/* Points to note regarding variable names: total_loss and prev_loss actually refer not to loss, but the metric (usually BLEU) */ #include <sstream> #include <iostream> #include <vector> #include <cassert> #include <cmath> //boost libraries #include <boost/shared_ptr.hpp> #include <boost/program_options.hpp> #include <boost/program_options/variables_map.hpp> //cdec libraries #include "config.h" #include "hg_sampler.h" #include "sentence_metadata.h" #include "scorer.h" #include "verbose.h" #include "viterbi.h" #include "hg.h" #include "prob.h" #include "kbest.h" #include "ff_register.h" #include "decoder.h" #include "filelib.h" #include "fdict.h" #include "weights.h" #include "sparse_vector.h" #include "sampler.h" using namespace std; namespace po = boost::program_options; bool invert_score; boost::shared_ptr<MT19937> rng; //random seed ptr void RandomPermutation(int len, vector<int>* p_ids) { vector<int>& ids = *p_ids; ids.resize(len); for (int i = 0; i < len; ++i) ids[i] = i; for (int i = len; i > 0; --i) { int j = rng->next() * i; if (j == i) i--; swap(ids[i-1], ids[j]); } } bool InitCommandLine(int argc, char** argv, po::variables_map* conf) { po::options_description opts("Configuration options"); opts.add_options() ("weights,w",po::value<string>(),"[REQD] Input feature weights file") ("input,i",po::value<string>(),"[REQD] Input source file for development set") ("passes,p", po::value<int>()->default_value(15), "Number of passes through the training data") ("weights_write_interval,n", po::value<int>()->default_value(1000), "Number of lines between writing out weights") ("reference,r",po::value<vector<string> >(), "[REQD] Reference translation(s) (tokenized text file)") ("mt_metric,m",po::value<string>()->default_value("ibm_bleu"), "Scoring metric (ibm_bleu, nist_bleu, koehn_bleu, ter, combi)") ("regularizer_strength,C", po::value<double>()->default_value(0.01), "regularization strength") ("mt_metric_scale,s", po::value<double>()->default_value(1.0), "Cost function is -mt_metric_scale*BLEU") ("costaug_log_bleu,l", "Flag converts BLEU to log space. Cost function is thus -mt_metric_scale*log(BLEU). Not on by default") ("average,A", "Average the weights (this is a weighted average due to the scaling factor)") ("mu,u", po::value<double>()->default_value(0.0), "weight (between 0 and 1) to scale model score by for oracle selection") ("stepsize_param,a", po::value<double>()->default_value(0.01), "Stepsize parameter, during optimization") ("stepsize_reduce,t", "Divide step size by sqrt(number of examples seen so far), as per Ratliff et al., 2007") ("metric_threshold,T", po::value<double>()->default_value(0.0), "Threshold for diff between oracle BLEU and cost-aug BLEU for updating the weights") ("check_positive,P", "Check that the loss is positive before updating") ("k_best_size,k", po::value<int>()->default_value(250), "Size of hypothesis list to search for oracles") ("best_ever,b", "Keep track of the best hypothesis we've ever seen (metric score), and use that as the reference") ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)") ("decoder_config,c",po::value<string>(),"Decoder configuration file"); po::options_description clo("Command line options"); clo.add_options() ("config", po::value<string>(), "Configuration file") ("help,h", "Print this help message and exit"); po::options_description dconfig_options, dcmdline_options; dconfig_options.add(opts); dcmdline_options.add(opts).add(clo); po::store(parse_command_line(argc, argv, dcmdline_options), *conf); if (conf->count("config")) { ifstream config((*conf)["config"].as<string>().c_str()); po::store(po::parse_config_file(config, dconfig_options), *conf); } po::notify(*conf); if (conf->count("help") || !conf->count("weights") || !conf->count("input") || !conf->count("decoder_config") || !conf->count("reference")) { cerr << dcmdline_options << endl; return false; } return true; } double scaling_trick = 1; // see http://blog.smola.org/post/940672544/fast-quadratic-regularization-for-online-learning /*computes and