11.3

bert.py

@@ -0,0 +1,27 @@
+import csv
+
+import pandas as pd
+import json
+import sentence_transformers.util
+import torch
+from sentence_transformers import SentenceTransformer
+from torch import nn
+
+if __name__ == '__main__':
+    # model = SentenceTransformer('E:\\Data\\Research\\Models\\roberta-large-nli-stsb-mean-tokens')
+    # sentences = ['公积金转入深圳', '公积金转出深圳', None, None, 114514, 114514, 1919810]
+    # embedding = model.encode(sentences, device='cuda')
+    # outcome1 = sentence_transformers.util.cos_sim(embedding[4], embedding[5])
+    # outcome2 = sentence_transformers.util.cos_sim(embedding[4], embedding[6])
+    # print(outcome1.item())
+    # print(outcome2.item())
+    train = pd.read_csv(r'E:\Data\Research\Datasets\DeepMatcher dataset\Dirty\Walmart-Amazon\train.csv', encoding='ISO-8859-1')
+    valid = pd.read_csv(r'E:\Data\Research\Datasets\DeepMatcher dataset\Dirty\Walmart-Amazon\valid.csv', encoding='ISO-8859-1')
+    test = pd.read_csv(r'E:\Data\Research\Datasets\DeepMatcher dataset\Dirty\Walmart-Amazon\test.csv', encoding='ISO-8859-1')
+    train = train[train['label'] == 1]
+    valid = valid[valid['label'] == 1]
+    test = test[test['label'] == 1]
+    matches = pd.concat([train, valid, test])
+    matches.drop(columns=['label'], inplace=True)
+    matches = matches.sort_values(by='ltable_id')
+    matches.to_csv(r'E:\Data\Research\Projects\matching_dependency\datasets\Walmart-Amazon_dirty\matches.csv', sep=',', index=False, header=True)

draw.py

@@ -0,0 +1,55 @@
+import os
+
+import pyecharts
+from pyecharts.charts import Line
+from pyecharts import options as opts
+from pyecharts.globals import ThemeType
+
+if __name__ == '__main__':
+    dir_path = r'E:\Data\Research\Outcome\Abt-Buy'
+    filename_list = os.listdir(dir_path)
+    iter_list = []
+    precision = []
+    recall = []
+    f1 = []
+    interpretability = []
+    performance = []
+    for _ in filename_list:
+        if _.startswith('eval_result'):
+            it = int(_[12:13])
+            iter_list.append(str(it))
+            with open(dir_path + '\\' + _, 'r') as f:
+                # 读取每一行的md，加入该文件的md列表
+                for line in f.readlines():
+                    if line.startswith('Precision'):
+                        lt = line.split(' ')
+                        value = float(lt[2].replace('%', ''))/100
+                        precision.append(value)
+                    elif line.startswith('Recall'):
+                        lt = line.split(' ')
+                        value = float(lt[2].replace('%', ''))/100
+                        recall.append(value)
+                    elif line.startswith('F1'):
+                        lt = line.split(' ')
+                        value = float(lt[2].replace('%', ''))/100
+                        f1.append(value)
+                    elif line.startswith('interpretability'):
+                        lt = line.split(':')
+                        value = float(lt[1])
+                        interpretability.append(value)
+                    elif line.startswith('performance'):
+                        lt = line.split(':')
+                        value = float(lt[1])
+                        performance.append(value)
+
+    line = (
+        Line(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
+        .add_xaxis(iter_list)
+        .add_yaxis('Precision', precision)
+        .add_yaxis('Recall', recall)
+        .add_yaxis('F1', f1)
+        .add_yaxis('Interpretability', interpretability)
+        .add_yaxis('Performance', performance)
+        .set_global_opts(title_opts=opts.TitleOpts(title=dir_path.split('\\')[-1]))
+    )
+    line.render(dir_path + '\\' + "line.html")

