matching_dependency/hpo/er_model_hpo.py

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:
        # 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,
                                             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_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']
        train_feature_vecs.fillna(0, inplace=True)
        test_feature_vecs.fillna(0, inplace=True)
        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]
        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:
            f1 = indicators["F1"]
        else:
            f1 = (2.0 * indicators["precision"] * indicators["block_recall"]) / (indicators["precision"] + indicators["block_recall"])
        # if indicators["block_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()
    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=10,  # 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()