315 lines
14 KiB
Python
315 lines
14 KiB
Python
import json
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import os
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import re
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import time
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import unicodedata
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import torch
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from pprint import pprint
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from concurrent.futures import ThreadPoolExecutor, as_completed, wait
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from transformers import BertTokenizer, BertModel
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from sklearn.metrics.pairwise import cosine_similarity
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'''
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========== SameWeb_merge(folder_path) 运行顺序 ==========
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1. 创建新列表，依次读取文件夹内的json文件并录入，对列表按年份进行排序
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2. 采用梯度合并的方式，将需要合并的元素根据合并时有把握的程度放入堆叠的
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列表中，形如[[[a,b],c],d,e]，每次根据不同合并条件将外围的元素逐渐
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合并进内部的元素。对于最后没有把握完全合并的元素一起作为单个列表保留
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3. 合并顺序：作者同名称优先合并（发现了不同作者公用相同邮箱的情况，所以
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针对同一个网站优先考虑相同名字合并），之后针对affiliation和邮箱是
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否完全相同经行二次合并，最后使用文本嵌入法经行相似度比较，对于相似度
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大于0.8的数据经行第三次合并
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4. 相似度比较时使用了BERT的预置模型，需要提前安装。目前看来运行时间会
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因此延长许多，在10线程下相似度比较速度约50份/分钟，但从最后表现结果
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来看准确率较为不错，针对大型数据（>1500份）处理速度可能偏慢，后续可
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以考虑优化代码结构或者其他方式提高效率
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5. 大部分函数使用
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for i in range(len(Data)):
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Merge(i, Data):
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for j in range(i+1, len(Data)):
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结构进
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行遍历，每次扫描到符合条件的 Data[j] 元素后，添加至 Data[i]元素
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中，然后删除 Data[j] 元素再次调用Merge函数重新遍历，直至Data中
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不再包含符合条件的 Data[j] 元素为止
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*Tips: 部分网站出现了不同作者共用同一邮箱账号的情况，因此针对同网站合并时，
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优先进行同名称合并，再进行同邮箱合并以及后续操作
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建议：
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1. 在合并部分首先将拉丁文字等非英文字符统一为英文再进行对比(已解决)
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2. 对于随意增添标点符号和任意额外信息的数据进行局部处理再对比（已解
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决，通过删除多余空格，全部标点符号，再进行相似度比较）
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3. 相似度比较需要一个更加快捷的方式，通过预置的模型对比耗时过长，cpu
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占用率也较高
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'''
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def SameWeb_merge(folder_path):
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# Function
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def SameName_merge(i, count1, count2, Data, ml):
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# Same name merge
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for j in range(i+1, len(Data)):
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if j < len(Data):
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a = Data[i]
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aa = Data[j]
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if isinstance(a, dict) and isinstance(aa, dict):
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if len(a['from_article']) == 1 and len(aa['from_article']) == 1:
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if a.get("first_name") == aa.get("first_name") and a.get("middle_name") == aa.get(
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"middle_name") and a.get("last_name") == aa.get("last_name"):
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if a['from_article'] == aa['from_article']: # Remove same data
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Data.remove(Data[j])
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count2[0] += 1
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return SameName_merge(i, count1, count2, Data, ml)
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else:
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ml.append(aa)
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# Update the counter
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count1[0] += 1
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if count1[0] % 100 == 0 and count1[0] != 0:
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print(str(count1[0]) + ' copies of data have been merged by same name.')
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# Delete extra elements
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Data.remove(Data[j])
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return SameName_merge(i, count1, count2, Data, ml)
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# Detail merge
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if len(ml) > 0:
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ml.append(Data[i]) # Add first element
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Data.remove(Data[i])
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ml = sorted(ml, key=lambda x: x.get('affiliation')[0].get('year')) # Sorted by year
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# Merge same affiliation data
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for i in range(len(ml)):
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for j in range(i+1, len(ml)):
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if j < len(ml):
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m = ml[i]
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n = ml[j]
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if m.get('affiliation')[-1].get('affiliation') == n.get('affiliation')[0].get('affiliation'):
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if m.get('affiliation')[-1].get('year') != n.get('affiliation')[0].get('year'):
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m['from_article'] += n['from_article']
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m['affiliation'] += n['affiliation']
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ml.remove(ml[j])
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elif m.get('affiliation')[-1].get('year') == n.get('affiliation')[0].get('year'):
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m['from_article'] += n['from_article']
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ml.remove(ml[j])
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# Merge same email data
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def SameEmail_merge(i, ml):
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for j in range(i + 1, len(ml)):
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if j < len(ml):
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m = ml[i]
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n = ml[j]
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A = m.get('affiliation')
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AA = n.get('affiliation')
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num = 0 # Merge counter
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for a in A:
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if num == 0:
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for aa in AA:
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if a.get('email') == aa.get('email'):
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m['from_article'] += n['from_article']
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m['affiliation'] += n['affiliation']
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ml.remove(n) # Delete merged element
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num += 1 # Update counter
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# Sorted by year
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m['affiliation'] = sorted(m['affiliation'], key=lambda x: x['year'])
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break
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else:
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return SameEmail_merge(i, ml)
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# Loop of merging data by same email
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for i in range(len(ml)):
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SameEmail_merge(i, ml)
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# Add into Data list
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if len(ml) == 1:
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Data.insert(-1, ml[0])
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else:
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Data.insert(-1, ml)
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def Similarity_merge(M):
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for i in range(len(M)):
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for j in range(i+1, len(M)):
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if j < len(M):
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m = M[i]
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n = M[j]
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A = m.get('affiliation')
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AA = n.get('affiliation')
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num = 0 # Merge counter
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for a in A:
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if num == 0:
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for aa in AA:
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# ========== Comparing document embeddings for similarity ==========
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# Get the data of affiliation
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text1 = a['affiliation']
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text2 = aa['affiliation']
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# Uniform characters in English
