matching_dependency/ml_er/ml_entity_resolver.py

import os
import sys

from py_entitymatching.debugmatcher.debug_gui_utils import _get_metric

sys.path.append('/home/w/PycharmProjects/py_entitymatching/py_entitymatching')

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.functions.multi_process_infer_by_pairs import my_Levenshtein_ratio
from settings import *


def process_prediction_for_md_discovery(pred: pd.DataFrame, tp_single_tuple_path: str = "ml_er/output/tp_single_tuple.csv", fn_single_tuple_path: str = "ml_er/output/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)
    my_recall = num_true_positives / (matching_number * test_proportion)

    return {"precision": precision, "recall": recall, "F1": F1, "my_recall": my_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 my_Levenshtein_ratio(str(getattr(row, 'ltable_'+a)), 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_discovery/output/tp_mds.txt', 'md_discovery/output/tp_vio.txt',
                'md_discovery/output/fn_mds.txt', 'md_discovery/output/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["my_recall"] >= 0.8:
        f1 = indicators["F1"]
    else:
        f1 = (2.0 * indicators["precision"] * indicators["my_recall"]) / (indicators["precision"] + indicators["my_recall"])
    performance = interpre_weight * interpretability + (1 - interpre_weight) * f1
    ################################################################################################################
    process_prediction_for_md_discovery(predictions)

    output_path = "ml_er/output/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('my_recall:' + str(indicators["my_recall"]) + '\n')
        f.write('interpretability:' + str(interpretability) + '\n')
        f.write('performance:' + str(performance) + '\n')