I am getting an error on my modeling of lightgbm searching for optimal auc. Any help would be appreciated. import optuna from sklearn.model_selection import StratifiedKFold from optuna.integration import LightGBMPruningCallback def objective(trial, X, y): param = { "objective": "binary", "metric": "auc", "verbosity": -1, "boosting_type": "gbdt", "lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 10.0), "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 10.0), "num_leaves": trial.suggest_int("num_leaves", 2, 256), "feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0), "bagging_fraction": trial.suggest_uniform("bagging_fraction", 0.4, 1.0), "bagging_freq": trial.suggest_int("bagging_freq", 1, 7), "min_child_samples": trial.suggest_int("min_child_samples", 5, 100), } cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1121218) cv_scores = np.empty(5) for idx, (train_idx, test_idx) in enumerate(cv.split(X, y)): X_train, X_test = X.iloc[train_idx], X.iloc[test_idx] y_train, y_test = y[train_idx], y[test_idx] pruning_callback = optuna.integration.LightGBMPruningCallback(trial, "auc") model = lgb.LGBMClassifier(**param) model.fit( X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=100, callbacks=[pruning_callback]) preds = model.predict_proba(X_test) cv_scores[idx] = log_loss(y_test, preds) auc_scores[idx] = roc_auc_score(y_test, preds) return np.mean(cv_scores), np.mean(auc_scores) study = optuna.create_study(direction="minimize", study_name="LGBM Classifier") func = lambda trial: objective(trial, sample_df[cols_to_keep], sample_df[target]) study.optimize(func, n_trials=1) Trial 0 failed because of the following error: ValueError('The intermediate values are inconsistent with the objective values in terms of study directions. Please specify a metric to be minimized for LightGBMPruningCallback.',)* Continue reading...
