Implement ovo strategy (#37)

* Implement ovo strategy
* Set ovo strategy as default
* Add kernel liblinear with LinearSVC classifier
* Fix weak test

stree/Strees.py

@@ -155,6 +155,10 @@ class Siterator:
         self._stack = []
         self._push(tree)
 
+    def __iter__(self):
+        # To complete the iterator interface
+        return self
+
     def _push(self, node: Snode):
         if node is not None:
             self._stack.append(node)
@@ -373,16 +377,17 @@ class Splitter:
         tuple
             indices of the features selected
         """
+        # No feature reduction
         if dataset.shape[1] == max_features:
-            # No feature reduction applies
             return tuple(range(dataset.shape[1]))
+        # Random feature reduction
         if self._feature_select == "random":
             features_sets = self._generate_spaces(
                 dataset.shape[1], max_features
             )
             return self._select_best_set(dataset, labels, features_sets)
+        # return the KBest features
         if self._feature_select == "best":
-            # Take KBest features
             return (
                 SelectKBest(k=max_features)
                 .fit(dataset, labels)
@@ -569,6 +574,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         min_samples_split: int = 0,
         max_features=None,
         splitter: str = "random",
+        multiclass_strategy: str = "ovo",
         normalize: bool = False,
     ):
         self.max_iter = max_iter
@@ -585,6 +591,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         self.criterion = criterion
         self.splitter = splitter
         self.normalize = normalize
+        self.multiclass_strategy = multiclass_strategy
 
     def _more_tags(self) -> dict:
         """Required by sklearn to supply features of the classifier
@@ -629,7 +636,23 @@ class Stree(BaseEstimator, ClassifierMixin):
                 f"Maximum depth has to be greater than 1... got (max_depth=\
                     {self.max_depth})"
             )
-        kernels = ["linear", "rbf", "poly", "sigmoid"]
+        if self.multiclass_strategy not in ["ovr", "ovo"]:
+            raise ValueError(
+                "mutliclass_strategy has to be either ovr or ovo"
+                f" but got {self.multiclass_strategy}"
+            )
+        if self.multiclass_strategy == "ovo":
+            if self.kernel == "liblinear":
+                raise ValueError(
+                    "The kernel liblinear is incompatible with ovo "
+                    "multiclass_strategy"
+                )
+            if self.split_criteria == "max_samples":
+                raise ValueError(
+                    "The multiclass_strategy 'ovo' is incompatible with "
+                    "split_criteria 'max_samples'"
+                )
+        kernels = ["liblinear", "linear", "rbf", "poly", "sigmoid"]
         if self.kernel not in kernels:
             raise ValueError(f"Kernel {self.kernel} not in {kernels}")
         check_classification_targets(y)
@@ -749,7 +772,7 @@ class Stree(BaseEstimator, ClassifierMixin):
                 C=self.C,
                 tol=self.tol,
             )
-            if self.kernel == "linear"
+            if self.kernel == "liblinear"
             else SVC(
                 kernel=self.kernel,
                 max_iter=self.max_iter,
@@ -758,6 +781,7 @@ class Stree(BaseEstimator, ClassifierMixin):
                 gamma=self.gamma,
                 degree=self.degree,
                 random_state=self.random_state,
+                decision_function_shape=self.multiclass_strategy,
             )
         )