def load_subgraph()

in src/sagemaker/FD_SL_DGL/clients_python/client_boto_demo.py [0:0]

• 7 lines of code
• 7 McCabe index (conditional complexity)

def load_subgraph(file_path):
    """
    Revise this function to fit with new input format
    :param file_path:
    :return:
    """
    print('------------------ Loading Graph -------------------')
    with open(file_path, 'rb') as f:
        graph_dict = pickle.load(f)
        # print(subgraph_dict)
        subgraph_dict = graph_dict['subgraph_dict']
        n_feat_dict = graph_dict['n_feat_dict']

        # generate fake node features
        # new_subgraph_dict = {}
        # n_lists = {}
        # for can_etype, e_lists in subgraph_dict.items():
        #     src_type, etype, dst_type = can_etype
        #     src_list, dst_list = e_lists
        #     src_list = [src_type + '-' + str(nid) for nid in src_list]
        #     dst_list = [dst_type + '-' + str(nid) for nid in dst_list]
        #
        #     # change canonical etype from tuple to string
        #     new_subgraph_dict['{}<>{}'.format(src_type, dst_type)] = (src_list.tolist(), dst_list.tolist())
        #
        #     if n_lists.get(src_type) is not None:
        #         n_lists[src_type] = np.append(n_lists.get(src_type), src_list)
        #     else:
        #         n_lists[src_type] = src_list
        #
        #     if n_lists.get(dst_type) is not None:
        #         n_lists[dst_type] = np.append(n_lists.get(dst_type), dst_list)
        #     else:
        #         n_lists[dst_type] = dst_list
        #
        # n_feats = {}
        # for ntype, n_list in n_lists.items():
        #     # print(np.unique(n_list))
        #     num_nodes = np.unique(n_list).size
        #     print(ntype, num_nodes)
        #     # n_feats[ntype] = np.random.randint(0,100, size=(num_nodes, 390)).tolist()
        #     n_feats[ntype] = np.random.rand(size=(num_nodes, 390)).astype(np.float32).tolist()

    return subgraph_dict, n_feat_dict