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-rw-r--r--training/dtrain/README.md25
-rw-r--r--training/dtrain/dtrain.cc840
-rw-r--r--training/dtrain/dtrain.h162
-rwxr-xr-xtraining/dtrain/lplp.rb12
-rwxr-xr-xtraining/dtrain/parallelize.rb134
-rw-r--r--training/dtrain/sample.h59
-rw-r--r--training/dtrain/score.h411
-rw-r--r--training/dtrain/update.h150
8 files changed, 869 insertions, 924 deletions
diff --git a/training/dtrain/README.md b/training/dtrain/README.md
index aa1ab3e7..73a6a5a5 100644
--- a/training/dtrain/README.md
+++ b/training/dtrain/README.md
@@ -1,35 +1,28 @@
This is a simple (and parallelizable) tuning method for cdec
-which is able to train the weights of very many (sparse) features
-on the training set.
+which enables training weights of very many (sparse) features
+on the full training set.
-It was used in these papers:
+Please cite as:
> "Joint Feature Selection in Distributed Stochastic
> Learning for Large-Scale Discriminative Training in
> SMT" (Simianer, Riezler, Dyer; ACL 2012)
>
-> "Multi-Task Learning for Improved Discriminative
-> Training in SMT" (Simianer, Riezler; WMT 2013)
->
-
Building
--------
-Builds when building cdec, see ../BUILDING .
-To build only parts needed for dtrain do
-```
- autoreconf -ifv
- ./configure
- cd training/dtrain/; make
-```
+Builds when building cdec, see ../../BUILDING .
Running
-------
-See directories under examples/ .
+Download runnable examples for all use cases from [1] and extract here.
Legal
-----
-Copyright (c) 2012-2013 by Patrick Simianer <p@simianer.de>
+Copyright (c) 2012-2015 by Patrick Simianer <p@simianer.de>
See the file LICENSE.txt in the root folder for the licensing terms that this software is
released under.
+
+[1] http://simianer.de/dtrain-example.tar.gz
+
diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index ccb50af2..b39fff3e 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -1,698 +1,288 @@
#include "dtrain.h"
+#include "sample.h"
#include "score.h"
-#include "kbestget.h"
-#include "ksampler.h"
-#include "pairsampling.h"
+#include "update.h"
using namespace dtrain;
-
-bool
-dtrain_init(int argc, char** argv, po::variables_map* cfg)
-{
- po::options_description ini("Configuration File Options");
- ini.add_options()
- ("input", po::value<string>(), "input file (src)")
- ("refs,r", po::value<string>(), "references")
- ("bitext,b", po::value<string>(), "bitext: 'src ||| tgt'")
- ("output", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT")
- ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)")
- ("decoder_config", po::value<string>(), "configuration file for cdec")
- ("print_weights", po::value<string>(), "weights to print on each iteration")
- ("stop_after", po::value<unsigned>()->default_value(0), "stop after X input sentences")
- ("keep", po::value<bool>()->zero_tokens(), "keep weights files for each iteration")
- ("epochs", po::value<unsigned>()->default_value(10), "# of iterations T (per shard)")
- ("k", po::value<unsigned>()->default_value(100), "how many translations to sample")
- ("sample_from", po::value<string>()->default_value("kbest"), "where to sample translations from: 'kbest', 'forest'")
- ("filter", po::value<string>()->default_value("uniq"), "filter kbest list: 'not', 'uniq'")
- ("pair_sampling", po::value<string>()->default_value("XYX"), "how to sample pairs: 'all', 'XYX' or 'PRO'")
- ("hi_lo", po::value<float>()->default_value(0.1), "hi and lo (X) for XYX (default 0.1), <= 0.5")
- ("pair_threshold", po::value<score_t>()->default_value(0.), "bleu [0,1] threshold to filter pairs")
- ("N", po::value<unsigned>()->default_value(4), "N for Ngrams (BLEU)")
- ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring: bleu, stupid_, smooth_, approx_, lc_")
- ("learning_rate", po::value<weight_t>()->default_value(1.0), "learning rate")
- ("gamma", po::value<weight_t>()->default_value(0.), "gamma for SVM (0 for perceptron)")
- ("select_weights", po::value<string>()->default_value("last"), "output best, last, avg weights ('VOID' to throw away)")
- ("rescale", po::value<bool>()->zero_tokens(), "rescale weight vector after each input")
- ("l1_reg", po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010) UNTESTED")
- ("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength")
- ("fselect", po::value<weight_t>()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPLEMENTED") // TODO
- ("approx_bleu_d", po::value<score_t>()->default_value(0.9), "discount for approx. BLEU")
- ("scale_bleu_diff", po::value<bool>()->zero_tokens(), "learning rate <- bleu diff of a misranked pair")
- ("loss_margin", po::value<weight_t>()->default_value(0.), "update if no error in pref pair but model scores this near")
- ("max_pairs", po::value<unsigned>()->default_value(std::numeric_limits<unsigned>::max()), "max. # of pairs per Sent.")
