Add BaggingStree and BaggingWodt models

src/Experiments.py

@@ -159,7 +159,6 @@ class Experiment:
         self.title = title
         self.stratified = stratified == "1"
         self.stratified_class = StratifiedKFold if self.stratified else KFold
-        self.model = Models.get_model(model_name)
         self.datasets = datasets
         dictionary = json.loads(hyperparams_dict)
         hyper = BestResults(
@@ -185,8 +184,10 @@ class Experiment:
         return self.output_file
 
     def _build_classifier(self, random_state, hyperparameters):
-        clf = self.model(random_state=random_state)
+        self.model = Models.get_model(self.model_name, random_state)
+        clf = self.model
         clf.set_params(**hyperparameters)
+        clf.set_params(random_state=random_state)
         return clf
 
     def _init_experiment(self):

src/Models.py

@@ -9,23 +9,27 @@ from odte import Odte
 
 class Models:
     @staticmethod
-    def get_model(name):
+    def get_model(name, random_state=None):
         if name == "STree":
-            return Stree
+            return Stree()
         if name == "Cart":
-            return DecisionTreeClassifier
+            return DecisionTreeClassifier()
         if name == "ExtraTree":
-            return ExtraTreeClassifier
+            return ExtraTreeClassifier()
         if name == "Wodt":
-            return Wodt
+            return Wodt()
         if name == "SVC":
-            return SVC
+            return SVC()
         if name == "ODTE":
-            return Odte
-        if name == "Bagging":
-            return BaggingClassifier
+            return Odte()
+        if name == "BaggingStree":
+            clf = Stree(random_state=random_state)
+            return BaggingClassifier(base_estimator=clf)
+        if name == "BaggingWodt":
+            clf = Wodt(random_state=random_state)
+            return BaggingClassifier(base_estimator=clf)
         if name == "RandomForest":
-            return RandomForestClassifier
+            return RandomForestClassifier()
         msg = f"No model recognized {name}"
         if name in ("Stree", "stree"):
             msg += ", did you mean STree?"
@@ -43,7 +47,7 @@ class Models:
             nodes = 0
             leaves = result.get_n_leaves()
             depth = 0
-        elif name == "Bagging":
+        elif name.startswith("Bagging"):
             if hasattr(result.base_estimator_, "nodes_leaves"):
                 nodes, leaves = list(
                     zip(*[x.nodes_leaves() for x in result.estimators_])