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import json
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import os
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import sys
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import time
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import ConfigSpace
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import pandas
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import torch
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from py_entitymatching.debugmatcher.debug_gui_utils import _get_metric
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from ConfigSpace.read_and_write import json as csj
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import py_entitymatching as em
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import py_entitymatching.catalog.catalog_manager as cm
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import pandas as pd
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import six
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from ConfigSpace import Configuration
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from tqdm import tqdm
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from md_discovery.md_discover import md_discover
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from settings import *
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def prepare_file_for_md_discovery(train, t_single_tuple_path=er_output_dir + "t_single_tuple.csv"):
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df = train[train['gold'] == 1]
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# 元组对左右ID调整一致
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for index, row in df.iterrows():
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df.loc[index, "rtable_" + rtable_id] = row["ltable_" + rtable_id]
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train_columns = train.columns.values.tolist()
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l_columns = []
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r_columns = []
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cols = []
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# 左表和右表字段名分别加入两个列表
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for _ in train_columns:
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if _.startswith('ltable'):
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l_columns.append(_)
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elif _.startswith('rtable'):
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r_columns.append(_)
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# 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)
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for _ in l_columns:
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cols.append(_.replace('ltable_', ''))
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ldf = df[l_columns]
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rdf = df[r_columns]
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ldf.columns = cols
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rdf.columns = cols
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t_single_tuple = pd.concat([ldf, rdf])
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t_single_tuple = t_single_tuple.reset_index(drop=True)
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t_single_tuple.to_csv(t_single_tuple_path, sep=',', index=False, header=True, quoting=1)
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def evaluate_prediction(df: pd.DataFrame, labeled_attr: str, predicted_attr: str, matching_number: int,
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candidate: pd.DataFrame) -> dict:
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new_df = df.reset_index(drop=False, inplace=False)
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gold = new_df[labeled_attr]
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predicted = new_df[predicted_attr]
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gold_negative = gold[gold == 0].index.values
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gold_positive = gold[gold == 1].index.values
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predicted_negative = predicted[predicted == 0].index.values
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predicted_positive = predicted[predicted == 1].index.values
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false_positive_indices = list(set(gold_negative).intersection(predicted_positive))
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true_positive_indices = list(set(gold_positive).intersection(predicted_positive))
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false_negative_indices = list(set(gold_positive).intersection(predicted_negative))
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num_true_positives = float(len(true_positive_indices))
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num_false_positives = float(len(false_positive_indices))
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num_false_negatives = float(len(false_negative_indices))
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precision_denominator = num_true_positives + num_false_positives
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recall_denominator = num_true_positives + num_false_negatives
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precision = 0.0 if precision_denominator == 0.0 else num_true_positives / precision_denominator
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recall = 0.0 if recall_denominator == 0.0 else num_true_positives / recall_denominator
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F1 = 0.0 if precision == 0.0 and recall == 0.0 else (2.0 * precision * recall) / (precision + recall)
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block_recall = len(candidate[candidate['gold'] == 1]) / matching_number
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return {"precision": precision, "recall": recall, "F1": F1, "block_recall": block_recall}
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def load_mds(paths: list) -> list:
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if len(paths) == 0:
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return []
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all_mds = []
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# 传入md路径列表
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for md_path in paths:
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if not os.path.exists(md_path):
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continue
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mds = []
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# 打开每一个md文件
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with open(md_path, 'r') as f:
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# 读取每一行的md，加入该文件的md列表
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for line in f.readlines():
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md_metadata = line.strip().split('\t')
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# todo 如果MD文件的形式改了 这里也要改
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md = eval(md_metadata[1])
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mds.append(md)
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all_mds.extend(mds)
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return all_mds
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def is_explicable(row, all_mds: list, st_dict):
