stacked_classifier.py 文件源码

def _extract_features(self, topNIndices, topNDistances, y, distances):
        samples = self._split_samples(topNIndices, topNDistances, y)
        training_data_list = Parallel(n_jobs=self.n_jobs)(
            delayed(_analyze)(tI, tD, y, distances, self.dependencies) for tI, tD, y in samples)

        # merge training data
        training_data = defaultdict(list)
        for training_data_dict in training_data_list:
            for label, training_samples_of_label in training_data_dict.items():
                training_data[label].extend(training_data_dict[label])
        return training_data