Update csqa_dataset.py

Author: yuchenlin

models/csqa_dataset.py

@@ -489,73 +489,3 @@ def collate_csqa_graphs_and_paths(samples):
     return sents_vecs,  torch.Tensor([[i] for i in correct_labels]), batched_graph, cpt_path_data, rel_path_data, qa_pair_data, concept_mapping_dicts
 
 
-# csqa_data = CSQA_Path("../datasets/csqa_new/dev_rand_split.jsonl.statements",
-#                       "../datasets/csqa_new/dev_rand_split.jsonl.statements.mcp.pf",
-#                       "../datasets/csqa_new/dev_rand_split.jsonl.statements.-2.mean.large.npy",
-#                       num_choice=5)
-
-
-# if __name__ == '__main__':
-#     port = 22019
-#     pf_json_data_client = PathFindingClient.getPathData(port)
-#
-#
-#     # with open(pf_json_file, 'r') as f:
-#     #     pf_json_data = json.load(f)
-#
-#     statement_json_data = []
-#     with open("../datasets/csqa_new/dev_rand_split.jsonl.statements", "r") as fp:
-#         for line in fp.readlines():
-#             statement_data = json.loads(line.strip())
-#             statement_json_data.append(statement_data)
-#
-#
-#     num_choice = 5
-#     port = 22019
-#     pf_json_data_client = PathFindingClient.getPathData(port)
-#
-#
-#     # pf_json_data = [pf_json_data_client.getPaths(index) for index in list(range(len(statement_json_data) * num_choice))]
-#
-#     start_time = timeit.default_timer()
-#     pf_json_data = pf_json_data_client.getAll()
-#     print('\t Done! Time: ', "{0:.2f} sec".format(float(timeit.default_timer() - start_time)))
-#
-#     start_time = timeit.default_timer()
-#     pf_json_data = pf_json_data_client.getAll()
-#     print('\t Done! Time: ', "{0:.2f} sec".format(float(timeit.default_timer() - start_time)))
-
-
-# csqa_graph_data = data_with_graphs("../datasets/csqa_new/dev_rand_split.jsonl.statements",
-#                       "../datasets/csqa_new/dev_rand_split.jsonl.statements.pnxg",
-#                       "../datasets/csqa_new/dev_rand_split.jsonl.statements.-2.mean.large.npy",
-#                       num_choice=5, reload=True)
-#
-#
-# dataset_loader = data.DataLoader(csqa_graph_data, batch_size=10, num_workers=0, shuffle=True,
-#                                      collate_fn=collate_csqa_graphs)
-#
-# for k, (statements, correct_labels, graphs) in enumerate(
-#             tqdm(dataset_loader, desc="Train Batch")):
-#     # print(k)
-#     bgs = dgl.unbatch(graphs)
-
-
-
-# csqa_graph_path_data = data_with_graphs_and_paths("../datasets/csqa_new/dev_rand_split.jsonl.statements",
-#                       "../datasets/csqa_new/dev_rand_split.jsonl.statements.pnxg",
-#                       "../datasets/csqa_new/dev_rand_split.jsonl.statements.mcp.pf.pickle",
-#                       "../datasets/csqa_new/dev_rand_split.jsonl.statements.-2.mean.large.npy",
-#                       num_choice=5, reload=True, cut_off=4)
-#
-#
-# dataset_loader = data.DataLoader(csqa_graph_path_data, batch_size=10, num_workers=0, shuffle=True,
-#                                      collate_fn=collate_csqa_graphs_and_paths)
-#
-# for k, (statements, correct_labels, graphs, cpt_path_data, rel_path_data, qa_pair_data, concept_mapping_dicts) in enumerate(
-#             tqdm(dataset_loader, desc="Train Batch")):
-#     # print(k)
-#     bgs = dgl.unbatch(graphs)
-
-
-