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127 lines
3.4 KiB
127 lines
3.4 KiB
import json
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import multiprocessing
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import os
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import time
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import ConfigSpace
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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 ConfigSpace.read_and_write import json as csj
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from md_discovery import tmp_discover
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from settings import er_output_dir, similarity_threshold, target_attr, hpo_output_dir
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def fuck(i):
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i = i * i + 1
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def test1():
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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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def test2():
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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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def test3():
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dic = {'description': 0, 'id': 1, 'manufacturer': 0, 'name': 0.9309734582901001, 'price': 0.912541675567627}
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ll = list(dic.values())
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ten = torch.Tensor(ll)
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t = ten.unsqueeze(1)
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t = t.unsqueeze(2)
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y = t.repeat(1, 742, 742)
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print(ten)
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print(y)
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print(torch.isfinite(ten))
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print(torch.count_nonzero(y).item())
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def test4():
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one_bool_tensor = torch.ones((3, 3, 3), dtype=torch.bool)
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print(torch.count_nonzero(one_bool_tensor).item())
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def test5():
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ten1 = torch.tensor([[1, 2, 3],
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[7, 8, 9]])
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ten2 = torch.tensor([[4, 5, 6],
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[11, 12, 15]])
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result = ten1 * ten2
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r = torch.sum(result, 1)
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print('\n')
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print(result)
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print(r)
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def test6():
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table_tensor = torch.tensor([[[1., 2., 3.],
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[4., 5., 6.],
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[7., 8., 9.]],
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[[1., 2., 3.],
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[4., 5., 6.],
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[7., 8., 9.]]])
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t = torch.tensor([[1., 2., 3.],
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[4., 5., 6.]])
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norm1 = torch.nn.functional.normalize(table_tensor, dim=1)
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norm2 = torch.nn.functional.normalize(table_tensor, dim=2)
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print('\n')
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print(norm1)
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print(norm2)
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print(t.shape)
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def test7():
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iterations = 1
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filename_list = os.listdir(er_output_dir)
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if len(filename_list) > 0:
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for _ in filename_list:
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if _.startswith('eval_result'):
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iterations = int(_[12:13]) + 1
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print(iterations)
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def test8():
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cum = np.load(hpo_output_dir + 'incumbent.npy')
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with open(hpo_output_dir + "configspace.json", 'r') as load_f:
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dict_configspace = json.load(load_f)
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str_configspace = json.dumps(dict_configspace)
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configspace = csj.read(str_configspace)
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config = ConfigSpace.Configuration(configspace, vector=cum)
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print(cum)
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if __name__ == '__main__':
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start = time.time()
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t_single_tuple_path = er_output_dir + "t_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.pairs_inference(t_single_tuple_path, similarity_threshold, target_attr)
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print(time.time() - start)
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