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            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.7",
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        "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)\n[![Downloads](https://pepy.tech/badge/scikit-lego/month)](https://pepy.tech/project/scikit-lego)\n[![Version](https://img.shields.io/pypi/v/scikit-lego)](https://pypi.org/project/scikit-lego/)\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/scikit-lego.svg)](https://anaconda.org/conda-forge/scikit-lego)\n![](https://img.shields.io/github/license/koaning/scikit-lego)\n![](https://img.shields.io/pypi/pyversions/scikit-lego)\n![](https://img.shields.io/github/contributors/koaning/scikit-lego)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![DOI](https://zenodo.org/badge/166836939.svg)](https://zenodo.org/badge/latestdoi/166836939)\n\n# scikit-lego\n\n<a href=\"https://scikit-lego.readthedocs.io/en/latest/\"><img src=\"images/logo.png\" width=\"35%\" height=\"35%\" align=\"right\" /></a>\n\nWe love scikit learn but very often we find ourselves writing\ncustom transformers, metrics and models. The goal of this project\nis to attempt to consolidate these into a package that offers\ncode quality/testing. This project started as a collaboration between\nmultiple companies in the Netherlands but has since received contributions\nfrom around the globe. It was initiated by [Matthijs Brouns](https://www.mbrouns.com/)\nand [Vincent D. Warmerdam](https://koaning.io) as a tool to teach people how\nto contribute to open source.\n\nNote that we're not formally affiliated with the scikit-learn project at all,\nbut we aim to strictly adhere to their standards.\n\nThe same holds with lego. LEGO\u00ae is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this project.\n\n## Installation\n\nInstall `scikit-lego` via pip with\n\n```bash\npython -m pip install scikit-lego\n```\n\nVia [conda](https://conda.io/projects/conda/en/latest/) with\n\n```bash\nconda install -c conda-forge scikit-lego\n```\n\nAlternatively, to edit and contribute you can fork/clone and run:\n\n```bash\npython -m pip install -e \".[dev]\"\npython setup.py develop\n```\n\n## Documentation\n\nThe documentation can be found [here](https://scikit-lego.netlify.app).\n\n## Usage\n\nWe offer custom metrics, models and transformers. You can import them just like you would\nin scikit-learn.\n\n```python\n# the scikit learn stuff we love\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import Pipeline\n\n# from scikit lego stuff we add\nfrom sklego.preprocessing import RandomAdder\nfrom sklego.mixture import GMMClassifier\n\n...\n\nmod = Pipeline([\n    (\"scale\", StandardScaler()),\n    (\"random_noise\", RandomAdder()),\n    (\"model\", GMMClassifier())\n])\n\n...\n```\n\n## Features\n\nHere's a list of features that this library currently offers:\n\n- `sklego.datasets.load_abalone` loads in the abalone dataset\n- `sklego.datasets.load_arrests` loads in a dataset with fairness concerns\n- `sklego.datasets.load_chicken` loads in the joyful chickweight dataset\n- `sklego.datasets.load_heroes` loads a heroes of the storm dataset\n- `sklego.datasets.load_hearts` loads a dataset about hearts\n- `sklego.datasets.load_penguins` loads a lovely dataset about penguins\n- `sklego.datasets.fetch_creditcard` fetch a fraud dataset from openml\n- `sklego.datasets.make_simpleseries` make a simulated timeseries\n- `sklego.pandas_utils.add_lags` adds lag values in a pandas dataframe\n- `sklego.pandas_utils.log_step` a useful decorator to log your pipeline steps\n- `sklego.dummy.RandomRegressor` dummy benchmark that predicts random values\n- `sklego.linear_model.DeadZoneRegressor` experimental feature that has a deadzone in the cost function\n- `sklego.linear_model.DemographicParityClassifier` logistic classifier constrained on demographic parity\n- `sklego.linear_model.EqualOpportunityClassifier` logistic classifier constrained on equal opportunity\n- `sklego.linear_model.ProbWeightRegression` linear model that treats coefficients as probabilistic weights\n- `sklego.linear_model.LowessRegression` locally weighted linear regression\n- `sklego.linear_model.LADRegression` least absolute deviation regression\n- `sklego.linear_model.QuantileRegression` linear quantile regression, generalizes LADRegression\n- `sklego.linear_model.ImbalancedLinearRegression` punish over/under-estimation of a model directly\n- `sklego.naive_bayes.GaussianMixtureNB` classifies by training a 1D GMM per column per class\n- `sklego.naive_bayes.BayesianGaussianMixtureNB` classifies by training a bayesian 1D GMM per class\n- `sklego.mixture.BayesianGMMClassifier` classifies by training a bayesian GMM per class\n- `sklego.mixture.BayesianGMMOutlierDetector` detects outliers based on a trained bayesian GMM\n- `sklego.mixture.GMMClassifier` classifies by training a GMM per class\n- `sklego.mixture.GMMOutlierDetector` detects outliers based on a trained GMM\n- `sklego.meta.ConfusionBalancer` experimental feature that allows you to balance the confusion matrix\n- `sklego.meta.DecayEstimator` adds decay to the sample_weight that the model accepts\n- `sklego.meta.EstimatorTransformer` adds a model output as a feature\n- `sklego.meta.OutlierClassifier` turns outlier models into classifiers for gridsearch\n- `sklego.meta.GroupedPredictor` can split the data into runs and run a model on each\n- `sklego.meta.GroupedTransformer` can split the data into runs and run a transformer on each\n- `sklego.meta.SubjectiveClassifier` experimental feature to add a prior to your classifier\n- `sklego.meta.Thresholder` meta model that allows you to gridsearch over the threshold\n- `sklego.meta.RegressionOutlierDetector` meta model that finds outliers by adding a threshold to regression\n- `sklego.meta.ZeroInflatedRegressor` predicts zero or applies a regression based on a classifier\n- `sklego.preprocessing.ColumnCapper` limits extreme values of the model features\n- `sklego.preprocessing.ColumnDropper` drops a column from pandas\n- `sklego.preprocessing.ColumnSelector` selects columns based on column name\n- `sklego.preprocessing.InformationFilter` transformer that can de-correlate features\n- `sklego.preprocessing.IdentityTransformer` returns the same data, allows for concatenating pipelines\n- `sklego.preprocessing.OrthogonalTransformer` makes all features linearly independent\n- `sklego.preprocessing.PandasTypeSelector` selects columns based on pandas type\n- `sklego.preprocessing.PatsyTransformer` applies a [patsy](https://patsy.readthedocs.io/en/latest/formulas.html) formula\n- `sklego.preprocessing.RandomAdder` adds randomness in training\n- `sklego.preprocessing.RepeatingBasisFunction` repeating feature engineering, useful for timeseries\n- `sklego.preprocessing.DictMapper` assign numeric values on categorical columns\n- `sklego.preprocessing.OutlierRemover` experimental method to remove outliers during training\n- `sklego.model_selection.KlusterFoldValidation` experimental feature that does K folds based on clustering\n- `sklego.model_selection.TimeGapSplit` timeseries Kfold with a gap between train/test\n- `sklego.pipeline.DebugPipeline` adds debug information to make debugging easier\n- `sklego.pipeline.make_debug_pipeline` shorthand 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