regression_stealer.py 文件源码

def evaluate(self, wopt, int_opt, X_test, base_model=None):
        # get the true class labels
        y_true = self.query(X_test)

        if X_test.shape[1] != self.num_features():
            X_test = self.encode(X_test)

        # predict classes using the optimized coefficients
        y_pred = predict_classes(X_test, wopt, int_opt, self.classes)

        """
        _, _, X, _, _ = utils.prepare_data(self.model_id, onehot=False)
        X = X.values
        for i in range(len(y_true)):
            if y_true[i] != y_pred[i]:
                print y_true[i], y_pred[i], X[i]
        """

        if base_model is not None:

            y_pred_base = base_model.predict(X_test)

            return accuracy_score(y_true, y_pred), \
                   accuracy_score(y_true, y_pred_base)

        return accuracy_score(y_true, y_pred)