SelectionPipeline

class lightautoml.pipelines.selection.base.SelectionPipeline(features_pipeline=None, ml_algo=None, imp_estimator=None, fit_on_holdout=False, **kwargs)

Bases: object

Abstract class, performing feature selection.

Instance should accept train/valid datasets and select features.

Parameters:

• features_pipeline (Optional[FeaturesPipeline]) – Composition of feature transforms.

• ml_algo (Union[MLAlgo, Tuple[MLAlgo, ParamsTuner], None]) – Tuple (MlAlgo, ParamsTuner).

• imp_estimator (Optional[ImportanceEstimator]) – Feature importance estimator.

• fit_on_holdout (bool) – If use the holdout iterator.

• **kwargs (Any) – Not used.

property is_fitted

Check if selection pipeline is already fitted.

Returns:

True for fitted pipeline and False for not fitted.

property selected_features

Get selected features.

Returns:

List of selected feature names.

property in_features

Input features to the selector.

Raises exception if not fitted beforehand.

Returns:

List of input features.

property dropped_features

Features that were dropped.

Returns:

list of dropped features.

perform_selection(train_valid)

Select features from train-valid iterator.

Method is used to perform selection based on features pipeline and ml model. Should save _selected_features attribute in the end of working.

Parameters:

train_valid (Optional[TrainValidIterator]) – Classical cv-iterator.

Raises:

NotImplementedError. –

fit(train_valid)

Selection pipeline fit.

Find features selection for given dataset based on features pipeline and ml model.

Parameters:

train_valid (TrainValidIterator) – Dataset iterator.

select(dataset)

Takes only selected features from giving dataset and creates new dataset.

Parameters:

dataset (LAMLDataset) – Dataset for feature selection.

Return type:

LAMLDataset

Returns:

New dataset with selected features only.

map_raw_feature_importances(raw_importances)

Calculate input feature importances.

Calculated as sum of importances on different levels of pipeline.

Parameters:

raw_importances (Series) – Importances of output features.

# noqa: DAR201

get_features_score()

Get input feature importances.

Returns:

Series with importances in not ascending order.