mosesdecoder/mert/kbmira.cpp
2015-01-14 11:07:42 +00:00

341 lines
12 KiB
C++

// $Id$
// vim:tabstop=2
/***********************************************************************
K-best Batch MIRA for Moses
Copyright (C) 2012, National Research Council Canada / Conseil national
de recherches du Canada
***********************************************************************/
/**
* k-best Batch Mira, as described in:
*
* Colin Cherry and George Foster
* Batch Tuning Strategies for Statistical Machine Translation
* NAACL 2012
*
* Implemented by colin.cherry@nrc-cnrc.gc.ca
*
* To license implementations of any of the other tuners in that paper,
* please get in touch with any member of NRC Canada's Portage project
*
* Input is a set of n-best lists, encoded as feature and score files.
*
* Output is a weight file that results from running MIRA on these
* n-btest lists for J iterations. Will return the set that maximizes
* training BLEU.
**/
#include <cmath>
#include <cstddef>
#include <cstdlib>
#include <ctime>
#include <cassert>
#include <iostream>
#include <string>
#include <vector>
#include <utility>
#include <algorithm>
#include <boost/program_options.hpp>
#include <boost/scoped_ptr.hpp>
#include "util/exception.hh"
#include "BleuScorer.h"
#include "HopeFearDecoder.h"
#include "MiraFeatureVector.h"
#include "MiraWeightVector.h"
#include "Scorer.h"
#include "ScorerFactory.h"
using namespace std;
using namespace MosesTuning;
namespace po = boost::program_options;
int main(int argc, char** argv)
{
bool help;
string denseInitFile;
string sparseInitFile;
string type = "nbest";
string sctype = "BLEU";
string scconfig = "";
vector<string> scoreFiles;
vector<string> featureFiles;
vector<string> referenceFiles; //for hg mira
string hgDir;
int seed;
string outputFile;
float c = 0.01; // Step-size cap C
float decay = 0.999; // Pseudo-corpus decay \gamma
int n_iters = 60; // Max epochs J
bool streaming = false; // Stream all k-best lists?
bool streaming_out = false; // Stream output after each sentence?
bool no_shuffle = false; // Don't shuffle, even for in memory version
bool model_bg = false; // Use model for background corpus
bool verbose = false; // Verbose updates
bool safe_hope = false; // Model score cannot have more than BLEU_RATIO times more influence than BLEU
size_t hgPruning = 50; //prune hypergraphs to have this many edges per reference word
// Command-line processing follows pro.cpp
po::options_description desc("Allowed options");
desc.add_options()
("help,h", po::value(&help)->zero_tokens()->default_value(false), "Print this help message and exit")
("type,t", po::value<string>(&type), "Either nbest or hypergraph")
("sctype", po::value<string>(&sctype), "the scorer type (default BLEU)")
("scconfig,c", po::value<string>(&scconfig), "configuration string passed to scorer")
("scfile,S", po::value<vector<string> >(&scoreFiles), "Scorer data files")
("ffile,F", po::value<vector<string> > (&featureFiles), "Feature data files")
("hgdir,H", po::value<string> (&hgDir), "Directory containing hypergraphs")
("reference,R", po::value<vector<string> > (&referenceFiles), "Reference files, only required for hypergraph mira")
("random-seed,r", po::value<int>(&seed), "Seed for random number generation")
("output-file,o", po::value<string>(&outputFile), "Output file")
("cparam,C", po::value<float>(&c), "MIRA C-parameter, lower for more regularization (default 0.01)")
("decay,D", po::value<float>(&decay), "BLEU background corpus decay rate (default 0.999)")
("iters,J", po::value<int>(&n_iters), "Number of MIRA iterations to run (default 60)")
("dense-init,d", po::value<string>(&denseInitFile), "Weight file for dense features. This should have 'name= value' on each line, or (legacy) should be the Moses mert 'init.opt' format.")
