Generates 5 random subspaces at most

stree/Strees.py

@@ -10,7 +10,8 @@ import os
 import numbers
 import random
 import warnings
-from math import log
+from math import log, factorial
+from typing import Optional
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.svm import SVC, LinearSVC
@@ -255,8 +256,14 @@ class Splitter:
     @staticmethod
     def _generate_spaces(features: int, max_features: int) -> list:
         comb = set()
-        # Generate at most 3 combinations
-        set_length = 1 if max_features == features else 3
+        # Generate at most 5 combinations
+        if max_features == features:
+            set_length = 1
+        else:
+            number = factorial(features) / (
+                factorial(max_features) * factorial(features - max_features)
+            )
+            set_length = min(5, number)
         while len(comb) < set_length:
             comb.add(
                 tuple(sorted(random.sample(range(features), max_features)))
@@ -494,7 +501,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         sample_weight: np.ndarray,
         depth: int,
         title: str,
-    ) -> Snode:
+    ) -> Optional[Snode]:
         """Recursive function to split the original dataset into predictor
         nodes (leaves)
 

stree/tests/Splitter_test.py

@@ -166,6 +166,14 @@ class Splitter_test(unittest.TestCase):
         self.assertEqual((6,), computed_data.shape)
         self.assertListEqual(expected.tolist(), computed_data.tolist())
 
+    def test_generate_subspaces(self):
+        features = 250
+        for max_features in range(2, features):
+            num = len(Splitter._generate_spaces(features, max_features))
+            self.assertEqual(5, num)
+        self.assertEqual(3, len(Splitter._generate_spaces(3, 2)))
+        self.assertEqual(4, len(Splitter._generate_spaces(4, 3)))
+
     def test_best_splitter_few_sets(self):
         X, y = load_iris(return_X_y=True)
         X = np.delete(X, 3, 1)
@@ -176,14 +184,14 @@ class Splitter_test(unittest.TestCase):
 
     def test_splitter_parameter(self):
         expected_values = [
-            [0, 4, 6, 12],  # best   entropy max_samples
+            [1, 4, 9, 12],  # best   entropy max_samples
             [1, 3, 6, 10],  # best   entropy impurity
-            [0, 1, 5, 11],  # best   gini    max_samples
-            [0, 1, 7, 9],  # best   gini    impurity
-            [0, 4, 6, 8],  # random entropy max_samples
-            [4, 5, 8, 9],  # random entropy impurity
-            [0, 4, 10, 12],  # random gini    max_samples
-            [1, 5, 8, 12],  # random gini    impurity
+            [6, 8, 10, 12],  # best   gini    max_samples
+            [7, 8, 10, 11],  # best   gini    impurity
+            [0, 3, 8, 12],  # random entropy max_samples
+            [0, 3, 9, 11],  # random entropy impurity
+            [0, 4, 7, 12],  # random gini    max_samples
+            [0, 2, 5, 6],  # random gini    impurity
         ]
         X, y = load_wine(return_X_y=True)
         rn = 0