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main.cpp
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#include<iostream>
#include<string>
#include<vector>
#include<numeric>
#include "word2vec.h"
#ifdef ZH
//using Model = Word2vec<std::u16string>;
#else
using Model = Word2vec<std::string>;
#endif
using Sentence = Model::Sentence;
using SentenceP = Model::SentenceP;
#ifdef ZH
const std::u16string MARKER = u"#m#";
std::vector<SentenceP> load_sentence(const std::string &path,bool with_marker,bool with_tag){
auto is_word = [](char16_t ch){return ch >=0x4e00 && ch <= 0x9fff;}; //中日韩统一汉字 unihan
auto close_tag = [](SentenceP& sentence){
Model::Tag &t = sentence->tags_.back();
if(t == Model::B) t = Model::S;
else if(t == Model::M) t = Model::E;
};
size_t count = 0 ;
const size_t max_sentence_len = 200;
std::vector<SentenceP> sentences;
SentenceP sentence(new Sentence);
std::ifstream in(path);
while(1){
std::string s;
in >> s;
if(s.empty()) break;
std::u16string us = Cvt<std::u16string>::from_utf8(s);
for(auto ch: us){
if(is_word(ch)){
if(sentence->tokens_.empty() && with_marker)
sentence->tokens_.push_back(MARKER);
sentence->tokens_.push_back(std::u16string(1,ch));
if(with_tag){
if(sentence->tags_.empty())
sentence->tags_.push_back(Model::B);
else{
auto &t = sentence->tags_.back();
Model::Tag nt = (t == Model::S||t==Model::E)?Model::B : Model::M;
sentence->tags_.push_back(nt);
}
}
}
if(!is_word(ch) || sentence->tokens_.size() == max_sentence_len){ //不是句子开头的 不是unihan中的字符都用特殊字符MARKER表示
if(sentence->tokens_.empty())continue;
if(with_tag) close_tag(sentence);
if(ch == u'，' || ch == '、')continue;
if(with_marker) sentence->tokens_.push_back(MARKER);
sentence->words_.reserve(sentence->tokens_.size());
sentences.push_back(std::move(sentence));
sentence.reset(new Sentence);
}
}
if(!sentence->tokens_.empty() && with_tag) close_tag(sentence);
}
if(!sentence->tokens_.empty()){
if(with_tag) close_tag(sentence);
if(with_marker) sentence->tokens_.push_back(MARKER);
sentences.push_back(std::move(sentence));
}
in.close();
return sentences;
}
#else
std::vector<SentenceP> load_english(const std::string &path){
std::ifstream in(path);
std::vector<SentenceP> sentences;
SentenceP sentence(new Sentence);
const size_t max_sentence_len = 200;
int count = 0;
while(true){
std::string s;
in >> s;
if(s.empty()) break;
++count;
sentence->tokens_.push_back(std::move(s));
if(count == max_sentence_len){
count = 0;
sentence->words_.reserve(sentence->tokens_.size());
sentences.push_back(std::move(sentence));
sentence.reset(new Sentence);
}
}
if(!sentence->tokens_.empty())
sentences.push_back(std::move(sentence));
in.close();
return sentences;
}
#endif
int accuracy(Model &model,std::string questions,int restrict_vocab=30000);
int main(int argc,char* argv[]){
Model model;
int n_workers = 4;
::srand(::time(NULL));
auto distance = [&model](){
while(1){
std::string s;
std::cout<<"\nFind nearest word for(:quit to break):";
std::cin>>s;
if(s == ":quit") break;
std::vector<std::pair<std::string,float> > p = model.most_similar(std::vector<std::string>{s},std::vector<std::string>(),10);
size_t i = 0;
for(auto& v:p){
std::cout<< i++ <<" " <<v.first<< " "<<v.second<<std::endl;
}
}
};
bool train = true;
bool test = false;
if(argc>1 && std::string(argv[1]) == "test"){
std::swap(train,test);
}
if(train){
