Python数据分析及可视化实例之文本处理文本相似度（29）

# coding: utf-8



# In[1]:

from gensim import corpora, models, similarities
from pprint import pprint


# 读取词典和文档，计算tf idf 和 lsi 的表达方式，并生成相似度矩阵
dictionary = corpora.Dictionary.load('../../tmp/deerwester.dict')
corpus = corpora.MmCorpus('../../tmp/deerwester.mm') # 上一示例中生成的数据
tfidf_model = models.TfidfModel(corpus)
corpus_tfidf = tfidf_model[corpus]  # TIDIF 逆文档频率
lsi_model = models.LsiModel(corpus_tfidf,id2word=dictionary,num_topics=2)
corpus_lsi = lsi_model[corpus_tfidf] # LSI 潜在语义索引
corpus_simi_matrix = similarities.MatrixSimilarity(corpus_lsi)
corpus_simi_matrix.save('../../tmp/deerwester.index')
similarities.MatrixSimilarity.load('../../tmp/deerwester.index') # 下次调用


# In[5]:

# 基于tfidf的文本相似度分析 
index = similarities.MatrixSimilarity(corpus_tfidf) 
vec_bow =[dictionary.doc2bow(text) for text in raw_data]   #把用户raw_data语料转为词包
all_reult_sims = []
times_v2 = 0 

###对每个用户预料与标准预料计算相似度

for i in vec_bow:    
     #直接使用上面得出的tf-idf 模型即可得出商品描述的tf-idf 值
    sims = index[tfidf_model[i]]
    sims = sorted(enumerate(sims), key=lambda item: -item[1])
    result_sims = []    
    for i,j in sims:
        result_sims.append([map_value_user[times_v2],map_value[i],j])
    times_v2 += 1
    all_reult_sims.append(result_sims[:20])
print(all_reult_sims)  # 查看前20条显示相似文本


# In[7]:

# 基于lsi的文本相似度分析 
index = similarities.MatrixSimilarity(corpus_lsi) 
vec_bow =[dictionary.doc2bow(text) for text in raw_data]  #把用户语料raw_data转为词包
all_reult_sims = []
times_v2 = 0 
for i in vec_bow:    
     #直接使用上面得出的tf-idf 模型即可得出商品描述的tf-idf 值
    sims = index[lsi[tfidf_model[i]]]
    sims = sorted(enumerate(sims), key=lambda item: -item[1])
    result_sims = []    
    for i,j in sims:
        result_sims.append([map_value_user[times_v2],map_value[i],j])
    times_v2 += 1
    all_reult_sims.append(result_sims[:20])
print(all_reult_sims)  # 查看前20条显示相似文本