returns cost augmented score for negative example selection*/ double cost_augmented_score(const LogVal<double> model_score, const double mt_metric_score, const double mt_metric_scale, const bool logbleu) { if(logbleu) { if(mt_metric_score != 0) // NOTE: log(model_score) is just the model score feature weights * features return log(model_score) * scaling_trick + (- mt_metric_scale * log(mt_metric_score)); else return -1000000; } // NOTE: log(model_score) is just the model score feature weights * features return log(model_score) * scaling_trick + (- mt_metric_scale * mt_metric_score); } /*computes and returns mu score, for oracle selection*/ double muscore(const vector<weight_t>& feature_weights, const SparseVector<double>& feature_values, const double mt_metric_score, const double mu, const bool logbleu) { if(logbleu) { if(mt_metric_score != 0) return feature_values.dot(feature_weights) * mu + (1 - mu) * log(mt_metric_score); else return feature_values.dot(feature_weights) * mu + (1 - mu) * (-1000000); // log(0) is -inf } return feature_values.dot(feature_weights) * mu + (1 - mu) * mt_metric_score; } static const double kMINUS_EPSILON = -1e-6; struct HypothesisInfo { SparseVector<double> features; double mt_metric_score; // The model score changes when the feature weights change, so it is not stored here // It must be recomputed every time }; struct GoodOracle { boost::shared_ptr<HypothesisInfo> good; }; struct TrainingObserver : public DecoderObserver { TrainingObserver(const int k, const DocScorer& d, vector<GoodOracle>* o, const vector<weight_t>& feat_weights, const double metric_scale, const double Mu, const bool bestever, const bool LogBleu) : ds(d), feature_weights(feat_weights), oracles(*o), kbest_size(k), mt_metric_scale(metric_scale), mu(Mu), best_ever(bestever), log_bleu(LogBleu) {} const DocScorer& ds; const vector<weight_t>& feature_weights; vector<GoodOracle>& oracles; boost::shared_ptr<HypothesisInfo> cur_best; boost::shared_ptr<HypothesisInfo> cur_costaug_best; boost::shared_ptr<HypothesisInfo> cur_ref; const int kbest_size; const double mt_metric_scale; const double mu; const bool best_ever; const bool log_bleu; const HypothesisInfo& GetCurrentBestHypothesis() const { return *cur_best; } const HypothesisInfo& GetCurrentCostAugmentedHypothesis() const { return *cur_costaug_best; } const HypothesisInfo& GetCurrentReference() const { return *cur_ref; } virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) { UpdateOracles(smeta.GetSentenceID(), *hg); } boost::shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double metric) { boost::shared_ptr<HypothesisInfo> h(new HypothesisInfo); h->features = feats; h->mt_metric_score = metric; return h; } void UpdateOracles(int sent_id, const Hypergraph& forest) { //shared_ptr<HypothesisInfo>& cur_ref = oracles[sent_id].good; cur_ref = oracles[sent_id].good; if(!best_ever) cur_ref.reset(); KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, kbest_size); double costaug_best_score = 0; for (int i = 0; i < kbest_size; ++i) { const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d = kbest.LazyKthBest(forest.nodes_.size() - 1, i); if (!d) break; double mt_metric_score = ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore(); //this might need to change!! const SparseVector<double>& feature_vals = d->feature_values; double costaugmented_score = cost_augmented_score(d->score, mt_metric_score, mt_metric_scale, log_bleu); //note that d->score, i.e., model score, is passed in if (i == 0) { //i.e., setting up cur_best to be model score highest, and initializing costaug_best cur_best = MakeHypothesisInfo(feature_vals, mt_metric_score); cur_costaug_best = cur_best; costaug_best_score = costaugmented_score; } if (costaugmented_score > costaug_best_score) { // kbest_mira's cur_bad, i.e., "fear" derivation cur_costaug_best = MakeHypothesisInfo(feature_vals, mt_metric_score); costaug_best_score = costaugmented_score; } double cur_muscore = mt_metric_score; if (!cur_ref) // kbest_mira's cur_good, i.e., "hope" derivation