Add complete classes counts to node and tests

stree/Splitter.py

@@ -68,6 +68,7 @@ class Snode:
         self._impurity = impurity
         self._partition_column: int = -1
         self._scaler = scaler
+        self._proba = None
 
     @classmethod
     def copy(cls, node: "Snode") -> "Snode":
@@ -127,22 +128,21 @@ class Snode:
     def get_up(self) -> "Snode":
         return self._up
 
-    def make_predictor(self):
+    def make_predictor(self, num_classes: int) -> None:
         """Compute the class of the predictor and its belief based on the
         subdataset of the node only if it is a leaf
         """
         if not self.is_leaf():
             return
         classes, card = np.unique(self._y, return_counts=True)
-        if len(classes) > 1:
+        self._proba = np.zeros((num_classes,))
+        for c, n in zip(classes, card):
+            self._proba[c] = n
+        try:
             max_card = max(card)
             self._class = classes[card == max_card][0]
             self._belief = max_card / np.sum(card)
-        else:
+        except ValueError:
-            self._belief = 1
-            try:
-                self._class = classes[0]
-            except IndexError:
             self._class = None
 
     def graph(self):
@@ -155,7 +155,7 @@ class Snode:
             output += (
                 f'N{id(self)} [shape=box style=filled label="'
                 f"class={self._class} impurity={self._impurity:.3f} "
-                f'classes={count_values[0]} samples={count_values[1]}"];\n'
+                f'counts={self._proba}"];\n'
             )
         else:
             output += (

stree/Strees.py

@@ -314,7 +314,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         if np.unique(y).shape[0] == 1:
             # only 1 class => pure dataset
             node.set_title(title + ", <pure>")
-            node.make_predictor()
+            node.make_predictor(self.n_classes_)
             return node
         # Train the model
         clf = self._build_clf()
@@ -333,7 +333,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         if X_U is None or X_D is None:
             # didn't part anything
             node.set_title(title + ", <cgaf>")
-            node.make_predictor()
+            node.make_predictor(self.n_classes_)
             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

@@ -67,10 +67,28 @@ class Snode_test(unittest.TestCase):
 
     def test_make_predictor_on_leaf(self):
         test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
-        test.make_predictor()
+        test.make_predictor(2)
         self.assertEqual(1, test._class)
         self.assertEqual(0.75, test._belief)
         self.assertEqual(-1, test._partition_column)
+        self.assertListEqual([1, 3], test._proba.tolist())
+
+    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"))
+        test.make_predictor(2)
+        self.assertIsNone(test._class)
+        self.assertEqual(0, test._belief)
+        self.assertEqual(-1, test._partition_column)
+        self.assertEqual(-1, test.get_up()._partition_column)
+        self.assertIsNone(test._proba)
+
+    def test_make_predictor_on_leaf_bogus_data(self):
+        test = Snode(None, [1, 2, 3, 4], [], [], 0.0, "test")
+        test.make_predictor(2)
+        self.assertIsNone(test._class)
+        self.assertEqual(-1, test._partition_column)
+        self.assertListEqual([0, 0], test._proba.tolist())
 
     def test_set_title(self):
         test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
@@ -97,21 +115,6 @@ class Snode_test(unittest.TestCase):
         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"))
-        test.make_predictor()
-        self.assertIsNone(test._class)
-        self.assertEqual(0, test._belief)
-        self.assertEqual(-1, test._partition_column)
-        self.assertEqual(-1, test.get_up()._partition_column)
-
-    def test_make_predictor_on_leaf_bogus_data(self):
-        test = Snode(None, [1, 2, 3, 4], [], [], 0.0, "test")
-        test.make_predictor()
-        self.assertIsNone(test._class)
-        self.assertEqual(-1, test._partition_column)
-
     def test_copy_node(self):
         px = [1, 2, 3, 4]
         py = [1]

stree/tests/Stree_test.py

@@ -695,7 +695,7 @@ class Stree_test(unittest.TestCase):
         )
         expected_tail = (
             ' [shape=box style=filled label="class=1 impurity=0.000 '
-            'classes=[1] samples=[1]"];\n}\n'
+            'counts=[0. 1. 0.]"];\n}\n'
         )
         self.assertEqual(clf.graph(), expected_head + "}\n")
         clf.fit(X, y)
@@ -715,7 +715,7 @@ class Stree_test(unittest.TestCase):
         )
         expected_tail = (
             ' [shape=box style=filled label="class=1 impurity=0.000 '
-            'classes=[1] samples=[1]"];\n}\n'
+            'counts=[0. 1. 0.]"];\n}\n'
         )
         self.assertEqual(clf.graph("Sample title"), expected_head + "}\n")
         clf.fit(X, y)