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@@ -0,0 +1 @@ +/scikit-lego-0.6.14.tar.gz diff --git a/python-scikit-lego.spec b/python-scikit-lego.spec new file mode 100644 index 0000000..1085487 --- /dev/null +++ b/python-scikit-lego.spec @@ -0,0 +1,568 @@ +%global _empty_manifest_terminate_build 0 +Name: python-scikit-lego +Version: 0.6.14 +Release: 1 +Summary: a collection of lego bricks for scikit-learn pipelines +License: MIT License +URL: https://scikit-lego.netlify.app/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/71/24/a01d392933898f660ac2f9d667131f5031083cbad06447d05f5cf61257de/scikit-lego-0.6.14.tar.gz +BuildArch: noarch + +Requires: python3-scikit-learn +Requires: python3-pandas +Requires: python3-patsy +Requires: python3-autograd +Requires: python3-Deprecated +Requires: python3-umap-learn +Requires: python3-cvxpy +Requires: python3-scikit-learn +Requires: python3-pandas +Requires: python3-patsy +Requires: python3-autograd +Requires: python3-Deprecated +Requires: python3-umap-learn +Requires: python3-cvxpy +Requires: python3-sphinx +Requires: python3-sphinx-rtd-theme +Requires: python3-nbsphinx +Requires: python3-recommonmark +Requires: python3-cvxpy +Requires: python3-flake8 +Requires: python3-nbval +Requires: python3-pytest +Requires: python3-pytest-xdist +Requires: python3-black +Requires: python3-pytest-cov +Requires: python3-pytest-mock +Requires: python3-pre-commit +Requires: python3-matplotlib +Requires: python3-jupyter +Requires: python3-jupyterlab +Requires: python3-sphinx +Requires: python3-sphinx-rtd-theme +Requires: python3-nbsphinx +Requires: python3-recommonmark +Requires: python3-cvxpy +Requires: python3-flake8 +Requires: python3-nbval +Requires: python3-pytest +Requires: python3-pytest-xdist +Requires: python3-black +Requires: python3-pytest-cov +Requires: python3-pytest-mock +Requires: python3-pre-commit + +%description +[](https://github.com/{github_id}/{repository}/workflows/{workflow_name}/badge.svg) +[](https://pepy.tech/project/scikit-lego) +[](https://pypi.org/project/scikit-lego/) +[](https://anaconda.org/conda-forge/scikit-lego) + + + +[](https://github.com/psf/black) +[](https://zenodo.org/badge/latestdoi/166836939) + +# scikit-lego + +<a href="https://scikit-lego.readthedocs.io/en/latest/"><img src="images/logo.png" width="35%" height="35%" align="right" /></a> + +We love scikit learn but very often we find ourselves writing +custom transformers, metrics and models. The goal of this project +is to attempt to consolidate these into a package that offers +code quality/testing. This project started as a collaboration between +multiple companies in the Netherlands but has since received contributions +from around the globe. It was initiated by [Matthijs Brouns](https://www.mbrouns.com/) +and [Vincent D. Warmerdam](https://koaning.io) as a tool to teach people how +to contribute to open source. + +Note that we're not formally affiliated with the scikit-learn project at all, +but we aim to strictly adhere to their standards. + +The same holds with lego. LEGO® is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this project. + +## Installation + +Install `scikit-lego` via pip with + +```bash +python -m pip install scikit-lego +``` + +Via [conda](https://conda.io/projects/conda/en/latest/) with + +```bash +conda install -c conda-forge scikit-lego +``` + +Alternatively, to edit and contribute you can fork/clone and run: + +```bash +python -m pip install -e ".[dev]" +python setup.py develop +``` + +## Documentation + +The documentation can be found [here](https://scikit-lego.netlify.app). + +## Usage + +We offer custom metrics, models and transformers. You can import them just like you would +in scikit-learn. + +```python +# the scikit learn stuff we love +from sklearn.preprocessing import StandardScaler +from sklearn.pipeline import Pipeline + +# from scikit lego stuff we add +from sklego.preprocessing import RandomAdder +from sklego.mixture import GMMClassifier + +... + +mod = Pipeline([ + ("scale", StandardScaler()), + ("random_noise", RandomAdder()), + ("model", GMMClassifier()) +]) + +... +``` + +## Features + +Here's a list of features that this library currently offers: + +- `sklego.datasets.load_abalone` loads in the abalone dataset +- `sklego.datasets.load_arrests` loads in a dataset with fairness concerns +- `sklego.datasets.load_chicken` loads in the joyful chickweight dataset +- `sklego.datasets.load_heroes` loads a heroes of the storm dataset +- `sklego.datasets.load_hearts` loads a dataset about hearts +- `sklego.datasets.load_penguins` loads a lovely dataset about penguins +- `sklego.datasets.fetch_creditcard` fetch a fraud dataset from openml +- `sklego.datasets.make_simpleseries` make a simulated timeseries +- `sklego.pandas_utils.add_lags` adds lag values in a pandas dataframe +- `sklego.pandas_utils.log_step` a useful decorator to log your pipeline steps +- `sklego.dummy.RandomRegressor` dummy benchmark that predicts random values +- `sklego.linear_model.DeadZoneRegressor` experimental feature that has a deadzone in the cost function +- `sklego.linear_model.DemographicParityClassifier` logistic classifier constrained on demographic parity +- `sklego.linear_model.EqualOpportunityClassifier` logistic classifier constrained on equal opportunity +- `sklego.linear_model.ProbWeightRegression` linear model that treats coefficients as probabilistic weights +- `sklego.linear_model.LowessRegression` locally weighted linear regression +- `sklego.linear_model.LADRegression` least absolute deviation regression +- `sklego.linear_model.QuantileRegression` linear quantile regression, generalizes LADRegression +- `sklego.linear_model.ImbalancedLinearRegression` punish over/under-estimation of a model directly +- `sklego.naive_bayes.GaussianMixtureNB` classifies by training a 1D GMM per column per class +- `sklego.naive_bayes.BayesianGaussianMixtureNB` classifies by training a bayesian 1D GMM per class +- `sklego.mixture.BayesianGMMClassifier` classifies by training a bayesian GMM per class +- `sklego.mixture.BayesianGMMOutlierDetector` detects outliers based on a trained bayesian GMM +- `sklego.mixture.GMMClassifier` classifies by training a GMM per class +- `sklego.mixture.GMMOutlierDetector` detects outliers based on a trained GMM +- `sklego.meta.ConfusionBalancer` experimental feature that allows you to balance the confusion matrix +- `sklego.meta.DecayEstimator` adds decay to the sample_weight that the model accepts +- `sklego.meta.EstimatorTransformer` adds a model output as a feature +- `sklego.meta.OutlierClassifier` turns outlier models into classifiers for gridsearch +- `sklego.meta.GroupedPredictor` can split the data into runs and run a model on each +- `sklego.meta.GroupedTransformer` can split the data into runs and run a transformer on each +- `sklego.meta.SubjectiveClassifier` experimental feature to add a prior to your classifier +- `sklego.meta.Thresholder` meta model that allows you to gridsearch over the threshold +- `sklego.meta.RegressionOutlierDetector` meta model that finds outliers by adding a threshold to regression +- `sklego.meta.ZeroInflatedRegressor` predicts zero or applies a regression based on a classifier +- `sklego.preprocessing.ColumnCapper` limits extreme values of the model features +- `sklego.preprocessing.ColumnDropper` drops a column from pandas +- `sklego.preprocessing.ColumnSelector` selects columns based on column name +- `sklego.preprocessing.InformationFilter` transformer that can de-correlate features +- `sklego.preprocessing.IdentityTransformer` returns the same data, allows for concatenating pipelines +- `sklego.preprocessing.OrthogonalTransformer` makes all features linearly independent +- `sklego.preprocessing.PandasTypeSelector` selects columns based on pandas type +- `sklego.preprocessing.PatsyTransformer` applies a [patsy](https://patsy.readthedocs.io/en/latest/formulas.html) formula +- `sklego.preprocessing.RandomAdder` adds randomness in training +- `sklego.preprocessing.RepeatingBasisFunction` repeating feature engineering, useful for timeseries +- `sklego.preprocessing.DictMapper` assign numeric values on categorical columns +- `sklego.preprocessing.OutlierRemover` experimental method to remove outliers during training +- `sklego.model_selection.GroupTimeSeriesSplit` timeseries Kfold for groups with different amount of observations per group +- `sklego.model_selection.KlusterFoldValidation` experimental feature that does K folds based on clustering +- `sklego.model_selection.TimeGapSplit` timeseries