Implement predict and score methods & tests

trees/Snode.py

@@ -2,7 +2,7 @@
 __author__ = "Ricardo Montañana Gómez"
 __copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
 __license__ = "MIT"
-__version__ = "1.0"
+__version__ = "0.9"
 Node of the Stree (binary tree)
 '''
 
@@ -11,10 +11,10 @@ from sklearn.svm import LinearSVC
 
 
 class Snode:
-    def __init__(self, model: LinearSVC, X: np.ndarray, y: np.ndarray, title: str):
-        self._model = model
-        self._vector = None if model is None else model.coef_
-        self._interceptor = 0 if model is None else model.intercept_
+    def __init__(self, clf: LinearSVC, X: np.ndarray, y: np.ndarray, title: str):
+        self._clf = clf
+        self._vector = None if clf is None else clf.coef_
+        self._interceptor = 0 if clf is None else clf.intercept_
         self._title = title
         self._belief = 0  # belief of the prediction in a leaf node based on samples
         self._X = X
@@ -60,6 +60,6 @@ class Snode:
                 num = max(num, self._y[self._y == i].shape[0])
             den = self._y.shape[0]
             accuracy = num / den if den != 0 else 1
-            return f"{self._title} LEAF accuracy={accuracy:.2f}\n"
+            return f"{self._title} LEAF accuracy={accuracy:.2f}, belief={self._belief:.2f} class={self._class}\n"
         else:
             return f"{self._title}\n"

trees/Stree.py

@@ -2,8 +2,8 @@
 __author__ = "Ricardo Montañana Gómez"
 __copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
 __license__ = "MIT"
-__version__ = "1.0"
-Create a oblique tree classifier based on SVM Trees
+__version__ = "0.9"
+Build an oblique tree classifier based on SVM Trees
 Uses LinearSVC
 '''
 
@@ -25,6 +25,7 @@ class Stree:
         self._tree = None
         self.__folder = 'data/'
         self.__use_predictions = use_predictions
+        self.__trained = False
 
     def _split_data(self, clf: LinearSVC, X: np.ndarray, y: np.ndarray) -> list:
         if self.__use_predictions:
@@ -46,10 +47,11 @@ class Stree:
 
     def fit(self, X: np.ndarray, y: np.ndarray, title: str = 'root') -> 'Stree':
         self._tree = self.train(X, y, title)
-        self._predictor()
+        self._build_predictor()
+        self.__trained = True
         return self
 
-    def _predictor(self):
+    def _build_predictor(self):
         """Process the leaves to make them predictors
         """
         def run_tree(node: Snode):
@@ -79,6 +81,28 @@ class Stree:
                                  str(np.unique(y_d, return_counts=True))))
         return tree
 
+    def predict(self, X: np.array) -> np.array:
+        def predict_class(xp: np.array, tree: Snode) -> np.array:
+            if tree.is_leaf():
+                return tree._class
+            coef = tree._vector[0, :].reshape(-1, xp.shape[1])
+            if xp.dot(coef.T) + tree._interceptor[0] > 0:
+                return predict_class(xp, tree.get_down())
+            return predict_class(xp, tree.get_up())
+        y = np.array([], dtype=int)
+        for xp in X:
+            y = np.append(y, predict_class(xp.reshape(-1, X.shape[1]), self._tree))
+        return y
+
+    def score(self, X: np.array, y: np.array, print_out=True) -> float:
+        self.fit(X, y)
+        yp = self.predict(X)
+        right = (yp == y).astype(int)
+        accuracy = sum(right) / len(y)
+        if print_out:
+            print(f"Accuracy: {accuracy:.6f}")
+        return accuracy
+
     def __str__(self):
         def print_tree(tree: Snode) -> str:
             output = str(tree)