COVID-19 literature searching (BM25)文献搜寻-BM25方法

今天选个大资料集，来试试看BM25的语义搜寻。(据说BM25不必先做”断词处理”，说错了，是不必处理stopwords)
59万笔COVID-19相关文献860MB
资料集来源：Kaggle COVID-19 metadata.csv 568,230笔(不重覆者)(title有内容的有48万笔)

先前两篇请参考 < 语义检索 Semantic Search NLP >
< Semantic search BM25 COVID-19 dataset 自然语言BM25搜寻新冠文献资料>
搜寻关键字: Taiwan vaccine mortality
搜寻标的： 文献Title

程序就简单一点，少一点花俏。

• 读档csv (读档较费时，请稍待)
• 取我们要的相关栏位
• 给关键字s (可多词、空白隔开)
• 把文献title tokenize
• 计算 BM25 score
• 列出最高分数前10篇
• 结果存档
  Source Code

''' article_search01.py 
    searching article title 
    BM25 method       '''
from rank_bm25 import BM25Okapi
import pandas as pd
import numpy as np
         
''' main flow '''        
# load csv file
# https://www.kaggle.com/maksimeren/covid-19-literature-clustering/data?select=metadata.csv
print('读档中，请稍候...')
df_raw = pd.read_csv('ArticleCOVID.csv',dtype=object)
# 测试 sample 1万笔
#df = df_raw.sample(n = 10000, random_state=20)
df = df_raw 
#print(df.head())
# 取我们要的相关栏位
mtitle   = df['title'].astype(str)
mabs     = df['abstract'].astype(str)
murl     = df['url'].astype(str) 
mpubtime = df['publish_time'].astype(str)
mpmcid   = df['pmcid'].astype(str)
mauthor  = df['authors'].astype(str)
mjournal = df['journal'].astype(str)
mdoi     = df['doi'].astype(str)

#print(len(mtitle),mtitle.shape)
#print(mtitle.iloc[5])

#--- 把文献title tokenize
tokenized_corpus = [doc.split(" ") for doc in mtitle]
print(f'文献数量: {len(tokenized_corpus)}')
print(f'前五篇 title token\n{tokenized_corpus[:5]}')

#--- initiate BM25
bm = BM25Okapi(tokenized_corpus)

# query --> 要查询的 keywords 可多词，以空格间隔
query = input('搜寻【文献标题】之关键字s:>> ')

tokenized_query = query.split(" ")
print(f'keywords数目: {len(tokenized_query)}\n tokenized: {tokenized_query}')

# 计算 BM25 score (log)
scores = bm.get_scores(tokenized_query)
# sort scores (take index)
s1 = np.argsort(scores)
sidx = s1[::-1]   # reverse s1
print(sidx[:10])   # top 10 highest score papers 

fw = open('article_result.txt','w',encoding='utf-8')
print(f'Searching keywords: {query}')
print('Top 10 aritcles listed below:')
print(f'Searching keywords: {query}',file=fw)
print('Top 10 aritcles listed below:',file=fw)
for i in range(10):
    no = sidx[i]
    tmp =       f'Location: {no}\n'
    tmp = tmp + f'BM25 score: {scores[no]}\n'
    tmp = tmp + f'Title: {mtitle.iloc[no]}\n'
    tmp = tmp + f'Authors: {mauthor.iloc[no]}\n'
    tmp = tmp + f'PubTime: {mpubtime.iloc[no]}\n'
    tmp = tmp + f'Abstract: {mabs.iloc[no][:500]}\n'   
    tmp = tmp + f'Journal: {mjournal.iloc[no]}\n'
    tmp = tmp + f'URL: {murl.iloc[no]}\n'
    tmp = tmp + f'pmcid: {mpmcid.iloc[no]}\n'
    tmp = tmp + f'doi: {mdoi.iloc[no]}\n\n'
    print(tmp)
    print(tmp,file=fw)
    
fw.close()    
print('搜寻结果，已存档完成: article_result.txt')