import json import os import pickle import time import ConfigSpace import pandas as pd import py_entitymatching as em import torch from ConfigSpace import Configuration from ConfigSpace.read_and_write import json as csj import py_entitymatching.catalog.catalog_manager as cm from tqdm import tqdm from colorama import Fore from settings import * def matching(config: Configuration): print(Fore.BLUE + f'Config: {config}') with open(md_output_dir + r"\mds.pickle", "rb") as file: md_list = pickle.load(file) train_set = pd.read_csv(directory_path + r'\train_whole.csv', encoding='ISO-8859-1') test_set = pd.read_csv(directory_path + r'\test_whole.csv', encoding='ISO-8859-1') ltable = pd.read_csv(directory_path + r'\tableA.csv', encoding='ISO-8859-1') rtable = pd.read_csv(directory_path + r'\tableB.csv', encoding='ISO-8859-1') ml_matcher = config["ml_matcher"] match ml_matcher: case "dt": matcher = em.DTMatcher(name='DecisionTree', random_state=0, criterion=config['tree_criterion'], max_depth=config['tree_max_depth'], splitter=config['dt_splitter'], max_features=config['dt_max_features']) case "svm": matcher = em.SVMMatcher(name='SVM', random_state=0, kernel=config['svm_kernel'], degree=config['svm_degree'], gamma=config['svm_gamma'], C=config['svm_C'], coef0=config['svm_constant']) case "rf": matcher = em.RFMatcher(name='RandomForest', random_state=0, criterion=config['tree_criterion'], max_depth=config['tree_max_depth'], n_estimators=config['number_of_tree'], max_features=config['rf_max_features']) cm.set_key(train_set, '_id') cm.set_fk_ltable(train_set, 'ltable_id') cm.set_fk_rtable(train_set, 'rtable_id') cm.set_ltable(train_set, ltable) cm.set_rtable(train_set, rtable) cm.set_key(ltable, 'id') cm.set_key(rtable, 'id') cm.set_key(test_set, '_id') cm.set_fk_ltable(test_set, 'ltable_id') cm.set_fk_rtable(test_set, 'rtable_id') cm.set_ltable(test_set, ltable) cm.set_rtable(test_set, rtable) feature_table = em.get_features_for_matching(ltable, rtable, validate_inferred_attr_types=False) train_feature_vecs = em.extract_feature_vecs(train_set, feature_table=feature_table, attrs_after=['label'], show_progress=False) train_feature_vecs.fillna(value=0, inplace=True) test_feature_after = ['ltable_' + i for i in ltable.columns.values.tolist()] for _ in test_feature_after[:]: test_feature_after.append(_.replace('ltable_', 'rtable_')) for _ in test_feature_after: if _.endswith('id'): test_feature_after.remove(_) test_feature_after.append('label') test_feature_vecs = em.extract_feature_vecs(test_set, feature_table=feature_table, attrs_after=test_feature_after, show_progress=False) test_feature_vecs.fillna(value=0, inplace=True) fit_exclude = ['_id', 'ltable_id', 'rtable_id', 'label'] matcher.fit(table=train_feature_vecs, exclude_attrs=fit_exclude, target_attr='label') test_feature_after.extend(['_id', 'ltable_id', 'rtable_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, 'label', 'predicted') em.print_eval_summary(eval_result) indicators = evaluate_prediction(predictions, 'label', 'predicted') test_feature_after.remove('_id') test_feature_after.append('predicted') predictions = predictions[test_feature_after] predictions = predictions.reset_index(drop=True) predictions = predictions.astype(str) # 目前predictions包含的属性：左右表全部属性+gold+predicted sim_tensor_dict = build_col_pairs_sim_tensor_dict(predictions) predictions['confidence'] = 0 predictions['md'] = '' epl_match = 0 # 可解释，预测match if len(md_list) > 0: for row in tqdm(predictions.itertuples()): if str(getattr(row, 'predicted')) == str(1): conf, md_dict = is_explicable(row, md_list, sim_tensor_dict) if conf > 0: predictions.loc[row[0], 'confidence'] = conf predictions.loc[row[0], 'md'] = str(md_dict) epl_match += 1 df = predictions[predictions['predicted'] == str(1)] interpretability = epl_match / len(df) # 可解释性 indicators['interpretability'] = interpretability performance = interpre_weight * interpretability + (1 - interpre_weight) * indicators["F1"] indicators['performance'] = performance print(Fore.BLUE + f'ER Indicators: {indicators}') predictions.to_csv(er_output_dir + r'\predictions.csv', sep=',', index=False, header=True) print(Fore.CYAN + f'Finish Time: {time.time()}') return indicators def evaluate_prediction(prediction_: pd.DataFrame, labeled_attr: str, predicted_attr: str) -> dict: new_df = prediction_.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) return {"precision": precision, "recall": recall, "F1": F1} def build_col_pairs_sim_tensor_dict(predictions: pd.DataFrame): predictions_attrs = predictions.columns.values.tolist() col_tuple_list = [] for _ in predictions_attrs: if _.startswith('ltable'): left_index = predictions_attrs.index(_) right_index = predictions_attrs.index(_.replace('ltable_', 'rtable_')) col_tuple_list.append((left_index, right_index)) length = predictions.shape[0] # width = predictions.shape[1] predictions = predictions.reset_index(drop=True) sentences = predictions.values.flatten(order='F').tolist() embedding = model.encode(sentences, convert_to_tensor=True, device="cuda", batch_size=256, show_progress_bar=True) split_embedding = torch.split(embedding, length, dim=0) table_tensor = torch.stack(split_embedding, dim=0, out=None) # prediction的归一化嵌入张量 norm_table_tensor = torch.nn.functional.normalize(table_tensor, dim=2) sim_tensor_dict = {} for col_tuple in col_tuple_list: lattr_tensor = norm_table_tensor[col_tuple[0]] rattr_tensor = norm_table_tensor[col_tuple[1]] mul_tensor = lattr_tensor * rattr_tensor sim_tensor = torch.sum(mul_tensor, 1) sim_tensor = torch.round(sim_tensor, decimals=2) sim_tensor_dict[predictions_attrs[col_tuple[0]].replace('ltable_', '')] = sim_tensor return sim_tensor_dict def is_explicable(row, all_mds: list, st_dict): attrs = all_mds[0][0].keys() # 从第一条md_tuple中的md字典中读取所有字段 for md_tuple in all_mds: explicable = True # 假设这条md能解释当前元组 for a in attrs: if st_dict[a][row[0]].item() < md_tuple[0][a]: 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 md_tuple[2], md_tuple[0] # 任意一条md能解释，直接返回 return -1.0, {} # 遍历结束，不能解释 def ml_er(config: Configuration): indicators = matching(config) output_path = er_output_dir + r"\eval_result.txt" with open(output_path, 'w') as _f: _f.write('Precision:' + str(indicators["precision"]) + '\n') _f.write('Recall:' + str(indicators["recall"]) + '\n') _f.write('F1:' + str(indicators["F1"]) + '\n') _f.write('interpretability:' + str(indicators['interpretability']) + '\n') _f.write('performance:' + str(indicators['performance']) + '\n') if __name__ == '__main__': if os.path.isfile(hpo_output_dir + r"\incumbent.json"): with open(hpo_output_dir + r"\configspace.json", 'r') as f: dict_configspace = json.load(f) str_configspace = json.dumps(dict_configspace) configspace = csj.read(str_configspace) with open(hpo_output_dir + r"\incumbent.json", 'r') as f: dic = json.load(f) configuration = ConfigSpace.Configuration(configspace, values=dic) ml_er(configuration)