|
|
import json
|
|
|
import os
|
|
|
import pickle
|
|
|
import time
|
|
|
|
|
|
import ConfigSpace
|
|
|
import pandas as pd
|
|
|
import py_entitymatching as em
|
|
|
import torch
|
|
|
from ConfigSpace import Configuration
|
|
|
from ConfigSpace.read_and_write import json as csj
|
|
|
import py_entitymatching.catalog.catalog_manager as cm
|
|
|
from tqdm import tqdm
|
|
|
from colorama import Fore
|
|
|
|
|
|
from settings import *
|
|
|
|
|
|
|
|
|
def matching(config: Configuration):
|
|
|
print(Fore.BLUE + f'Config: {config}')
|
|
|
with open(md_output_dir + r"\mds.pickle", "rb") as file:
|
|
|
md_list = pickle.load(file)
|
|
|
|
|
|
train_set = pd.read_csv(directory_path + r'\train_whole.csv', encoding='ISO-8859-1')
|
|
|
test_set = pd.read_csv(directory_path + r'\test_whole.csv', encoding='ISO-8859-1')
|
|
|
ltable = pd.read_csv(directory_path + r'\tableA.csv', encoding='ISO-8859-1')
|
|
|
rtable = pd.read_csv(directory_path + r'\tableB.csv', encoding='ISO-8859-1')
|
|
|
ml_matcher = config["ml_matcher"]
|
|
|
match ml_matcher:
|
|
|
case "dt":
|
|
|
matcher = em.DTMatcher(name='DecisionTree', random_state=0, criterion=config['tree_criterion'],
|
|
|
max_depth=config['tree_max_depth'], splitter=config['dt_splitter'],
|
|
|
max_features=config['dt_max_features'])
|
|
|
case "svm":
|
|
|
matcher = em.SVMMatcher(name='SVM', random_state=0, kernel=config['svm_kernel'], degree=config['svm_degree'],
|
|
|
gamma=config['svm_gamma'], C=config['svm_C'], coef0=config['svm_constant'])
|
|
|
case "rf":
|
|
|
matcher = em.RFMatcher(name='RandomForest', random_state=0, criterion=config['tree_criterion'],
|
|
|
max_depth=config['tree_max_depth'], n_estimators=config['number_of_tree'],
|
|
|
max_features=config['rf_max_features'])
|
|
|
|
|
|
cm.set_key(train_set, '_id')
|
|
|
cm.set_fk_ltable(train_set, 'ltable_id')
|
|
|
cm.set_fk_rtable(train_set, 'rtable_id')
|
|
|
cm.set_ltable(train_set, ltable)
|
|
|
cm.set_rtable(train_set, rtable)
|
|
|
cm.set_key(ltable, 'id')
|
|
|
cm.set_key(rtable, 'id')
|
|
|
|
|
|
cm.set_key(test_set, '_id')
|
|
|
cm.set_fk_ltable(test_set, 'ltable_id')
|
|
|
cm.set_fk_rtable(test_set, 'rtable_id')
|
|
|
cm.set_ltable(test_set, ltable)
|
|
|
cm.set_rtable(test_set, rtable)
|
|
|
feature_table = em.get_features_for_matching(ltable, rtable, validate_inferred_attr_types=False)
|
|
|
train_feature_vecs = em.extract_feature_vecs(train_set,
|
|
|
feature_table=feature_table,
|
|
|
attrs_after=['label'],
|
|
|
show_progress=False)
|
|
|
train_feature_vecs.fillna(value=0, inplace=True)
|
|
|
|
|
|
test_feature_after = ['ltable_' + i for i in ltable.columns.values.tolist()]
|
|
|
for _ in test_feature_after[:]:
|
|
|
test_feature_after.append(_.replace('ltable_', 'rtable_'))
|
|
|
for _ in test_feature_after:
|
|
|
if _.endswith('id'):
|
|
|
test_feature_after.remove(_)
|
|
|
test_feature_after.append('label')
|
|
|
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_id', 'rtable_id', 'label']
|
|
|
matcher.fit(table=train_feature_vecs, exclude_attrs=fit_exclude, target_attr='label')
|
|
|
test_feature_after.extend(['_id', 'ltable_id', 'rtable_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, 'label', 'predicted')