- ("pclr", po::value<string>()->default_value("no"), "use a (simple|adagrad) per-coordinate learning rate")
- ("batch", po::value<bool>()->zero_tokens(), "do batch optimization")
- ("repeat", po::value<unsigned>()->default_value(1), "repeat optimization over kbest list this number of times")
- ("check", po::value<bool>()->zero_tokens(), "produce list of loss differentials")
- ("noup", po::value<bool>()->zero_tokens(), "do not update weights");
- po::options_description cl("Command Line Options");
- cl.add_options()
- ("config,c", po::value<string>(), "dtrain config file")
- ("quiet,q", po::value<bool>()->zero_tokens(), "be quiet")
- ("verbose,v", po::value<bool>()->zero_tokens(), "be verbose");
- cl.add(ini);
- po::store(parse_command_line(argc, argv, cl), *cfg);
- if (cfg->count("config")) {
- ifstream ini_f((*cfg)["config"].as<string>().c_str());
- po::store(po::parse_config_file(ini_f, ini), *cfg);
- }
- po::notify(*cfg);
- if (!cfg->count("decoder_config")) {
- cerr << cl << endl;
- return false;
- }
- if ((*cfg)["sample_from"].as<string>() != "kbest"
- && (*cfg)["sample_from"].as<string>() != "forest") {
- cerr << "Wrong 'sample_from' param: '" << (*cfg)["sample_from"].as<string>() << "', use 'kbest' or 'forest'." << endl;
- return false;
- }
- if ((*cfg)["sample_from"].as<string>() == "kbest" && (*cfg)["filter"].as<string>() != "uniq" &&
- (*cfg)["filter"].as<string>() != "not") {
- cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'uniq' or 'not'." << endl;
- return false;
- }
- if ((*cfg)["pair_sampling"].as<string>() != "all" && (*cfg)["pair_sampling"].as<string>() != "XYX" &&
- (*cfg)["pair_sampling"].as<string>() != "PRO") {
- cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "'." << endl;
- return false;
- }
- if (cfg->count("hi_lo") && (*cfg)["pair_sampling"].as<string>() != "XYX") {
- cerr << "Warning: hi_lo only works with pair_sampling XYX." << endl;
- }
- if ((*cfg)["hi_lo"].as<float>() > 0.5 || (*cfg)["hi_lo"].as<float>() < 0.01) {
- cerr << "hi_lo must lie in [0.01, 0.5]" << endl;
- return false;
- }
- if ((cfg->count("input")>0 || cfg->count("refs")>0) && cfg->count("bitext")>0) {
- cerr << "Provide 'input' and 'refs' or 'bitext', not both." << endl;
- return false;
- }
- if ((*cfg)["pair_threshold"].as<score_t>() < 0) {
- cerr << "The threshold must be >= 0!" << endl;
- return false;
- }
- if ((*cfg)["select_weights"].as<string>() != "last" && (*cfg)["select_weights"].as<string>() != "best" &&
- (*cfg)["select_weights"].as<string>() != "avg" && (*cfg)["select_weights"].as<string>() != "VOID") {
- cerr << "Wrong 'select_weights' param: '" << (*cfg)["select_weights"].as<string>() << "', use 'last' or 'best'." << endl;
- return false;
- }
- return true;
-}
-
int
main(int argc, char** argv)
{
- // handle most parameters
- po::variables_map cfg;
- if (!dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong
- bool quiet = false;
- if (cfg.count("quiet")) quiet = true;
- bool verbose = false;
- if (cfg.count("verbose")) verbose = true;
- bool noup = false;
- if (cfg.count("noup")) noup = true;
- bool rescale = false;
- if (cfg.count("rescale")) rescale = true;
- bool keep = false;
- if (cfg.count("keep")) keep = true;
-
- const unsigned k = cfg["k"].as<unsigned>();
- const unsigned N = cfg["N"].as<unsigned>();
- const unsigned T = cfg["epochs"].as<unsigned>();
- const unsigned stop_after = cfg["stop_after"].as<unsigned>();
- const string filter_type = cfg["filter"].as<string>();
- const string sample_from = cfg["sample_from"].as<string>();
