--- a
+++ b/python/feature_selection.py
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+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
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