mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-09-11 11:25:40 +03:00
git-svn-id: https://mosesdecoder.svn.sourceforge.net/svnroot/mosesdecoder/trunk@1719 1f5c12ca-751b-0410-a591-d2e778427230
315 lines
10 KiB
C++
315 lines
10 KiB
C++
#include <cassert>
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#include <vector>
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#include <limits>
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#include <list>
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#include <cfloat>
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#include <iostream>
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#include "Optimizer.h"
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using namespace std;
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static const float MIN_FLOAT=numeric_limits<float>::min();
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static const float MAX_FLOAT=numeric_limits<float>::max();
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enum OptType{POWELL=0,NOPTIMIZER};//Add new optimizetr here
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string names[NOPTIMIZER]={string("powell")};
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Optimizer *BuildOptimizer(unsigned dim,vector<unsigned>to,vector<parameter_t>s,string type){
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int thetype;
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for(thetype=0;thetype<(int)NOPTIMIZER;thetype++)
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if(names[thetype]==type)
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break;
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switch((OptType)thetype){
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case POWELL:
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return new SimpleOptimizer(dim,to,s);
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case NOPTIMIZER:
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cerr<<"error unknwon optimizer"<<type<<endl;
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return NULL;
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}
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return NULL;//Should nver go there
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};
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void Optimizer::SetScorer(Scorer *S){
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if(scorer)
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delete scorer;
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scorer=S;
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}
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void Optimizer::SetFData(FeatureData *F){
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if(FData)
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delete FData;
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FData=F;
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};
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Optimizer::Optimizer(unsigned Pd,vector<unsigned> i2O,vector<parameter_t> start):scorer(NULL),FData(NULL){
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//warning: the init vector is a full set of parameters, of dimension pdim!
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Point::pdim=Pd;
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assert(start.size()==Pd);
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Point::dim=i2O.size();
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Point::optindices=i2O;
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if(Point::pdim<Point::dim){
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for(int i=0;i<Point::pdim;i++){
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int j;
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for(j=0;j<Point::dim;j++)
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if(i==i2O[j])
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break;
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if(j==Point::dim)//the index i wasnt found on optindices, it is a fixed index, we use the valu of hte start vector
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Point::fixedweights[i]=start[i];
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}
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}
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};
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Optimizer::~Optimizer(){
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delete scorer;
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delete FData;
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}
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statscore_t Optimizer::GetStatScore(const Point& param)const{
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vector<unsigned> bests;
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Get1bests(param,bests);
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return GetStatScore(bests);
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};
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/**compute the intersection of 2 lines*/
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float intersect (float m1, float b1,float m2,float b2){
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if(m1==m2)
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return MAX_FLOAT;//parrallel lines
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return((b2-b1)/(m1-m2));
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}
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statscore_t Optimizer::LineOptimize(const Point& origin,const Point& direction,Point& bestpoint)const{
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// we are looking for the best Point on the line y=Origin+x*direction
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float min_int=0.0001;
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typedef pair<unsigned,unsigned> diff;//first the sentence that changes, second is the new 1best for this sentence
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typedef pair<float,vector<diff> > threshold;
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list<threshold> thresholdlist;
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thresholdlist.push_back(threshold(MIN_FLOAT,vector<diff>()));
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vector<unsigned> first1best;//the vector of nbrests for x=-inf
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for(int S=0;S<size();S++){
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cerr<<"SENTENCE"<<S;
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//first we determine the translation with the best feature score for each sentence and each value of x
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multimap<float,unsigned> gradient;
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vector<float> f0;
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f0.resize(size());
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for(unsigned j=0;j<FData->get(S).size();j++){
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gradient.insert(pair<float,unsigned>(direction*(FData->get(S,j)),j));//gradient of the feature function for this particular target sentence
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f0[j]=origin*FData->get(S,j);//compute the feature function at the origin point
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}
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//now lets compute the 1best for each value of x
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vector<pair<float,unsigned> > onebest;
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multimap<float,unsigned>::iterator it=gradient.begin();
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float smallest=it->first;//smallest gradient
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unsigned index=it->second;
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float biggestf0=f0[index];
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//several candidates can have the lowest slope (eg for word penalty where the gradient is an integer)
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it++;
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while(it!=gradient.end()&&it->first==smallest){
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if(f0[it->second]>f0[index])
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index=it->second;//the highest line is the one with he highest f0
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it++;
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}
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--it;//we went one step too far in the while loop
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onebest.push_back(pair<float,unsigned>(MIN_FLOAT,index));//first 1best is the lowest gradient.
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//now we look for the intersections points indicating a change of 1 best
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//we use the fact that the function is convex, which means that the gradient can only go up
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int c=0;
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while(it!=gradient.end()){
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cerr<<c++<<endl;
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map<float,unsigned>::iterator leftmost=it;
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float leftmostx=onebest.back().first;
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float m=it->first;
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float b=f0[it->second];
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multimap<float,unsigned>::iterator it2=it;
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it2++;
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int d=0;
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for(;it2!=gradient.end();it2++){
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//cerr<<"--"<<d++<<' '<<it2->first<<' '<<it2->second<<endl;
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//look for all candidate with a gradient bigger than the current one and fond the one with the leftmost intersection
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float curintersect=intersect(m,b,it2->first,f0[it2->second]);
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if(curintersect<leftmostx){
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//we have found and intersection to the left of the leftmost we had so far.
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leftmostx=curintersect;
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leftmost=it2;//this is the new reference
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}
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}
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//we have found the next intersection!
