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/**
* \description This is the main for the new version of the mert algorithm developed during the 2nd MT marathon
*/
#include <limits>
#include <unistd.h>
#include <cstdlib>
#include <iostream>
#include <fstream>
#include <cmath>
#include <ctime>
#include <getopt.h>
#include "Data.h"
#include "Point.h"
#include "Scorer.h"
#include "ScorerFactory.h"
#include "ScoreData.h"
#include "FeatureData.h"
#include "Optimizer.h"
#include "Types.h"
#include "Timer.h"
#include "Util.h"
#include "../moses/src/ThreadPool.h"
using namespace std;
namespace {
/**
* Runs an optimisation, or a random restart.
*/
class OptimizationTask : public Moses::Task {
public:
OptimizationTask(Optimizer* optimizer, const Point& point)
: m_optimizer(optimizer), m_point(point) {}
~OptimizationTask() {}
void resetOptimizer() {
if (m_optimizer) {
delete m_optimizer;
m_optimizer = NULL;
}
}
bool DeleteAfterExecution() const {
return false;
}
void Run() {
m_score = m_optimizer->Run(m_point);
}
statscore_t getScore() const {
return m_score;
}
const Point& getPoint() const {
return m_point;
}
private:
// Do not allow the user to instanciate without arguments.
OptimizationTask() {}
Optimizer* m_optimizer;
Point m_point;
statscore_t m_score;
};
void usage(int ret)
{
cerr<<"usage: mert -d <dimensions> (mandatory )"<<endl;
cerr<<"[-n] retry ntimes (default 1)"<<endl;
cerr<<"[-m] number of random directions in powell (default 0)"<<endl;
cerr<<"[-o] the indexes to optimize(default all)"<<endl;
cerr<<"[-t] the optimizer(default powell)"<<endl;
cerr<<"[-r] the random seed (defaults to system clock)"<<endl;
cerr<<"[--sctype|-s] the scorer type (default BLEU)"<<endl;
cerr<<"[--scconfig|-c] configuration string passed to scorer"<<endl;
cerr<<"[--scfile|-S] comma separated list of scorer data files (default score.data)"<<endl;
cerr<<"[--ffile|-F] comma separated list of feature data files (default feature.data)"<<endl;
cerr<<"[--ifile|-i] the starting point data file (default init.opt)"<<endl;
#ifdef WITH_THREADS
cerr<<"[--threads|-T] use multiple threads (default 1)"<<endl;
#endif
cerr<<"[--shard-count] Split data into shards, optimize for each shard and average"<<endl;
cerr<<"[--shard-size] Shard size as proportion of data. If 0, use non-overlapping shards"<<endl;
cerr<<"[-v] verbose level"<<endl;
cerr<<"[--help|-h] print this message and exit"<<endl;
exit(ret);
}
static struct option long_options[] = {
{"pdim", 1, 0, 'd'},
{"ntry",1,0,'n'},
{"nrandom",1,0,'m'},
{"rseed",required_argument,0,'r'},
{"optimize",1,0,'o'},
{"pro",required_argument,0,'p'},
{"type",1,0,'t'},
{"sctype",1,0,'s'},
{"scconfig",required_argument,0,'c'},
{"scfile",1,0,'S'},
{"ffile",1,0,'F'},
{"ifile",1,0,'i'},
#ifdef WITH_THREADS
{"threads", required_argument,0,'T'},
#endif
{"shard-count", required_argument, 0, 'a'},
{"shard-size", required_argument, 0, 'b'},
{"verbose",1,0,'v'},
{"help",no_argument,0,'h'},
{0, 0, 0, 0}
};
int option_index;
} // anonymous namespace
int main (int argc, char **argv)
{
ResetUserTime();
/*
Timer timer;
timer.start("Starting...");
*/
int c,pdim;
pdim=-1;
int ntry=1;
int nrandom=0;
int seed=0;
bool hasSeed = false;
#ifdef WITH_THREADS
size_t threads=1;
#endif
float shard_size = 0;
size_t shard_count = 0;
string type("powell");
string scorertype("BLEU");
string scorerconfig("");
string scorerfile("statscore.data");
string featurefile("features.data");
string initfile("init.opt");
string tooptimizestr("");
vector<unsigned> tooptimize;
vector<vector<parameter_t> > start_list;
vector<parameter_t> min;
vector<parameter_t> max;
// NOTE: those mins and max are the bound for the starting points of the algorithm, not strict bound on the result!
