def _init_decision_function(self, X):
        self._check_initialized()
        X = self.estimators_[0, 0]._validate_X_predict(X, check_input=True)
        if X.shape[1] != self.n_features:
            raise ValueError("X.shape[1] should be {0:d}, not {1:d}.".format(
                self.n_features, X.shape[1]))
        score = self.init_.predict(X).astype(np.float64)
        return score
Validates a chkcrontab command line and logs any errors and warnings.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/gradient_boosting.py/BaseGradientBoosting._init_decision_function--crossover with--dataset/ETHPy150Open lyda/chkcrontab/chkcrontab_lib.py/CronLineChkCrontabCmd.ValidateAndLog