- const string pair_sampling = cfg["pair_sampling"].as<string>();
- const score_t pair_threshold = cfg["pair_threshold"].as<score_t>();
- const string select_weights = cfg["select_weights"].as<string>();
- const float hi_lo = cfg["hi_lo"].as<float>();
- const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>();
- const unsigned max_pairs = cfg["max_pairs"].as<unsigned>();
- int repeat = cfg["repeat"].as<unsigned>();
- bool check = false;
- if (cfg.count("check")) check = true;
- weight_t loss_margin = cfg["loss_margin"].as<weight_t>();
- bool batch = false;
- if (cfg.count("batch")) batch = true;
- if (loss_margin > 9998.) loss_margin = std::numeric_limits<float>::max();
- bool scale_bleu_diff = false;
- if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true;
- const string pclr = cfg["pclr"].as<string>();
- bool average = false;
- if (select_weights == "avg")
- average = true;
+ // get configuration
+ po::variables_map conf;
+ if (!dtrain_init(argc, argv, &conf))
+ return 1;
+ const size_t k = conf["k"].as<size_t>();
+ const string score_name = conf["score"].as<string>();
+ const size_t N = conf["N"].as<size_t>();
+ const size_t T = conf["iterations"].as<size_t>();
+ const weight_t eta = conf["learning_rate"].as<weight_t>();
+ const weight_t margin = conf["margin"].as<weight_t>();
+ const bool average = conf["average"].as<bool>();
+ const bool structured = conf["struct"].as<bool>();
+ const weight_t l1_reg = conf["l1_reg"].as<weight_t>();
+ const bool keep = conf["keep"].as<bool>();
+ const bool noup = conf["disable_learning"].as<bool>();
+ const string output_fn = conf["output"].as<string>();
+ const string output_data_which = conf["output_data"].as<string>();
+ const bool output_data = output_data_which!="";
vector<string> print_weights;
- if (cfg.count("print_weights"))
- boost::split(print_weights, cfg["print_weights"].as<string>(), boost::is_any_of(" "));
+ boost::split(print_weights, conf["print_weights"].as<string>(),
+ boost::is_any_of(" "));
- // setup decoder
+ // setup decoder and scorer
register_feature_functions();
SetSilent(true);
- ReadFile ini_rf(cfg["decoder_config"].as<string>());
- if (!quiet)
- cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
- Decoder decoder(ini_rf.stream());
-
- // scoring metric/scorer
- string scorer_str = cfg["scorer"].as<string>();
- LocalScorer* scorer;
- if (scorer_str == "bleu") {
- scorer = static_cast<BleuScorer*>(new BleuScorer);
- } else if (scorer_str == "stupid_bleu") {
- scorer = static_cast<StupidBleuScorer*>(new StupidBleuScorer);
- } else if (scorer_str == "fixed_stupid_bleu") {
- scorer = static_cast<FixedStupidBleuScorer*>(new FixedStupidBleuScorer);
- } else if (scorer_str == "smooth_bleu") {
- scorer = static_cast<SmoothBleuScorer*>(new SmoothBleuScorer);
- } else if (scorer_str == "sum_bleu") {
- scorer = static_cast<SumBleuScorer*>(new SumBleuScorer);
- } else if (scorer_str == "sumexp_bleu") {
- scorer = static_cast<SumExpBleuScorer*>(new SumExpBleuScorer);
- } else if (scorer_str == "sumwhatever_bleu") {
- scorer = static_cast<SumWhateverBleuScorer*>(new SumWhateverBleuScorer);
- } else if (scorer_str == "approx_bleu") {
- scorer = static_cast<ApproxBleuScorer*>(new ApproxBleuScorer(N, approx_bleu_d));
- } else if (scorer_str == "lc_bleu") {
- scorer = static_cast<LinearBleuScorer*>(new LinearBleuScorer(N));
+ ReadFile f(conf["decoder_conf"].as<string>());
+ Decoder decoder(f.stream());
+ Scorer* scorer;
+ if (score_name == "nakov") {
