import os import numpy as np import torch import json from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Integer from ConfigSpace.conditions import InCondition from ConfigSpace.read_and_write import json as csj import py_entitymatching as em import py_entitymatching.catalog.catalog_manager as cm import pandas as pd from smac import HyperparameterOptimizationFacade, Scenario from settings import * from ml_er.ml_entity_resolver import evaluate_prediction, load_mds, is_explicable, build_col_pairs_sim_tensor_dict # 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)] # if len(lr_attrs_map) > 0: # 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 ######################################################################################################################## 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: 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, allow_missing=True) 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, allow_missing=True) candidate['gold'] = 0 candidate = candidate.reset_index(drop=True) candidate_match_rows = [] for line in candidate.itertuples(): l_id = getattr(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 == getattr(line, 'rtable_' + tables_id): candidate_match_rows.append(line[0]) else: continue for _ in candidate_match_rows: candidate.loc[_, 'gold'] = 1 candidate.fillna("", inplace=True) # 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 candidate_for_train_test = candidate_for_train_test.reset_index(drop=True) 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) train_feature_vecs.fillna(value=0, inplace=True) 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) test_feature_vecs.fillna(value=0, inplace=True) 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, candidate_for_train_test) 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] predictions = predictions.reset_index(drop=True) predictions = predictions.astype(str) sim_tensor_dict = build_col_pairs_sim_tensor_dict(predictions) # 默认路径为 "../md_discovery/output/xxx.txt" # mds/vio 共2个md文件 md_paths = [md_output_dir + 'mds.txt', md_output_dir + 'vio.txt'] md_list = load_mds(md_paths) # 从全局变量中读取所有的md epl_match = 0 # 可解释，预测match if len(md_list) > 0: for line in predictions.itertuples(): if is_explicable(line, md_list, sim_tensor_dict) and str(getattr(line, 'predicted')) == str(1): epl_match += 1 ppre = predictions[predictions['predicted'] == str(1)] interpretability = epl_match / len(ppre) # 可解释性 if (indicators["block_recall"] < 0.8) and (indicators["block_recall"] < indicators["recall"]): f1 = (2.0 * indicators["precision"] * indicators["block_recall"]) / ( indicators["precision"] + indicators["block_recall"]) else: f1 = indicators["F1"] # if indicators["block_recall"] < 0.8: # return 1 # f1 = indicators["F1"] performance = interpre_weight * interpretability + (1 - interpre_weight) * f1 print('Interpretability: ', interpretability) return 1 - performance def ml_er_hpo(): classifier = Classifier() cs = classifier.configspace str_configspace = csj.write(cs) dict_configspace = json.loads(str_configspace) with open(hpo_output_dir + "configspace.json", "w") as f: json.dump(dict_configspace, f) # Next, we create an object, holding general information about the run scenario = Scenario( cs, deterministic=True, n_trials=12, # We want to run max 50 trials (combination of config and seed) n_workers=1 ) 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() incumbent_cost = smac.validate(incumbent) default = cs.get_default_configuration() default_cost = smac.validate(default) print(f"Default Cost: {default_cost}") print(f"Incumbent Cost: {incumbent_cost}") if incumbent_cost > default_cost: incumbent = default print(f"Updated Incumbent Cost: {default_cost}") print(f"Optimized Configuration:{incumbent.values()}") incumbent_ndarray = incumbent.get_array() np.save(hpo_output_dir + 'incumbent.npy', incumbent_ndarray) return incumbent if __name__ == '__main__': ml_er_hpo()