mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-09-11 11:25:40 +03:00
74fb532e00
git-svn-id: https://mosesdecoder.svn.sourceforge.net/svnroot/mosesdecoder/trunk@1781 1f5c12ca-751b-0410-a591-d2e778427230
444 lines
14 KiB
C++
444 lines
14 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=-1.0*numeric_limits<float>::max();
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static const float MAX_FLOAT=numeric_limits<float>::max();
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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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//cerr << "1BESTS: ";
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//copy(bests.begin(),bests.end(),ostream_iterator<unsigned>(cerr," "));
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statscore_t score = GetStatScore(bests);
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//cerr << " score = " << score << endl;
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return score;
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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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//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(FData->get(S).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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//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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// cerr<<"ni"<<it->second<<endl;;
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//cerr<<"fos"<<f0[it->second]<<" "<<f0[index]<<" "<<index<<endl;
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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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while(it!=gradient.end()){
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map<float,unsigned>::iterator leftmost=it;
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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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float leftmostx=MAX_FLOAT;
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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 find the one with the leftmost intersection
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float curintersect;
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if(m!=it2->first){
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curintersect=intersect(m,b,it2->first,f0[it2->second]);
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//cerr << "curintersect: " << curintersect << " leftmostx: " << leftmostx << endl;
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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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}
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if (leftmost == it) {
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//we didn't find any more intersections
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break;
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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(abs(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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}
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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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/*
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cerr << "Sentence: " << S << endl;
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for (size_t i = 0; i < onebest.size(); ++i) {
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cerr << "x: " << onebest[i].first << " i: " << onebest[i].second << " ";
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}
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cerr << endl;
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*/
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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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/*
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cerr << "Thresholds: " << endl;
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for (list<threshold>::iterator i = thresholdlist.begin();
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i != thresholdlist.end(); ++i) {
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cerr << "x: " << i->first << " diffs";
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for (size_t j = 0; j < i->second.size(); ++j) {
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cerr << " " << i->second[j].first << "," << i->second[j].second;
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}
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cerr << endl;
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}*/
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// cerr<<"thesholdlist size"<<thresholdlist.size()<<endl;
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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=MIN_FLOAT;
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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++){
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//cerr << "Threshold id: " << sc << " Score: " << scores[sc] << endl;
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if (scores[sc] > bestscore) {
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//This is the score for the interval [lit2->first, (lit2+1)->first]
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//unless we're at the last score, when it's the score
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//for the interval [lit2->first,+inf]
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bestscore = scores[sc];
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//if we're not in [-inf,x1] or [xn,+inf] then just take the value
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//if x which splits the interval in half. For the rightmost interval,
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//take x to be the last interval boundary + 0.1, and for the leftmost
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//interval, take x to be the first interval boundary - 1000.
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//These values are taken from cmert.
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float leftx = lit2->first;
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if (lit2 == thresholdlist.begin()) {
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leftx = MIN_FLOAT;
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}
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++lit2;
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float rightx = MAX_FLOAT;
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if (lit2 != thresholdlist.end()) {
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rightx = lit2->first;
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}
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--lit2;
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//cerr << "leftx: " << leftx << " rightx: " << rightx << endl;
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if (leftx == MIN_FLOAT) {
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bestx = rightx-1000;
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} else if (rightx == MAX_FLOAT) {
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bestx = leftx+0.1;
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} else {
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bestx = 0.5 * (rightx + leftx);
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}
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//cerr << "x = " << "set new bestx to: " << bestx << endl;
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}
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++lit2;
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}
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if(abs(bestx)<0.00015){
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bestx=0.0;//the origin of the line is the best point!we put it back at 0 so we do not propagate rounding erros
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//finally! we manage to extract the best score;
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//now we convert bestx (position on the line) to a point!