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attrs = all_mds[0][0].keys() # 从第一条md_tuple中的md字典中读取所有字段
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for md_tuple in all_mds:
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explicable = True # 假设这条md能解释当前元组
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for a in attrs:
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if a != target_attr:
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if st_dict[a][row[0]].item() < md_tuple[0][a]:
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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
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break # 不再与当前md的其他相似度阈值比较，跳转到下一条md
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if explicable:
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return md_tuple[2] # 任意一条md能解释，直接返回
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return -1.0 # 遍历结束，不能解释
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def build_col_pairs_sim_tensor_dict(predictions: pandas.DataFrame):
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predictions_attrs = predictions.columns.values.tolist()
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col_tuple_list = []
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for _ in predictions_attrs:
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if _.startswith('ltable'):
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left_index = predictions_attrs.index(_)
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right_index = predictions_attrs.index(_.replace('ltable_', 'rtable_'))
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col_tuple_list.append((left_index, right_index))
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length = predictions.shape[0]
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width = predictions.shape[1]
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sentences = []
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for col in range(0, width):
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for row in range(0, length):
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cell_value = predictions.values[row, col]
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sentences.append(cell_value)
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embedding = model.encode(sentences, convert_to_tensor=True, device="cuda")
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split_embedding = torch.split(embedding, length, dim=0)
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table_tensor = torch.stack(split_embedding, dim=0, out=None)
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# prediction的归一化嵌入张量
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norm_table_tensor = torch.nn.functional.normalize(table_tensor, dim=2)
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sim_tensor_dict = {}
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for col_tuple in col_tuple_list:
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lattr_tensor = norm_table_tensor[col_tuple[0]]
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rattr_tensor = norm_table_tensor[col_tuple[1]]
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mul_tensor = lattr_tensor * rattr_tensor
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sim_tensor = torch.sum(mul_tensor, 1)
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sim_tensor = torch.round(sim_tensor, decimals=4)
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sim_tensor_dict[predictions_attrs[col_tuple[0]].replace('ltable_', '')] = sim_tensor
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return sim_tensor_dict
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def er_process(config: Configuration):
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start = time.time()
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ltable = pd.read_csv(ltable_path, encoding='ISO-8859-1')
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cm.set_key(ltable, ltable_id)
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# ltable.fillna("", inplace=True)
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rtable = pd.read_csv(rtable_path, encoding='ISO-8859-1')
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cm.set_key(rtable, rtable_id)
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# rtable.fillna("", inplace=True)
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mappings = pd.read_csv(mapping_path, encoding='ISO-8859-1')
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# 仅保留两表中出现在映射表中的行，增大正样本比例
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lid_mapping_list = []
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rid_mapping_list = []
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# 全部转为字符串
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# ltable = ltable.astype(str)
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# rtable = rtable.astype(str)
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# mappings = mappings.astype(str)
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matching_number = len(mappings) # 所有阳性样本数
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for index, row in mappings.iterrows():
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lid_mapping_list.append(row[mapping_lid])
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rid_mapping_list.append(row[mapping_rid])
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selected_ltable = ltable[ltable[ltable_id].isin(lid_mapping_list)]
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# if len(lr_attrs_map) > 0:
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# 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
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tables_id = rtable_id
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selected_rtable = rtable[rtable[rtable_id].isin(rid_mapping_list)]
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selected_attrs = selected_ltable.columns.values.tolist() # 两张表中的字段名
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attrs_with_l_prefix = ['ltable_' + i for i in selected_attrs]
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attrs_with_r_prefix = ['rtable_' + i for i in selected_attrs]
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cm.set_key(selected_ltable, tables_id)
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cm.set_key(selected_rtable, tables_id)
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if config["ml_blocker"] == "over_lap":
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blocker = em.OverlapBlocker()
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candidate = blocker.block_tables(selected_ltable, selected_rtable, config["block_attr"],
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config["block_attr"], allow_missing=True,
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l_output_attrs=selected_attrs, r_output_attrs=selected_attrs,
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overlap_size=1, show_progress=False)
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elif config["ml_blocker"] == "attr_equiv":
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blocker = em.AttrEquivalenceBlocker()
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candidate = blocker.block_tables(selected_ltable, selected_rtable, config["block_attr"],
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config["block_attr"], allow_missing=True,
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l_output_attrs=selected_attrs, r_output_attrs=selected_attrs)
