automatic import of python-scikit-lego

3 files changed, 570 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..debd744 100644
--- a/.gitignore
+++ b/.gitignore
@@ -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
+[![Build status](https://github.com/koaning/scikit-lego/workflows/Unit%20Tests/badge.svg)](https://github.com/{github_id}/{repository}/workflows/{workflow_name}/badge.svg)
+[![Downloads](https://pepy.tech/badge/scikit-lego/month)](https://pepy.tech/project/scikit-lego)
+[![Version](https://img.shields.io/pypi/v/scikit-lego)](https://pypi.org/project/scikit-lego/)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/scikit-lego.svg)](https://anaconda.org/conda-forge/scikit-lego)
+![](https://img.shields.io/github/license/koaning/scikit-lego)
+![](https://img.shields.io/pypi/pyversions/scikit-lego)
+![](https://img.shields.io/github/contributors/koaning/scikit-lego)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![DOI](https://zenodo.org/badge/166836939.svg)](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
+[![Build status](https://github.com/koaning/scikit-lego/workflows/Unit%20Tests/badge.svg)](https://github.com/{github_id}/{repository}/workflows/{workflow_name}/badge.svg)
+[![Downloads](https://pepy.tech/badge/scikit-lego/month)](https://pepy.tech/project/scikit-lego)
+[![Version](https://img.shields.io/pypi/v/scikit-lego)](https://pypi.org/project/scikit-lego/)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/scikit-lego.svg)](https://anaconda.org/conda-forge/scikit-lego)
+![](https://img.shields.io/github/license/koaning/scikit-lego)
+![](https://img.shields.io/pypi/pyversions/scikit-lego)
+![](https://img.shields.io/github/contributors/koaning/scikit-lego)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![DOI](https://zenodo.org/badge/166836939.svg)](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
+[![Build status](https://github.com/koaning/scikit-lego/workflows/Unit%20Tests/badge.svg)](https://github.com/{github_id}/{repository}/workflows/{workflow_name}/badge.svg)
+[![Downloads](https://pepy.tech/badge/scikit-lego/month)](https://pepy.tech/project/scikit-lego)
+[![Version](https://img.shields.io/pypi/v/scikit-lego)](https://pypi.org/project/scikit-lego/)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/scikit-lego.svg)](https://anaconda.org/conda-forge/scikit-lego)
+![](https://img.shields.io/github/license/koaning/scikit-lego)
+![](https://img.shields.io/pypi/pyversions/scikit-lego)
+![](https://img.shields.io/github/contributors/koaning/scikit-lego)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![DOI](https://zenodo.org/badge/166836939.svg)](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
diff --git a/sources b/sources
new file mode 100644
index 0000000..59c1071
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+4143a9582b6758a02470faa209f068d6  scikit-lego-0.6.14.tar.gz