Completed def of 2.1 and 2.4 as well, and identified typo in hyperparam script...

Completed def of 2.1 and 2.4 as well, and identified typo in hyperparam script leading to incorrect results in 2.2 and 2.3

model_selection/test_models.py

@@ -62,7 +62,6 @@ def get_tuned_models(group_id, method_id):
             "LR": LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'none', 'max_iter': 1000, 'class_weight': 'balanced'}),
             "SVM": SVC(**{'C': 1.5550524351360953, 'kernel': 'linear', 'max_iter': 1000, 'class_weight': 'balanced'}),
             }
-
         # 1.3) Trained with oversampled training dataset
         elif method_id == 2:
             tuned_models = {
@@ -103,13 +102,38 @@ def get_tuned_models(group_id, method_id):
             }
         # 2.2) Trained with original dataset and cost-sensitive learning
         elif method_id == 1:
-            ...
+            tuned_models = {
+            # "DT": DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'log2', 'criterion': 'entropy', 'class_weight': 'balanced'}),
+            # "RF": RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 118, 'class_weight': 'balanced'}),
+            # "Bagging": BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 15, 'warm_start': False, 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
+            # "AB": AdaBoostClassifier(**{'learning_rate': 0.8159074545140872, 'n_estimators': 121, 'algorithm': 'SAMME', 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
+            # "LR": LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'none', 'max_iter': 1000, 'class_weight': 'balanced'}),
+            # "SVM": SVC(**{'C': 1.5550524351360953, 'kernel': 'linear', 'max_iter': 1000, 'class_weight': 'balanced'}),
+            }
         # 2.3) Trained with oversampled training dataset
         elif method_id == 2:
-            ...
+            tuned_models = {
+            # "DT" : DecisionTreeClassifier(**{'splitter': 'random', 'max_features': 'sqrt', 'criterion': 'log_loss'}), 
+            # "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 135}), 
+            # "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 26, 'warm_start': True}),
+            # "AB" : AdaBoostClassifier(**{'learning_rate': 1.6590924545876917, 'n_estimators': 141, 'algorithm': 'SAMME'}), 
+            # "XGB": XGBClassifier(**{'learning_rate': 0.26946295284728783, 'max_depth': 7, 'n_estimators': 893}),
+            # "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'l2', 'max_iter': 1000}), 
+            # "SVM" : SVC(**{'C': 1.676419306008229, 'kernel': 'poly', 'max_iter':1000}), 
+            # "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 116, 'learning_rate': 'invscaling', 'max_iter':500})
+            }
         # 2.4) Trained with undersampled training dataset
         elif method_id == 3:
-            ...
+            tuned_models = {
+            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'entropy'}), 
+            "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 224}), 
+            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 0.8, 'n_estimators': 13, 'warm_start': True}),
+            "AB" : AdaBoostClassifier(**{'learning_rate': 1.836659462701278, 'n_estimators': 138, 'algorithm': 'SAMME'}), 
+            "XGB": XGBClassifier(**{'learning_rate': 0.2517946893282251, 'max_depth': 4, 'n_estimators': 646}),
+            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'l2', 'max_iter': 1000}), 
+            "SVM" : SVC(**{'C': 1.8414678085000697, 'kernel': 'linear', 'max_iter':1000}), 
+            "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 76, 'learning_rate': 'constant', 'max_iter':500})
+            }
     return tuned_models
 # --------------------------------------------------------------------------------------------------------