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from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Integer, Float
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import py_entitymatching as em
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import py_entitymatching.catalog.catalog_manager as cm
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import pandas as pd
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from py_entitymatching.blocker.blocker import Blocker
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from py_entitymatching.matcher.mlmatcher import MLMatcher
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from smac import HyperparameterOptimizationFacade, Scenario
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from md_discovery.functions.multi_process_infer_by_pairs import my_Levenshtein_ratio
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from entrance import *
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# todo 距离度量用户可设置
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# 全局变量，每次迭代后清空列表，加入新的md路径
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# todo:
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# 默认路径为 "../md_discovery/output/xxx.txt"
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# 真阳/假阴 mds/vio 共4个md文件
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def evaluate_prediction(df: pd.DataFrame, labeled_attr: str, predicted_attr: str, matching_number: int,
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test_proportion: float) -> dict:
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new_df = df.reset_index(drop=False, inplace=False)
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gold = new_df[labeled_attr]
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predicted = new_df[predicted_attr]
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gold_negative = gold[gold == 0].index.values
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gold_positive = gold[gold == 1].index.values
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predicted_negative = predicted[predicted == 0].index.values
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predicted_positive = predicted[predicted == 1].index.values
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false_positive_indices = list(set(gold_negative).intersection(predicted_positive))
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true_positive_indices = list(set(gold_positive).intersection(predicted_positive))
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false_negative_indices = list(set(gold_positive).intersection(predicted_negative))
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num_true_positives = float(len(true_positive_indices))
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num_false_positives = float(len(false_positive_indices))
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num_false_negatives = float(len(false_negative_indices))
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precision_denominator = num_true_positives + num_false_positives
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recall_denominator = num_true_positives + num_false_negatives
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precision = 0.0 if precision_denominator == 0.0 else num_true_positives / precision_denominator
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recall = 0.0 if recall_denominator == 0.0 else num_true_positives / recall_denominator
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F1 = 0.0 if precision == 0.0 and recall == 0.0 else (2.0 * precision * recall) / (precision + recall)
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my_recall = num_true_positives / (matching_number * test_proportion)
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return {"precision": precision, "recall": recall, "F1": F1, "my_recall": my_recall}
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def load_mds(paths: list) -> list:
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if len(paths) == 0:
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return []
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all_mds = []
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# 传入md路径列表
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for md_path in paths:
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mds = []
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# 打开每一个md文件
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with open(md_path, 'r') as f:
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# 读取每一行的md，加入该文件的md列表
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for line in f.readlines():
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md_metadata = line.strip().split('\t')
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md = eval(md_metadata[0].replace('md:', ''))
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confidence = eval(md_metadata[2].replace('confidence:', ''))
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if confidence > 0:
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mds.append(md)
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all_mds.extend(mds)
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return all_mds
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def is_explicable(row, all_mds: list) -> bool:
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attrs = all_mds[0].keys() # 从第一条md中读取所有字段
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for md in all_mds:
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explicable = True # 假设这条md能解释当前元组
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for a in attrs:
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threshold = md[a]
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if my_Levenshtein_ratio(str(getattr(row, 'ltable_'+a)), str(getattr(row, 'rtable_'+a))) < threshold:
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explicable = False # 任意一个字段的相似度达不到阈值，这条md就不能解释当前元组
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break # 不再与当前md的其他相似度阈值比较，跳转到下一条md
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if explicable:
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return True # 任意一条md能解释，直接返回
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return False # 遍历结束，不能解释
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class SVM:
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@property
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def configspace(self) -> ConfigurationSpace:
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# Build Configuration Space which defines all parameters and their ranges
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cs = ConfigurationSpace(seed=0)
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# todo
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# block_attr 取消打桩
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block_attr = Categorical("block_attr", ["name", "description", "manufacturer", "price"], default="title")
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overlap_size = Integer("overlap_size", (1, 3), default=1)
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ml_matcher = Categorical("ml_matcher", ["dt", "svm", "rf", "lg", "ln", "nb"], default="rf")
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ml_blocker = Categorical("ml_blocker", ["over_lap", "attr_equiv"], default="over_lap")
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cs.add_hyperparameters([block_attr, overlap_size, ml_matcher, ml_blocker])
