matching_dependency/script/get_support_and_confidence.py

import time
from functions.multi_process_infer_by_pairs import inference_from_record_pairs
from functions.multi_process_infer_by_pairs import get_mds_metadata

if __name__ == '__main__':
    # 目前可以仿照这个main函数写
    path = "/home/w/PycharmProjects/matching_dependency/input/T_positive_with_id_concat_single_tuple.csv"
    start = time.time()
    # 输入：csv文件路径，md左侧相似度阈值，md右侧目标字段
    # 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)
    # 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
    mds, mds_vio = inference_from_record_pairs(path, 0.1, 'id_concat')

    # 如果不需要输出support和confidence，去掉下面两行
    mds_meta = get_mds_metadata(mds, path, 'id_concat')
    mds_vio_meta = get_mds_metadata(mds_vio, path, 'id_concat')

    # # 若不输出support和confidence，使用以下两块代码
    # # 将列表1写入本地，路径需自己修改
    # md_path = '/home/w/A-New Folder/8.14/Paper Dataset/TP_md_list.txt'
    # with open(md_path, 'w') as f:
    #     for _ in mds:
    #         f.write(str(_) + '\n')
    #
    # # 将列表2写入本地，路径需自己修改
    # vio_path = '/home/w/A-New Folder/8.14/Paper Dataset/TP_vio_list.txt'
    # with open(vio_path, 'w') as f:
    #     for _ in mds_vio:
    #         f.write(str(_) + '\n')

    # 若输出support和confidence，使用以下两块代码
    # 将列表1写入本地，路径需自己修改
    md_path = "output/md.txt"
    with open(md_path, 'w') as f:
        for _ in mds_meta:
            for i in _.keys():
                f.write(i + ':' + str(_[i]) + '\t')
            f.write('\n')

    # 将列表2写入本地，路径需自己修改
    vio_path = "output/vio.txt"
    with open(vio_path, 'w') as f:
        for _ in mds_vio_meta:
            for i in _.keys():
                f.write(i + ':' + str(_[i]) + '\t')
            f.write('\n')

    print(time.time() - start)