Refactor train method

stree/Strees.py

@@ -79,6 +79,30 @@ class Snode:
     def set_down(self, son):
         self._down = son
 
+    def set_title(self, title):
+        self._title = title
+
+    def set_classifier(self, clf):
+        self._clf = clf
+
+    def set_features(self, features):
+        self._features = features
+
+    def set_impurity(self, impurity):
+        self._impurity = impurity
+
+    def get_title(self) -> str:
+        return self._title
+
+    def get_classifier(self) -> SVC:
+        return self._clf
+
+    def get_impurity(self) -> float:
+        return self._impurity
+
+    def get_features(self) -> np.array:
+        return self._features
+
     def set_up(self, son):
         self._up = son
 
@@ -636,38 +660,26 @@ class Stree(BaseEstimator, ClassifierMixin):
             y = y[~indices_zero]
             sample_weight = sample_weight[~indices_zero]
         self.depth_ = max(depth, self.depth_)
+        node = Snode(None, X, y, X.shape[1], 0.0, title, sample_weight)
         if np.unique(y).shape[0] == 1:
             # only 1 class => pure dataset
-            return Snode(
-                clf=None,
-                X=X,
-                y=y,
-                features=X.shape[1],
-                impurity=0.0,
-                title=title + ", <pure>",
-                weight=sample_weight,
-            )
+            node.set_title(title + ", <pure>")
+            return node
         # Train the model
         clf = self._build_clf()
         Xs, features = self.splitter_.get_subspace(X, y, self.max_features_)
         clf.fit(Xs, y, sample_weight=sample_weight)
-        impurity = self.splitter_.partition_impurity(y)
-        node = Snode(clf, X, y, features, impurity, title, sample_weight)
+        node.set_impurity(self.splitter_.partition_impurity(y))
+        node.set_classifier(clf)
+        node.set_features(features)
         self.splitter_.partition(X, node, True)
         X_U, X_D = self.splitter_.part(X)
         y_u, y_d = self.splitter_.part(y)
         sw_u, sw_d = self.splitter_.part(sample_weight)
         if X_U is None or X_D is None:
             # didn't part anything
-            return Snode(
-                clf,
-                X,
-                y,
-                features=X.shape[1],
-                impurity=impurity,
-                title=title + ", <cgaf>",
-                weight=sample_weight,
-            )
+            node.set_title(title + ", <cgaf>")
+            return node
         node.set_up(
             self.train(X_U, y_u, sw_u, depth + 1, title + f" - Up({depth+1})")
         )

stree/tests/Snode_test.py

@@ -69,6 +69,31 @@ class Snode_test(unittest.TestCase):
         self.assertEqual(0.75, test._belief)
         self.assertEqual(-1, test._partition_column)
 
+    def test_set_title(self):
+        test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
+        self.assertEqual("test", test.get_title())
+        test.set_title("another")
+        self.assertEqual("another", test.get_title())
+
+    def test_set_classifier(self):
+        test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
+        clf = Stree()
+        self.assertIsNone(test.get_classifier())
+        test.set_classifier(clf)
+        self.assertEqual(clf, test.get_classifier())
+
+    def test_set_impurity(self):
+        test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
+        self.assertEqual(0.0, test.get_impurity())
+        test.set_impurity(54.7)
+        self.assertEqual(54.7, test.get_impurity())
+
+    def test_set_features(self):
+        test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [0, 1], 0.0, "test")
+        self.assertListEqual([0, 1], test.get_features())
+        test.set_features([1, 2])
+        self.assertListEqual([1, 2], test.get_features())
+
     def test_make_predictor_on_not_leaf(self):
         test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
         test.set_up(Snode(None, [1], [1], [], 0.0, "another_test"))