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import os
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from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Integer
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from ConfigSpace.conditions import InCondition
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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 smac import HyperparameterOptimizationFacade, Scenario
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from settings import *
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from ml_er.ml_entity_resolver import evaluate_prediction, load_mds, is_explicable
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# 数据在外部加载
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########################################################################################################################
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ltable = pd.read_csv(ltable_path, encoding='ISO-8859-1')
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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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rtable.fillna("", inplace=True)
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mappings = pd.read_csv(mapping_path)
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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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# 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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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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tables_id = rtable_id # 不论左表右表ID字段名是否一致，经上一行调整，统一以右表为准
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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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########################################################################################################################
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class Classifier:
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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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block_attr_items = selected_attrs[:]
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block_attr_items.remove(tables_id)
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block_attr = Categorical("block_attr", block_attr_items)
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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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use_overlap_size = InCondition(child=overlap_size, parent=ml_blocker, values=["over_lap"])
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cs.add_hyperparameters([block_attr, overlap_size, ml_matcher, ml_blocker])
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cs.add_conditions([use_overlap_size])
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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, seed: int = 0) -> float:
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# print(f"BRAINFUCK:{config.values()}")
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cm.del_catalog()
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attrs_with_l_prefix = ['ltable_' + i for i in selected_attrs] # 字段名加左前缀
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attrs_with_r_prefix = ['rtable_' + i for i in selected_attrs] # 字段名加右前缀
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cm.set_key(selected_ltable, tables_id)
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cm.set_key(selected_rtable, tables_id)
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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)
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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)
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candidate['gold'] = 0
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candidate_match_rows = []
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for index_num, line in candidate.iterrows():
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l_id = line['ltable_' + tables_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 == line['rtable_' + tables_id]:
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candidate_match_rows.append(line["_id"])
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else:
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continue
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for line in candidate_match_rows:
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candidate.loc[line, '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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if len(candidate_for_train_test) == 0:
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return 1
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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_' + tables_id)
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cm.set_fk_rtable(candidate_for_train_test, 'rtable_' + tables_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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cm.set_key(train_set, '_id')
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cm.set_fk_ltable(train_set, 'ltable_' + tables_id)
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cm.set_fk_rtable(train_set, 'rtable_' + tables_id)
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cm.set_ltable(train_set, selected_ltable)
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cm.set_rtable(train_set, selected_rtable)
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cm.set_key(test_set, '_id')
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cm.set_fk_ltable(test_set, 'ltable_' + tables_id)
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cm.set_fk_rtable(test_set, 'rtable_' + tables_id)
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cm.set_ltable(test_set, selected_ltable)
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cm.set_rtable(test_set, selected_rtable)
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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_after = attrs_with_l_prefix[:]
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test_feature_after.extend(attrs_with_r_prefix)
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for _ in test_feature_after:
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if _.endswith(tables_id):
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test_feature_after.remove(_)
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test_feature_after.append('gold')
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test_feature_vecs = em.extract_feature_vecs(test_set, feature_table=feature_table,
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attrs_after=test_feature_after, show_progress=False)
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fit_exclude = ['_id', 'ltable_' + tables_id, 'rtable_' + tables_id, 'gold']
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matcher.fit(table=train_feature_vecs, exclude_attrs=fit_exclude, target_attr='gold')
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test_feature_after.extend(['_id', 'ltable_' + tables_id, 'rtable_' + tables_id])
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predictions = matcher.predict(table=test_feature_vecs, exclude_attrs=test_feature_after,
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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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# 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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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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if len(md_list) > 0:
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for line in predictions.itertuples():
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if is_explicable(line, md_list):
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if getattr(line, 'predicted') == 1:
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epl_match += 1
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else:
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if getattr(line, 'predicted') == 0:
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nepl_mismatch += 1
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interpretability = (epl_match + nepl_mismatch) / len(predictions) # 可解释性
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# if indicators["my_recall"] >= 0.8:
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# f1 = indicators["F1"]
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# else:
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# f1 = (2.0 * indicators["precision"] * indicators["my_recall"]) / (indicators["precision"] + indicators["my_recall"])
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if indicators["my_recall"] < 0.8:
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return 1
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f1 = indicators["F1"]
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performance = interpre_weight * interpretability + (1 - interpre_weight) * f1
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return 1 - performance
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def ml_er_hpo():
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classifier = Classifier()
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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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deterministic=True,
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n_trials=10, # We want to run max 50 trials (combination of config and seed)
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n_workers=1
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)
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initial_design = HyperparameterOptimizationFacade.get_initial_design(scenario, n_configs=5)
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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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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"Optimized_Configuration:{incumbent.values()}")
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return incumbent
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