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/* Require that the intersection Point be at least min_int
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to the right of the previous one. If not, we replace the
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previous intersection Point with this one. Yes, it can even
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happen that the new intersection Point is slightly to the
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left of the old one, because of numerical imprecision. We
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don't check that the new Point is also min_interval to the
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right of the penultimate one. In that case, the Points would
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switch places in the sort, resulting in a bogus score for
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that interval. */
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if((leftmostx-onebest.back().first)<min_int)
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onebest.back()=pair<float,unsigned>(leftmostx,leftmost->second);//leftmost->first is the gradient, we are interested in the value of the intersection
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else //normal case: we add a new threshold
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onebest.push_back(pair<float,unsigned>(leftmostx,leftmost->second));
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if(it==leftmost)
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it=gradient.end();
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else
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cerr<<"ok"<<endl;
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it=leftmost;
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}
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//we have the onebest list and the threshold for the current sentence.
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//now we update the thresholdlist: we add the new threshold and the value of the onebest.
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//add the 1best for x=-inf to the corresponding threshold
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// (this first threshold is the same for everybody)
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first1best.push_back(onebest[0].second);
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assert(first1best.size()==S+1);
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list<threshold >::iterator current=thresholdlist.begin();
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list<threshold >::iterator lit;
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unsigned prev1best=onebest[0].second;
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for(int t=1;t<onebest.size();t++){
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float ref=onebest[t].first;
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for( lit=current;lit!=thresholdlist.end()&&ref>lit->first;lit++)
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;
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//we have found where we must insert the new threshold(before lit)
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if(lit==thresholdlist.end()){
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thresholdlist.push_back(threshold(ref,vector<diff>()));
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lit--;
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}
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else
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if(ref!=lit->first)//normal case
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lit=thresholdlist.insert(lit,threshold(ref,vector<diff>()));
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//if ref==lit->first:unlikely (but this is the reason why we have a vector of diff); in that case the threshold is already created
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//lit is at the right place now we add the diff pair
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lit->second.push_back(diff(S,onebest[t].second));
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current=lit;//we will continue after that point
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current++;
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prev1best=onebest[t].second;
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}//loop on onebest.size()
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}//loop on S
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//now the thresholdlist is up to date:
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//it contains a list of all the parameter_ts where the function changed its value, along with the nbest list for the interval after each threshold
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//last thing to do is compute the Stat score (ie BLEU) and find the minimum
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list<threshold>::iterator lit2=thresholdlist.begin();
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++lit2;
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vector<vector<diff> > diffs;
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for(;lit2!=thresholdlist.end();lit2++)
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diffs.push_back(lit2->second);
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vector<statscore_t> scores=GetIncStatScore(first1best,diffs);
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lit2=thresholdlist.begin();
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statscore_t bestscore=MIN_FLOAT;
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float bestx;
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assert(scores.size()==thresholdlist.size());//we skipped the first el of thresholdlist but GetIncStatScore return 1 more for first1best
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for(int sc=0;sc!=scores.size();sc++,lit2++){
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//We move the list iterator and the vector index at the same time
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//because we need to get the value of lambda back from the list
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if(scores[sc]>bestscore){
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bestscore=scores[sc];
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if(lit2!=thresholdlist.end()){
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//we dont want to stay exactly at the threshold where the function is discontinuous so we move just a little to the right
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bestx=lit2->first;
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lit2++;
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bestx+=lit2->first;
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bestx/=2.0;
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lit2--;
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}else
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bestx=lit2->first+0.001;
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}
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}
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//finally! we manage to extract the best score;
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//nowwe convert bestx (position on the line) to a point!
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bestpoint=direction*bestx+origin;
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bestpoint.score=bestscore;
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return bestscore;
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};
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void Optimizer::Get1bests(const Point& P,vector<unsigned>& bests)const{
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assert(FData);
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bests.clear();
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bests.resize(size());
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for(unsigned i=0;i<size();i++){
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float bestfs=MIN_FLOAT;
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unsigned idx=0;
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unsigned j;
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for(j=0;j<FData->get(i).size();j++){
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float curfs=P*FData->get(i,j);
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if(curfs>bestfs){
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bestfs=curfs;
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idx=j;
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}
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}
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bests[i]=idx;
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}
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}
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statscore_t Optimizer::Run(Point& P)const{
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if(!FData){
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cerr<<"error trying to optimize without Feature loaded"<<endl;
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exit(2);
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}
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if(!scorer){
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cerr<<"error trying to optimize without a Scorer loaded"<<endl;
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exit(2);
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}
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if (scorer->getReferenceSize()!=FData->size()){
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cerr<<"errror size mismatch between FeatureData and Scorer"<<endl;
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exit(2);
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}
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statscore_t s=TrueRun(P);
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P.score=s;//just in case its not done in TrueRun
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return s;
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}
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vector<statscore_t> Optimizer::GetIncStatScore(vector<unsigned> thefirst,vector<vector <pair<unsigned,unsigned> > > thediffs)const{
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assert(scorer);
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vector<statscore_t> theres;
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scorer->score(thefirst,thediffs,theres);
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return theres;
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};
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//---------------- code for the powell optimizer
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float SimpleOptimizer::eps=0.0001;
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statscore_t SimpleOptimizer::TrueRun(Point& P)const{
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statscore_t prevscore=MAX_FLOAT;
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statscore_t bestscore=MAX_FLOAT;
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do{
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Point best;
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Point linebest;
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for(int d=0;d<Point::getdim();d++){
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Point direction;
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direction[d]=1.0;
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statscore_t curscore=LineOptimize(P,direction,linebest);//find the minimum on the line
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if(curscore>bestscore){
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bestscore=curscore;
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best=linebest;
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}
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}
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P=best;//update the current vector with the best points on all line tested
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}while(bestscore-prevscore<eps);
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return bestscore;
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}
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