Combinatorial explosion (#19)

* Remove itertools combinations from subspaces

* Generates 5 random subspaces at most

stree/Strees.py

@@ -10,8 +10,8 @@ import os
 import numbers
 import random
 import warnings
-from math import log
+from math import log, factorial
-from itertools import combinations
+from typing import Optional
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.svm import SVC, LinearSVC
@@ -253,19 +253,32 @@ class Splitter:
                 selected = feature_set
         return selected if selected is not None else feature_set
 
+    @staticmethod
+    def _generate_spaces(features: int, max_features: int) -> list:
+        comb = set()
+        # 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)))
+            )
+        return list(comb)
+
     def _get_subspaces_set(
         self, dataset: np.array, labels: np.array, max_features: int
     ) -> np.array:
-        features = range(dataset.shape[1])
+        features_sets = self._generate_spaces(dataset.shape[1], max_features)
-        features_sets = list(combinations(features, max_features))
         if len(features_sets) > 1:
             if self._splitter_type == "random":
                 index = random.randint(0, len(features_sets) - 1)
                 return features_sets[index]
             else:
-                # get only 3 sets at most
-                if len(features_sets) > 3:
-                    features_sets = random.sample(features_sets, 3)
                 return self._select_best_set(dataset, labels, features_sets)
         else:
             return features_sets[0]
@@ -488,7 +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, 1, 7, 9],  # best   entropy max_samples
+            [1, 4, 9, 12],  # best   entropy max_samples
-            [3, 8, 10, 11],  # best   entropy impurity
+            [1, 3, 6, 10],  # best   entropy impurity
-            [0, 2, 8, 12],  # best   gini    max_samples
+            [6, 8, 10, 12],  # best   gini    max_samples
-            [1, 2, 5, 12],  # best   gini    impurity
+            [7, 8, 10, 11],  # best   gini    impurity
-            [1, 2, 5, 10],  # random entropy max_samples
+            [0, 3, 8, 12],  # random entropy max_samples
-            [4, 8, 9, 12],  # random entropy impurity
+            [0, 3, 9, 11],  # random entropy impurity
-            [3, 9, 11, 12],  # random gini    max_samples
+            [0, 4, 7, 12],  # random gini    max_samples
-            [1, 5, 6, 9],  # random gini    impurity
+            [0, 2, 5, 6],  # random gini    impurity
         ]
         X, y = load_wine(return_X_y=True)
         rn = 0

stree/tests/Stree_test.py

@@ -313,7 +313,7 @@ class Stree_test(unittest.TestCase):
         X, y = load_dataset(self._random_state)
         clf = Stree(random_state=self._random_state, max_features=2)
         clf.fit(X, y)
-        self.assertAlmostEqual(0.944, clf.score(X, y))
+        self.assertAlmostEqual(0.9246666666666666, clf.score(X, y))
 
     def test_bogus_splitter_parameter(self):
         clf = Stree(splitter="duck")