("sparse-init,s", po::value<string>(&sparseInitFile), "Weight file for sparse features")
("streaming", po::value(&streaming)->zero_tokens()->default_value(false), "Stream n-best lists to save memory, implies --no-shuffle")
("streaming-out", po::value(&streaming_out)->zero_tokens()->default_value(false), "Stream weights to stdout after each sentence")
("no-shuffle", po::value(&no_shuffle)->zero_tokens()->default_value(false), "Don't shuffle hypotheses before each epoch")
("model-bg", po::value(&model_bg)->zero_tokens()->default_value(false), "Use model instead of hope for BLEU background")
("verbose", po::value(&verbose)->zero_tokens()->default_value(false), "Verbose updates")
("safe-hope", po::value(&safe_hope)->zero_tokens()->default_value(false), "Mode score's influence on hope decoding is limited")
("hg-prune", po::value<size_t>(&hgPruning), "Prune hypergraphs to have this many edges per reference word")
;
po::options_description cmdline_options;
cmdline_options.add(desc);
po::variables_map vm;
po::store(po::command_line_parser(argc,argv).
options(cmdline_options).run(), vm);
po::notify(vm);
if (help) {
cout << "Usage: " + string(argv[0]) + " [options]" << endl;
cout << desc << endl;
exit(0);
}
cerr << "kbmira with c=" << c << " decay=" << decay << " no_shuffle=" << no_shuffle << endl;
if (vm.count("random-seed")) {
cerr << "Initialising random seed to " << seed << endl;
srand(seed);
} else {
cerr << "Initialising random seed from system clock" << endl;
srand(time(NULL));
}
// Initialize weights
///
// Dense
vector<parameter_t> initParams;
if(!denseInitFile.empty()) {
ifstream opt(denseInitFile.c_str());
string buffer;
if (opt.fail()) {
cerr << "could not open dense initfile: " << denseInitFile << endl;
exit(3);
}
if (verbose) cerr << "Reading dense features:" << endl;
parameter_t val;
getline(opt,buffer);
if (buffer.find_first_of("=") == buffer.npos) {
UTIL_THROW_IF(type == "hypergraph", util::Exception, "For hypergraph version, require dense features in 'name= value' format");
cerr << "WARN: dense features in deprecated Moses mert format. Prefer 'name= value' format." << endl;
istringstream strstrm(buffer);
while(strstrm >> val) {
initParams.push_back(val);
if(verbose) cerr << val << endl;
}
} else {
vector<string> names;
string last_name = "";
size_t feature_ctr = 1;
do {
size_t equals = buffer.find_last_of("=");
UTIL_THROW_IF(equals == buffer.npos, util::Exception, "Incorrect format in dense feature file: '"
<< buffer << "'");
string name = buffer.substr(0,equals);
names.push_back(name);
initParams.push_back(boost::lexical_cast<ValType>(buffer.substr(equals+2)));
//Names for features with several values need to have their id added
if (name != last_name) feature_ctr = 1;
last_name = name;
if (feature_ctr>1) {
stringstream namestr;
namestr << names.back() << "_" << feature_ctr;
names[names.size()-1] = namestr.str();
if (feature_ctr == 2) {
stringstream namestr;
namestr << names[names.size()-2] << "_" << (feature_ctr-1);
names[names.size()-2] = namestr.str();
}
}
++feature_ctr;
} while(getline(opt,buffer));
//Make sure that SparseVector encodes dense feature names as 0..n-1.
for (size_t i = 0; i < names.size(); ++i) {
size_t id = SparseVector::encode(names[i]);
assert(id == i);
if (verbose) cerr << names[i] << " " << initParams[i] << endl;
}
}
opt.close();
}
size_t initDenseSize = initParams.size();
// Sparse
if(!sparseInitFile.empty()) {
if(initDenseSize==0) {
cerr << "sparse initialization requires dense initialization" << endl;
exit(3);
}
ifstream opt(sparseInitFile.c_str());
if(opt.fail()) {
cerr << "could not open sparse initfile: " << sparseInitFile << endl;
exit(3);
}
int sparseCount=0;
parameter_t val;
std::string name;
while(opt >> name >> val) {
