From af39854399a5643e9d6019c87f2752405cbb1fa3 Mon Sep 17 00:00:00 2001 From: joaquintb Date: Fri, 10 May 2024 12:26:51 +0200 Subject: [PATCH] style fix --- model_selection/test_models.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/model_selection/test_models.py b/model_selection/test_models.py index 3eda93c..3004bf2 100644 --- a/model_selection/test_models.py +++ b/model_selection/test_models.py @@ -55,13 +55,14 @@ def get_tuned_models(group_id, method_id): # 1.2) Trained with original dataset and cost-sensitive learning elif method_id == 1: tuned_models = { - "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'log2', 'criterion': 'log_loss', '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}, estimator= DecisionTreeClassifier(class_weight='balanced'), algorithm='SAMME'), - "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'), + "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'}), } + # 1.3) Trained with oversampled training dataset elif method_id == 2: tuned_models = { -- 2.24.1