def load_data_split(self, ent2id, root_path):
        """Load data split

        Parameters
        ----------
        ent2id: func
            A function mapping entity to id
        root_path: str
            Root path containing the data

        Return
        ------
        train_idx: Numpy array
            Training set
        test_idx: Numpy array
            Testing set
        labels: Numpy array
            Labels
        num_classes: int
            Number of classes
        """
        label_dict = {}
        labels = np.zeros((self._hg.num_nodes(self.predict_category),)) - 1
        train_idx = self.parse_idx_file(
            os.path.join(root_path, "trainingSet.tsv"),
            ent2id,
            label_dict,
            labels,
        )
        test_idx = self.parse_idx_file(
            os.path.join(root_path, "testSet.tsv"), ent2id, label_dict, labels
        )
        train_idx = np.array(train_idx)
        test_idx = np.array(test_idx)
        labels = np.array(labels)
        num_classes = len(label_dict)
        return train_idx, test_idx, labels, num_classes
Load data split

        Parameters
        ----------
        ent2id: func
            A function mapping entity to id
        root_path: str
            Root path containing the data

        Return
        ------
        train_idx: Numpy array
            Training set
        test_idx: Numpy array
            Testing set
        labels: Numpy array
            Labels
        num_classes: int
            Number of classes
        
python/dgl/data/rdf.py