+ scorer = static_cast<PerSentenceBleuScorer*>(new PerSentenceBleuScorer(N));
+ } else if (score_name == "papineni") {
+ scorer = static_cast<BleuScorer*>(new BleuScorer(N));
+ } else if (score_name == "lin") {
+ scorer = static_cast<OriginalPerSentenceBleuScorer*>\
+ (new OriginalPerSentenceBleuScorer(N));
+ } else if (score_name == "liang") {
+ scorer = static_cast<SmoothPerSentenceBleuScorer*>\
+ (new SmoothPerSentenceBleuScorer(N));
+ } else if (score_name == "chiang") {
+ scorer = static_cast<ApproxBleuScorer*>(new ApproxBleuScorer(N));
} else {
- cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl;
- exit(1);
+ assert(false);
}
- vector<score_t> bleu_weights;
- scorer->Init(N, bleu_weights);
-
- // setup decoder observer
- MT19937 rng; // random number generator, only for forest sampling
- HypSampler* observer;
- if (sample_from == "kbest")
- observer = static_cast<KBestGetter*>(new KBestGetter(k, filter_type));
- else
- observer = static_cast<KSampler*>(new KSampler(k, &rng));
- observer->SetScorer(scorer);
-
- // init weights
+ ScoredKbest* observer = new ScoredKbest(k, scorer);
+
+ // weights
vector<weight_t>& decoder_weights = decoder.CurrentWeightVector();
- SparseVector<weight_t> lambdas, cumulative_penalties, w_average;
- if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &decoder_weights);
- Weights::InitSparseVector(decoder_weights, &lambdas);
-
- // meta params for perceptron, SVM
- weight_t eta = cfg["learning_rate"].as<weight_t>();
- weight_t gamma = cfg["gamma"].as<weight_t>();
-
- // faster perceptron: consider only misranked pairs, see
- bool faster_perceptron = false;
- if (gamma==0 && loss_margin==0) faster_perceptron = true;
-
- // l1 regularization
- bool l1naive = false;
- bool l1clip = false;
- bool l1cumul = false;
- weight_t l1_reg = 0;
- if (cfg["l1_reg"].as<string>() != "none") {
- string s = cfg["l1_reg"].as<string>();
- if (s == "naive") l1naive = true;
- else if (s == "clip") l1clip = true;
- else if (s == "cumul") l1cumul = true;
- l1_reg = cfg["l1_reg_strength"].as<weight_t>();
+ SparseVector<weight_t> lambdas, w_average;
+ if (conf.count("input_weights")) {
+ Weights::InitFromFile(conf["input_weights"].as<string>(), &decoder_weights);
+ Weights::InitSparseVector(decoder_weights, &lambdas);
}
- // output
- string output_fn = cfg["output"].as<string>();
// input
- bool read_bitext = false;
- string input_fn;
- if (cfg.count("bitext")) {
- read_bitext = true;
- input_fn = cfg["bitext"].as<string>();
- } else {
- input_fn = cfg["input"].as<string>();
- }
+ string input_fn = conf["bitext"].as<string>();
ReadFile input(input_fn);
- // buffer input for t > 0
- vector<string> src_str_buf; // source strings (decoder takes only strings)
- vector<vector<WordID> > ref_ids_buf; // references as WordID vecs
- ReadFile refs;
- string refs_fn;
- if (!read_bitext) {
- refs_fn = cfg["refs"].as<string>();
- refs.Init(refs_fn);
- }
-
- unsigned in_sz = std::numeric_limits<unsigned>::max(); // input index, input size
- vector<pair<score_t, score_t> > all_scores;
- score_t max_score = 0.;
- unsigned best_it = 0;
- float overall_time = 0.;
-
- // output cfg
- if (!quiet) {
- cerr << _p5;
- cerr << endl << "dtrain" << endl << "Parameters:" << endl;
- cerr << setw(25) << "k " << k << endl;
- cerr << setw(25) << "N " << N << endl;
- cerr << setw(25) << "T " << T << endl;
- cerr << setw(25) << "batch " << batch << endl;
- cerr << setw(26) << "scorer '" << scorer_str << "'" << endl;
- if (scorer_str == "approx_bleu")