hpo/er_model_hpo.py

@@ -17,17 +17,17 @@ from ml_er.ml_entity_resolver import evaluate_prediction, load_mds, is_explicabl
 # 数据在外部加载
 ########################################################################################################################
 ltable = pd.read_csv(ltable_path, encoding='ISO-8859-1')
-# ltable.fillna("", inplace=True)
+ltable.fillna("", inplace=True)
 rtable = pd.read_csv(rtable_path, encoding='ISO-8859-1')
-# rtable.fillna("", inplace=True)
+rtable.fillna("", inplace=True)
 mappings = pd.read_csv(mapping_path)
 
 lid_mapping_list = []
 rid_mapping_list = []
 # 全部转为字符串
-# ltable = ltable.astype(str)
-# rtable = rtable.astype(str)
-# mappings = mappings.astype(str)
+ltable = ltable.astype(str)
+rtable = rtable.astype(str)
+mappings = mappings.astype(str)
 matching_number = len(mappings)  # 所有阳性样本数，商品数据集应为1300
 
 for index, row in mappings.iterrows():
@@ -162,8 +162,6 @@ class Classifier:
                                                     attrs_after=test_feature_after, show_progress=False)
 
         fit_exclude = ['_id', 'ltable_' + tables_id, 'rtable_' + tables_id, 'gold']
-        train_feature_vecs.fillna(0, inplace=True)
-        test_feature_vecs.fillna(0, inplace=True)
         matcher.fit(table=train_feature_vecs, exclude_attrs=fit_exclude, target_attr='gold')
 
         test_feature_after.extend(['_id', 'ltable_' + tables_id, 'rtable_' + tables_id])
@@ -181,7 +179,7 @@ class Classifier:
         predictions_attrs.extend(['gold', 'predicted'])
         predictions = predictions[predictions_attrs]
         predictions = predictions.reset_index(drop=True)
-        # predictions = predictions.astype(str)
+        predictions = predictions.astype(str)
         sim_tensor_dict = build_col_pairs_sim_tensor_dict(predictions)
 
         #  默认路径为 "../md_discovery/output/xxx.txt"
@@ -197,10 +195,11 @@ class Classifier:
         ppre = predictions[predictions['predicted'] == str(1)]
         interpretability = epl_match / len(ppre)  # 可解释性
 
-        if indicators["block_recall"] >= 0.8:
-            f1 = indicators["F1"]
+        if (indicators["block_recall"] < 0.8) and (indicators["block_recall"] < indicators["recall"]):
+            f1 = (2.0 * indicators["precision"] * indicators["block_recall"]) / (
+                        indicators["precision"] + indicators["block_recall"])
         else:
-            f1 = (2.0 * indicators["precision"] * indicators["block_recall"]) / (indicators["precision"] + indicators["block_recall"])
+            f1 = indicators["F1"]
         # if indicators["block_recall"] < 0.8:
         #     return 1
         # f1 = indicators["F1"]

ml_er/ml_entity_resolver.py

@@ -157,9 +157,9 @@ def ml_er(iter_round: int, config: Configuration = None, ):
     lid_mapping_list = []
     rid_mapping_list = []
     # 全部转为字符串
-    # ltable = ltable.astype(str)
-    # rtable = rtable.astype(str)
-    # mappings = mappings.astype(str)
+    ltable = ltable.astype(str)
+    rtable = rtable.astype(str)
+    mappings = mappings.astype(str)
     matching_number = len(mappings)  # 所有阳性样本数
 
     for index, row in mappings.iterrows():
@@ -206,9 +206,9 @@ def ml_er(iter_round: int, config: Configuration = None, ):
                                              config["block_attr"], allow_missing=True,
                                              l_output_attrs=selected_attrs, r_output_attrs=selected_attrs)
     else:
-        matcher = em.RFMatcher(name='RF', random_state=0)
+        matcher = em.SVMMatcher(name='SVM', random_state=0)
         blocker = em.OverlapBlocker()
-        candidate = blocker.block_tables(selected_ltable, selected_rtable, items_but_id[0], items_but_id[0],
+        candidate = blocker.block_tables(selected_ltable, selected_rtable, selected_attrs[-1], selected_attrs[-1],
                                          l_output_attrs=selected_attrs, r_output_attrs=selected_attrs,
                                          overlap_size=1, show_progress=False, allow_missing=True)
 