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candidate['gold'] = 0
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candidate = candidate.reset_index(drop=True)
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candidate_match_rows = []
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for row in candidate.itertuples():
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l_id = getattr(row, 'ltable_' + tables_id)
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map_row = mappings[mappings[mapping_lid] == l_id]
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if map_row is not None:
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r_id = map_row[mapping_rid]
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for value in r_id:
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if value == getattr(row, 'rtable_' + tables_id):
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candidate_match_rows.append(row[0])
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else:
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continue
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for _ in candidate_match_rows:
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candidate.loc[_, 'gold'] = 1
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candidate.fillna(value="", inplace=True)
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# 裁剪负样本，保持正负样本数量一致
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candidate_mismatch = candidate[candidate['gold'] == 0]
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candidate_match = candidate[candidate['gold'] == 1]
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if len(candidate_mismatch) > len(candidate_match):
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candidate_mismatch = candidate_mismatch.sample(n=len(candidate_match))
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# 拼接正负样本
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candidate_for_train_test = pd.concat([candidate_mismatch, candidate_match])
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# if len(candidate_for_train_test) == 0:
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# return 0
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# 如果拼接后不重设索引可能导致索引重复
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candidate_for_train_test = candidate_for_train_test.reset_index(drop=True)
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cm.set_key(candidate_for_train_test, '_id')
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cm.set_fk_ltable(candidate_for_train_test, 'ltable_' + tables_id)
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cm.set_fk_rtable(candidate_for_train_test, 'rtable_' + tables_id)
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cm.set_ltable(candidate_for_train_test, selected_ltable)
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cm.set_rtable(candidate_for_train_test, selected_rtable)
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# 分为训练测试集
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train_proportion = 0.7
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sets = em.split_train_test(candidate_for_train_test, train_proportion=train_proportion, random_state=0)
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train_set = sets['train']
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test_set = sets['test']
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# cm.set_key(train_set, '_id')
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# cm.set_fk_ltable(train_set, 'ltable_' + tables_id)
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# cm.set_fk_rtable(train_set, 'rtable_' + tables_id)
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# cm.set_ltable(train_set, selected_ltable)
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# cm.set_rtable(train_set, selected_rtable)
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#
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# cm.set_key(test_set, '_id')
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# cm.set_fk_ltable(test_set, 'ltable_' + tables_id)
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# cm.set_fk_rtable(test_set, 'rtable_' + tables_id)
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# cm.set_ltable(test_set, selected_ltable)
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# cm.set_rtable(test_set, selected_rtable)
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ml_matcher = config["ml_matcher"]
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if ml_matcher == "dt":
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matcher = em.DTMatcher(name='DecisionTree', random_state=0)
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elif ml_matcher == "svm":
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matcher = em.SVMMatcher(name='SVM', random_state=0)
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elif ml_matcher == "rf":
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matcher = em.RFMatcher(name='RF', random_state=0)
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elif ml_matcher == "lg":
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matcher = em.LogRegMatcher(name='LogReg', random_state=0)
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elif ml_matcher == "ln":
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matcher = em.LinRegMatcher(name='LinReg')
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elif ml_matcher == "nb":
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matcher = em.NBMatcher(name='NaiveBayes')
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feature_table = em.get_features_for_matching(selected_ltable, selected_rtable, validate_inferred_attr_types=False)
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train_feature_vecs = em.extract_feature_vecs(train_set,
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feature_table=feature_table,
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attrs_after=['gold'],
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show_progress=False)
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train_feature_vecs.fillna(value=0, inplace=True)
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test_feature_after = attrs_with_l_prefix[:]
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test_feature_after.extend(attrs_with_r_prefix)
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for _ in test_feature_after:
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if _.endswith(tables_id):
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test_feature_after.remove(_)
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test_feature_after.append('gold')
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test_feature_vecs = em.extract_feature_vecs(test_set, feature_table=feature_table,
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attrs_after=test_feature_after, show_progress=False)
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test_feature_vecs.fillna(value=0, inplace=True)
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fit_exclude = ['_id', 'ltable_' + tables_id, 'rtable_' + tables_id, 'gold']
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matcher.fit(table=train_feature_vecs, exclude_attrs=fit_exclude, target_attr='gold')
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test_feature_after.extend(['_id', 'ltable_' + tables_id, 'rtable_' + tables_id])
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predictions = matcher.predict(table=test_feature_vecs, exclude_attrs=test_feature_after,
