Implement optimized predict and new predict_proba

stree/Splitter.py

@@ -135,7 +135,7 @@ class Snode:
         if not self.is_leaf():
             return
         classes, card = np.unique(self._y, return_counts=True)
-        self._proba = np.zeros((num_classes,))
+        self._proba = np.zeros((num_classes,), dtype=np.int64)
         for c, n in zip(classes, card):
             self._proba[c] = n
         try:

stree/Strees.py

@@ -367,28 +367,66 @@ class Stree(BaseEstimator, ClassifierMixin):
             )
         )
 
-    @staticmethod
-    def _reorder_results(y: np.array, indices: np.array) -> np.array:
-        """Reorder an array based on the array of indices passed
+    def __predict_class(self, X: np.array) -> np.array:
+        def compute_prediction(xp, indices, node):
+            if xp is None:
+                return
+            if node.is_leaf():
+                # set a class for indices
+                result[indices] = node._proba
+                return
+            self.splitter_.partition(xp, node, train=False)
+            x_u, x_d = self.splitter_.part(xp)
+            i_u, i_d = self.splitter_.part(indices)
+            compute_prediction(x_u, i_u, node.get_up())
+            compute_prediction(x_d, i_d, node.get_down())
+
+        # setup prediction & make it happen
+        result = np.zeros((X.shape[0], self.n_classes_))
+        indices = np.arange(X.shape[0])
+        compute_prediction(X, indices, self.tree_)
+        return result
+
+    def check_predict(self, X) -> np.array:
+        check_is_fitted(self, ["tree_"])
+        # Input validation
+        X = check_array(X)
+        if X.shape[1] != self.n_features_:
+            raise ValueError(
+                f"Expected {self.n_features_} features but got "
+                f"({X.shape[1]})"
+            )
+        return X
+
+    def predict_proba(self, X: np.array) -> np.array:
+        """Predict class probabilities of the input samples X.
+
+        The predicted class probability is the fraction of samples of the same
+        class in a leaf.
 
         Parameters
         ----------
-        y : np.array
-            data untidy
-        indices : np.array
-            indices used to set order
+        X : dataset of samples.
 
         Returns
         -------
-        np.array
-            array y ordered
+        proba : array of shape (n_samples, n_classes)
+            The class probabilities of the input samples.
+
+        Raises
+        ------
+        ValueError
+            if dataset with inconsistent number of features
+        NotFittedError
+            if model is not fitted
         """
-        # return array of same type given in y
-        y_ordered = y.copy()
-        indices = indices.astype(int)
-        for i, index in enumerate(indices):
-            y_ordered[index] = y[i]
-        return y_ordered
+
+        X = self.check_predict(X)
+        # return # of samples of each class in leaf node
+        values = self.__predict_class(X)
+        normalizer = values.sum(axis=1)[:, np.newaxis]
+        normalizer[normalizer == 0.0] = 1.0
+        return values / normalizer
 
     def predict(self, X: np.array) -> np.array:
         """Predict labels for each sample in dataset passed
@@ -410,40 +448,8 @@ class Stree(BaseEstimator, ClassifierMixin):
         NotFittedError
             if model is not fitted
         """
-
-        def predict_class(
-            xp: np.array, indices: np.array, node: Snode
-        ) -> np.array:
-            if xp is None:
-                return [], []
-            if node.is_leaf():
-                # set a class for every sample in dataset
-                prediction = np.full((xp.shape[0], 1), node._class)
-                return prediction, indices
-            self.splitter_.partition(xp, node, train=False)
-            x_u, x_d = self.splitter_.part(xp)
-            i_u, i_d = self.splitter_.part(indices)
-            prx_u, prin_u = predict_class(x_u, i_u, node.get_up())
-            prx_d, prin_d = predict_class(x_d, i_d, node.get_down())
-            return np.append(prx_u, prx_d), np.append(prin_u, prin_d)
-
-        # sklearn check
-        check_is_fitted(self, ["tree_"])
-        # Input validation
-        X = check_array(X)
-        if X.shape[1] != self.n_features_:
-            raise ValueError(
-                f"Expected {self.n_features_} features but got "
-                f"({X.shape[1]})"
-            )
-        # setup prediction & make it happen
-        indices = np.arange(X.shape[0])
-        result = (
-            self._reorder_results(*predict_class(X, indices, self.tree_))
-            .astype(int)
-            .ravel()
-        )
-        return self.classes_[result]
+        X = self.check_predict(X)
+        return self.classes_[np.argmax(self.__predict_class(X), axis=1)]
 
     def nodes_leaves(self) -> tuple:
         """Compute the number of nodes and leaves in the built tree

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 '
-            'counts=[0. 1. 0.]"];\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 '
-            'counts=[0. 1. 0.]"];\n}\n'
+            'counts=[0 1 0]"];\n}\n'
         )
         self.assertEqual(clf.graph("Sample title"), expected_head + "}\n")
         clf.fit(X, y)