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return cs
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# train 就是整个函数 只需将返回结果由预测变成预测结果的评估
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def train(self, config: Configuration) -> float:
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ltable = pd.read_csv(ltable_path, encoding='ISO-8859-1')
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cm.set_key(ltable, ltable_id)
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ltable.fillna("", inplace=True)
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rtable = pd.read_csv(rtable_path, encoding='ISO-8859-1')
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cm.set_key(rtable, rtable_id)
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rtable.fillna("", inplace=True)
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mappings = pd.read_csv(mapping_path)
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# 仅保留两表中出现在映射表中的行，增大正样本比例
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lid_mapping_list = []
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rid_mapping_list = []
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# 全部转为字符串
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ltable = ltable.astype(str)
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rtable = rtable.astype(str)
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mappings = mappings.astype(str)
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matching_number = len(mappings) # 所有阳性样本数，商品数据集应为1300
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for index, row in mappings.iterrows():
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lid_mapping_list.append(row[mapping_lid])
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rid_mapping_list.append(row[mapping_rid])
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selected_ltable = ltable[ltable[ltable_id].isin(lid_mapping_list)]
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selected_ltable = selected_ltable.rename(columns=lr_attrs_map) # 参照右表，修改左表中与右表对应但不同名的字段
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selected_rtable = rtable[rtable[rtable_id].isin(rid_mapping_list)]
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selected_attrs = selected_ltable.columns.values.tolist() # 两张表中的字段名
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attrs_with_l_prefix = ['ltable_'+i for i in selected_attrs]
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attrs_with_r_prefix = ['rtable_'+i for i in selected_attrs]
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cm.set_key(selected_ltable, ltable_id)
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cm.set_key(selected_rtable, rtable_id)
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blocker = None
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if config["ml_blocker"] == "over_lap":
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blocker = em.OverlapBlocker()
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candidate = blocker.block_tables(selected_ltable, selected_rtable, config["block_attr"], config["block_attr"],
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l_output_attrs=selected_attrs, r_output_attrs=selected_attrs,
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overlap_size=config["overlap_size"], show_progress=False, n_jobs=-1)
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elif config["ml_blocker"] == "attr_equiv":
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blocker = em.AttrEquivalenceBlocker()
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candidate = blocker.block_tables(selected_ltable, selected_rtable, config["block_attr"], config["block_attr"],
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l_output_attrs=selected_attrs, r_output_attrs=selected_attrs, n_jobs=-1)
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candidate['gold'] = 0
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candidate_match_rows = []
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for index, row in candidate.iterrows():
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l_id = row['ltable_' + ltable_id]
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map_row = mappings[mappings[mapping_lid] == l_id]
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if map_row is not None:
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r_id = map_row[mapping_rid]
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for value in r_id:
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if value == row['rtable_' + rtable_id]:
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candidate_match_rows.append(row["_id"])
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else:
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continue
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for row in candidate_match_rows:
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candidate.loc[row, 'gold'] = 1
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# 裁剪负样本，保持正负样本数量一致
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candidate_mismatch = candidate[candidate['gold'] == 0]
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candidate_match = candidate[candidate['gold'] == 1]
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if len(candidate_mismatch) > len(candidate_match):
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candidate_mismatch = candidate_mismatch.sample(n=len(candidate_match))
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# 拼接正负样本
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candidate_for_train_test = pd.concat([candidate_mismatch, candidate_match])
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cm.set_key(candidate_for_train_test, '_id')
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cm.set_fk_ltable(candidate_for_train_test, 'ltable_' + ltable_id)
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cm.set_fk_rtable(candidate_for_train_test, 'rtable_' + rtable_id)
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cm.set_ltable(candidate_for_train_test, selected_ltable)
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cm.set_rtable(candidate_for_train_test, selected_rtable)
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# 分为训练测试集
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train_proportion = 0.7
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test_proportion = 0.3
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sets = em.split_train_test(candidate_for_train_test, train_proportion=train_proportion, random_state=0)
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train_set = sets['train']
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test_set = sets['test']
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matcher = None
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if config["ml_matcher"] == "dt":
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matcher = em.DTMatcher(name='DecisionTree', random_state=0)
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elif config["ml_matcher"] == "svm":
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matcher = em.SVMMatcher(name='SVM', random_state=0)
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elif config["ml_matcher"] == "rf":
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matcher = em.RFMatcher(name='RF', random_state=0)
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elif config["ml_matcher"] == "lg":