Kfold with a gap between train/test +- `sklego.pipeline.DebugPipeline` adds debug information to make debugging easier +- `sklego.pipeline.make_debug_pipeline` shorthand function to create a debugable pipeline +- `sklego.metrics.correlation_score` calculates correlation between model output and feature +- `sklego.metrics.equal_opportunity_score` calculates equal opportunity metric +- `sklego.metrics.p_percent_score` proxy for model fairness with regards to sensitive attribute +- `sklego.metrics.subset_score` calculate a score on a subset of your data (meant for fairness tracking) + +## New Features + +We want to be rather open here in what we accept but we do demand three +things before they become added to the project: + +1. any new feature contributes towards a demonstratable real-world usecase +2. any new feature passes standard unit tests (we use the ones from scikit-learn) +3. the feature has been discussed in the issue list beforehand + +We automate all of our testing and use pre-commit hooks to keep the code working. + + + + +%package -n python3-scikit-lego +Summary: a collection of lego bricks for scikit-learn pipelines +Provides: python-scikit-lego +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-scikit-lego +[](https://github.com/{github_id}/{repository}/workflows/{workflow_name}/badge.svg) +[](https://pepy.tech/project/scikit-lego) +[](https://pypi.org/project/scikit-lego/) +[](https://anaconda.org/conda-forge/scikit-lego) + + + +[](https://github.com/psf/black) +[](https://zenodo.org/badge/latestdoi/166836939) + +# scikit-lego + +<a href="https://scikit-lego.readthedocs.io/en/latest/"><img src="images/logo.png" width="35%" height="35%" align="right" /></a> + +We love scikit learn but very often we find ourselves writing +custom transformers, metrics and models. The goal of this project +is to attempt to consolidate these into a package that offers +code quality/testing. This project started as a collaboration between +multiple companies in the Netherlands but has since received contributions +from around the globe. It was initiated by [Matthijs Brouns](https://www.mbrouns.com/) +and [Vincent D. Warmerdam](https://koaning.io) as a tool to teach people how +to contribute to open source. + +Note that we're not formally affiliated with the scikit-learn project at all, +but we aim to strictly adhere to their standards. + +The same holds with lego. LEGO® is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this project. + +## Installation + +Install `scikit-lego` via pip with + +```bash +python -m pip install scikit-lego +``` + +Via [conda](https://conda.io/projects/conda/en/latest/) with + +```bash +conda install -c conda-forge scikit-lego +``` + +Alternatively, to edit and contribute you can fork/clone and run: + +```bash +python -m pip install -e ".[dev]" +python setup.py develop +``` + +## Documentation + +The documentation can be found [here](https://scikit-lego.netlify.app). + +## Usage + +We offer custom metrics, models and transformers. You can import them just like you would +in scikit-learn. + +```python +# the scikit learn stuff we love +from sklearn.preprocessing import StandardScaler +from sklearn.pipeline import Pipeline + +# from scikit lego stuff we add +from sklego.preprocessing import RandomAdder +from sklego.mixture import GMMClassifier + +... + +mod = Pipeline([ + ("scale", StandardScaler()), + ("random_noise", RandomAdder()), + ("model", GMMClassifier()) +]) + +... +``` + +## Features + +Here's a list of features that this library currently offers: + +- `sklego.datasets.load_abalone` loads in the abalone dataset +- `sklego.datasets.load_arrests` loads in a dataset with fairness concerns +- `sklego.datasets.load_chicken` loads in the joyful chickweight dataset +- `sklego.datasets.load_heroes` loads a heroes of the storm dataset +- `sklego.datasets.load_hearts` loads a dataset about hearts +- `sklego.datasets.load_penguins` loads a lovely dataset about penguins +- `sklego.datasets.fetch_creditcard` fetch a fraud dataset from openml +- `sklego.datasets.make_simpleseries` make a simulated timeseries +- `sklego.pandas_utils.add_lags` adds lag values in a pandas dataframe +- `sklego.pandas_utils.log_step` a useful decorator to log your pipeline steps +- `sklego.dummy.RandomRegressor` dummy benchmark that predicts random values +- `sklego.linear_model.DeadZoneRegressor` experimental feature that has a deadzone in the cost function +- `sklego.linear_model.DemographicParityClassifier` logistic classifier constrained on demographic parity +- `sklego.linear_model.EqualOpportunityClassifier` logistic classifier constrained on equal opportunity +- `sklego.linear_model.ProbWeightRegression` linear model that treats coefficients as probabilistic weights +- `sklego.linear_model.LowessRegression` locally weighted linear regression +- `sklego.linear_model.LADRegression` least absolute deviation regression +- `sklego.linear_model.QuantileRegression` linear quantile regression, generalizes LADRegression +- `sklego.linear_model.ImbalancedLinearRegression` punish over/under-estimation of a model directly +- `sklego.naive_bayes.GaussianMixtureNB` classifies by training a 1D GMM per column per class +- `sklego.naive_bayes.BayesianGaussianMixtureNB` classifies by training a bayesian 1D GMM per class +- `sklego.mixture.BayesianGMMClassifier` classifies by training a bayesian GMM per class +- `sklego.mixture.BayesianGMMOutlierDetector` detects outliers based on a trained bayesian GMM +- `sklego.mixture.GMMClassifier` classifies by training a GMM per class +- `sklego.mixture.GMMOutlierDetector` detects outliers based on a trained GMM +- `sklego.meta.ConfusionBalancer` experimental feature that allows you to balance the confusion matrix +- `sklego.meta.DecayEstimator` adds decay to the sample_weight that the model accepts +- `sklego.meta.EstimatorTransformer` adds a model output as a feature +- `sklego.meta.OutlierClassifier` turns outlier models into classifiers for gridsearch +- `sklego.meta.GroupedPredictor` can split the data into runs and run a model on each +- `sklego.meta.GroupedTransformer` can split the data into runs and run a transformer on each +- `sklego.meta.SubjectiveClassifier` experimental feature to add a prior to your classifier +- `sklego.meta.Thresholder` meta model that allows you to gridsearch over the threshold +- `sklego.meta.RegressionOutlierDetector` meta model that finds outliers by adding a threshold to regression +- `sklego.meta.ZeroInflatedRegressor` predicts zero or applies a regression based on a classifier +- `sklego.preprocessing.ColumnCapper` limits extreme values of the model features +- `sklego.preprocessing.ColumnDropper` drops a column from pandas +- `sklego.preprocessing.ColumnSelector` selects columns based on column name +- `sklego.preprocessing.InformationFilter` transformer that can de-correlate features +- `sklego.preprocessing.IdentityTransformer` returns the same data, allows for concatenating pipelines +- `sklego.preprocessing.OrthogonalTransformer` makes all features linearly independent +- `sklego.preprocessing.PandasTypeSelector` selects columns based on pandas type +- `sklego.preprocessing.PatsyTransformer` applies a [patsy](https://patsy.readthedocs.io/en/latest/formulas.html) formula +- `sklego.preprocessing.RandomAdder` adds randomness in training +- `sklego.preprocessing.RepeatingBasisFunction` repeating feature engineering, useful for timeseries +- `sklego.preprocessing.DictMapper` assign numeric values on categorical columns +- `sklego.preprocessing.OutlierRemover` experimental method to remove outliers during training +- `sklego.model_selection.GroupTimeSeriesSplit` timeseries Kfold for groups with different amount of observations per group +- `sklego.model_selection.KlusterFoldValidation` experimental feature that does K folds based on clustering +- `sklego.model_selection.TimeGapSplit` timeseries Kfold with a gap between train/test +- `sklego.pipeline.DebugPipeline` adds debug information to make debugging easier +- `sklego.pipeline.make_debug_pipeline` shorthand function to create a debugable pipeline +- `sklego.metrics.correlation_score` calculates correlation between model output and feature +- `sklego.metrics.equal_opportunity_score` calculates equal opportunity metric +- `sklego.metrics.p_percent_score` proxy for model fairness with regards to sensitive attribute +- `sklego.metrics.subset_score` calculate a score on a subset of your data (meant for fairness tracking) + +## New Features + +We want to