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Utilities for scikit-learn.

Project description

Sklearn Utilities

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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.
  • 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

Contributors ✨

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