4c3790c4553cef37eae4a4e308307f05772579cf,AutoML2015/models/evaluate.py,,evaluate,#Any#Any#Any#,16

Before Change

        Y_optimization = Y_optimization_binary

    if task_type == "multilabel.classification":
        Y_pred = np.hstack([Y_pred[i][:, 1].reshape((-1, 1))
                             for i in range(len(Y_pred))])

    scoring_func = getattr(libscores, metric)

After Change

                                                       Y_optimization_pred)
    score = scoring_func(csolution, cprediction, task=task_type)

    Y_valid_pred = predict_proba(X_valid, model, task_type)
    Y_test_pred = predict_proba(X_test, model, task_type)

    err = 1 - score

Instances