matching_dependency/ml_er/ml_entity_resolver.py

import json
import os
import sys
import time

import ConfigSpace
import pandas
import torch
from py_entitymatching.debugmatcher.debug_gui_utils import _get_metric
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
import six
from ConfigSpace import Configuration
from tqdm import tqdm

from md_discovery.md_discover import md_discover
from settings import *


def prepare_file_for_md_discovery(train, t_single_tuple_path=er_output_dir + "t_single_tuple.csv"):
    df = train[train['gold'] == 1]
    # 元组对左右ID调整一致
    for index, row in df.iterrows():
        df.loc[index, "rtable_" + rtable_id] = row["ltable_" + rtable_id]

    train_columns = train.columns.values.tolist()
    l_columns = []
    r_columns = []
    cols = []
    # 左表和右表字段名分别加入两个列表
    for _ in train_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:
        cols.append(_.replace('ltable_', ''))

    ldf = df[l_columns]
    rdf = df[r_columns]
    ldf.columns = cols
    rdf.columns = cols
    t_single_tuple = pd.concat([ldf, rdf])
    t_single_tuple = t_single_tuple.reset_index(drop=True)

    t_single_tuple.to_csv(t_single_tuple_path, sep=',', index=False, header=True, quoting=1)


def evaluate_prediction(df: pd.DataFrame, labeled_attr: str, predicted_attr: str, matching_number: int,
                        candidate: pd.DataFrame) -> 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 = len(candidate[candidate['gold'] == 1]) / matching_number

    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')
                # todo 如果MD文件的形式改了 这里也要改
                md = eval(md_metadata[1])
                mds.append(md)
        all_mds.extend(mds)
    return all_mds


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 a != target_attr:
                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]  # 任意一条md能解释，直接返回
    return -1.0  # 遍历结束，不能解释


# 形成一个字典，key为字段名称，value为一维张量，记录了预测表中这一字段每行的左右属性的相似度
def build_col_pairs_sim_tensor_dict(predictions: pandas.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]
    sentences = []
    for col in range(0, width):
        for row in range(0, length):
            cell_value = predictions.values[row, col]
            sentences.append(cell_value)
    embedding = model.encode(sentences, convert_to_tensor=True, device="cuda")
    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=4)
        sim_tensor_dict[predictions_attrs[col_tuple[0]].replace('ltable_', '')] = sim_tensor
    return sim_tensor_dict


def er_process(config: Configuration):
    start = time.time()
    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, encoding='ISO-8859-1')

    # 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

    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)]
    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
    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)

    blocker = em.OverlapBlocker()
    candidate = blocker.block_tables(selected_ltable, selected_rtable, config["block_attr"],
                                     config["block_attr"], allow_missing=True,
                                     l_output_attrs=selected_attrs, r_output_attrs=selected_attrs,
                                     overlap_size=1, show_progress=False)

    candidate['gold'] = 0
    candidate = candidate.reset_index(drop=True)
    candidate_match_rows = []
    for row in candidate.itertuples():
        l_id = getattr(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 == getattr(row, 'rtable_' + tables_id):
                    candidate_match_rows.append(row[0])
        else:
            continue

    for _ in candidate_match_rows:
        candidate.loc[_, 'gold'] = 1

    candidate.fillna(value="", 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 0
    # 如果拼接后不重设索引可能导致索引重复
    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)
    block_recall = len(candidate_match) / matching_number

    # 分为训练测试集
    train_proportion = 0.7
    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)

    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')

    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)

    # 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]
    # 必须从训练集内挖MD
    train_attrs = predictions_attrs[:]
    train_attrs.remove('predicted')
    train_set = train_set[train_attrs]
    prepare_file_for_md_discovery(train_set)
    predictions = predictions.reset_index(drop=True)
    predictions = predictions.astype(str)
    sim_tensor_dict = build_col_pairs_sim_tensor_dict(predictions)
    predictions['confidence'] = 0

    md_discover(config, er_output_dir + "t_single_tuple.csv", md_output_dir + "mds.txt")
    md_paths = [md_output_dir + 'mds.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

    unexplainable = pd.DataFrame()

    if len(md_list) > 0:
        for row in tqdm(predictions.itertuples()):
            x = is_explicable(row, md_list, sim_tensor_dict)
            if x > 0 and str(getattr(row, 'predicted')) == str(1):
                predictions.loc[row[0], 'confidence'] = x
                epl_match += 1
        #     else:
        #         series = pd.Series(row)
        #         unexplainable = unexplainable._append(series, ignore_index=True)
        # unexplainable.drop(columns=unexplainable.columns[[-1, 0]], inplace=True)
        # unexplainable.columns = predictions_attrs
        # unexplainable = unexplainable[train_attrs]
        # if len(unexplainable[unexplainable['gold'] == str(1)]) > 0:
        #     prepare_file_for_md_discovery(unexplainable, t_single_tuple_path=er_output_dir + 'unexplainable_tst.csv')
        #     md_discover(config, er_output_dir + 'unexplainable_tst.csv', md_output_dir + "from_unexplainable.txt")

    df = predictions[predictions['predicted'] == str(1)]
    interpretability = epl_match / len(df)  # 可解释性
    indicators['interpretability'] = interpretability
    if indicators["block_recall"] < indicators["recall"]:
        f1 = (2.0 * indicators["precision"] * indicators["block_recall"]) / (
                    indicators["precision"] + indicators["block_recall"])
    else:
        f1 = indicators["F1"]
    performance = interpre_weight * interpretability + (1 - interpre_weight) * f1
    indicators['performance'] = performance
    indicators['eval_result'] = eval_result
    print(indicators)
    predictions.to_csv(er_output_dir + 'predictions.csv', sep=',', index=False, header=True)
    ################################################################################################################
    print(f'\033[33mTime consumed by ML-ER in seconds: {time.time() - start}\033[0m')
    return indicators


def ml_er(config: Configuration = None):
    indicators = er_process(config)
    output_path = er_output_dir + "eval_result.txt"
    with open(output_path, 'w') as f:
        for key, value in six.iteritems(_get_metric(indicators['eval_result'])):
            f.write(key + " : " + value)
            f.write('\n')
        f.write('block_recall:' + str(indicators["block_recall"]) + '\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 + "incumbent.json"):
        with open(hpo_output_dir + "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 + "incumbent.json", 'r') as f:
            dic = json.load(f)
        configuration = ConfigSpace.Configuration(configspace, values=dic)
        ml_er(configuration)