%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
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
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
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 - 0.6.14-1
- Package Spec generated