import os import time from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Integer from ConfigSpace.conditions import InCondition import py_entitymatching as em import py_entitymatching.catalog.catalog_manager as cm import pandas as pd from smac import HyperparameterOptimizationFacade, Scenario from md_discovery.functions.multi_process_infer_by_pairs import my_Levenshtein_ratio from settings import * # 数据在外部加载 ######################################################################################################################## ltable = pd.read_csv(ltable_path, encoding='ISO-8859-1') ltable.fillna("", inplace=True) rtable = pd.read_csv(rtable_path, encoding='ISO-8859-1') 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) 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(): lid_mapping_list.append(row[mapping_lid]) rid_mapping_list.append(row[mapping_rid]) # 仅保留两表中出现在映射表中的行，增大正样本比例 selected_ltable = ltable[ltable[ltable_id].isin(lid_mapping_list)] 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 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 selected_rtable = rtable[rtable[rtable_id].isin(rid_mapping_list)] selected_attrs = selected_ltable.columns.values.tolist() # 两张表中的字段名 ######################################################################################################################## def evaluate_prediction(df: pd.DataFrame, labeled_attr: str, predicted_attr: str, couple_number: int, test_proportion: float) -> dict: new_df = df.reset_index(drop=False, inplace=False) gold = new_df[labeled_attr] predicted = new_df[predicted_attr] gold_negative = gold[gold == 0].index.values gold_positive = gold[gold == 1].index.values predicted_negative = predicted[predicted == 0].index.values predicted_positive = predicted[predicted == 1].index.values false_positive_indices = list(set(gold_negative).intersection(predicted_positive)) true_positive_indices = list(set(gold_positive).intersection(predicted_positive)) false_negative_indices = list(set(gold_positive).intersection(predicted_negative)) num_true_positives = float(len(true_positive_indices)) num_false_positives = float(len(false_positive_indices)) num_false_negatives = float(len(false_negative_indices)) precision_denominator = num_true_positives + num_false_positives recall_denominator = num_true_positives + num_false_negatives precision = 0.0 if precision_denominator == 0.0 else num_true_positives / precision_denominator recall = 0.0 if recall_denominator == 0.0 else num_true_positives / recall_denominator F1 = 0.0 if precision == 0.0 and recall == 0.0 else (2.0 * precision * recall) / (precision + recall) my_recall = num_true_positives / (couple_number * test_proportion) return {"precision": precision, "recall": recall, "F1": F1, "my_recall": my_recall} def load_mds(paths: list) -> list: if len(paths) == 0: return [] all_mds = [] # 传入md路径列表 for md_path in paths: if not os.path.exists(md_path): continue mds = [] # 打开每一个md文件 with open(md_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(): md_metadata = line.strip().split('\t') md = eval(md_metadata[0].replace('md:', '')) confidence = eval(md_metadata[2].replace('confidence:', '')) if confidence > 0: mds.append(md) all_mds.extend(mds) return all_mds def is_explicable(line, all_mds: list) -> bool: attrs = all_mds[0].keys() # 从第一条md中读取所有字段 for md in all_mds: explicable = True # 假设这条md能解释当前元组 for a in attrs: threshold = md[a] if my_Levenshtein_ratio(str(getattr(line, 'ltable_' + a)), str(getattr(line, 'rtable_' + a))) < threshold: 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 break # 不再与当前md的其他相似度阈值比较，跳转到下一条md if explicable: return True # 任意一条md能解释，直接返回 return False # 遍历结束，不能解释 class Classifier: @property def configspace(self) -> ConfigurationSpace: # Build Configuration Space which defines all parameters and their ranges cs = ConfigurationSpace(seed=0) block_attr_items = selected_attrs[:] block_attr_items.remove(tables_id) block_attr = Categorical("block_attr", block_attr_items) overlap_size = Integer("overlap_size", (1, 3), default=1) ml_matcher = Categorical("ml_matcher", ["dt", "svm", "rf", "lg", "ln", "nb"], default="rf") ml_blocker = Categorical("ml_blocker", ["over_lap", "attr_equiv"], default="over_lap") use_overlap_size = InCondition(child=overlap_size, parent=ml_blocker, values=["over_lap"]) cs.add_hyperparameters([block_attr, overlap_size, ml_matcher, ml_blocker]) cs.add_conditions([use_overlap_size]) return cs # 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 def train(self, config: Configuration, seed: int = 0) -> float: # print(f"BRAINFUCK:{config.values()}") cm.del_catalog() attrs_with_l_prefix = ['ltable_' + i for i in selected_attrs] # 字段名加左前缀 attrs_with_r_prefix = ['rtable_' + i for i in selected_attrs] # 字段名加右前缀 cm.set_key(selected_ltable, tables_id) cm.set_key(selected_rtable, tables_id) if config["ml_blocker"] == "over_lap": blocker = em.OverlapBlocker() candidate = blocker.block_tables(selected_ltable, selected_rtable, config["block_attr"], config["block_attr"], l_output_attrs=selected_attrs, r_output_attrs=selected_attrs, overlap_size=config["overlap_size"], show_progress=False) elif config["ml_blocker"] == "attr_equiv": blocker = em.AttrEquivalenceBlocker() candidate = blocker.block_tables(selected_ltable, selected_rtable, config["block_attr"], config["block_attr"], l_output_attrs=selected_attrs, r_output_attrs=selected_attrs) candidate['gold'] = 