predictor.py 文件源码

def score(self, X_test, y_test, advanced_scoring=True, verbose=2):

        if isinstance(X_test, list):
            X_test = pd.DataFrame(X_test)
        y_test = list(y_test)

        X_test, y_test = utils.drop_missing_y_vals(X_test, y_test, self.output_column)

        if self._scorer is not None:
            if self.type_of_estimator == 'regressor':
                return self._scorer.score(self.trained_pipeline, X_test, y_test, self.took_log_of_y, advanced_scoring=advanced_scoring, verbose=verbose, name=self.name)

            elif self.type_of_estimator == 'classifier':
                # TODO: can probably refactor accuracy score now that we've turned scoring into it's own class
                if self._scorer == accuracy_score:
                    predictions = self.trained_pipeline.predict(X_test)
                    return self._scorer.score(y_test, predictions)
                elif advanced_scoring:
                    score, probas = self._scorer.score(self.trained_pipeline, X_test, y_test, advanced_scoring=advanced_scoring)
                    utils_scoring.advanced_scoring_classifiers(probas, y_test, name=self.name)
                    return score
                else:
                    return self._scorer.score(self.trained_pipeline, X_test, y_test, advanced_scoring=advanced_scoring)
        else:
            return self.trained_pipeline.score(X_test, y_test)