be rather open here in what we accept but we do demand three +things before they become added to the project: + +1. any new feature contributes towards a demonstratable real-world usecase +2. any new feature passes standard unit tests (we use the ones from scikit-learn) +3. the feature has been discussed in the issue list beforehand + +We automate all of our testing and use pre-commit hooks to keep the code working. + + + + +%package help +Summary: Development documents and examples for scikit-lego +Provides: python3-scikit-lego-doc +%description help +[](https://github.com/{github_id}/{repository}/workflows/{workflow_name}/badge.svg) +[](https://pepy.tech/project/scikit-lego) +[](https://pypi.org/project/scikit-lego/) +[](https://anaconda.org/conda-forge/scikit-lego) + + + +[](https://github.com/psf/black) +[](https://zenodo.org/badge/latestdoi/166836939) + +# scikit-lego + +<a href="https://scikit-lego.readthedocs.io/en/latest/"><img src="images/logo.png" width="35%" height="35%" align="right" /></a> + +We love scikit learn but very often we find ourselves writing +custom transformers, metrics and models. The goal of this project +is to attempt to consolidate these into a package that offers +code quality/testing. This project started as a collaboration between +multiple companies in the Netherlands but has since received contributions +from around the globe. It was initiated by [Matthijs Brouns](https://www.mbrouns.com/) +and [Vincent D. Warmerdam](https://koaning.io) as a tool to teach people how +to contribute to open source. + +Note that we're not formally affiliated with the scikit-learn project at all, +but we aim to strictly adhere to their standards. + +The same holds with lego. LEGO® is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this project. + +## Installation + +Install `scikit-lego` via pip with + +```bash +python -m pip install scikit-lego +``` + +Via [conda](https://conda.io/projects/conda/en/latest/) with + +```bash +conda install -c conda-forge scikit-lego +``` + +Alternatively, to edit and contribute you can fork/clone and run: + +```bash +python -m pip install -e ".[dev]" +python setup.py develop +``` + +## Documentation + +The documentation can be found [here](https://scikit-lego.netlify.app). + +## Usage + +We offer custom metrics, models and transformers. You can import them just like you would +in scikit-learn. + +```python +# the scikit learn stuff we love +from sklearn.preprocessing import StandardScaler +from sklearn.pipeline import Pipeline + +# from scikit lego stuff we add +from sklego.preprocessing import RandomAdder +from sklego.mixture import GMMClassifier + +... + +mod = Pipeline([ + ("scale", StandardScaler()), + ("random_noise", RandomAdder()), + ("model", GMMClassifier()) +]) + +... +``` + +## Features + +Here's a list of features that this library currently offers: + +- `sklego.datasets.load_abalone` loads in the abalone dataset +- `sklego.datasets.load_arrests` loads in a dataset with fairness concerns +- `sklego.datasets.load_chicken` loads in the joyful chickweight dataset +- `sklego.datasets.load_heroes` loads a heroes of the storm dataset +- `sklego.datasets.load_hearts` loads a dataset about hearts +- `sklego.datasets.load_penguins` loads a lovely dataset about penguins +- `sklego.datasets.fetch_creditcard` fetch a fraud dataset from openml +- `sklego.datasets.make_simpleseries` make a simulated timeseries +- `sklego.pandas_utils.add_lags` adds lag values in a pandas dataframe +- `sklego.pandas_utils.log_step` a useful decorator to log your pipeline steps +- `sklego.dummy.RandomRegressor` dummy benchmark that predicts random values +- `sklego.linear_model.DeadZoneRegressor` experimental feature that has a deadzone in the cost function +- `sklego.linear_model.DemographicParityClassifier` logistic classifier constrained on demographic parity +- `sklego.linear_model.EqualOpportunityClassifier` logistic classifier constrained on equal opportunity +- `sklego.linear_model.ProbWeightRegression` linear model that treats coefficients as probabilistic weights +- `sklego.linear_model.LowessRegression` locally weighted linear regression +- `sklego.linear_model.LADRegression` least absolute deviation regression +- `sklego.linear_model.QuantileRegression` linear