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append=True, target_attr='predicted', inplace=False)
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eval_result = em.eval_matches(predictions, 'gold', 'predicted')
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em.print_eval_summary(eval_result)
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indicators = evaluate_prediction(predictions, 'gold', 'predicted', matching_number, candidate_for_train_test)
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# 计算可解释性
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################################################################################################################
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predictions_attrs = []
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predictions_attrs.extend(attrs_with_l_prefix)
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predictions_attrs.extend(attrs_with_r_prefix)
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predictions_attrs.extend(['gold', 'predicted'])
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predictions = predictions[predictions_attrs]
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train_attrs = predictions_attrs[:]
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train_attrs.remove('predicted')
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train_set = train_set[train_attrs]
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prepare_file_for_md_discovery(train_set)
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predictions = predictions.reset_index(drop=True)
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predictions = predictions.astype(str)
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sim_tensor_dict = build_col_pairs_sim_tensor_dict(predictions)
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predictions['confidence'] = 0
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md_discover(config, er_output_dir + "t_single_tuple.csv", md_output_dir + "mds.txt")
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md_paths = [md_output_dir + 'mds.txt']
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md_list = load_mds(md_paths) # 从全局变量中读取所有的md
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epl_match = 0 # 可解释，预测match
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unexplainable = pd.DataFrame()
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if len(md_list) > 0:
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for row in tqdm(predictions.itertuples()):
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x = is_explicable(row, md_list, sim_tensor_dict)
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if x > 0 and str(getattr(row, 'predicted')) == str(1):
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predictions.loc[row[0], 'confidence'] = x
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epl_match += 1
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# else:
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# series = pd.Series(row)
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# unexplainable = unexplainable._append(series, ignore_index=True)
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# unexplainable.drop(columns=unexplainable.columns[[-1, 0]], inplace=True)
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# unexplainable.columns = predictions_attrs
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# unexplainable = unexplainable[train_attrs]
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# if len(unexplainable[unexplainable['gold'] == str(1)]) > 0:
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# prepare_file_for_md_discovery(unexplainable, t_single_tuple_path=er_output_dir + 'unexplainable_tst.csv')
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# md_discover(config, er_output_dir + 'unexplainable_tst.csv', md_output_dir + "from_unexplainable.txt")
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df = predictions[predictions['predicted'] == str(1)]
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interpretability = epl_match / len(df) # 可解释性
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indicators['interpretability'] = interpretability
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if indicators["block_recall"] < indicators["recall"]:
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f1 = (2.0 * indicators["precision"] * indicators["block_recall"]) / (
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indicators["precision"] + indicators["block_recall"])
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else:
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f1 = indicators["F1"]
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performance = interpre_weight * interpretability + (1 - interpre_weight) * f1
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indicators['performance'] = performance
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indicators['eval_result'] = eval_result
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print(indicators)
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predictions.to_csv(er_output_dir + 'predictions.csv', sep=',', index=False, header=True)
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################################################################################################################
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print(f'\033[33mTime consumed by ML-ER in seconds: {time.time() - start}\033[0m')
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return indicators
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def ml_er(config: Configuration = None):
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indicators = er_process(config)
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output_path = er_output_dir + "eval_result.txt"
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with open(output_path, 'w') as f:
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for key, value in six.iteritems(_get_metric(indicators['eval_result'])):
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f.write(key + " : " + value)
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f.write('\n')
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f.write('block_recall:' + str(indicators["block_recall"]) + '\n')
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f.write('interpretability:' + str(indicators['interpretability']) + '\n')
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f.write('performance:' + str(indicators['performance']) + '\n')
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if __name__ == '__main__':
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if os.path.isfile(hpo_output_dir + "incumbent.json"):
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with open(hpo_output_dir + "configspace.json", 'r') as f:
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dict_configspace = json.load(f)
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str_configspace = json.dumps(dict_configspace)
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configspace = csj.read(str_configspace)
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with open(hpo_output_dir + "incumbent.json", 'r') as f:
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dic = json.load(f)
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configuration = ConfigSpace.Configuration(configspace, values=dic)
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ml_er(configuration)
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