#3 Complete multiclass in Stree

Add multiclass dimensions management in distances method
Add gamma hyperparameter for non linear kernels

stree/Strees.py

@@ -126,6 +126,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         random_state: int = None,
         max_depth: int = None,
         tol: float = 1e-4,
+        gamma="scale",
         min_samples_split: int = 0,
     ):
         self.max_iter = max_iter
@@ -134,6 +135,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         self.random_state = random_state
         self.max_depth = max_depth
         self.tol = tol
+        self.gamma = gamma
         self.min_samples_split = min_samples_split
 
     def _more_tags(self) -> dict:
@@ -144,21 +146,6 @@ class Stree(BaseEstimator, ClassifierMixin):
         """
         return {"binary_only": True, "requires_y": True}
 
-    def _linear_function(self, data: np.array, node: Snode) -> np.array:
-        """Compute the distance of set of samples to a hyperplane, in
-        multiclass classification it should compute the distance to a
-        hyperplane of each class
-
-        :param data: dataset of samples
-        :type data: np.array shape(m, n)
-        :param node: the node that contains the hyperplance coefficients
-        :type node: Snode shape(1, n)
-        :return: array of distances of each sample to the hyperplane
-        :rtype: np.array
-        """
-        coef = node._clf.coef_[0, :].reshape(-1, data.shape[1])
-        return data.dot(coef.T) + node._clf.intercept_[0]
-
     def _split_array(self, origin: np.array, down: np.array) -> list:
         """Split an array in two based on indices passed as down and its complement
 
@@ -170,7 +157,6 @@ class Stree(BaseEstimator, ClassifierMixin):
         :rtype: list
         """
         up = ~down
-        print(self.kernel, up.shape, down.shape)
         return (
             origin[up[:, 0]] if any(up) else None,
             origin[down[:, 0]] if any(down) else None,
@@ -187,7 +173,12 @@ class Stree(BaseEstimator, ClassifierMixin):
         the hyperplane of the node
         :rtype: np.array
         """
-        return np.expand_dims(node._clf.decision_function(data), 1)
+        res = node._clf.decision_function(data)
+        if res.ndim == 1:
+            return np.expand_dims(res, 1)
+        elif res.shape[1] > 1:
+            res = np.delete(res, slice(1, res.shape[1]), axis=1)
+        return res
 
     def _split_criteria(self, data: np.array) -> np.array:
         """Set the criteria to split arrays
@@ -256,9 +247,8 @@ class Stree(BaseEstimator, ClassifierMixin):
         run_tree(self.tree_)
 
     def _build_clf(self):
-        """ Select the correct classifier for the node
+        """ Build the correct classifier for the node
         """
-
         return (
             LinearSVC(
                 max_iter=self.max_iter,
@@ -272,6 +262,7 @@ class Stree(BaseEstimator, ClassifierMixin):
                 max_iter=self.max_iter,
                 tol=self.tol,
                 C=self.C,
+                gamma=self.gamma,
             )
         )