better solution to the sklearn bagging problem

Add better tests
enhance .coveragerc

stree/Strees.py

@@ -40,6 +40,7 @@ class Snode:
         features: np.array,
         impurity: float,
         title: str,
+        weight: np.ndarray = None,
     ):
         self._clf = clf
         self._title = title
@@ -51,7 +52,9 @@ class Snode:
         self._up = None
         self._class = None
         self._feature = None
-        self._sample_weight = None
+        self._sample_weight = (
+            weight if os.environ.get("TESTING", "NS") != "NS" else None
+        )
         self._features = features
         self._impurity = impurity
 
@@ -443,9 +446,6 @@ class Stree(BaseEstimator, ClassifierMixin):
         sample_weight = _check_sample_weight(
             sample_weight, X, dtype=np.float64
         )
-        # solve WARNING: class label 0 specified in weight is not found
-        # in bagging
-        sample_weight += 1e-5
         check_classification_targets(y)
         # Initialize computed parameters
         self.splitter_ = Splitter(
@@ -505,13 +505,22 @@ class Stree(BaseEstimator, ClassifierMixin):
                 features=X.shape[1],
                 impurity=0.0,
                 title=title + ", <pure>",
+                weight=sample_weight,
             )
         # Train the model
         clf = self._build_clf()
         Xs, features = self.splitter_.get_subspace(X, y, self.max_features_)
+        # solve WARNING: class label 0 specified in weight is not found
+        # in bagging
+        if any(sample_weight == 0):
+            indices = sample_weight == 0
+            y_next = y[~indices]
+            # touch weights if removing any class
+            if np.unique(y_next).shape[0] != self.n_classes_:
+                sample_weight += 1e-5
         clf.fit(Xs, y, sample_weight=sample_weight)
         impurity = self.splitter_.impurity(y)
-        node = Snode(clf, X, y, features, impurity, title)
+        node = Snode(clf, X, y, features, impurity, title, sample_weight)
         self.depth_ = max(depth, self.depth_)
         self.splitter_.partition(X, node)
         X_U, X_D = self.splitter_.part(X)
@@ -526,6 +535,7 @@ class Stree(BaseEstimator, ClassifierMixin):
                 features=X.shape[1],
                 impurity=impurity,
                 title=title + ", <cgaf>",
+                weight=sample_weight,
             )
         node.set_up(self.train(X_U, y_u, sw_u, depth + 1, title + " - Up"))
         node.set_down(self.train(X_D, y_d, sw_d, depth + 1, title + " - Down"))