size_t id = SparseVector::encode(name) + initDenseSize;
while(initParams.size()<=id) initParams.push_back(0.0);
initParams[id] = val;
sparseCount++;
}
cerr << "Found " << sparseCount << " initial sparse features" << endl;
opt.close();
}
MiraWeightVector wv(initParams);
// Initialize scorer
if(sctype != "BLEU" && type == "hypergraph") {
UTIL_THROW(util::Exception, "hypergraph mira only supports BLEU");
}
boost::scoped_ptr<Scorer> scorer(ScorerFactory::getScorer(sctype, scconfig));
// Initialize background corpus
vector<ValType> bg(scorer->NumberOfScores(), 1);
boost::scoped_ptr<HopeFearDecoder> decoder;
if (type == "nbest") {
decoder.reset(new NbestHopeFearDecoder(featureFiles, scoreFiles, streaming, no_shuffle, safe_hope, scorer.get()));
} else if (type == "hypergraph") {
decoder.reset(new HypergraphHopeFearDecoder(hgDir, referenceFiles, initDenseSize, streaming, no_shuffle, safe_hope, hgPruning, wv, scorer.get()));
} else {
UTIL_THROW(util::Exception, "Unknown batch mira type: '" << type << "'");
}
// Training loop
if (!streaming_out)
cerr << "Initial BLEU = " << decoder->Evaluate(wv.avg()) << endl;
ValType bestBleu = 0;
for(int j=0; j<n_iters; j++) {
// MIRA train for one epoch
int iNumExamples = 0;
int iNumUpdates = 0;
ValType totalLoss = 0.0;
size_t sentenceIndex = 0;
for(decoder->reset(); !decoder->finished(); decoder->next()) {
HopeFearData hfd;
decoder->HopeFear(bg,wv,&hfd);
// Update weights
if (!hfd.hopeFearEqual && hfd.hopeBleu > hfd.fearBleu) {
// Vector difference
MiraFeatureVector diff = hfd.hopeFeatures - hfd.fearFeatures;
// Bleu difference
//assert(hfd.hopeBleu + 1e-8 >= hfd.fearBleu);
ValType delta = hfd.hopeBleu - hfd.fearBleu;
// Loss and update
ValType diff_score = wv.score(diff);
ValType loss = delta - diff_score;
if(verbose) {
cerr << "Updating sent " << sentenceIndex << endl;
cerr << "Wght: " << wv << endl;
cerr << "Hope: " << hfd.hopeFeatures << " BLEU:" << hfd.hopeBleu << " Score:" << wv.score(hfd.hopeFeatures) << endl;
cerr << "Fear: " << hfd.fearFeatures << " BLEU:" << hfd.fearBleu << " Score:" << wv.score(hfd.fearFeatures) << endl;
cerr << "Diff: " << diff << " BLEU:" << delta << " Score:" << diff_score << endl;
cerr << "Loss: " << loss << " Scale: " << 1 << endl;
cerr << endl;
}
if(loss > 0) {
ValType eta = min(c, loss / diff.sqrNorm());
wv.update(diff,eta);
totalLoss+=loss;
iNumUpdates++;
}
// Update BLEU statistics
for(size_t k=0; k<bg.size(); k++) {
bg[k]*=decay;
if(model_bg)
bg[k]+=hfd.modelStats[k];
else
bg[k]+=hfd.hopeStats[k];
}
}
iNumExamples++;
++sentenceIndex;
if (streaming_out)
cout << wv << endl;
}
// Training Epoch summary
cerr << iNumUpdates << "/" << iNumExamples << " updates"
<< ", avg loss = " << (totalLoss / iNumExamples);
// Evaluate current average weights
AvgWeightVector avg = wv.avg();
ValType bleu = decoder->Evaluate(avg);
cerr << ", BLEU = " << bleu << endl;
if(bleu > bestBleu) {
/*
size_t num_dense = train->num_dense();
if(initDenseSize>0 && initDenseSize!=num_dense) {
cerr << "Error: Initial dense feature count and dense feature count from n-best do not match: "
<< initDenseSize << "!=" << num_dense << endl;
exit(1);
}*/
// Write to a file
ostream* out;
ofstream outFile;
if (!outputFile.empty() ) {
outFile.open(outputFile.c_str());
if (!(outFile)) {
cerr << "Error: Failed to open " << outputFile << endl;
exit(1);
}
out = &outFile;
} else {
out = &cout;
}
for(size_t i=0; i<avg.size(); i++) {
if(i<initDenseSize)
*out << "F" << i << " " << avg.weight(i) << endl;
else {
if(abs(avg.weight(i))>1e-8)
*out << SparseVector::decode(i-initDenseSize) << " " << avg.weight(i) << endl;
}
}
outFile.close();
bestBleu = bleu;
}
}
cerr << "Best BLEU = " << bestBleu << endl;
}
// --Emacs trickery--
// Local Variables:
// mode:c++
// c-basic-offset:2
// End: