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154 lines
7.1 KiB
154 lines
7.1 KiB
import sys
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from py_entitymatching.debugmatcher.debug_gui_utils import _get_metric
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sys.path.append('/home/w/PycharmProjects/py_entitymatching/py_entitymatching')
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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 time
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import six
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def load_data(left_path: str, right_path: str, mapping_path: str):
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left = pd.read_csv(left_path, encoding='ISO-8859-1')
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cm.set_key(left, left.columns.values.tolist()[0])
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left.fillna("", inplace=True)
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left = left.astype(str)
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right = pd.read_csv(right_path, encoding='ISO-8859-1')
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cm.set_key(right, right.columns.values.tolist()[0])
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right.fillna("", inplace=True)
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right = right.astype(str)
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mapping = pd.read_csv(mapping_path)
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mapping = mapping.astype(str)
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return left, right, mapping
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if __name__ == '__main__':
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# 读入公开数据，注册并填充空值
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path_Amazon = '/home/w/PycharmProjects/py_entitymatching/py_entitymatching/datasets/end-to-end/Amazon-GoogleProducts/Amazon.csv'
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path_Google = '/home/w/PycharmProjects/py_entitymatching/py_entitymatching/datasets/end-to-end/Amazon-GoogleProducts/GoogleProducts.csv'
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path_Mappings = '/home/w/PycharmProjects/py_entitymatching/py_entitymatching/datasets/end-to-end/Amazon-GoogleProducts/Amzon_GoogleProducts_perfectMapping.csv'
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Amazon = pd.read_csv(path_Amazon, encoding='ISO-8859-1')
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cm.set_key(Amazon, 'id')
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Amazon.fillna("", inplace=True)
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Google = pd.read_csv(path_Google, encoding='ISO-8859-1')
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cm.set_key(Google, 'id')
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Google.fillna("", inplace=True)
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Mappings = pd.read_csv(path_Mappings)
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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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l_id_list = []
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r_id_list = []
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# 全部转为字符串
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Amazon = Amazon.astype(str)
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Google = Google.astype(str)
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Mappings = Mappings.astype(str)
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for index, row in Mappings.iterrows():
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l_id_list.append(row["idAmazon"])
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r_id_list.append(row["idGoogleBase"])
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selected_Amazon = Amazon[Amazon['id'].isin(l_id_list)]
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selected_Amazon = selected_Amazon.rename(columns={'title': 'name'})
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selected_Google = Google[Google['id'].isin(r_id_list)]
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cm.set_key(selected_Amazon, 'id')
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cm.set_key(selected_Google, 'id')
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#########################################################################
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# False-retain True-remove
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def match_last_name(ltuple, rtuple):
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l_last_name = ltuple['name']
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r_last_name = rtuple['name']
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if l_last_name != r_last_name:
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return True
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else:
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return False
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bb = em.BlackBoxBlocker()
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bb.set_black_box_function(match_last_name)
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Candidate = bb.block_tables(selected_Amazon, selected_Google, l_output_attrs=['id', 'name', 'description', 'manufacturer', 'price'], r_output_attrs=['id', 'name', 'description', 'manufacturer', 'price'])
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#########################################################################
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# block 并将gold标记为0
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blocker = em.OverlapBlocker()
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candidate = blocker.block_tables(selected_Amazon, selected_Google, 'name', 'name',
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l_output_attrs=['id', 'name', 'description', 'manufacturer', 'price'],
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r_output_attrs=['id', 'name', 'description', 'manufacturer', 'price'],
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overlap_size=0, show_progress=False)
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candidate['gold'] = 0
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start = time.time()
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candidate_match_rows = []
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for index, row in candidate.iterrows():
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l_id = row["ltable_id"]
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map_row = Mappings[Mappings['idAmazon'] == l_id]
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if map_row is not None:
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r_id = map_row["idGoogleBase"]
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for value in r_id:
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if value == row["rtable_id"]:
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candidate_match_rows.append(row["_id"])
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else:
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continue
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for row in candidate_match_rows:
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candidate.loc[row, 'gold'] = 1
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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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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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cm.set_key(candidate_for_train_test, '_id')
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cm.set_fk_ltable(candidate_for_train_test, 'ltable_id')
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cm.set_fk_rtable(candidate_for_train_test, 'rtable_id')
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cm.set_ltable(candidate_for_train_test, selected_Amazon)
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cm.set_rtable(candidate_for_train_test, selected_Google)
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# 分为训练测试集
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sets = em.split_train_test(candidate_for_train_test, train_proportion=0.7, random_state=0)
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train_set = sets['train']
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test_set = sets['test']
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dt = em.DTMatcher(name='DecisionTree', random_state=0)
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svm = em.SVMMatcher(name='SVM', random_state=0)
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rf = em.RFMatcher(name='RF', random_state=0)
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lg = em.LogRegMatcher(name='LogReg', random_state=0)
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ln = em.LinRegMatcher(name='LinReg')
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nb = em.NBMatcher(name='NaiveBayes')
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feature_table = em.get_features_for_matching(selected_Amazon, selected_Google, 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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result = em.select_matcher([dt, rf, svm, ln, lg, nb], table=train_feature_vecs,
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exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold'],
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k=5,
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target_attr='gold', metric_to_select_matcher='f1', random_state=0)
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test_feature_vecs = em.extract_feature_vecs(test_set, feature_table=feature_table,
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attrs_after=['ltable_name', 'ltable_description', 'ltable_manufacturer',
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'ltable_price', 'rtable_name', 'rtable_description',
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'rtable_manufacturer', 'rtable_price', 'gold'], show_progress=False)
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rf.fit(table=train_feature_vecs,
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exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold'],
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target_attr='gold')
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predictions = rf.predict(table=test_feature_vecs, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'ltable_name',
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'ltable_description', 'ltable_manufacturer',
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'ltable_price', 'rtable_name', 'rtable_description',
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'rtable_manufacturer', 'rtable_price', 'gold'],
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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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output_path = "output/eval_result" + str(time.time()) + ".txt"
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with open(output_path, 'w') as f:
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for key, value in six.iteritems(_get_metric(eval_result)):
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f.write(key + " : " + value)
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f.write('\n')
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