data.py 文件源码

def train_val_split(self, split_ratio=0.1):
        """Generates train and validation sets from the training indices.

        Args:
            split_ratio: The split proportion in [0, 1] (Default value: 0.1)

        Returns:
            The stratified train and val subsets. Multi-label outputs are handled as well.
        """
        if self.is_multi_label:
            train_indices, val_indices = sampling.multi_label_train_test_split(self.y, split_ratio)
        else:
            sss = StratifiedShuffleSplit(n_splits=1, test_size=split_ratio)
            train_indices, val_indices = next(sss.split(self.X, self.y))
        return self.X[train_indices], self.X[val_indices], self.y[train_indices], self.y[val_indices]