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matcher = em.LogRegMatcher(name='LogReg', random_state=0)
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elif config["ml_matcher"] == "ln":
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matcher = em.LinRegMatcher(name='LinReg')
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elif config["ml_matcher"] == "nb":
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matcher = em.NBMatcher(name='NaiveBayes')
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feature_table = em.get_features_for_matching(selected_ltable, selected_rtable, validate_inferred_attr_types=False)
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train_feature_vecs = em.extract_feature_vecs(train_set,
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feature_table=feature_table,
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attrs_after=['gold'],
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show_progress=False)
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test_feature_vecs = em.extract_feature_vecs(test_set, feature_table=feature_table,
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attrs_after=['ltable_title', 'ltable_description', 'ltable_manufacturer',
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'ltable_price', 'rtable_name', 'rtable_description',
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'rtable_manufacturer', 'rtable_price', 'gold'], show_progress=False)
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# todo 参数可调 用drop删除特征向量中的列？
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# 1.exclude_attrs
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# 去掉id相关的相似度
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matcher.fit(table=train_feature_vecs,
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exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold'],
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target_attr='gold')
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# 1.exclude_attrs
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predictions = matcher.predict(table=test_feature_vecs, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'ltable_title',
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'ltable_description', 'ltable_manufacturer',
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'ltable_price', 'rtable_name', 'rtable_description',
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'rtable_manufacturer', 'rtable_price', 'gold'],
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append=True, target_attr='predicted', inplace=False)
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eval_result = em.eval_matches(predictions, 'gold', 'predicted')
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em.print_eval_summary(eval_result)
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indicators = evaluate_prediction(predictions, 'gold', 'predicted', matching_number, test_proportion)
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print(indicators)
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# 计算可解释性
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predictions_attrs = []
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predictions_attrs.extend(attrs_with_l_prefix)
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predictions_attrs.extend(attrs_with_r_prefix)
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predictions_attrs.extend(['gold', 'predicted'])
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predictions = predictions[predictions_attrs]
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md_paths = ['../md_discovery/output/tp_mds.txt', '../md_discovery/output/tp_vio.txt',
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'../md_discovery/output/fn_mds.txt', '../md_discovery/output/fn_vio.txt']
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epl_match = 0 # 可解释，预测match
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nepl_mismatch = 0 # 不可解释，预测mismatch
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md_list = load_mds(md_paths) # 从全局变量中读取所有的md
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for row in predictions.itertuples():
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if is_explicable(row, md_list):
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if getattr(row, 'predicted') == 1:
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epl_match += 1
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else:
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if getattr(row, 'predicted') == 0:
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nepl_mismatch += 1
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epl_ability = (epl_match + nepl_mismatch) / len(predictions) # 可解释性
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f1 = indicators['F1']
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performance = interpretability_weight * epl_ability + (1 - interpretability_weight) * f1
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return 1 - performance
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if __name__ == "__main__":
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classifier = SVM()
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# Next, we create an object, holding general information about the run
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scenario = Scenario(
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classifier.configspace,
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n_trials=12, # We want to run max 50 trials (combination of config and seed)
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)
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initial_design = HyperparameterOptimizationFacade.get_initial_design(scenario, n_configs=3)
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# Now we use SMAC to find the best hyperparameters
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smac = HyperparameterOptimizationFacade(
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scenario,
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classifier.train,
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initial_design=initial_design,
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overwrite=True, # If the run exists, we overwrite it; alternatively, we can continue from last state
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)
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# todo
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# 如果new_recall过低则避免其成为最优解
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# 将损失函数置为1/用new_recall降低F1从而提高损失函数
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incumbent = smac.optimize()
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# Get cost of default configuration
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default_cost = smac.validate(classifier.configspace.get_default_configuration())
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print(f"Default cost: {default_cost}")
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# Let's calculate the cost of the incumbent
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incumbent_cost = smac.validate(incumbent)
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print(f"Incumbent cost: {incumbent_cost}")
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print(f"Configuration:{incumbent.values()}")
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print(f"MAX_F1:{1-classifier.train(incumbent)}")
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