- cerr << setw(25) << "approx. B discount " << approx_bleu_d << endl;
- cerr << setw(25) << "sample from " << "'" << sample_from << "'" << endl;
- if (sample_from == "kbest")
- cerr << setw(25) << "filter " << "'" << filter_type << "'" << endl;
- if (!scale_bleu_diff) cerr << setw(25) << "learning rate " << eta << endl;
- else cerr << setw(25) << "learning rate " << "bleu diff" << endl;
- cerr << setw(25) << "gamma " << gamma << endl;
- cerr << setw(25) << "loss margin " << loss_margin << endl;
- cerr << setw(25) << "faster perceptron " << faster_perceptron << endl;
- cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl;
- if (pair_sampling == "XYX")
- cerr << setw(25) << "hi lo " << hi_lo << endl;
- cerr << setw(25) << "pair threshold " << pair_threshold << endl;
- cerr << setw(25) << "select weights " << "'" << select_weights << "'" << endl;
- if (cfg.count("l1_reg"))
- cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl;
- if (rescale)
- cerr << setw(25) << "rescale " << rescale << endl;
- cerr << setw(25) << "pclr " << pclr << endl;
- cerr << setw(25) << "max pairs " << max_pairs << endl;
- cerr << setw(25) << "repeat " << repeat << endl;
- //cerr << setw(25) << "test k-best " << test_k_best << endl;
- cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
- cerr << setw(25) << "input " << "'" << input_fn << "'" << endl;
- if (!read_bitext)
- cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl;
- cerr << setw(25) << "output " << "'" << output_fn << "'" << endl;
- if (cfg.count("input_weights"))
- cerr << setw(25) << "weights in " << "'" << cfg["input_weights"].as<string>() << "'" << endl;
- if (stop_after > 0)
- cerr << setw(25) << "stop_after " << stop_after << endl;
- if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " inputs)" << endl;
+ vector<string> buf; // decoder only accepts strings as input
+ vector<vector<Ngrams> > buf_ngs; // compute ngrams and lengths of references
+ vector<vector<size_t> > buf_ls; // just once
+ size_t input_sz = 0;
+
+ cerr << _p4;
+ // output configuration
+ cerr << "Parameters:" << endl;
+ cerr << setw(25) << "bitext " << "'" << input_fn << "'" << endl;
+ cerr << setw(25) << "k " << k << endl;
+ cerr << setw(25) << "score " << "'" << score_name << "'" << endl;
+ cerr << setw(25) << "N " << N << endl;
+ cerr << setw(25) << "T " << T << endl;
+ cerr << setw(25) << "learning rate " << eta << endl;
+ cerr << setw(25) << "margin " << margin << endl;
+ cerr << setw(25) << "average " << average << endl;
+ cerr << setw(25) << "l1 reg " << l1_reg << endl;
+ cerr << setw(25) << "decoder conf " << "'"
+ << conf["decoder_conf"].as<string>() << "'" << endl;
+ cerr << setw(25) << "input " << "'" << input_fn << "'" << endl;
+ cerr << setw(25) << "output " << "'" << output_fn << "'" << endl;
+ if (conf.count("input_weights")) {
+ cerr << setw(25) << "weights in " << "'"
+ << conf["input_weights"].as<string>() << "'" << endl;
}
+ cerr << "(1 dot per processed input)" << endl;
- // pclr
- SparseVector<weight_t> learning_rates;
- // batch
- SparseVector<weight_t> batch_updates;
- score_t batch_loss;
+ // meta
+ weight_t best=0., gold_prev=0.;
+ size_t best_iteration = 0;
+ time_t total_time = 0.;
- for (unsigned t = 0; t < T; t++) // T epochs
+ for (size_t t = 0; t < T; t++) // T iterations
{
time_t start, end;
time(&start);
- score_t score_sum = 0.;
- score_t model_sum(0);
- unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0, f_count = 0, list_sz = 0, kbest_loss_improve = 0;
- batch_loss = 0.;
- if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
