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python初步实现word2vec操作

 更新时间：2020年06月09日 11:24:09   作者：小拳头  
这篇文章主要介绍了python初步实现word2vec操作，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

一、前言

一开始看到word2vec环境的安装还挺复杂的，安了半天Cygwin也没太搞懂。后来突然发现，我为什么要去安c语言版本的呢，我应该去用python版本的，然后就发现了gensim，安装个gensim的包就可以用word2vec了，不过gensim只实现了word2vec里面的skip-gram模型。若要用到其他模型，就需要去研究其他语言的word2vec了。

二、语料准备

有了gensim包之后，看了网上很多教程都是直接传入一个txt文件，但是这个txt文件长啥样，是什么样的数据格式呢，很多博客都没有说明，也没有提供可以下载的txt文件作为例子。进一步理解之后发现这个txt是一个包含巨多文本的分好词的文件。如下图所示，是我自己训练的一个语料，我选取了自己之前用爬虫抓取的7000条新闻当做语料并进行分词。注意，词与词之间一定要用空格：

这里分词使用的是结巴分词。

这部分代码如下：

import jieba
f1 =open("fenci.txt")
f2 =open("fenci_result.txt", 'a')
lines =f1.readlines() # 读取全部内容
for line in lines:
  line.replace('\t', '').replace('\n', '').replace(' ','')
  seg_list = jieba.cut(line, cut_all=False)
  f2.write(" ".join(seg_list))
 
f1.close()
f2.close()

还要注意的一点就是语料中的文本一定要多，看网上随便一个语料都是好几个G，而且一开始我就使用了一条新闻当成语料库，结果很不好，输出都是0。然后我就用了7000条新闻作为语料库，分词完之后得到的fenci_result.txt是20M，虽然也不大，但是已经可以得到初步结果了。

三、使用gensim的word2vec训练模型

相关代码如下：

from gensim.modelsimport word2vec
import logging
 
# 主程序
logging.basicConfig(format='%(asctime)s:%(levelname)s: %(message)s', level=logging.INFO)
sentences =word2vec.Text8Corpus(u"fenci_result.txt") # 加载语料
model =word2vec.Word2Vec(sentences, size=200) #训练skip-gram模型，默认window=5
 
print model
# 计算两个词的相似度/相关程度
try:
  y1 = model.similarity(u"国家", u"国务院")
except KeyError:
  y1 = 0
print u"【国家】和【国务院】的相似度为：", y1
print"-----\n"
#
# 计算某个词的相关词列表
y2 = model.most_similar(u"控烟", topn=20) # 20个最相关的
print u"和【控烟】最相关的词有：\n"
for item in y2:
  print item[0], item[1]
print"-----\n"
 
# 寻找对应关系
print u"书-不错，质量-"
y3 =model.most_similar([u'质量', u'不错'], [u'书'], topn=3)
for item in y3:
  print item[0], item[1]
print"----\n"
 
# 寻找不合群的词
y4 =model.doesnt_match(u"书 书籍 教材 很".split())
print u"不合群的词：", y4
print"-----\n"
 
# 保存模型，以便重用
model.save(u"书评.model")
# 对应的加载方式
# model_2 =word2vec.Word2Vec.load("text8.model")
 
# 以一种c语言可以解析的形式存储词向量
#model.save_word2vec_format(u"书评.model.bin", binary=True)
# 对应的加载方式
# model_3 =word2vec.Word2Vec.load_word2vec_format("text8.model.bin",binary=True)