while ((c=getopt_long (argc, argv, "o:r:d:n:m:t:s:S:F:v:p:", long_options, &option_index)) != -1) {
switch (c) {
case 'o':
tooptimizestr = string(optarg);
break;
case 'd':
pdim = strtol(optarg, NULL, 10);
break;
case 'n':
ntry=strtol(optarg, NULL, 10);
break;
case 'm':
nrandom=strtol(optarg, NULL, 10);
break;
case 'r':
seed=strtol(optarg, NULL, 10);
hasSeed = true;
break;
case 't':
type=string(optarg);
break;
case's':
scorertype=string(optarg);
break;
case 'c':
scorerconfig = string(optarg);
break;
case 'S':
scorerfile=string(optarg);
break;
case 'F':
featurefile=string(optarg);
break;
case 'i':
initfile=string(optarg);
break;
case 'v':
setverboselevel(strtol(optarg,NULL,10));
break;
#ifdef WITH_THREADS
case 'T':
threads = strtol(optarg, NULL, 10);
if (threads < 1) threads = 1;
break;
#endif
case 'a':
shard_count = strtof(optarg,NULL);
break;
case 'b':
shard_size = strtof(optarg,NULL);
break;
case 'h':
usage(0);
break;
default:
usage(1);
}
}
if (pdim < 0)
usage(1);
cerr << "shard_size = " << shard_size << " shard_count = " << shard_count << endl;
if (shard_size && !shard_count) {
cerr << "Error: shard-size provided without shard-count" << endl;
exit(1);
}
if (shard_size > 1 || shard_size < 0) {
cerr << "Error: shard-size should be between 0 and 1" << endl;
exit(1);
}
if (hasSeed) {
cerr << "Seeding random numbers with " << seed << endl;
srandom(seed);
} else {
cerr << "Seeding random numbers with system clock " << endl;
srandom(time(NULL));
}
// read in starting points
std::string onefile;
while (!initfile.empty()) {
getNextPound(initfile, onefile, ",");
vector<parameter_t> start;
ifstream opt(onefile.c_str());
if(opt.fail()) {
cerr<<"could not open initfile: " << initfile << endl;
exit(3);
}
start.resize(pdim);//to do:read from file
int j;
for( j=0; j<pdim&&!opt.fail(); j++)
opt>>start[j];
if(j<pdim) {
cerr<<initfile<<":Too few starting weights." << endl;
exit(3);
}
start_list.push_back(start);
// for the first time, also read in the min/max values for scores
if (start_list.size() == 1) {
min.resize(pdim);
for( j=0; j<pdim&&!opt.fail(); j++)
opt>>min[j];
if(j<pdim) {
cerr<<initfile<<":Too few minimum weights." << endl;
cerr<<"error could not initialize start point with " << initfile << endl;
std::cerr << "j: " << j << ", pdim: " << pdim << std::endl;
exit(3);
}
max.resize(pdim);
for( j=0; j<pdim&&!opt.fail(); j++)
opt>>max[j];
if(j<pdim) {
cerr<<initfile<<":Too few maximum weights." << endl;
exit(3);
}
}
opt.close();
}
vector<string> ScoreDataFiles;
if (scorerfile.length() > 0) {
Tokenize(scorerfile.c_str(), ',', &ScoreDataFiles);
}
vector<string> FeatureDataFiles;
if (featurefile.length() > 0) {
Tokenize(featurefile.c_str(), ',', &FeatureDataFiles);
}
if (ScoreDataFiles.size() != FeatureDataFiles.size()) {
throw runtime_error("Error: there is a different number of previous score and feature files");
}
// it make sense to know what parameter set were used to generate the nbest
Scorer *TheScorer = ScorerFactory::getScorer(scorertype,scorerconfig);
//load data
Data D(*TheScorer);
for (size_t i=0; i < ScoreDataFiles.size(); i++) {
cerr<<"Loading Data from: "<< ScoreDataFiles.at(i) << " and " << FeatureDataFiles.at(i) << endl;
D.load(FeatureDataFiles.at(i), ScoreDataFiles.at(i));
}
//ADDED_BY_TS
D.remove_duplicates();
//END_ADDED
PrintUserTime("Data loaded");
// starting point score over latest n-best, accumulative n-best
//vector<unsigned> bests;
//compute bests with sparse features needs to be implemented
//currently sparse weights are not even loaded
//statscore_t score = TheScorer->score(bests);
