First Approach

stree/Strees.py

@@ -7,9 +7,9 @@ Build an oblique tree classifier based on SVM Trees
 """
 
 import os
-import numbers
 import random
 import warnings
+from typing import Optional, List, Union, Tuple
 from math import log
 from itertools import combinations
 import numpy as np
@@ -78,10 +78,10 @@ class Snode:
     def is_leaf(self) -> bool:
         return self._up is None and self._down is None
 
-    def get_down(self) -> "Snode":
+    def get_down(self) -> Optional["Snode"]:
         return self._down
 
-    def get_up(self) -> "Snode":
+    def get_up(self) -> Optional["Snode"]:
         return self._up
 
     def make_predictor(self):
@@ -123,11 +123,11 @@ class Siterator:
     """Stree preorder iterator
     """
 
-    def __init__(self, tree: Snode):
+    def __init__(self, tree: Optional[Snode]):
-        self._stack = []
+        self._stack: List[Snode] = []
         self._push(tree)
 
-    def _push(self, node: Snode):
+    def _push(self, node: Optional[Snode]) -> None:
         if node is not None:
             self._stack.append(node)
 
@@ -150,7 +150,7 @@ class Splitter:
         min_samples_split: int = None,
         random_state=None,
     ):
-        self._clf = clf
+        self._clf: Union[SVC, LinearSVC] = clf
         self._random_state = random_state
         if random_state is not None:
             random.seed(random_state)
@@ -230,8 +230,8 @@ class Splitter:
     def _select_best_set(
         self, dataset: np.array, labels: np.array, features_sets: list
     ) -> list:
-        max_gain = 0
+        max_gain: float = 0.0
-        selected = None
+        selected: Union[List[int], None] = None
         warnings.filterwarnings("ignore", category=ConvergenceWarning)
         for feature_set in features_sets:
             self._clf.fit(dataset[:, feature_set], labels)
@@ -265,7 +265,7 @@ class Splitter:
 
     def get_subspace(
         self, dataset: np.array, labels: np.array, max_features: int
-    ) -> list:
+    ) -> Tuple[np.array, np.array]:
         """Return the best subspace to make a split
         """
         indices = self._get_subspaces_set(dataset, labels, max_features)
@@ -478,7 +478,7 @@ 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)
 
@@ -543,11 +543,13 @@ class Stree(BaseEstimator, ClassifierMixin):
         node.set_down(self.train(X_D, y_d, sw_d, depth + 1, title + " - Down"))
         return node
 
-    def _build_predictor(self):
+    def _build_predictor(self) -> None:
         """Process the leaves to make them predictors
         """
 
-        def run_tree(node: Snode):
+        def run_tree(node: Optional[Snode]) -> None:
+            if node is None:
+                raise ValueError("Can't build predictors on None")
             if node.is_leaf():
                 node.make_predictor()
                 return
@@ -556,7 +558,7 @@ class Stree(BaseEstimator, ClassifierMixin):
 
         run_tree(self.tree_)
 
-    def _build_clf(self):
+    def _build_clf(self) -> Union[LinearSVC, SVC]:
         """ Build the correct classifier for the node
         """
         return (
@@ -605,7 +607,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         """
 
         def predict_class(
-            xp: np.array, indices: np.array, node: Snode
+            xp: np.array, indices: np.array, node: Optional[Snode]
         ) -> np.array:
             if xp is None:
                 return [], []
@@ -704,7 +706,7 @@ class Stree(BaseEstimator, ClassifierMixin):
                 )
         elif self.max_features is None:
             max_features = self.n_features_
-        elif isinstance(self.max_features, numbers.Integral):
+        elif isinstance(self.max_features, int):
             max_features = self.max_features
         else:  # float
             if self.max_features > 0.0:

stree/tests/Stree_test.py

@@ -414,3 +414,24 @@ class Stree_test(unittest.TestCase):
         # zero weights are ok when they don't erase a class
         _ = clf.train(X, y, weights_no_zero, 1, "test")
         self.assertListEqual(weights_no_zero.tolist(), original.tolist())
+
+    def test_build_predictor(self):
+        X, y = load_dataset(self._random_state)
+        clf = Stree(random_state=self._random_state)
+        with self.assertRaises(ValueError):
+            clf.tree_ = None
+            clf._build_predictor()
+        clf.fit(X, y)
+        node = clf.tree_.get_down().get_down()
+        expected_impurity = 0.04686951386893923
+        expected_class = 1
+        expected_belief = 0.9759887005649718
+        self.assertAlmostEqual(expected_impurity, node._impurity)
+        self.assertAlmostEqual(expected_belief, node._belief)
+        self.assertEqual(expected_class, node._class)
+        node._belief = 0.0
+        node._class = None
+        clf._build_predictor()
+        node = clf.tree_.get_down().get_down()
+        self.assertAlmostEqual(expected_belief, node._belief)
+        self.assertEqual(expected_class, node._class)