Windows下编译word2vec

首先要声明,如果条件允许,不要在windows下做类似的事情,我这里是在折腾。

如果只需要下载代码,相应的代码,我已经上传了github,可以在这里下载到:
word2vec_win32

编译工具为:VS2013

具体的做法为:

1、到google code下载代码https://code.google.com/p/word2vec/

2、根据makefile,创建VS2013工程

3、进行调整,保证编译成功
3.1、所有c文件,天津下面的宏定义

#define _CRT_SECURE_NO_WARNINGS

3.2、将部分const修改为define,比如

    #define MAX_STRING 100

3.3、用_aligned_malloc函数,替换posix_memalign函数

    #define posix_memalign(p, a, s) (((*(p)) = _aligned_malloc((s), (a))), *(p) ?0 :errno)

3.4、下载windows下的pthread库,pthreads-win32,并修改include及link配置

3.5、编译成功

4、可执行文件说明
word2vec:词转向量,或者进行聚类
word2phrase:词转词组,用于预处理,可重复使用(运行一遍则生成2个词的短语,运行两遍则形成4个词的短语)
compute-accuracy:校验模型精度
distance:输入一个词A,返回最相近的词(A=》?)
word-analogy:输入三个词A,B,C,返回(如果A=》B,C=》?)

5、进行测试
5.1下载测试资料
http://mattmahoney.net/dc/text8.zip

5.2建立模型

>word2vec -train text8 -output vectors.bin -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -iter 15
Starting training using file text8
Vocab size: 71291
Words in train file: 16718843
Alpha: 0.000005  Progress: 100.10%  Words/thread/sec: 13.74k

5.3校验模型精度

>compute-accuracy vectors.bin 30000 < questions-word
s.txt
capital-common-countries:
ACCURACY TOP1: 80.83 %  (409 / 506)
Total accuracy: 80.83 %   Semantic accuracy: 80.83 %   Syntactic accuracy: -1.#J
 %
capital-world:
ACCURACY TOP1: 62.65 %  (884 / 1411)
Total accuracy: 67.45 %   Semantic accuracy: 67.45 %   Syntactic accuracy: -1.#J
 %
currency:
ACCURACY TOP1: 23.13 %  (62 / 268)
Total accuracy: 62.01 %   Semantic accuracy: 62.01 %   Syntactic accuracy: -1.#J
 %
city-in-state:
ACCURACY TOP1: 46.85 %  (736 / 1571)
Total accuracy: 55.67 %   Semantic accuracy: 55.67 %   Syntactic accuracy: -1.#J
 %
family:
ACCURACY TOP1: 77.45 %  (237 / 306)
Total accuracy: 57.31 %   Semantic accuracy: 57.31 %   Syntactic accuracy: -1.#J
 %
gram1-adjective-to-adverb:
ACCURACY TOP1: 19.44 %  (147 / 756)
Total accuracy: 51.37 %   Semantic accuracy: 57.31 %   Syntactic accuracy: 19.44
 %
gram2-opposite:
ACCURACY TOP1: 24.18 %  (74 / 306)
Total accuracy: 49.75 %   Semantic accuracy: 57.31 %   Syntactic accuracy: 20.81
 %
gram3-comparative:
ACCURACY TOP1: 64.92 %  (818 / 1260)
Total accuracy: 52.74 %   Semantic accuracy: 57.31 %   Syntactic accuracy: 44.75
 %
gram4-superlative:
ACCURACY TOP1: 39.53 %  (200 / 506)
Total accuracy: 51.77 %   Semantic accuracy: 57.31 %   Syntactic accuracy: 43.81
 %
gram5-present-participle:
ACCURACY TOP1: 40.32 %  (400 / 992)
Total accuracy: 50.33 %   Semantic accuracy: 57.31 %   Syntactic accuracy: 42.91
 %
gram6-nationality-adjective:
ACCURACY TOP1: 84.46 %  (1158 / 1371)
Total accuracy: 55.39 %   Semantic accuracy: 57.31 %   Syntactic accuracy: 53.88
 %
gram7-past-tense:
ACCURACY TOP1: 39.79 %  (530 / 1332)
Total accuracy: 53.42 %   Semantic accuracy: 57.31 %   Syntactic accuracy: 51.00
 %
gram8-plural:
ACCURACY TOP1: 61.39 %  (609 / 992)
Total accuracy: 54.11 %   Semantic accuracy: 57.31 %   Syntactic accuracy: 52.38
 %
gram9-plural-verbs:
ACCURACY TOP1: 33.38 %  (217 / 650)
Total accuracy: 53.01 %   Semantic accuracy: 57.31 %   Syntactic accuracy: 50.86
 %
Questions seen / total: 12227 19544   62.56 %