+ weight_t gold_sum=0., model_sum=0.;
+ size_t i=0, num_up=0, feature_count=0, list_sz=0;
+
+ cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
while(true)
{
+ bool next = true;
- string in;
- string ref;
- bool next = false, stop = false; // next iteration or premature stop
+ // getting input
if (t == 0) {
- if(!getline(*input, in)) next = true;
- if(read_bitext) {
- vector<string> strs;
- boost::algorithm::split_regex(strs, in, boost::regex(" \\|\\|\\| "));
- in = strs[0];
- ref = strs[1];
- }
- } else {
- if (ii == in_sz) next = true; // stop if we reach the end of our input
- }
- // stop after X sentences (but still go on for those)
- if (stop_after > 0 && stop_after == ii && !next) stop = true;
-
- // produce some pretty output
- if (!quiet && !verbose) {
- if (ii == 0) cerr << " ";
- if ((ii+1) % (DTRAIN_DOTS) == 0) {
- cerr << ".";
- cerr.flush();
- }
- if ((ii+1) % (20*DTRAIN_DOTS) == 0) {
- cerr << " " << ii+1 << endl;
- if (!next && !stop) cerr << " ";
- }
- if (stop) {
- if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl;
- cerr << "Stopping after " << stop_after << " input sentences." << endl;
+ string in;
+ if(!getline(*input, in)) {
+ next = false;
} else {
- if (next) {
- if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl;
+ vector<string> parts;
+ boost::algorithm::split_regex(parts, in, boost::regex(" \\|\\|\\| "));
+ buf.push_back(parts[0]);
+ parts.erase(parts.begin());
+ buf_ngs.push_back({});
+ buf_ls.push_back({});
+ for (auto s: parts) {
+ vector<WordID> r;
+ vector<string> toks;
+ boost::split(toks, s, boost::is_any_of(" "));
+ for (auto tok: toks)
+ r.push_back(TD::Convert(tok));
+ buf_ngs.back().emplace_back(MakeNgrams(r, N));
+ buf_ls.back().push_back(r.size());
}
}
+ } else {
+ next = i<input_sz;
}
- // next iteration
- if (next || stop) break;
-
- // weights
- lambdas.init_vector(&decoder_weights);
-
- // getting input
- vector<WordID> ref_ids; // reference as vector<WordID>
- if (t == 0) {
- if (!read_bitext) {
- getline(*refs, ref);
- }
- vector<string> ref_tok;
- boost::split(ref_tok, ref, boost::is_any_of(" "));
- register_and_convert(ref_tok, ref_ids);
- ref_ids_buf.push_back(ref_ids);
- src_str_buf.push_back(in);
+ // produce some pretty output
+ if (next) {
+ if (i%20 == 0)
+ cerr << " ";
+ cerr << ".";
+ if ((i+1)%20==0)
+ cerr << " " << i+1 << endl;
} else {
- ref_ids = ref_ids_buf[ii];
+ if (i%20 != 0)
+ cerr << " " << i << endl;
}
- observer->SetRef(ref_ids);
- if (t == 0)
- decoder.Decode(in, observer);
- else
- decoder.Decode(src_str_buf[ii], observer);
+ cerr.flush();
+
+ // stop iterating
+ if (!next) break;
- // get (scored) samples
+ // decode
+ if (t > 0 || i > 0)
+ lambdas.init_vector(&decoder_weights);
+ observer->SetReference(buf_ngs[i], buf_ls[i]);
+ decoder.Decode(buf[i], observer);
vector<ScoredHyp>* samples = observer->GetSamples();
- if (verbose) {
- cerr << "--- ref for " << ii << ": ";
- if (t > 0) printWordIDVec(ref_ids_buf[ii]);
- else printWordIDVec(ref_ids);
- cerr << endl;
- for (unsigned u = 0; u < samples->size(); u++) {
- cerr << _p2 << _np << "[" << u << ". '";
- printWordIDVec((*samples)[u].w);
- cerr << "'" << endl;
- cerr << "SCORE=" << (*samples)[u].score << ",model="<< (*samples)[u].model << endl;
- cerr << "F{" << (*samples)[u].f << "} ]" << endl << endl;
+ // stats for 1best
+ gold_sum += samples->front().gold;
+ model_sum += samples->front().model;
+ feature_count += observer->GetFeatureCount();
+ list_sz += observer->GetSize();
+