|
|
|
em.print_eval_summary(eval_result)
|
|
|
indicators = evaluate_prediction(predictions, 'label', 'predicted')
|
|
|
|
|
|
test_feature_after.remove('_id')
|
|
|
test_feature_after.append('predicted')
|
|
|
predictions = predictions[test_feature_after]
|
|
|
|
|
|
predictions = predictions.reset_index(drop=True)
|
|
|
predictions = predictions.astype(str)
|
|
|
# 目前predictions包含的属性：左右表全部属性+gold+predicted
|
|
|
sim_tensor_dict = build_col_pairs_sim_tensor_dict(predictions)
|
|
|
predictions['confidence'] = 0
|
|
|
predictions['md'] = ''
|
|
|
|
|
|
epl_match = 0 # 可解释，预测match
|
|
|
if len(md_list) > 0:
|
|
|
for row in tqdm(predictions.itertuples()):
|
|
|
if str(getattr(row, 'predicted')) == str(1):
|
|
|
conf, md_dict = is_explicable(row, md_list, sim_tensor_dict)
|
|
|
if conf > 0:
|
|
|
predictions.loc[row[0], 'confidence'] = conf
|
|
|
predictions.loc[row[0], 'md'] = str(md_dict)
|
|
|
epl_match += 1
|
|
|
|
|
|
df = predictions[predictions['predicted'] == str(1)]
|
|
|
interpretability = epl_match / len(df) # 可解释性
|
|
|
indicators['interpretability'] = interpretability
|
|
|
performance = interpre_weight * interpretability + (1 - interpre_weight) * indicators["F1"]
|
|
|
indicators['performance'] = performance
|
|
|
print(Fore.BLUE + f'ER Indicators: {indicators}')
|
|
|
predictions.to_csv(er_output_dir + r'\predictions.csv', sep=',', index=False, header=True)
|
|
|
print(Fore.CYAN + f'Finish Time: {time.time()}')
|
|
|
return indicators
|
|
|
|
|
|
|
|
|
def evaluate_prediction(prediction_: pd.DataFrame, labeled_attr: str, predicted_attr: str) -> dict:
|
|
|
new_df = prediction_.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)
|
|
|
|
|
|
return {"precision": precision, "recall": recall, "F1": F1}
|
|
|
|
|
|
|
|
|
def build_col_pairs_sim_tensor_dict(predictions: pd.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]
|
|
|
predictions = predictions.reset_index(drop=True)
|
|
|
sentences = predictions.values.flatten(order='F').tolist()
|
|
|
|
|
|
embedding = model.encode(sentences, convert_to_tensor=True, device="cuda", batch_size=256, show_progress_bar=True)
|
|
|
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=2)
|
|
|
sim_tensor_dict[predictions_attrs[col_tuple[0]].replace('ltable_', '')] = sim_tensor
|
|
|
return sim_tensor_dict
|
|
|
|
|
|
|
|
|
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 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], md_tuple[0] # 任意一条md能解释，直接返回
|
|
|
return -1.0, {} # 遍历结束，不能解释
|
|
|
|
|
|
|
|
|
def ml_er(config: Configuration):
|
|
|
indicators = matching(config)
|
|
|
output_path = er_output_dir + r"\eval_result.txt"
|
|
|
with open(output_path, 'w') as _f:
|
|
|
_f.write('Precision:' + str(indicators["precision"]) + '\n')
|
|
|
_f.write('Recall:' + str(indicators["recall"]) + '\n')
|
|
|
_f.write('F1:' + str(indicators["F1"]) + '\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 + r"\incumbent.json"):
|
|
|
with open(hpo_output_dir + r"\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 + r"\incumbent.json", 'r') as f:
|
|
|
dic = json.load(f)
|
|
|
configuration = ConfigSpace.Configuration(configspace, values=dic)
|
|
|
ml_er(configuration)
|