std::string sfilename = "./text8";
std::vector<SentenceP> sentences;// = load_english(sfilename);
size_t count = 0;
const size_t max_sentence_len = 200;
SentenceP sentence(new Sentence);
std::ifstream in("text8");
while(true){
std::string s;
in >> s;
if(s.empty()) break;
++count;
sentence->tokens_.push_back(std::move(s));
if(count == max_sentence_len){
count = 0 ;
sentence->words_.reserve(sentence->tokens_.size());
sentences.push_back(std::move(sentence));
sentence.reset(new Sentence);
}
}
if(!sentence->tokens_.empty()){
sentences.push_back(std::move(sentence));
}
//std::cout<<sentences.size()<<" sentences, "<<std::accumulate(sentences.begin(),sentences.end(),(int)0,[](int x,const SentenceP &s){ return x+s->tokens_.size();})<<" words loaded."<<std::endl;
auto cstart = std::chrono::high_resolution_clock::now();
model.build_vocab(sentences);
auto cend = std::chrono::high_resolution_clock::now();
printf("load vocab: %.4f seconds\n",std::chrono::duration_cast<std::chrono::microseconds>(cend-cstart).count() / 1000000.0);
cstart = cend;
model.train(sentences,n_workers);
cend = std::chrono::high_resolution_clock::now();
printf("train: %.4f seconds\n",std::chrono::duration_cast<std::chrono::microseconds>(cend-cstart).count()/1000000.0);
cstart = cend;
model.save_text("./vectors.txt");
cend = std::chrono::high_resolution_clock::now();
printf("save_text: %.4f seconds\n",std::chrono::duration_cast<std::chrono::microseconds>(cend-cstart).count()/1000000.0);
}
if(test){
std::cout<<"testing ..."<<std::endl;
auto cstart = std::chrono::high_resolution_clock::now();
model.load_text("vectors.txt");
auto cend = std::chrono::high_resolution_clock::now();
printf("load model: %.4f seconds\n",std::chrono::duration_cast<std::chrono::microseconds>(cstart-cend).count()/1000000.0);
cstart = cend;
accuracy(model,"questions-words.txt");
cend = std::chrono::high_resolution_clock::now();
printf("load model: %.4f seconds\n",std::chrono::duration_cast<std::chrono::microseconds>(cstart-cend).count()/1000000.0);
}
distance();
}
int accuracy(Model &model,std::string questions,int restrict_vocab){
std::ifstream in(questions);
std::string line;
auto lower = [](std::string &data){
std::transform(data.begin(),data.end(),data.begin(),::tolower);
};
size_t count = 0,correct = 0,ignore=0,almost_correct=0;
const int topn = 10;
while(std::getline(in,line)){
if(line[0] == ':'){
printf("%s\n",line.c_str());
continue;
}
std::istringstream iss(line);
std::string a,b,c,expected;
iss>>a>>b>>c>>expected;
lower(a);lower(b);lower(c);lower(expected);
if(!model.has(a) || !model.has(b) || !model.has(c) || !model.has(expected)){
printf("unhandled: %s %s %s %s\n",a.c_str(),b.c_str(),c.c_str(),expected.c_str());
++ignore;
continue;
}
++count;
std::vector<std::string> positive{b,c},negtive{a};
auto predict = model.most_similar(positive,negtive,topn);
if(predict[0].first == expected){ ++correct; ++almost_correct;}
else{
bool found = false;
for(auto &v : predict){
if(v.first == expected){ found = true; break;}
}
if(found) ++almost_correct;
else printf("predicted: %s, expected: %s\n",predict[0].first.c_str(),expected.c_str());
}
}
if(count > 0)
printf("predict %lu out of %lu (%f%%),almost correct %lu (%f%%) ignore %lu\n",correct,count,correct * 100.0 / count , almost_correct, almost_correct * 100.0 / count, ignore);
return 0;
}