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-rw-r--r--latent_svm/latent_svm.cc412
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diff --git a/latent_svm/latent_svm.cc b/latent_svm/latent_svm.cc
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-/*
-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;
-using boost::shared_ptr;
-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 {
- 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;
- shared_ptr<HypothesisInfo> cur_best;
- shared_ptr<HypothesisInfo> cur_costaug_best;
- 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);
- }
-
- shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double metric) {
- 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;
-}
-
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