+ if (output_data) {
+ if (output_data_which == "kbest") {
+ OutputKbest(samples);
+ } else if (output_data_which == "default") {
+ OutputMultipartitePairs(samples, margin);
+ } else if (output_data_which == "all") {
+ OutputAllPairs(samples);
}
}
- if (repeat == 1) {
- score_sum += (*samples)[0].score; // stats for 1best
- model_sum += (*samples)[0].model;
- }
-
- f_count += observer->get_f_count();
- list_sz += observer->get_sz();
-
- // weight updates
+ // get pairs and update
if (!noup) {
- // get pairs
- vector<pair<ScoredHyp,ScoredHyp> > pairs;
- if (pair_sampling == "all")
- all_pairs(samples, pairs, pair_threshold, max_pairs, faster_perceptron);
- if (pair_sampling == "XYX")
- partXYX(samples, pairs, pair_threshold, max_pairs, faster_perceptron, hi_lo);
- if (pair_sampling == "PRO")
- PROsampling(samples, pairs, pair_threshold, max_pairs);
- int cur_npairs = pairs.size();
- npairs += cur_npairs;
-
- score_t kbest_loss_first = 0.0, kbest_loss_last = 0.0;
-
- if (check) repeat = 2;
- vector<float> losses; // for check
-
- for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
- it != pairs.end(); it++) {
- score_t model_diff = it->first.model - it->second.model;
- score_t loss = max(0.0, -1.0 * model_diff);
- losses.push_back(loss);
- kbest_loss_first += loss;
- }
- score_t kbest_loss = 0.0;
- for (int ki=0; ki < repeat; ki++) {
-
- SparseVector<weight_t> lambdas_copy; // for l1 regularization
- SparseVector<weight_t> sum_up; // for pclr
- if (l1naive||l1clip||l1cumul) lambdas_copy = lambdas;
-
- unsigned pair_idx = 0; // for check
- for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
- it != pairs.end(); it++) {
- score_t model_diff = it->first.model - it->second.model;
- score_t loss = max(0.0, -1.0 * model_diff);
-
- if (check && ki==repeat-1) cout << losses[pair_idx] - loss << endl;
- pair_idx++;
-
- if (repeat > 1) {
- model_diff = lambdas.dot(it->first.f) - lambdas.dot(it->second.f);
- kbest_loss += loss;
- }
- bool rank_error = false;
- score_t margin;
- if (faster_perceptron) { // we only have considering misranked pairs
- rank_error = true; // pair sampling already did this for us
- margin = std::numeric_limits<float>::max();
- } else {
- rank_error = model_diff<=0.0;
- margin = fabs(model_diff);
- if (!rank_error && margin < loss_margin) margin_violations++;
- }
- if (rank_error && ki==0) rank_errors++;
- if (scale_bleu_diff) eta = it->first.score - it->second.score;
- if (rank_error || margin < loss_margin) {
- SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
- if (batch) {
- batch_loss += max(0., -1.0 * model_diff);
- batch_updates += diff_vec;
- continue;
- }
- if (pclr != "no") {
- sum_up += diff_vec;
- } else {
- lambdas.plus_eq_v_times_s(diff_vec, eta);
- if (gamma) lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./cur_npairs));
- }
- }
- }
-
- // per-coordinate learning rate
- if (pclr != "no") {
- SparseVector<weight_t>::iterator it = sum_up.begin();
- for (; it != sum_up.end(); ++it) {
- if (pclr == "simple") {
- lambdas[it->first] += it->second / max(1.0, learning_rates[it->first]);
- learning_rates[it->first]++;
- } else if (pclr == "adagrad") {
- if (learning_rates[it->first] == 0) {
- lambdas[it->first] += it->second * eta;
- } else {
- lambdas[it->first] += it->second * eta * learning_rates[it->first];
- }
- learning_rates[it->first] += pow(it->second, 2.0);
- }
- }
- }
-
- // l1 regularization
- // please note that this regularizations happen
- // after a _sentence_ -- not after each example/pair!
