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| -rw-r--r-- | latent_svm/Makefile.am | 6 | ||||
| -rw-r--r-- | latent_svm/latent_svm.cc | 412 |
2 files changed, 0 insertions, 418 deletions
diff --git a/latent_svm/Makefile.am b/latent_svm/Makefile.am deleted file mode 100644 index 673b9159..00000000 --- a/latent_svm/Makefile.am +++ /dev/null @@ -1,6 +0,0 @@ -bin_PROGRAMS = latent_svm - -latent_svm_SOURCES = latent_svm.cc -latent_svm_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz - -AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval diff --git a/latent_svm/latent_svm.cc b/latent_svm/latent_svm.cc deleted file mode 100644 index ab9c1d5d..00000000 --- a/latent_svm/latent_svm.cc +++ /dev/null @@ -1,412 +0,0 @@ -/* -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; -} - |