import os import sys from py_entitymatching.debugmatcher.debug_gui_utils import _get_metric import py_entitymatching as em import py_entitymatching.catalog.catalog_manager as cm import pandas as pd import six from ConfigSpace import Configuration from md_discovery.multi_process_infer_by_pairs import my_Levenshtein_ratio, norm_cos_sim from settings import * def process_prediction_for_md_discovery(pred: pd.DataFrame, tp_single_tuple_path: str = er_output_dir + "tp_single_tuple.csv", fn_single_tuple_path: str = er_output_dir + "fn_single_tuple.csv"): # 提取预测表中真阳和假阴部分 tp = pred[(pred['gold'] == 1) & (pred['predicted'] == 1)] fn = pred[(pred['gold'] == 1) & (pred['predicted'] == 0)] # 将真阳/假阴表中左右ID调整一致 for index, row in tp.iterrows(): tp.loc[index, "rtable_" + rtable_id] = row["ltable_" + rtable_id] for index, row in fn.iterrows(): fn.loc[index, "rtable_" + rtable_id] = row["ltable_" + rtable_id] pred_columns = pred.columns.values.tolist() l_columns = [] r_columns = [] columns = [] # 将预测表中左表和右表字段名分别加入两个列表 for _ in pred_columns: if _.startswith('ltable'): l_columns.append(_) elif _.startswith('rtable'): r_columns.append(_) # 将左表中字段名去掉前缀，作为统一的字段名列表(前提是两张表内对应字段名调整一致) for _ in l_columns: columns.append(_.replace('ltable_', '')) # 将表拆分成左右两部分 tpl = tp[l_columns] tpr = tp[r_columns] # 将左右两部分字段名统一 tpl.columns = columns tpr.columns = columns fnl = fn[l_columns] fnr = fn[r_columns] fnl.columns = columns fnr.columns = columns tp_single_tuple = pd.concat([tpl, tpr]) fn_single_tuple = pd.concat([fnl, fnr]) tp_single_tuple.to_csv(tp_single_tuple_path, sep=',', index=False, header=True) fn_single_tuple.to_csv(fn_single_tuple_path, sep=',', index=False, header=True) def evaluate_prediction(df: pd.DataFrame, labeled_attr: str, predicted_attr: str, matching_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) block_recall = num_true_positives / (matching_number * test_proportion) return {"precision": precision, "recall": recall, "F1": F1, "block_recall": block_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(row, 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 norm_cos_sim(embedding_dict[str(getattr(row, 'ltable_'+a))], embedding_dict[str(getattr(row, '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 def load_data(left_path: str, right_path: str, mapping_path: str): left = pd.read_csv(left_path, encoding='ISO-8859-1') cm.set_key(left, left.columns.values.tolist()[0]) left.fillna("", inplace=True) left = left.astype(str) right = pd.read_csv(right_path, encoding='ISO-8859-1') cm.set_key(right, right.columns.values.tolist()[0]) right.fillna("", inplace=True) right = right.astype(str) mapping = pd.read_csv(mapping_path) mapping = mapping.astype(str) return left, right, mapping def ml_er(iter_round: int, config: Configuration = None, ): # todo: # if config is not None -> load configs # else -> use default configs ltable = pd.read_csv(ltable_path, encoding='ISO-8859-1') cm.set_key(ltable, ltable_id) ltable.fillna("", inplace=True) rtable = pd.read_csv(rtable_path, encoding='ISO-8859-1') cm.set_key(rtable, rtable_id) rtable.fillna("", inplace=True) mappings = pd.read_csv(mapping_path) # 仅保留两表中出现在映射表中的行，增大正样本比例 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]) 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 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 items_but_id = selected_attrs[:] items_but_id.remove(tables_id) # 两张表中除了id的字段名 attrs_with_l_prefix = ['ltable_'+i for i in selected_attrs] attrs_with_r_prefix = ['rtable_'+i for i in selected_attrs] cm.set_key(selected_ltable, tables_id) cm.set_key(selected_rtable, tables_id) if config is not None: ml_matcher = config["ml_matcher"] if ml_matcher == "dt": matcher = em.DTMatcher(name='DecisionTree', random_state=0) elif ml_matcher == "svm": matcher = em.SVMMatcher(name='SVM', random_state=0) elif ml_matcher == "rf": matcher = em.RFMatcher(name='RF', random_state=0) elif ml_matcher == "lg": matcher = em.LogRegMatcher(name='LogReg', random_state=0) elif ml_matcher == "ln": matcher = em.LinRegMatcher(name='LinReg') elif ml_matcher == "nb": matcher = em.NBMatcher(name='NaiveBayes') 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) else: matcher = em.RFMatcher(name='RF', random_state=0) blocker = em.OverlapBlocker() candidate = blocker.block_tables(selected_ltable, selected_rtable, items_but_id[0], items_but_id[0], l_output_attrs=selected_attrs, r_output_attrs=selected_attrs, overlap_size=1, show_progress=False) candidate['gold'] = 0 candidate_match_rows = [] for index, row in candidate.iterrows(): l_id = row['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 == row['rtable_' + tables_id]: candidate_match_rows.append(row["_id"]) else: continue for row in candidate_match_rows: candidate.loc[row, '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]) 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'] 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] md_paths = [md_output_dir + 'tp_mds.txt', md_output_dir + 'tp_vio.txt', md_output_dir + 'fn_mds.txt', md_output_dir + '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 row in predictions.itertuples(): if is_explicable(row, md_list): if getattr(row, 'predicted') == 1: epl_match += 1 else: if getattr(row, 'predicted') == 0: nepl_mismatch += 1 interpretability = (epl_match + nepl_mismatch) / len(predictions) # 可解释性 if indicators["block_recall"] >= 0.8: f1 = indicators["F1"] else: f1 = (2.0 * indicators["precision"] * indicators["block_recall"]) / (indicators["precision"] + indicators["block_recall"]) performance = interpre_weight * interpretability + (1 - interpre_weight) * f1 ################################################################################################################ process_prediction_for_md_discovery(predictions) output_path = er_output_dir + "eval_result_" + str(iter_round) + ".txt" with open(output_path, 'w') as f: for key, value in six.iteritems(_get_metric(eval_result)): f.write(key + " : " + value) f.write('\n') f.write('block_recall:' + str(indicators["block_recall"]) + '\n') f.write('interpretability:' + str(interpretability) + '\n') f.write('performance:' + str(performance) + '\n') if __name__ == '__main__': ml_er(1)