Utilities for scikit-learn.
Project description
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
: aFeatureUnion
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. Usefeature_engine.DropMissingData
for X.IntersectXY
: drops rows where the index of X and y do not intersect. Use withfeature_engine.DropMissingData
.ReindexMissingColumns
: reindexes columns of X intransform()
to match the columns of X infit()
.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
: aMultiOutputEstimator
that supports tuple of arrays inpredict()
and supports pandasSeries
andDataFrame
.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 passeseval_set
tofit()
usingtrain_test_split()
, mainly forCatBoost
. The latter shows progress bar (usingtqdm
) as well. Useful for early stopping. For LightGBM, seelightgbm-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 fornn.Linear
andnn.Conv1d/2d
respectively. (Fornn.Conv2d
, usesnp.sliding_window_view()
.)AllowNaN
: wraps a loss module and assign 0 to y and y_hat for indices where y contains NaN inforward()
..
See also
Contributors ✨
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!
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