小编Kyr*_*kos的帖子

我正在尝试使用 Optuna 调整额外的树分类器。

我在所有的试验中都收到这样的消息：

[W 2022-02-10 12:13:12,501] 试验 2 失败，因为值 None 无法转换为浮点数。

下面是我的代码。我所有的考验都会发生这种情况。谁能告诉我我做错了什么？

    def objective(trial, X, y):
    
        param = {
            'verbose': trial.suggest_categorical('verbosity', [1]),
            'random_state': trial.suggest_categorical('random_state', [RS]),
            'n_estimators': trial.suggest_int('n_estimators', 100, 150),
            'n_jobs': trial.suggest_categorical('n_jobs', [-1]),
        }
            
    
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True, random_state=RS)
    
        clf = ExtraTreesClassifier(**param)
        
        clf.fit(X_train, y_train)
        
        y_pred = clf.predict(X_test)
        
        acc = accuracy_score(y_pred, y_test)
        print(f"Model Accuracy: {round(acc, 6)}")
        print(f"Model Parameters: {param}")
        print('='*50)
        return`
    
    
        study = optuna.create_study(
            direction='maximize',
            sampler=optuna.samplers.TPESampler(),
            pruner=optuna.pruners.HyperbandPruner(),
            study_name='ExtraTrees-Hyperparameter-Tuning')

    func = lambda trial: objective(trial, X, y)

    %%time …