if (tooptimizestr.length() > 0) {
cerr << "Weights to optimize: " << tooptimizestr << endl;
// Parse string to get weights to optimize, and set them as active
std::string substring;
int index;
while (!tooptimizestr.empty()) {
getNextPound(tooptimizestr, substring, ",");
index = D.getFeatureIndex(substring);
cerr << "FeatNameIndex:" << index << " to insert" << endl;
//index = strtol(substring.c_str(), NULL, 10);
if (index >= 0 && index < pdim) {
tooptimize.push_back(index);
} else {
cerr << "Index " << index << " is out of bounds. Allowed indexes are [0," << (pdim-1) << "]." << endl;
}
}
} else {
//set all weights as active
tooptimize.resize(pdim);//We'll optimize on everything
for(int i=0; i<pdim; i++) {
tooptimize[i]=1;
}
}
// treat sparse features just like regular features
if (D.hasSparseFeatures()) {
D.mergeSparseFeatures();
}
#ifdef WITH_THREADS
cerr << "Creating a pool of " << threads << " threads" << endl;
Moses::ThreadPool pool(threads);
#endif
Point::setpdim(pdim);
Point::setdim(tooptimize.size());
//starting points consist of specified points and random restarts
vector<Point> startingPoints;
for (size_t i = 0; i < start_list.size(); ++i) {
startingPoints.push_back(Point(start_list[i],min,max));
}
for (int i = 0; i < ntry; ++i) {
startingPoints.push_back(Point(start_list[0],min,max));
startingPoints.back().Randomize();
}
vector<vector<OptimizationTask*> > allTasks(1);
//optional sharding
vector<Data> shards;
if (shard_count) {
D.createShards(shard_count, shard_size, scorerconfig, shards);
allTasks.resize(shard_count);
}
// launch tasks
for (size_t i = 0 ; i < allTasks.size(); ++i) {
Data& data = D;
if (shard_count) data = shards[i]; //use the sharded data if it exists
vector<OptimizationTask*>& tasks = allTasks[i];
Optimizer *O = OptimizerFactory::BuildOptimizer(pdim,tooptimize,start_list[0],type,nrandom);
O->SetScorer(data.getScorer());
O->SetFData(data.getFeatureData());
//A task for each start point
for (size_t j = 0; j < startingPoints.size(); ++j) {
OptimizationTask* task = new OptimizationTask(O, startingPoints[j]);
tasks.push_back(task);
#ifdef WITH_THREADS
pool.Submit(task);
#else
task->Run();
#endif
}
}
// wait for all threads to finish
#ifdef WITH_THREADS
pool.Stop(true);
#endif
statscore_t total = 0;
Point totalP;
// collect results
for (size_t i = 0; i < allTasks.size(); ++i) {
statscore_t best=0, mean=0, var=0;
Point bestP;
for (size_t j = 0; j < allTasks[i].size(); ++j) {
statscore_t score = allTasks[i][j]->getScore();
mean += score;
var += score*score;
if (score > best) {
bestP = allTasks[i][j]->getPoint();
best = score;
}
}
mean/=(float)ntry;
var/=(float)ntry;
var=sqrt(abs(var-mean*mean));
if (verboselevel()>1)
cerr<<"shard " << i << " best score: "<< best << " variance of the score (for "<<ntry<<" try): "<<var<<endl;
totalP += bestP;
total += best;
if (verboselevel()>1)
cerr << "bestP " << bestP << endl;
}
//cerr << "totalP: " << totalP << endl;
Point finalP = totalP * (1.0 / allTasks.size());
statscore_t final = total / allTasks.size();
if (verboselevel()>1)
cerr << "bestP: " << finalP << endl;
// L1-Normalization of the best Point
if ((int)tooptimize.size() == pdim)
finalP.NormalizeL1();
cerr << "Best point: " << finalP << " => " << final << endl;
ofstream res("weights.txt");
res<<finalP<<endl;
for (size_t i = 0; i < allTasks.size(); ++i) {
allTasks[i][0]->resetOptimizer();
for (size_t j = 0; j < allTasks[i].size(); ++j) {
delete allTasks[i][j];
}
}
delete TheScorer;
PrintUserTime("Stopping...");
}
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