatures + * SmartCorrelationSelection + * ShuffleFeaturesSelector + * SelectBySingleFeaturePerformance + * SelectByTargetMeanPerformance + * RecursiveFeatureElimination + * RecursiveFeatureAddition + * DropHighPSIFeatures + * SelectByInformationValue + * ProbeFeatureSelection + +### Datetime + * DatetimeFeatures + * DatetimeSubtraction + +### Time Series + * LagFeatures + * WindowFeatures + * ExpandingWindowFeatures + +### Preprocessing + * MatchCategories + * MatchVariables + +### Wrappers: + * SklearnTransformerWrapper + +## Installation + +From PyPI using pip: + +``` +pip install feature_engine +``` + +From Anaconda: + +``` +conda install -c conda-forge feature_engine +``` + +Or simply clone it: + +``` +git clone https://github.com/feature-engine/feature_engine.git +``` + +## Example Usage + +```python +>>> import pandas as pd +>>> from feature_engine.encoding import RareLabelEncoder + +>>> data = {'var_A': ['A'] * 10 + ['B'] * 10 + ['C'] * 2 + ['D'] * 1} +>>> data = pd.DataFrame(data) +>>> data['var_A'].value_counts() +``` + +``` +Out[1]: +A 10 +B 10 +C 2 +D 1 +Name: var_A, dtype: int64 +``` + +```python +>>> rare_encoder = RareLabelEncoder(tol=0.10, n_categories=3) +>>> data_encoded = rare_encoder.fit_transform(data) +>>> data_encoded['var_A'].value_counts() +``` + +``` +Out[2]: +A 10 +B 10 +Rare 3 +Name: var_A, dtype: int64 +``` + +Find more examples in our [Jupyter Notebook Gallery](https://nbviewer.org/github/feature-engine/feature-engine-examples/tree/main/) +or in the [documentation](https://feature-engine.trainindata.com). + +## Contribute + +Details about how to contribute can be found in the [Contribute Page](https://feature-engine.trainindata.com/en/latest/contribute/index.html) + +Briefly: + +- Fork the repo +- Clone your fork into your local computer: ``git clone https://github.com/<YOURUSERNAME>/feature_engine.git`` +- navigate into the repo folder ``cd feature_engine`` +- Install Feature-engine as a developer: ``pip install -e .`` +- Optional: Create and activate a virtual environment with any tool of choice +- Install Feature-engine dependencies: ``pip install -r requirements.txt`` and ``pip install -r test_requirements.txt`` +- Create a feature branch with a meaningful name for your feature: ``git checkout -b myfeaturebranch`` +- Develop your feature, tests and documentation +- Make sure the tests pass +- Make a PR + +Thank you!! + + +### Documentation + +Feature-engine documentation is built using [Sphinx](https://www.sphinx-doc.org) and is hosted on [Read the Docs](https://readthedocs.org/). + +To build the documentation make sure you have the dependencies installed: from the root directory: ``pip install -r docs/requirements.txt``. + +Now you can build the docs using: ``sphinx-build -b html docs build`` + + +## License + +BSD 3-Clause + +## Sponsor us + +[Sponsor us](https://github.com/sponsors/feature-engine) and support further our +mission to democratize machine learning and programming tools through open-source +software. + + + + +%prep +%autosetup -n feature-engine-1.6.0 + +%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-feature-engine -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.6.0-1 +- Package Spec generated @@ -0,0 +1 @@ +ae4122fd1983d050af3b60563422858c feature_engine-1.6.0.tar.gz |