5.4查找关系最近的单词

>distance vectors.bin
Enter word or sentence (EXIT to break): china

Word: china  Position in vocabulary: 486

                                              Word       Cosine distance
------------------------------------------------------------------------
                                            taiwan              0.649276
                                             japan              0.624836
                                            hainan              0.567946
                                          kalmykia              0.562871
                                             tibet              0.562600
                                               prc              0.553833
                                              tuva              0.553255
                                             korea              0.552685
                                           chinese              0.545661
                                            xiamen              0.542703
                                              liao              0.542607
                                             jiang              0.540888
                                         manchuria              0.540783
                                             wuhan              0.537735
                                            yunnan              0.535809
                                             hunan              0.535770
                                          hangzhou              0.524340
                                              yong              0.523802
                                           sichuan              0.517254
                                         guangdong              0.514874
                                             liang              0.511881
                                               jin              0.511389
                                             india              0.508853
                                          xinjiang              0.505971
                                         taiwanese              0.503072
                                              qing              0.502909
                                          shanghai              0.502771
                                          shandong              0.499169
                                           jiangxi              0.495940
                                           nanjing              0.492893
                                         guangzhou              0.492788
                                              zhao              0.490396
                                          shenzhen              0.489658
                                         singapore              0.489428
                                             hubei              0.488228
                                            harbin              0.488112
                                          liaoning              0.484283
                                          zhejiang              0.484192
                                            joseon              0.483718
                                          mongolia              0.481411
Enter word or sentence (EXIT to break):

5.5根据A=>B,得到C=>?

>word-analogy vectors.bin
Enter three words (EXIT to break): china beijing canada

Word: china  Position in vocabulary: 486

Word: beijing  Position in vocabulary: 3880

Word: canada  Position in vocabulary: 474

                                              Word              Distance
------------------------------------------------------------------------
                                           toronto              0.624131
                                          montreal              0.559667
                                            mcgill              0.519338
                                           calgary              0.518366
                                           ryerson              0.515524
                                            ottawa              0.515316
                                           alberta              0.509334
                                          edmonton              0.498436
                                           moncton              0.488861
                                            quebec              0.487712
                                          canadian              0.475655
                                      saskatchewan              0.460744
                                       fredericton              0.460354
                                           ontario              0.458213
                                       montrealers              0.435611
                                         vancouver              0.429893
                                         saskatoon              0.416954
                                            dieppe              0.404408
                                           iqaluit              0.401143
                                         canadians              0.398137
                                          winnipeg              0.397547
                                            labatt              0.393893
                                              city              0.386245
                                      bilingualism              0.386245
                                          columbia              0.384754
                                        provincial              0.383439
                                             banff              0.382603
                                             metro              0.382367
                                            molson              0.379343
                                           nunavut              0.375992
                                             montr              0.373883
                                      francophones              0.373512
                                         brunswick              0.364261
                                          manitoba              0.360447
                                               bec              0.359977
                                       francophone              0.358556
                                             leafs              0.353035
                                        ellensburg              0.352787
                                           curling              0.351973
                                               cdn              0.347580
Enter three words (EXIT to break):

5.6进行聚类,输出结果(classes为0时,就是向量输出了)

>word2vec -train text8 -output classes.txt -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -iter 15 -classes 500
Starting training using file text8
Vocab size: 71291
Words in train file: 16718843
Alpha: 0.000005  Progress: 100.10%  Words/thread/sec: 14.72k

5.7原来程序中,还有三个测试脚本,是处理词组的,由于要用到linux命令sed,awk等,大家还是到Cygwin或MinGW下运行吧