ensemble_model

def get_ensemble_models():
    rf =RandomForestClassifier(n_estimators=51,min_samples_leaf=5,min_samples_split=3)
    bagg = BaggingClassifier(n_estimators=51,random_state=42)
    extra = ExtraTreesClassifier(n_estimators=51,random_state=42)
    ada = AdaBoostClassifier(n_estimators=51,random_state=42)
    grad = GradientBoostingClassifier(n_estimators=51,random_state=42)
    classifier_list = [rf,bagg,extra,ada,grad]
    classifier_name_list = ['Random Forests','Bagging','Extra Trees','AdaBoost','Gradient Boost']
    return classifier_list,classifier_name_list
    
def print_evaluation_metrics(trained_model,trained_model_name,X_test,y_test):
    print('--------- Model : ', trained_model_name, ' ---------------\n')
    predicted_values = trained_model.predict(X_test)
    print(metrics.classification_report(y_test,predicted_values))
    print("Accuracy Score : ",metrics.accuracy_score(y_test,predicted_values))
    print("---------------------------------------\n")
    
classifier_list, classifier_name_list = get_ensemble_models()
for classifier,classifier_name in zip(classifier_list,classifier_name_list):
    classifier.fit(X_train,y_train)
    print_evaluation_metrics(classifier,classifier_name,X_test,y_test)