cur_ref = MakeHypothesisInfo(feature_vals, cur_muscore); else { double cur_ref_muscore = cur_ref->mt_metric_score; if(mu > 0) { //select oracle with mixture of model score and BLEU cur_ref_muscore = muscore(feature_weights, cur_ref->features, cur_ref->mt_metric_score, mu, log_bleu); cur_muscore = muscore(feature_weights, d->feature_values, mt_metric_score, mu, log_bleu); } if (cur_muscore > cur_ref_muscore) //replace oracle cur_ref = MakeHypothesisInfo(feature_vals, mt_metric_score); } } } }; void ReadTrainingCorpus(const string& fname, vector<string>* c) { ReadFile rf(fname); istream& in = *rf.stream(); string line; while(in) { getline(in, line); if (!in) break; c->push_back(line); } } bool ApproxEqual(double a, double b) { if (a == b) return true; return (fabs(a-b)/fabs(b)) < 0.000001; } int main(int argc, char** argv) { register_feature_functions(); SetSilent(true); // turn off verbose decoder output po::variables_map conf; if (!InitCommandLine(argc, argv, &conf)) return 1; if (conf.count("random_seed")) rng.reset(new MT19937(conf["random_seed"].as<uint32_t>())); else rng.reset(new MT19937); const bool best_ever = conf.count("best_ever") > 0; vector<string> corpus; ReadTrainingCorpus(conf["input"].as<string>(), &corpus); const string metric_name = conf["mt_metric"].as<string>(); //set up scoring; this may need to be changed!! ScoreType type = ScoreTypeFromString(metric_name); if (type == TER) { invert_score = true; } else { invert_score = false; } DocScorer ds(type, conf["reference"].as<vector<string> >(), ""); cerr << "Loaded " << ds.size() << " references for scoring with " << metric_name << endl; if (ds.size() != corpus.size()) { cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n"; return 1; } ReadFile ini_rf(conf["decoder_config"].as<string>()); Decoder decoder(ini_rf.stream()); // load initial weights vector<weight_t>& decoder_weights = decoder.CurrentWeightVector(); //equivalent to "dense_weights" vector in kbest_mira.cc SparseVector<weight_t> sparse_weights; //equivaelnt to kbest_mira.cc "lambdas" Weights::InitFromFile(conf["weights"].as<string>(), &decoder_weights); Weights::InitSparseVector(decoder_weights, &sparse_weights); //initializing other algorithm and output parameters const double c = conf["regularizer_strength"].as<double>(); const int weights_write_interval = conf["weights_write_interval"].as<int>(); const double mt_metric_scale = conf["mt_metric_scale"].as<double>(); const double mu = conf["mu"].as<double>(); const double metric_threshold = conf["metric_threshold"].as<double>(); const double stepsize_param = conf["stepsize_param"].as<double>(); //step size in structured SGD optimization step const bool stepsize_reduce = conf.count("stepsize_reduce") > 0; const bool costaug_log_bleu = conf.count("costaug_log_bleu") > 0; const bool average = conf.count("average") > 0; const bool checkpositive = conf.count("check_positive") > 0; assert(corpus.size() > 0); vector<GoodOracle> oracles(corpus.size()); TrainingObserver observer(conf["k_best_size"].as<int>(), // kbest size ds, // doc scorer &oracles, decoder_weights, mt_metric_scale, mu, best_ever, costaug_log_bleu); int cur_sent = 0; int line_count = 0; int normalizer = 0; double total_loss = 0; double prev_loss = 0; int dots = 0; // progess bar int cur_pass = 0; SparseVector<double> tot; tot += sparse_weights; //add initial weights to total normalizer++; //add 1 to normalizer int max_iteration = conf["passes"].as<int>(); string msg = "# LatentSVM tuned weights"; vector<int> order; int interval_counter = 0; RandomPermutation(corpus.size(), &order); //shuffle corpus while (line_count <= max_iteration * corpus.size()) { //loop over all (passes * num sentences) examples //if ((interval_counter * 40 / weights_write_interval) > dots) { ++dots; cerr << '.'; } //check this if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.';} if (interval_counter == weights_write_interval) { //i.e., we need to write out weights sparse_weights *= scaling_trick; tot *= scaling_trick; scaling_trick = 1; cerr << " [SENTENCE NUMBER= " << cur_sent << "\n"; cerr << " [AVG METRIC LAST INTERVAL =" << ((total_loss - prev_loss) / weights_write_interval) << "]\n"; cerr << " [AVG METRIC THIS PASS THUS FAR =" << (total_loss / cur_sent) << "]\n"; cerr << " [TOTAL LOSS: =" << total_loss << "\n"; Weights::ShowLargestFeatures(decoder_weights); //dots = 0; interval_counter = 0; prev_loss = total_loss; if (average){ SparseVector<double> x = tot; x /= normalizer; ostringstream sa; sa << "weights.latentsvm-" << line_count/weights_write_interval << "-avg.gz"; x.init_vector(&decoder_weights); Weights::WriteToFile(sa.str(), decoder_weights, true, &msg); } else { ostringstream os; os << "weights.latentsvm-" << line_count/weights_write_interval << ".gz"; sparse_weights.init_vector(&decoder_weights); Weights::WriteToFile(os.str(), decoder_weights, true, &msg); } } if (corpus.size() == cur_sent) { //i.e., finished a pass //cerr << " [AVG METRIC LAST PASS=" << (document_metric_score / corpus.size()) << "]\n"; cerr << " [AVG METRIC LAST PASS=" << (total_loss / corpus.size()) << "]\n"; cerr << " TOTAL LOSS: " << total_loss << "\n"; Weights::ShowLargestFeatures(decoder_weights); cur_sent = 0; total_loss = 0; dots = 0; if(average) { SparseVector<double> x = tot; x /= normalizer; ostringstream sa; sa << "weights.latentsvm-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "-avg.gz"; x.init_vector(&decoder_weights); Weights::WriteToFile(sa.str(), decoder_weights, true, &msg); } else { ostringstream os; os << "weights.latentsvm-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz"; Weights::WriteToFile(os.str(), decoder_weights, true, &msg); } cur_pass++; RandomPermutation(corpus.size(), &order); } if (cur_sent == 0) { //i.e., starting a new pass cerr << "PASS " << (line_count / corpus.size() + 1) << endl; } sparse_weights.init_vector(&decoder_weights); // copy sparse_weights to the decoder weights decoder.SetId(order[cur_sent]); //assign current sentence decoder.Decode(corpus[order[cur_sent]], &observer); // decode/update oracles const HypothesisInfo& cur_best = observer.GetCurrentBestHypothesis(); //model score best const HypothesisInfo& cur_costaug = observer.GetCurrentCostAugmentedHypothesis(); //(model + cost) best; cost = -metric_scale*log(BLEU) or -metric_scale*BLEU //const HypothesisInfo& cur_ref = *oracles[order[cur_sent]].good; //this oracle-best line only picks based on BLEU const HypothesisInfo& cur_ref = observer.GetCurrentReference(); //if mu > 0, this mu-mixed oracle will be picked; otherwise, only on BLEU total_loss += cur_best.mt_metric_score; double step_size = stepsize_param; if (stepsize_reduce){ // w_{t+1} = w_t - stepsize_t * grad(Loss) step_size /= (sqrt(cur_sent+1.0)); } //actual update step - compute gradient, and modify sparse_weights if(cur_ref.mt_metric_score - cur_costaug.mt_metric_score > metric_threshold) { const double loss = (cur_costaug.features.dot(decoder_weights) - cur_ref.features.dot(decoder_weights)) * scaling_trick + mt_metric_scale * (cur_ref.mt_metric_score - cur_costaug.mt_metric_score); if (!checkpositive || loss > 0.0) { //can update either all the time if check positive is off, or only when loss > 0 if it's on sparse_weights -= cur_costaug.features * step_size / ((1.0-2.0*step_size*c)*scaling_trick); // cost augmented hyp orig - sparse_weights += cur_ref.features * step_size / ((1.0-2.0*step_size*c)*scaling_trick); // ref orig + } } scaling_trick *= (1.0 - 2.0 * step_size * c); tot += sparse_weights; //for averaging purposes normalizer++; //for averaging purposes line_count++; interval_counter++; cur_sent++; } cerr << endl; if(average) { tot /= normalizer; tot.init_vector(decoder_weights); msg = "# Latent SSVM tuned weights (averaged vector)"; Weights::WriteToFile("weights.latentsvm-final-avg.gz", decoder_weights, true, &msg); cerr << "Optimization complete.\n" << "AVERAGED WEIGHTS: weights.latentsvm-final-avg.gz\n"; } else { Weights::WriteToFile("weights.latentsvm-final.gz", decoder_weights, true, &msg); cerr << "Optimization complete.\n"; } return 0; }