输出如下：

"D:\program files\python2.7.0\python.exe" "D:/pycharm workspace/毕设/cluster_test/word2vec.py"
D:\program files\python2.7.0\lib\site-packages\gensim\utils.py:840: UserWarning: detected Windows; aliasing chunkize to chunkize_serial
 warnings.warn("detected Windows; aliasing chunkize to chunkize_serial")
D:\program files\python2.7.0\lib\site-packages\gensim\utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.
 warnings.warn("Pattern library is not installed, lemmatization won't be available.")
2016-12-12 15:37:43,331: INFO: collecting all words and their counts
2016-12-12 15:37:43,332: INFO: PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
2016-12-12 15:37:45,236: INFO: collected 99865 word types from a corpus of 3561156 raw words and 357 sentences
2016-12-12 15:37:45,236: INFO: Loading a fresh vocabulary
2016-12-12 15:37:45,413: INFO: min_count=5 retains 29982 unique words (30% of original 99865, drops 69883)
2016-12-12 15:37:45,413: INFO: min_count=5 leaves 3444018 word corpus (96% of original 3561156, drops 117138)
2016-12-12 15:37:45,602: INFO: deleting the raw counts dictionary of 99865 items
2016-12-12 15:37:45,615: INFO: sample=0.001 downsamples 29 most-common words
2016-12-12 15:37:45,615: INFO: downsampling leaves estimated 2804247 word corpus (81.4% of prior 3444018)
2016-12-12 15:37:45,615: INFO: estimated required memory for 29982 words and 200 dimensions: 62962200 bytes
2016-12-12 15:37:45,746: INFO: resetting layer weights
2016-12-12 15:37:46,782: INFO: training model with 3 workers on 29982 vocabulary and 200 features, using sg=0 hs=0 sample=0.001 negative=5 window=5
2016-12-12 15:37:46,782: INFO: expecting 357 sentences, matching count from corpus used for vocabulary survey
2016-12-12 15:37:47,818: INFO: PROGRESS: at 1.96% examples, 267531 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:37:48,844: INFO: PROGRESS: at 3.70% examples, 254229 words/s, in_qsize 3, out_qsize 1
2016-12-12 15:37:49,871: INFO: PROGRESS: at 5.99% examples, 273509 words/s, in_qsize 3, out_qsize 1
2016-12-12 15:37:50,867: INFO: PROGRESS: at 8.18% examples, 281557 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:37:51,872: INFO: PROGRESS: at 10.20% examples, 280918 words/s, in_qsize 5, out_qsize 0
2016-12-12 15:37:52,898: INFO: PROGRESS: at 12.44% examples, 284750 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:37:53,911: INFO: PROGRESS: at 14.17% examples, 278948 words/s, in_qsize 0, out_qsize 0
2016-12-12 15:37:54,956: INFO: PROGRESS: at 16.47% examples, 284101 words/s, in_qsize 2, out_qsize 1
2016-12-12 15:37:55,934: INFO: PROGRESS: at 18.60% examples, 285781 words/s, in_qsize 6, out_qsize 1
2016-12-12 15:37:56,933: INFO: PROGRESS: at 20.84% examples, 288045 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:37:57,973: INFO: PROGRESS: at 23.03% examples, 289083 words/s, in_qsize 6, out_qsize 2
2016-12-12 15:37:58,993: INFO: PROGRESS: at 24.87% examples, 285990 words/s, in_qsize 6, out_qsize 1
2016-12-12 15:38:00,006: INFO: PROGRESS: at 27.17% examples, 288266 words/s, in_qsize 4, out_qsize 1
2016-12-12 15:38:01,081: INFO: PROGRESS: at 29.52% examples, 290197 words/s, in_qsize 1, out_qsize 2
2016-12-12 15:38:02,065: INFO: PROGRESS: at 31.88% examples, 292344 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:38:03,188: INFO: PROGRESS: at 34.01% examples, 291356 words/s, in_qsize 2, out_qsize 2
2016-12-12 15:38:04,161: INFO: PROGRESS: at 36.02% examples, 290805 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:38:05,174: INFO: PROGRESS: at 38.26% examples, 292174 words/s, in_qsize 3, out_qsize 0
2016-12-12 15:38:06,214: INFO: PROGRESS: at 40.56% examples, 293297 words/s, in_qsize 4, out_qsize 1
2016-12-12 15:38:07,201: INFO: PROGRESS: at 42.69% examples, 293428 words/s, in_qsize 4, out_qsize 1
2016-12-12 15:38:08,266: INFO: PROGRESS: at 44.65% examples, 292108 words/s, in_qsize 1, out_qsize 1
2016-12-12 15:38:09,295: INFO: PROGRESS: at 46.83% examples, 292097 words/s, in_qsize 4, out_qsize 1
