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import time
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from functions.multi_process_infer_by_pairs import inference_from_record_pairs
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from functions.multi_process_infer_by_pairs import get_mds_metadata
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if __name__ == '__main__':
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# 目前可以仿照这个main函数写
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path = "/home/w/PycharmProjects/matching_dependency/input/T_positive_with_id_concat_single_tuple.csv"
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start = time.time()
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# 输入：csv文件路径，md左侧相似度阈值，md右侧目标字段
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# 输出：2个md列表，列表1中md无violation,列表2中md有violation但confidence满足阈值(0.8)
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# 例如此处输入参数要求md左侧相似度字段至少为0.7，右侧指向'id'字段
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mds, mds_vio = inference_from_record_pairs(path, 0.1, 'id_concat')
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# 如果不需要输出support和confidence，去掉下面两行
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mds_meta = get_mds_metadata(mds, path, 'id_concat')
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mds_vio_meta = get_mds_metadata(mds_vio, path, 'id_concat')
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# # 若不输出support和confidence，使用以下两块代码
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# # 将列表1写入本地，路径需自己修改
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# md_path = '/home/w/A-New Folder/8.14/Paper Dataset/TP_md_list.txt'
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# with open(md_path, 'w') as f:
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# for _ in mds:
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# f.write(str(_) + '\n')
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#
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# # 将列表2写入本地，路径需自己修改
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# vio_path = '/home/w/A-New Folder/8.14/Paper Dataset/TP_vio_list.txt'
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# with open(vio_path, 'w') as f:
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# for _ in mds_vio:
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# f.write(str(_) + '\n')
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# 若输出support和confidence，使用以下两块代码
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# 将列表1写入本地，路径需自己修改
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md_path = "output/md.txt"
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with open(md_path, 'w') as f:
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for _ in mds_meta:
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for i in _.keys():
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f.write(i + ':' + str(_[i]) + '\t')
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f.write('\n')
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# 将列表2写入本地，路径需自己修改
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vio_path = "output/vio.txt"
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with open(vio_path, 'w') as f:
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for _ in mds_vio_meta:
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for i in _.keys():
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f.write(i + ':' + str(_[i]) + '\t')
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f.write('\n')
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print(time.time() - start)
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