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49 lines
1.6 KiB
49 lines
1.6 KiB
1 year ago
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import multiprocessing
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import time
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import numpy as np
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import pandas as pd
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import torch
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from tqdm import tqdm
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from md_discovery.multi_process_infer_by_pairs import table_encode, inference_from_record_pairs
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from md_discovery import tmp_discover
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from settings import er_output_dir, similarity_threshold, target_attr, embedding_dict
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def fuck(i):
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i = i*i+1
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if __name__ == '__main__':
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start = time.time()
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tp_single_tuple_path = er_output_dir + "tp_single_tuple.csv"
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# tp_mds, tp_vio = inference_from_record_pairs(tp_single_tuple_path, similarity_threshold, target_attr)
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tp_mds, tp_vio = tmp_discover.inference_from_record_pairs(tp_single_tuple_path, similarity_threshold, target_attr)
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print(time.time()-start)
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# li = [[[6, 6, 2],
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# [2, 4, 6],
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# [2, 4, 7],
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# [3, 6, 4]],
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# [[6, 2, 7],
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# [3, 2, 4],
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# [5, 3, 5],
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# [6, 2, 4]],
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# [[7, 2, 2],
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# [6, 3, 2],
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# [6, 4, 3],
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# [6, 5, 6]]]
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# tensor = torch.Tensor(li)
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# norm_tensor = torch.nn.functional.normalize(tensor, dim=2)
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# print(norm_tensor, '\n')
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# sim_ten = torch.matmul(norm_tensor, norm_tensor.transpose(1, 2))
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# print(sim_ten/2 + 0.5, '\n')
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# print(sim_ten.size())
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# multiprocessing.set_start_method("spawn")
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# manager = multiprocessing.Manager()
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# lock = manager.Lock()
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# pool = multiprocessing.Pool(16)
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# with manager:
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# for _ in tqdm(range(0, 1000)):
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# result = pool.apply_async(fuck, args=(_,))
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# print(result)
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