from sklearn.feature_selection import SelectFromModel

from sklearn.linear_model import LassoCV

from sklearn.feature_selection import SelectPercentile, f_classif

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import chi2

import numpy as np

# https://machinelearningmastery.com/feature-selection-machine-learning-python/

def run_feature_selection(features, labels, feature_selection, best_features):

    if feature_selection == 'select_K_Best':

        # feature extraction

        selector = SelectKBest(score_func=f_classif, k=4) # score_func=chi2 : only for non-negative features

        selector.fit(features, labels)

        # summarize scores

        scores = selector.scores_

        features_index_sorted = np.argsort(-scores)

        features_selected = features[:, features_index_sorted[0:best_features]]

    # SelectFromModel and LassoCV

    # We use the base estimator LassoCV since the L1 norm promotes sparsity of features.

    if feature_selection == 'LassoCV':

        clf = LassoCV()

        # Set a minimum threshold of 0.25

        sfm = SelectFromModel(clf, threshold=0.95)

        sfm.fit(features, labels)

        features_selected = sfm.transform(features).shape[1]

        """

        # Reset the threshold till the number of features equals two.

        # Note that the attribute can be set directly instead of repeatedly

        # fitting the metatransformer.

        while n_features > 2:

            sfm.threshold += 0.1

            X_transform = sfm.transform(X)

            n_features = X_transform.shape[1]

        """

    # Univariate feature selection

    # Univariate feature selection works by selecting the best features based on univariate statistical tests. 

    # It can be seen as a preprocessing step to an estimator. 

    # Scikit-learn exposes feature selection routines as objects that implement the transform method:

    #   - SelectKBest removes all but the k highest scoring features

    #   - SelectPercentile removes all but a user-specified highest scoring percentage of features

    #       common univariate statistical tests for each feature: false positive rate SelectFpr, false discovery rate SelectFdr, or family wise error SelectFwe.

    #   - GenericUnivariateSelect allows to perform univariate feature selection with a configurable strategy. This allows to select the best univariate selection strategy with hyper-parameter search estimator.

    if feature_selection == 'slct_percentile':

        selector = SelectPercentile(f_classif, percentile=10)

        selector.fit(features, labels)

        # The percentile not affect. 

        # Just select in order the top features by number or threshold

        # Keep best 8 values?

        scores = selector.scores_

        features_index_sorted = np.argsort(-scores)

        # scores = selector.scores_

        # scores = -np.log10(selector.pvalues_)

        # scores /= scores.max()

        features_selected = features[:, features_index_sorted[0:best_features]]

    print("Selected only " + str(features_selected.shape) + " features ")

    return features_selected, features_index_sorted