classifierpromax.FeatureSelector
================================

.. py:module:: classifierpromax.FeatureSelector


Functions
---------

.. autoapisummary::

   classifierpromax.FeatureSelector.FeatureSelector


Module Contents
---------------

.. py:function:: FeatureSelector(preprocessor, trained_models, X_train, y_train, method='RFE', n_features_to_select=None)

   Selects features for multiple classification models using RFE or Pearson methods.

   Parameters:
   -----------
   preprocessor : sklearn.pipeline.Pipeline or Transformer
       Preprocessing pipeline to include in the final pipeline.

   trained_models : dict
       A dictionary containing the names and corresponding trained best classification models.
       Keys are model names, and values are trained pipelines.

   X_train : array-like or DataFrame
       Training feature set.

   y_train : array-like or Series
       Training target labels.

   method : str, optional
       Feature selection method. Defaults to 'RFE'.
       Can be one of {'RFE', 'Pearson'}.

   n_features_to_select : int, optional
       The number of features to select.
       Required for both 'RFE' and 'Pearson' methods. Defaults to None.

   Returns:
   --------
   feature_selected_models : dict
       A dictionary containing the feature-selected models.
       Keys are model names, and values are pipelines with feature selection applied.

   Raises:
   -------
   ValueError
       If `n_features_to_select` is not provided or an invalid method is specified.

   Examples:
   ---------
   >>> from sklearn.ensemble import RandomForestClassifier
   >>> from sklearn.preprocessing import StandardScaler
   >>> from sklearn.datasets import make_classification
   >>> X_train, y_train = make_classification(n_samples=100, n_features=10, random_state=42)
   >>> trained_models = {
   ...     'RandomForest': make_pipeline(StandardScaler(), RandomForestClassifier())
   ... }
   >>> preprocessor = StandardScaler()
   >>> feature_selected_models = FeatureSelector(
   ...     preprocessor, trained_models, X_train, y_train, method='RFE', n_features_to_select=5
   ... )
   >>> print(feature_selected_models.keys())
   dict_keys(['RandomForest'])