From dc13d4b6c6a35563fb7dc5e426f8dc4142a03702 Mon Sep 17 00:00:00 2001
From: Avneesh Saluja <asaluja@gmail.com>
Date: Thu, 28 Mar 2013 18:58:31 -0700
Subject: latent SVM
---
training/latent_svm/Makefile.am | 6 +
training/latent_svm/latent_svm.cc | 412 ++++++++++++++++++++++++++++++++++++++
2 files changed, 418 insertions(+)
create mode 100644 training/latent_svm/Makefile.am
create mode 100644 training/latent_svm/latent_svm.cc
(limited to 'training')
diff --git a/training/latent_svm/Makefile.am b/training/latent_svm/Makefile.am
new file mode 100644
index 00000000..673b9159
--- /dev/null
+++ b/training/latent_svm/Makefile.am
@@ -0,0 +1,6 @@
+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/training/latent_svm/latent_svm.cc b/training/latent_svm/latent_svm.cc
new file mode 100644
index 00000000..ab9c1d5d
--- /dev/null
+++ b/training/latent_svm/latent_svm.cc
@@ -0,0 +1,412 @@
+/*
+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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