Merge pull request #272 from nzw0301/unify-intermediate-and-objective

Use auc score to unify the intermediate and objective values

lightgbm/lightgbm_integration.py

@@ -10,7 +10,6 @@
 
 """
 
-import numpy as np
 import optuna
 
 import lightgbm as lgb
@@ -46,9 +45,7 @@ def objective(trial):
     gbm = lgb.train(param, dtrain, valid_sets=[dvalid], callbacks=[pruning_callback])
 
     preds = gbm.predict(valid_x)
-    pred_labels = np.rint(preds)
-    accuracy = sklearn.metrics.accuracy_score(valid_y, pred_labels)
-    return accuracy
+    return sklearn.metrics.roc_auc_score(valid_y, preds)
 
 
 if __name__ == "__main__":