Implement splitter type mutual info

stree/Strees.py

@@ -11,7 +11,7 @@ from typing import Optional
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.svm import SVC, LinearSVC
-from sklearn.feature_selection import SelectKBest
+from sklearn.feature_selection import SelectKBest, mutual_info_classif
 from sklearn.preprocessing import StandardScaler
 from sklearn.utils.multiclass import check_classification_targets
 from sklearn.exceptions import ConvergenceWarning
@@ -205,9 +205,9 @@ class Splitter:
                 f"criteria has to be max_samples or impurity; got ({criteria})"
             )
 
-        if feature_select not in ["random", "best"]:
+        if feature_select not in ["random", "best", "mutual"]:
             raise ValueError(
-                "splitter must be either random or best, got "
+                "splitter must be in {random, best, mutual} got "
                 f"({feature_select})"
             )
         self.criterion_function = getattr(self, f"_{self._criterion}")
@@ -381,11 +381,19 @@ class Splitter:
                 dataset.shape[1], max_features
             )
             return self._select_best_set(dataset, labels, features_sets)
-        # Take KBest features
-        return (
-            SelectKBest(k=max_features)
-            .fit(dataset, labels)
-            .get_support(indices=True)
+        if self._feature_select == "best":
+            # Take KBest features
+            return (
+                SelectKBest(k=max_features)
+                .fit(dataset, labels)
+                .get_support(indices=True)
+            )
+        # return best features with mutual info with the label
+        feature_list = mutual_info_classif(dataset, labels)
+        return tuple(
+            sorted(
+                range(len(feature_list)), key=lambda sub: feature_list[sub]
+            )[-max_features:]
         )
 
     def get_subspace(