# coding: utf-8 #本代碼主要實現(xiàn)了對于商品名稱根據(jù)tfidf提取特征詞，然后基于已經(jīng)訓(xùn)練好的word2vec模型，對每行商品的tfidf值大于某一閾值的特征詞相似度匹配已經(jīng)給定的商品類別。import jieba import jieba.posseg as pseg import jieba.analyse import pymssql import xlwt from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer import pandas as pd jieba.load_userdict('C:\\Users\\Desktop\\s_proj\\dict.txt') #導(dǎo)入自定義的分詞詞典#連接數(shù)據(jù)庫 conn = pymssql.connect(host='1.1.1.1',user='username',password='password',database='database',charset='utf8') cur = conn.cursor() sql='select distinct(column) from table' cur.execute(sql) listl=cur.fetchall()#分詞 words=[] for word in listl:for i in word:seg_list = jieba.cut(i, cut_all=False)words.append(" ".join(seg_list))#計算tfidf tfidf_vectorizer = CountVectorizer(min_df=2,max_df = 0.8,token_pattern=u'(?u)\\b[^\\d\\W]\\w+\\b') transformer=TfidfTransformer() word_tfidf = tfidf_vectorizer.fit_transform(words) tfidf=transformer.fit_transform(word_tfidf) print(tfidf.shape)#獲取特征詞 features=tfidf_vectorizer.get_feature_names()#加載已訓(xùn)練好的word2vec模型 from gensim.models import word2vec import logging logging.basicConfig(format='%(asctime)s:%(levelname)s: %(message)s', level=logging.INFO) model=gensim.models.KeyedVectors.load_word2vec_format('model_word.bin',binary=True) #打開要匹配相似度的商品類文本 f4=open('C:\\Users\\Desktop\\s_proj\\c.txt','r',encoding='utf-8',errors='ignore') #對商品類文本做文本處理 ff=[] for j in f4.readlines():j=j.replace('\n','')ff.append(j) print(len(ff))#tfidf 稀疏矩陣 tfidf_tuple = tfidf.nonzero() tfidf_rows = tfidf_tuple[0] tfidf_columns = tfidf_tuple[1] size = len(tfidf_columns) print('nonzero.size=%s' % size)# 將TF-IDF>=某一閾值d的數(shù)據(jù)存入字典，Key為電商索引。 product_dict = {} for i in range(size):row = tfidf_rows[i]column = tfidf_columns[i]tfidf_value = tfidf[row,column]if tfidf_value <=0.4:continuekey_words = product_dict.setdefault(row, []) key_word = {}key_word["key_word"]=features[column]key_word["tfidf_value"]=tfidf_valuekey_words.append(key_word) print('product_dict.len=%s' % len(product_dict))#tfidf提取的所有features計算word2vec相似度 f=open('gabbage.txt','w',encoding='utf-8',errors='ignore') word2vec={} for i in range(size):column = tfidf_columns[i]cate=word2vec.setdefault(features[column],[])for jj in ff:try:y1 = model.wv.similarity(features[column],jj) insert={"category_name":jj,"similarity":y1}cate.append(insert)except:f.write('') print('word2vec.len=%s' % len(word2vec)) f.close()#相似度值排序 import operator for k,v in word2vec.items():new_dict_list=[]s=sorted(v,key=operator.itemgetter("similarity"),reverse=True)word2vec[k]=sfor w in word2vec[k]:if w not in new_dict_list:new_dict_list.append(w)word2vec[k]=new_dict_list#創(chuàng)建滿足某一閾值的tfidf的特征詞的相似度的詞典 l=[ll for l in product_dict.keys()for ll in product_dict[l]] for k in word2vec.keys():for m in l:if m["key_word"]==k:m["category_names"]=word2vec[k] print(len(product_dict))# 將字典轉(zhuǎn)為數(shù)組，索引轉(zhuǎn)為商品名稱。 result=[r for r in listl] product_list = [] for i in product_dict:product = {}product['product'] = result[i][0]product['key_words'] = product_dict[i]product_list.append(product) print('product_list.len=%s' % len(product_list)) print(len(product_list))#匹配填充tfidf值滿足某一閾值的相似度詞典 f=open('gabbage.txt','w',encoding='utf-8',errors='ignore') # 將TF-IDF>=0。4的數(shù)據(jù)存入字典，Key為電商索引。 product_dict = {} for i in range(size):row = tfidf_rows[i]column = tfidf_columns[i]tfidf_value = tfidf[row,column]if tfidf_value <=0.4:continuekey_words = product_dict.setdefault(row, [])for jj in ff:try:y1 = model.wv.similarity(features[column],jj) insert={'key_word':features[column],'tfidf_value':tfidf_value,'category_names':{'goods_name':jj,'word2vec':y1}}key_words.append(insert)except:f.write('') f.close()#將結(jié)果寫入json import json t=open('word2vec_result1.txt','w',encoding='utf-8',errors='ignore') json_string = json.dumps(product_list[0:11]).encode('utf-8').decode('unicode_escape') print(json_string) t.write(json_string) t.close() 創(chuàng)作挑戰(zhàn)賽新人創(chuàng)作獎勵來咯，堅持創(chuàng)作打卡瓜分現(xiàn)金大獎

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