Welcome to Sklearn Utilities documentation!

Installation & Usage

• Installation
• Usage

Project Info

• Changelog
• Contributing
  • Types of Contributions
  • Get Started!
  • Pull Request Guidelines
  • Tips
  • Making a new release

API Reference

• sklearn_utilities package
  • AppendPredictionToX
  • AppendPredictionToXSingle
  • AppendXPredictionToX
  • ComposeVarEstimator
  • DataFrameWrapper
  • DropByNoisePrediction
  • DropMissingColumns()
  • DropMissingRowsY
  • DummyRegressorVar
  • EstimatorWrapperBase
  • EvalSetWrapper
  • ExcludedColumnTransformerPandas
  • FeatureUnionPandas
  • IdTransformer
  • IncludedColumnTransformerPandas
  • IntersectXY
  • PipelineVar
  • RecursiveFitSubtractRegressor
  • ReindexMissingColumns
  • ReportNonFinite
  • SmartMultioutputEstimator
  • StandardScalerVar
  • TransformedTargetEstimatorVar
  • since_event()
  • until_event()
  • Subpackages
  • Submodules
  • sklearn_utilities.append_prediction_to_x module
  • sklearn_utilities.append_x_prediction_to_x module
  • sklearn_utilities.dataset module
  • sklearn_utilities.drop_by_noise_prediction module
  • sklearn_utilities.drop_missing_columns module
  • sklearn_utilities.drop_missing_rows_y module
  • sklearn_utilities.estimator_wrapper module
  • sklearn_utilities.eval_set module
  • sklearn_utilities.event module
  • sklearn_utilities.id_transformer module
  • sklearn_utilities.intersect module
  • sklearn_utilities.recursive_fit_subtract_regressor module
  • sklearn_utilities.reindex_missing_columns module
  • sklearn_utilities.report_non_finite module
  • sklearn_utilities.types module
  • sklearn_utilities.utils module

Sklearn Utilities

Utilities for scikit-learn.

Installation

Install this via pip (or your favourite package manager):

pip install sklearn-utilities

API

See Docs for more information.

• EstimatorWrapperBase: base class for wrappers. Redirects all attributes which are not in the wrapper to the wrapped estimator.

• DataFrameWrapper: tries to convert every estimator output to a pandas DataFrame or Series.

• FeatureUnionPandas: a FeatureUnion that works with pandas DataFrames.

• IncludedColumnTransformerPandas, ExcludedColumnTransformerPandas: select columns by name.

• AppendPredictionToX: appends the prediction of y to X.

• AppendXPredictionToX: appends the prediction of X to X.

• DropByNoisePrediction: drops columns which has high importance in predicting noise.

• DropMissingColumns: drops columns with missing values above a threshold.

• DropMissingRowsY: drops rows with missing values in y. Use feature_engine.DropMissingData for X.

• IntersectXY: drops rows where the index of X and y do not intersect. Use with feature_engine.DropMissingData.

• ReindexMissingColumns: reindexes columns of X in transform() to match the columns of X in fit().

• ReportNonFinite: reports non-finite values in X and/or y.

• IdTransformer: a transformer that does nothing.

• RecursiveFitSubtractRegressor: a regressor that recursively fits a regressor and subtracts the prediction from the target.

• SmartMultioutputEstimator: a MultiOutputEstimator that supports tuple of arrays in predict() and supports pandas Series and DataFrame.

• until_event(), since_event(): calculates the time since or until events (Series[bool])

• ComposeVarEstimator: composes mean and std/var estimators.

• DummyRegressorVar: DummyRegressor that returns 1.0 for std/var.

• TransformedTargetRegressorVar: TransformedTargetRegressor with std/var support.

• StandardScalerVar: StandardScaler with std/var support.

• EvalSetWrapper, CatBoostProgressBarWrapper: wrapper that passes eval_set to fit() using train_test_split(), mainly for CatBoost. The latter shows progress bar (using tqdm) as well. Useful for early stopping. For LightGBM, see lightgbm-callbacks.

sklearn_utilities.dataset

• add_missing_values(): adds missing values to a dataset.

sklearn_utilities.torch

• PCATorch: faster PCA using PyTorch with GPU support.

sklearn_utilities.torch.skorch

• SkorchReshaper, SkorchCNNReshaper: reshapes X and y for nn.Linear and nn.Conv1d/2d respectively. (For nn.Conv2d, uses np.sliding_window_view().)

• AllowNaN: wraps a loss module and assign 0 to y and y_hat for indices where y contains NaN in forward()..

See also

• ml-tooling/best-of-ml-python

Contributors ✨

Thanks goes to these wonderful people (emoji key):

This project follows the all-contributors specification. Contributions of any kind welcome!