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if(verboselevel()>4)
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cerr<<"best point on line at origin"<<endl;
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}
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if(verboselevel()>3)
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cerr<<"end Lineopt, bestx="<<bestx<<endl;
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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 Features 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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if(verboselevel()>1)
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cerr<<"starting Point "<<P;
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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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if (verboselevel()>1)
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cerr<<"best found Point "<<P<<"score="<<s<<endl;
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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=0;
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statscore_t bestscore=MIN_FLOAT;
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Point best;
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int nrun=0;
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do{
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++nrun;
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if(verboselevel()>2&&nrun>1)
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cerr<<"last diff="<<bestscore-prevscore<<"nrun "<<nrun<<endl;
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prevscore=bestscore;
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Point linebest;
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for(int d=0;d<Point::getdim();d++){
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if(verboselevel()>4)
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cerr<<"minimizing along direction "<<d<<endl;
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Point direction;
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for(int i=0;i<Point::getdim();i++)
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direction[i];
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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(verboselevel()>5){
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cerr<<"direction: "<<d<<" score="<<curscore<<endl;
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cerr<<"\tPoint: "<<linebest<<endl;
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}
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if(curscore>bestscore){
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bestscore=curscore;
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best=linebest;
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if(verboselevel()>3){
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cerr<<"new best dir:"<<d<<" ("<<nrun<<")"<<endl;
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cerr<<"new best Point "<<best<<endl;
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}
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}
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}
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P=best;//update the current vector with the best point on all line tested
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if(verboselevel()>3)
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cerr<<nrun<<"\t"<<P<<endl;
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}while(bestscore-prevscore>eps);
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if(verboselevel()>2){
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cerr<<"end Powell Algo, nrun="<<nrun<<endl;
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cerr<<"last diff="<<bestscore-prevscore<<endl;
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cerr<<"\t"<<P<<endl;
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}
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return bestscore;
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}
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/**RandomOptimizer to use as beaseline and test.\n
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Just return a random point*/
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statscore_t RandomOptimizer::TrueRun(Point& P)const{
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vector<parameter_t> min(Point::getdim());
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vector<parameter_t> max(Point::getdim());
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for(int d=0;d<Point::getdim();d++){
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min[d]=0.0;
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max[d]=1.0;
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}
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P.Randomize(min,max);
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statscore_t score=GetStatScore(P);
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P.score=score;
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return score;
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}
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//--------------------------------------
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vector<string> OptimizerFactory::typenames;
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void OptimizerFactory::SetTypeNames(){
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if(typenames.empty()){
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typenames.resize(NOPTIMIZER);
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typenames[POWELL]="powell";
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typenames[RANDOM]="random";
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//add new type there
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}
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}
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vector<string> OptimizerFactory::GetTypeNames(){
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if(typenames.empty())
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SetTypeNames();
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return typenames;
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}
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OptimizerFactory::OptType OptimizerFactory::GetOType(string type){
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int thetype;
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if(typenames.empty())
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SetTypeNames();
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for(thetype=0;thetype<typenames.size();thetype++)
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if(typenames[thetype]==type)
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break;
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return((OptType)thetype);
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};
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Optimizer* OptimizerFactory::BuildOptimizer(unsigned dim,vector<unsigned> i2o,vector<parameter_t> start,string type){
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OptType T=GetOType(type);
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if(T==NOPTIMIZER){
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cerr<<"Error: unknown Optimizer type "<<type<<endl;
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cerr<<"Known Algorithm are:"<<endl;
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int thetype;
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for(thetype=0;thetype<typenames.size();thetype++)
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cerr<<typenames[thetype]<<endl;
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throw ("unknown Optimizer Type");
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}
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switch((OptType)T){
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case POWELL:
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return new SimpleOptimizer(dim,i2o,start);
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break;
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case RANDOM:
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return new RandomOptimizer(dim,i2o,start);
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break;
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default:
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cerr<<"Error: unknown optimizer"<<type<<endl;
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return NULL;
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}
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}
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