quantile regression, generalizes LADRegression +- `sklego.linear_model.ImbalancedLinearRegression` punish over/under-estimation of a model directly +- `sklego.naive_bayes.GaussianMixtureNB` classifies by training a 1D GMM per column per class +- `sklego.naive_bayes.BayesianGaussianMixtureNB` classifies by training a bayesian 1D GMM per class +- `sklego.mixture.BayesianGMMClassifier` classifies by training a bayesian GMM per class +- `sklego.mixture.BayesianGMMOutlierDetector` detects outliers based on a trained bayesian GMM +- `sklego.mixture.GMMClassifier` classifies by training a GMM per class +- `sklego.mixture.GMMOutlierDetector` detects outliers based on a trained GMM +- `sklego.meta.ConfusionBalancer` experimental feature that allows you to balance the confusion matrix +- `sklego.meta.DecayEstimator` adds decay to the sample_weight that the model accepts +- `sklego.meta.EstimatorTransformer` adds a model output as a feature +- `sklego.meta.OutlierClassifier` turns outlier models into classifiers for gridsearch +- `sklego.meta.GroupedPredictor` can split the data into runs and run a model on each +- `sklego.meta.GroupedTransformer` can split the data into runs and run a transformer on each +- `sklego.meta.SubjectiveClassifier` experimental feature to add a prior to your classifier +- `sklego.meta.Thresholder` meta model that allows you to gridsearch over the threshold +- `sklego.meta.RegressionOutlierDetector` meta model that finds outliers by adding a threshold to regression +- `sklego.meta.ZeroInflatedRegressor` predicts zero or applies a regression based on a classifier +- `sklego.preprocessing.ColumnCapper` limits extreme values of the model features +- `sklego.preprocessing.ColumnDropper` drops a column from pandas +- `sklego.preprocessing.ColumnSelector` selects columns based on column name +- `sklego.preprocessing.InformationFilter` transformer that can de-correlate features +- `sklego.preprocessing.IdentityTransformer` returns the same data, allows for concatenating pipelines +- `sklego.preprocessing.OrthogonalTransformer` makes all features linearly independent +- `sklego.preprocessing.PandasTypeSelector` selects columns based on pandas type +- `sklego.preprocessing.PatsyTransformer` applies a [patsy](https://patsy.readthedocs.io/en/latest/formulas.html) formula +- `sklego.preprocessing.RandomAdder` adds randomness in training +- `sklego.preprocessing.RepeatingBasisFunction` repeating feature engineering, useful for timeseries +- `sklego.preprocessing.DictMapper` assign numeric values on categorical columns +- `sklego.preprocessing.OutlierRemover` experimental method to remove outliers during training +- `sklego.model_selection.GroupTimeSeriesSplit` timeseries Kfold for groups with different amount of observations per group +- `sklego.model_selection.KlusterFoldValidation` experimental feature that does K folds based on clustering +- `sklego.model_selection.TimeGapSplit` timeseries Kfold with a gap between train/test +- `sklego.pipeline.DebugPipeline` adds debug information to make debugging easier +- `sklego.pipeline.make_debug_pipeline` shorthand function to create a debugable pipeline +- `sklego.metrics.correlation_score` calculates correlation between model output and feature +- `sklego.metrics.equal_opportunity_score` calculates equal opportunity metric +- `sklego.metrics.p_percent_score` proxy for model fairness with regards to sensitive attribute +- `sklego.metrics.subset_score` calculate a score on a subset of your data (meant for fairness tracking) + +## New Features + +We want to be rather open here in what we accept but we do demand three +things before they become added to the project: + +1. any new feature contributes towards a demonstratable real-world usecase +2. any new feature passes standard unit tests (we use the ones from scikit-learn) +3. the feature has been discussed in the issue list beforehand + +We automate all of our testing and use pre-commit hooks to keep the code working. + + + + +%prep +%autosetup -n scikit-lego-0.6.14 + +%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-scikit-lego -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.14-1 +- Package Spec generated @@ -0,0 +1 @@ +4143a9582b6758a02470faa209f068d6 scikit-lego-0.6.14.tar.gz |