0 candidate_match_rows = [] for index_num, line in candidate.iterrows(): l_id = line['ltable_' + tables_id] map_row = mappings[mappings[mapping_lid] == l_id] if map_row is not None: r_id = map_row[mapping_rid] for value in r_id: if value == line['rtable_' + tables_id]: candidate_match_rows.append(line["_id"]) else: continue for line in candidate_match_rows: candidate.loc[line, 'gold'] = 1 # 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] if len(candidate_mismatch) > len(candidate_match): candidate_mismatch = candidate_mismatch.sample(n=len(candidate_match)) # 拼接正负样本 candidate_for_train_test = pd.concat([candidate_mismatch, candidate_match]) if len(candidate_for_train_test) == 0: return 1 cm.set_key(candidate_for_train_test, '_id') cm.set_fk_ltable(candidate_for_train_test, 'ltable_' + tables_id) cm.set_fk_rtable(candidate_for_train_test, 'rtable_' + tables_id) cm.set_ltable(candidate_for_train_test, selected_ltable) cm.set_rtable(candidate_for_train_test, selected_rtable) # 分为训练测试集 train_proportion = 0.7 test_proportion = 0.3 sets = em.split_train_test(candidate_for_train_test, train_proportion=train_proportion, random_state=0) train_set = sets['train'] test_set = sets['test'] cm.set_key(train_set, '_id') cm.set_fk_ltable(train_set, 'ltable_' + tables_id) cm.set_fk_rtable(train_set, 'rtable_' + tables_id) cm.set_ltable(train_set, selected_ltable) cm.set_rtable(train_set, selected_rtable) cm.set_key(test_set, '_id') cm.set_fk_ltable(test_set, 'ltable_' + tables_id) cm.set_fk_rtable(test_set, 'rtable_' + tables_id) cm.set_ltable(test_set, selected_ltable) cm.set_rtable(test_set, selected_rtable) if config["ml_matcher"] == "dt": matcher = em.DTMatcher(name='DecisionTree', random_state=0) elif config["ml_matcher"] == "svm": matcher = em.SVMMatcher(name='SVM', random_state=0) elif config["ml_matcher"] == "rf": matcher = em.RFMatcher(name='RF', random_state=0) elif config["ml_matcher"] == "lg": matcher = em.LogRegMatcher(name='LogReg', random_state=0) elif config["ml_matcher"] == "ln": matcher = em.LinRegMatcher(name='LinReg') elif config["ml_matcher"] == "nb": matcher = em.NBMatcher(name='NaiveBayes') feature_table = em.get_features_for_matching(selected_ltable, selected_rtable, validate_inferred_attr_types=False) train_feature_vecs = em.extract_feature_vecs(train_set, feature_table=feature_table, attrs_after=['gold'], show_progress=False) test_feature_after = attrs_with_l_prefix[:] test_feature_after.extend(attrs_with_r_prefix) for _ in test_feature_after: if _.endswith(tables_id): test_feature_after.remove(_) test_feature_after.append('gold') test_feature_vecs = em.extract_feature_vecs(test_set, feature_table=feature_table, attrs_after=test_feature_after, show_progress=False) fit_exclude = ['_id', 'ltable_' + tables_id, 'rtable_' + tables_id, 'gold'] 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, append=True, target_attr='predicted', inplace=False) eval_result = em.eval_matches(predictions, 'gold', 'predicted') em.print_eval_summary(eval_result) indicators = evaluate_prediction(predictions, 'gold', 'predicted', matching_number, test_proportion) print(indicators) # 计算可解释性 predictions_attrs = [] predictions_attrs.extend(attrs_with_l_prefix) predictions_attrs.extend(attrs_with_r_prefix) predictions_attrs.extend(['gold', 'predicted']) predictions = predictions[predictions_attrs] # 默认路径为 "../md_discovery/output/xxx.txt" # 真阳/假阴 mds/vio 共4个md文件 md_paths = ['md_discovery/output/tp_mds.txt', 'md_discovery/output/tp_vio.txt', 'md_discovery/output/fn_mds.txt', 'md_discovery/output/fn_vio.txt'] epl_match = 0 # 可解释，预测match nepl_mismatch = 0 # 不可解释，预测mismatch md_list = load_mds(md_paths) # 从全局变量中读取所有的md if len(md_list) > 0: for line in predictions.itertuples(): if is_explicable(line, md_list): if getattr(line, 'predicted') == 1: epl_match += 1 else: if getattr(line, 'predicted') == 0: nepl_mismatch += 1 interpretability = (epl_match + nepl_mismatch) / len(predictions) # 可解释性 # if indicators["my_recall"] >= 0.8: # f1 = indicators["F1"] # else: # f1 = (2.0 * indicators["precision"] * indicators["my_recall"]) / (indicators["precision"] + indicators["my_recall"]) if indicators["my_recall"] < 0.8: return 1 f1 = indicators["F1"] performance = interpre_weight * interpretability + (1 - interpre_weight) * f1 return 1 - performance def ml_er_hpo(): classifier = Classifier() # Next, we create an object, holding general information about the run scenario = Scenario( classifier.configspace, deterministic=True, n_trials=10, # We want to run max 50 trials (combination of config and seed) n_workers=2 ) initial_design = HyperparameterOptimizationFacade.get_initial_design(scenario, n_configs=5) # Now we use SMAC to find the best hyperparameters smac = HyperparameterOptimizationFacade( scenario, classifier.train, initial_design=initial_design, overwrite=True, # If the run exists, we overwrite it; alternatively, we can continue from last state ) incumbent = smac.optimize() # Get cost of default configuration default_cost = smac.validate(classifier.configspace.get_default_configuration()) print(f"Default cost: {default_cost}") # Let's calculate the cost of the incumbent incumbent_cost = smac.validate(incumbent) print(f"Incumbent cost: {incumbent_cost}") print(f"Optimized_Configuration:{incumbent.values()}") return incumbent