Utilities for scikit-learn. Append prediction to x, append prediction to x single, append x prediction to x, compose var estimator, data frame wrapper, drop by noise prediction, drop missing rows y, dummy regressor var, estimator wrapper base, excluded column transformer pandas, feature union pandas, id transformer, included column transformer pand
MIT License
Utilities for scikit-learn.
Install this via pip (or your favourite package manager):
pip install sklearn-utilities
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()
..Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!