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text1 = unicodedata.normalize('NFKD', text1).encode('ascii', 'ignore').decode('utf-8')
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text2 = unicodedata.normalize('NFKD', text2).encode('ascii', 'ignore').decode('utf-8')
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# Delete punctuation and lower the character
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text1 = re.sub(r'[^\w\s]', '', str(text1)).lower()
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text2 = re.sub(r'[^\w\s]', '', str(text2)).lower()
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# Delete extra spaces
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text1 = re.sub(r'\s+', ' ', text1).strip()
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text2 = re.sub(r'\s+', ' ', text2).strip()
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# Load the pre-trained BERT simulator and tokenizer
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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model = BertModel.from_pretrained('bert-base-uncased')
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# Tokenize and encode the text
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inputs1 = tokenizer.encode_plus(text1, add_special_tokens=True, return_tensors='pt')
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inputs2 = tokenizer.encode_plus(text2, add_special_tokens=True, return_tensors='pt')
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# Get the embedding vectors of the text
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with torch.no_grad():
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outputs1 = model(**inputs1)
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outputs2 = model(**inputs2)
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embeddings1 = torch.mean(outputs1.last_hidden_state, dim=1).squeeze()
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embeddings2 = torch.mean(outputs2.last_hidden_state, dim=1).squeeze()
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# Calculate text similarity (cosine similarity)
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similarity = cosine_similarity(embeddings1.unsqueeze(0), embeddings2.unsqueeze(0))[0][0]
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print('Similarity algorithm complete: the similarity score is', similarity)
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if similarity >= 0.8:
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m['from_article'] += n['from_article']
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m['affiliation'] += n['affiliation']
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M.remove(n) # Delete merged element
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num += 1 # Update counter
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# Sorted by year
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m['affiliation'] = sorted(m['affiliation'], key=lambda x: x['year'])
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break
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else:
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return Similarity_merge(M)
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# ========== Main code ==========
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Data = [] # List of all data
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count1 = [0] # Same name merged data counter
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count2 = [0] # Duplicate data counter
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num1 = 0 # Unique data counter
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num2 = 0 # Complete merged data counter
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num3 = 0 # Incomplete merged data counter
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num4 = 0 # Similarity algorithm merged data counter
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# Add data into list
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for filename in os.listdir(folder_path):
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if filename.endswith('.json'):
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file_path = os.path.join(folder_path, filename)
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with open(file_path, 'r', encoding='utf-8') as file:
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data = json.load(file)
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if len(data) > 0:
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Data.extend(data)
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Database = len(Data) # The length of the original data
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Data = sorted(Data, key=lambda x: x['affiliation'][0]['year'])
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# ========== Merge ==========
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# ----- Same name data merge -----
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ml = []
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if len(Data) > 1:
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for i in range(len(Data)):
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ml.clear()
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SameName_merge(i, count1, count2, Data, ml)
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print('\n----- Same name data merge complete -----\n')
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# ----- Similarity algorithm merge -----
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# Change the index of incomplete data before other data
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temp_list = [] # Temp list for incomplete merged data
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if len(Data) > 1:
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for i in range(len(Data)-1, -1, -1):
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if isinstance(Data[i], list):
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temp = Data[i]
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Data.remove(Data[i])
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temp_list.append(temp)
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print(str(len(temp_list)) + ' copies of incomplete merged data have been added to temporary list\n')
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st = time.time() # Start time
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if len(temp_list) > 1:
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executor = ThreadPoolExecutor(max_workers=10) # Thread pool
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futures = [executor.submit(Similarity_merge, temp_list[i]) for i in range(len(temp_list))]
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for future in as_completed(futures):
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pass
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wait(futures)
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et = time.time() # End time
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print('\nThread pool has been run for ' + str(et-st) + 's')
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# Combine Data with temp_list
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for i in temp_list:
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if len(i) == 1:
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num4 += 1
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Data.insert(0, i[0])
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else:
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Data.insert(-1, i)
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print('\n----- Similarity algorithm merge complete -----\n')
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# ========== Statistic data ==========
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# Data counter update
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for a in Data:
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if isinstance(a, dict) and len(a['from_article']) == 1:
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num1 += 1
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elif isinstance(a, dict) and len(a['from_article']) > 1:
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num2 += 1
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else:
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num3 += 1
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# Information
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print('\n========== Complete ==========\n')
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print(str(Database) + ' copies of data in total, before')
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print(str(count1[0]) + ' copies of data have been merged by same name.')
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print(str(count2[0]) + ' copies of duplicate data have been deleted')
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print(str(len(Data)) + ' copies of data in total, now.\n')
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print(str(num1) + ' copies of data are unique.')
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print(str(num2) + ' copies of data are complete merged')
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print(str(num4) + ' copies of data are complete merged by similarity algorithm')
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print(str(num3) + ' copies of data are incomplete merged')
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# Save into file
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path = os.path.dirname(folder_path) # parent path
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path = os.path.join(path, "Author_data(merged)")
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os.makedirs(path, exist_ok=True)
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path = os.path.join(path, "Author_data(merged).json")
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with open(path, 'w', encoding='utf-8') as file:
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json.dump(Data, file, indent=4)
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print('\nData has been added to ' + path)
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# =========== input the file path here ==========
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# SameWeb_merge('.\EJQTDE\EJQTDE_buffer\Author_output')
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# SameWeb_merge('.\SpringerOpen\SpringerOpen_buffer\Author_output')
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SameWeb_merge('.\ejde\ejde_buffer\Author_output')
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