- class lightautoml.pipelines.selection.base.SelectionPipeline(features_pipeline=None, ml_algo=None, imp_estimator=None, fit_on_holdout=False, **kwargs)
Abstract class, performing feature selection. Instance should accept train/valid datasets and select features.
- property is_fitted
Check if selection pipeline is already fitted.
- Return type
Truefor fitted pipeline and False for not fitted.
- property selected_features
Get selected features.
- property in_features
Input features to the selector.
Raises exception if not fitted beforehand.
- property dropped_features
Features that were dropped.
- __init__(features_pipeline=None, ml_algo=None, imp_estimator=None, fit_on_holdout=False, **kwargs)
Create features selection pipeline.
bool) – If use the holdout iterator.
**kwargs – Not used.
Select features from train-valid iterator.
Method is used to perform selection based on features pipeline and ml model. Should save
_selected_featuresattribute in the end of working.
Selection pipeline fit.
Find features selection for given dataset based on features pipeline and ml model.
TrainValidIterator) – Dataset iterator.
Takes only selected features from giving dataset and creates new dataset.
Calculate input feature importances. Calculated as sum of importances on different levels of pipeline.
Series) – Importances of output features.
Get input feature importances.
Series with importances in not ascending order.