2016-12-12 15:38:10,315: INFO: PROGRESS: at 49.13% examples, 292968 words/s, in_qsize 2, out_qsize 2
2016-12-12 15:38:11,326: INFO: PROGRESS: at 51.37% examples, 293621 words/s, in_qsize 5, out_qsize 0
2016-12-12 15:38:12,367: INFO: PROGRESS: at 53.39% examples, 292777 words/s, in_qsize 2, out_qsize 2
2016-12-12 15:38:13,348: INFO: PROGRESS: at 55.35% examples, 292187 words/s, in_qsize 5, out_qsize 0
2016-12-12 15:38:14,349: INFO: PROGRESS: at 57.31% examples, 291656 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:38:15,374: INFO: PROGRESS: at 59.50% examples, 292019 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:38:16,403: INFO: PROGRESS: at 61.68% examples, 292318 words/s, in_qsize 4, out_qsize 2
2016-12-12 15:38:17,401: INFO: PROGRESS: at 63.81% examples, 292275 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:38:18,410: INFO: PROGRESS: at 65.71% examples, 291495 words/s, in_qsize 4, out_qsize 1
2016-12-12 15:38:19,433: INFO: PROGRESS: at 67.62% examples, 290443 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:38:20,473: INFO: PROGRESS: at 69.58% examples, 289655 words/s, in_qsize 6, out_qsize 2
2016-12-12 15:38:21,589: INFO: PROGRESS: at 71.71% examples, 289388 words/s, in_qsize 2, out_qsize 2
2016-12-12 15:38:22,533: INFO: PROGRESS: at 73.78% examples, 289366 words/s, in_qsize 0, out_qsize 1
2016-12-12 15:38:23,611: INFO: PROGRESS: at 75.46% examples, 287542 words/s, in_qsize 5, out_qsize 1
2016-12-12 15:38:24,614: INFO: PROGRESS: at 77.25% examples, 286609 words/s, in_qsize 3, out_qsize 0
2016-12-12 15:38:25,609: INFO: PROGRESS: at 79.33% examples, 286732 words/s, in_qsize 5, out_qsize 1
2016-12-12 15:38:26,621: INFO: PROGRESS: at 81.40% examples, 286595 words/s, in_qsize 2, out_qsize 0
2016-12-12 15:38:27,625: INFO: PROGRESS: at 83.53% examples, 286807 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:38:28,683: INFO: PROGRESS: at 85.32% examples, 285651 words/s, in_qsize 5, out_qsize 3
2016-12-12 15:38:29,729: INFO: PROGRESS: at 87.56% examples, 286175 words/s, in_qsize 6, out_qsize 1
2016-12-12 15:38:30,706: INFO: PROGRESS: at 89.86% examples, 286920 words/s, in_qsize 5, out_qsize 0
2016-12-12 15:38:31,714: INFO: PROGRESS: at 92.10% examples, 287368 words/s, in_qsize 6, out_qsize 0
2016-12-12 15:38:32,756: INFO: PROGRESS: at 94.40% examples, 288070 words/s, in_qsize 4, out_qsize 2
2016-12-12 15:38:33,755: INFO: PROGRESS: at 96.30% examples, 287543 words/s, in_qsize 1, out_qsize 0
2016-12-12 15:38:34,802: INFO: PROGRESS: at 98.71% examples, 288375 words/s, in_qsize 4, out_qsize 0
2016-12-12 15:38:35,286: INFO: worker thread finished; awaiting finish of 2 more threads
2016-12-12 15:38:35,286: INFO: worker thread finished; awaiting finish of 1 more threads
Word2Vec(vocab=29982, size=200, alpha=0.025)
【国家】和【国务院】的相似度为： 0.387535493256
-----
2016-12-12 15:38:35,293: INFO: worker thread finished; awaiting finish of 0 more threads
2016-12-12 15:38:35,293: INFO: training on 17805780 raw words (14021191 effective words) took 48.5s, 289037 effective words/s
2016-12-12 15:38:35,293: INFO: precomputing L2-norms of word weight vectors
和【控烟】最相关的词有：
禁烟 0.6038454175
防烟 0.585186183453
执行 0.530897378922
烟控 0.516572892666
广而告之 0.508533298969
履约 0.507428050041
执法 0.494115233421
禁烟令 0.471616715193
修法 0.465247869492
该项 0.457907706499
落实 0.457776963711
控制 0.455987215042
这方面 0.450040221214
立法 0.44820779562
控烟办 0.436062157154
执行力 0.432559013367
控烟会 0.430508673191
进展 0.430286765099
监管 0.429748386145
惩罚 0.429243773222
-----
书-不错，质量-
生存 0.613928854465
稳定 0.595371186733
整体 0.592055797577
----
不合群的词： 很
-----
2016-12-12 15:38:35,515: INFO: saving Word2Vec object under 书评.model, separately None
2016-12-12 15:38:35,515: INFO: not storing attribute syn0norm
2016-12-12 15:38:35,515: INFO: not storing attribute cum_table
2016-12-12 15:38:36,490: INFO: saved 书评.model
Process finished with exit code 0

以上这篇python初步实现word2vec操作就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。

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