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import numpy as np
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import pandas as pd
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import time
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import Levenshtein
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import copy
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def my_Levenshtein_ratio(str1, str2):
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return 1 - Levenshtein.distance(str1, str2) / max(len(str1), len(str2))
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def if_minimal(md, md_list, target_col):
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# 假设这个md是minimal
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minimal = True
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for _ in md_list:
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if _ != md:
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# 假设列表中每一个md都使当前md不minimal
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exist = True
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# 如果左边任何一个大于，则假设不成立
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for col in list(set(_.keys()) - set([target_col])):
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if _[col] > md[col]:
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exist = False
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# 如果右边小于，假设也不成立
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if _[target_col] < md[target_col]:
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exist = False
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# 任何一次假设成立，当前md不minimal
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if exist:
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minimal = False
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break
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return minimal
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def satisfy_confidence(md, df, conf_thresh, target_col):
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support = 0
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support_plus = 0
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for row1 in df.itertuples():
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i = row1[0]
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df_slice = df[i + 1:]
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for row2 in df_slice.itertuples():
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left_satisfy = True
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both_satisfy = True
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for col in df.columns.values.tolist():
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sim = my_Levenshtein_ratio(getattr(row1, col), getattr(row2, col))
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if col == target_col:
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if sim < 1:
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both_satisfy = False
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else:
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if sim < md[col]:
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left_satisfy = False
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both_satisfy = False
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if left_satisfy:
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support += 1
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if both_satisfy:
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support_plus += 1
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confidence = support_plus / support
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return confidence >= conf_thresh
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def inference_from_record_pairs(path, threshold, target_col):
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data = pd.read_csv(path, low_memory=False, encoding='ISO-8859-1')
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data = data.astype(str)
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columns = data.columns.values.tolist()
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md_list = []
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minimal_vio = []
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init_md = {}
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for col in columns:
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init_md[col] = 1 if col == target_col else 0
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md_list.append(init_md)
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for row1 in data.itertuples():
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# 获取当前行的索引，从后一行开始切片
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i = row1[0]
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data1 = data[i + 1:]
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for row2 in data1.itertuples():
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violated_mds = []
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# sims是两行的相似度
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sims = {}
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for col in columns:
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similarity = my_Levenshtein_ratio(getattr(row1, col), getattr(row2, col))
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sims[col] = similarity
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# 寻找violated md,从md列表中删除并加入vio列表
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for md in md_list:
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lhs_satis = True
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rhs_satis = True
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for col in list(set(columns) - set([target_col])):
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if sims[col] < md[col]:
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lhs_satis = False
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if sims[target_col] < md[target_col]:
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rhs_satis = False
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if lhs_satis == True and rhs_satis == False:
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md_list.remove(md)
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violated_mds.append(md)
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minimal_vio.extend(violated_mds)
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for vio_md in violated_mds:
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# 特殊化右侧,我们需要右侧百分百相似，其实不需要降低右侧阈值
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# if sims[target_col] >= threshold:
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# new_rhs = sims[target_col]
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# spec_r_md = copy.deepcopy(vio_md)
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# spec_r_md[target_col] = new_rhs
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# if if_minimal(spec_r_md, md_list, target_col):
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# md_list.append(spec_r_md)
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# 特殊化左侧
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for col in list(set(columns) - set([target_col])):
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if sims[col] + 0.001 <= 1:
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spec_l_md = copy.deepcopy(vio_md)
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spec_l_md[col] = threshold if sims[col] < threshold else sims[col] + 0.001
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if if_minimal(spec_l_md, md_list, target_col):
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md_list.append(spec_l_md)
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for vio in minimal_vio:
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if not if_minimal(vio, md_list, target_col):
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minimal_vio.remove(vio)
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for _ in minimal_vio:
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if not satisfy_confidence(_, data, 0.8, target_col):
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minimal_vio.remove(_)
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list1 = copy.deepcopy(minimal_vio)
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for _ in list1:
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if not if_minimal(_, minimal_vio, target_col):
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minimal_vio.remove(_)
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return md_list, minimal_vio
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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/py_entitymatching/py_entitymatching/datasets/end-to-end/Amazon-GoogleProducts/output/8.14/TP_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, 0.7, 'id')
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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/Goods 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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# 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/Goods 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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print(time.time() - start)
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