@@ -229,6 +229,8 @@ def ml_er(iter_round: int, config: Configuration = None, ):
     for row in candidate_match_rows:
         candidate.loc[row, 'gold'] = 1
 
+    candidate.fillna("", inplace=True)
+
     # 裁剪负样本，保持正负样本数量一致
     candidate_mismatch = candidate[candidate['gold'] == 0]
     candidate_match = candidate[candidate['gold'] == 1]
@@ -266,8 +268,6 @@ def ml_er(iter_round: int, config: Configuration = None, ):
                                                 attrs_after=test_feature_after, show_progress=False)
 
     fit_exclude = ['_id', 'ltable_' + tables_id, 'rtable_' + tables_id, 'gold']
-    train_feature_vecs.fillna(0, inplace=True)
-    test_feature_vecs.fillna(0, inplace=True)
     matcher.fit(table=train_feature_vecs, exclude_attrs=fit_exclude, target_attr='gold')
     test_feature_after.extend(['_id', 'ltable_' + tables_id, 'rtable_' + tables_id])
     predictions = matcher.predict(table=test_feature_vecs, exclude_attrs=test_feature_after,
@@ -299,11 +299,11 @@ def ml_er(iter_round: int, config: Configuration = None, ):
 
     df = predictions[predictions['predicted'] == str(1)]
     interpretability = epl_match / len(df)  # 可解释性
-    if indicators["block_recall"] >= 0.8:
-        f1 = indicators["F1"]
-    else:
+    if (indicators["block_recall"] < 0.8) and (indicators["block_recall"] < indicators["recall"]):
         f1 = (2.0 * indicators["precision"] * indicators["block_recall"]) / (
                     indicators["precision"] + indicators["block_recall"])
+    else:
+        f1 = indicators["F1"]
     performance = interpre_weight * interpretability + (1 - interpre_weight) * f1
     ################################################################################################################
 

settings.py

@@ -4,16 +4,16 @@ import numpy as np
 ltable_path = r'E:\Data\Research\Projects\matching_dependency\datasets\Walmart-Amazon_dirty\tableA.csv'
 rtable_path = r'E:\Data\Research\Projects\matching_dependency\datasets\Walmart-Amazon_dirty\tableB.csv'
 mapping_path = r'E:\Data\Research\Projects\matching_dependency\datasets\Walmart-Amazon_dirty\matches.csv'
-mapping_lid = 'id1'  # mapping表中左表id名
-mapping_rid = 'id2'  # mapping表中右表id名
+mapping_lid = 'ltable_id'  # mapping表中左表id名
+mapping_rid = 'rtable_id'  # mapping表中右表id名
 ltable_id = 'id'  # 左表id字段名称
 rtable_id = 'id'  # 右表id字段名称
 target_attr = 'id'  # 进行md挖掘时的目标字段
 lr_attrs_map = {}  # 如果两个表中存在对应字段名称不一样的情况，将名称加入列表便于调整一致
 similarity_threshold = 0.2
 support_threshold = 1
-confidence_threshold = 0.5
-interpre_weight = 0.3  # 可解释性权重
+confidence_threshold = 0.4
+interpre_weight = 0.4  # 可解释性权重
 er_output_dir = 'E:\\Data\\Research\\Projects\\matching_dependency\\ml_er\\output\\'
 md_output_dir = 'E:\\Data\\Research\\Projects\\matching_dependency\\md_discovery\\output\\'
 hpo_output_dir = 'E:\\Data\\Research\\Projects\\matching_dependency\\hpo\\output\\'