- if (l1naive) {
- SparseVector<weight_t>::iterator it = lambdas.begin();
- for (; it != lambdas.end(); ++it) {
- if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
- it->second *= max(0.0000001, eta/(eta+learning_rates[it->first])); // FIXME
- learning_rates[it->first]++;
- it->second -= sign(it->second) * l1_reg;
- }
- }
- } else if (l1clip) {
- SparseVector<weight_t>::iterator it = lambdas.begin();
- for (; it != lambdas.end(); ++it) {
- if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
- if (it->second != 0) {
- weight_t v = it->second;
- if (v > 0) {
- it->second = max(0., v - l1_reg);
- } else {
- it->second = min(0., v + l1_reg);
- }
- }
- }
- }
- } else if (l1cumul) {
- weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input
- SparseVector<weight_t>::iterator it = lambdas.begin();
- for (; it != lambdas.end(); ++it) {
- if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
- if (it->second != 0) {
- weight_t v = it->second;
- weight_t penalized = 0.;
- if (v > 0) {
- penalized = max(0., v-(acc_penalty + cumulative_penalties.get(it->first)));
- } else {
- penalized = min(0., v+(acc_penalty - cumulative_penalties.get(it->first)));
- }
- it->second = penalized;
- cumulative_penalties.set_value(it->first, cumulative_penalties.get(it->first)+penalized);
- }
- }
+ SparseVector<weight_t> updates;
+ if (structured)
+ num_up += CollectUpdatesStruct(samples, updates);
+ else
+ num_up += CollectUpdates(samples, updates, margin);
+ SparseVector<weight_t> lambdas_copy;
+ if (l1_reg)
+ lambdas_copy = lambdas;
+ lambdas.plus_eq_v_times_s(updates, eta);
+
+ // update context for approx. BLEU
+ if (score_name == "chiang") {
+ for (auto it: *samples) {
+ if (it.rank == 0) {
+ scorer->UpdateContext(it.w, buf_ngs[i], buf_ls[i], 0.9);
+ break;
}
}
+ }
- if (ki==repeat-1) { // done
- kbest_loss_last = kbest_loss;
- if (repeat > 1) {
- score_t best_model = -std::numeric_limits<score_t>::max();
- unsigned best_idx = 0;
- for (unsigned i=0; i < samples->size(); i++) {
- score_t s = lambdas.dot((*samples)[i].f);
- if (s > best_model) {
- best_idx = i;
- best_model = s;
- }
+ // l1 regularization
+ // NB: regularization is done after each sentence,
+ // not after every single pair!
+ if (l1_reg) {
+ SparseVector<weight_t>::iterator it = lambdas.begin();
+ for (; it != lambdas.end(); ++it) {
+ weight_t v = it->second;
+ if (!v)
+ continue;
+ if (!lambdas_copy.get(it->first) // new or..
+ || lambdas_copy.get(it->first)!=v) // updated feature
+ {
+ if (v > 0) {
+ it->second = max(0., v - l1_reg);
+ } else {
+ it->second = min(0., v + l1_reg);
}
- score_sum += (*samples)[best_idx].score;
- model_sum += best_model;
}
}
- } // repeat
-
- if ((kbest_loss_first - kbest_loss_last) >= 0) kbest_loss_improve++;
+ }
} // noup
- if (rescale) lambdas /= lambdas.l2norm();
-
- ++ii;
+ i++;
} // input loop
- if (t == 0) in_sz = ii; // remember size of input (# lines)
-
-
- if (batch) {
- lambdas.plus_eq_v_times_s(batch_updates, eta);
- if (gamma) lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs));
- batch_updates.clear();
+ if (t == 0)
+ input_sz = i; // remember size of input (# lines)
+
+ // update average
+ if (average)
+ w_average += lambdas;
+
+ // stats
+ weight_t gold_avg = gold_sum/(weight_t)input_sz;
+ cerr << _p << "WEIGHTS" << endl;
+ for (auto name: print_weights)
+ cerr << setw(18) << name << " = " << lambdas.get(FD::Convert(name)) << endl;
+ cerr << " ---" << endl;
+ cerr << _np << " 1best avg score: " << gold_avg*100;
+ cerr << _p << " (" << (gold_avg-gold_prev)*100 << ")" << endl;
+ cerr << " 1best avg model score: "
+ << model_sum/(weight_t)input_sz << endl;
+ cerr << " avg # updates: ";
+ cerr << _np << num_up/(float)input_sz << endl;
+ cerr << " non-0 feature count: " << lambdas.num_nonzero() << endl;
+ cerr << " avg f count: " << feature_count/(float)list_sz << endl;
+ cerr << " avg list sz: " << list_sz/(float)input_sz << endl;
+
+ if (gold_avg > best) {
+ best = gold_avg;
+ best_iteration = t;
}
+ gold_prev = gold_avg;
- if (average) w_average += lambdas;
-
- if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset();
-
- // print some stats
- score_t score_avg = score_sum/(score_t)in_sz;
- score_t model_avg = model_sum/(score_t)in_sz;
- score_t score_diff, model_diff;
- if (t > 0) {
- score_diff = score_avg - all_scores[t-1].first;
- model_diff = model_avg - all_scores[t-1].second;
- } else {
- score_diff = score_avg;
- model_diff = model_avg;
- }
-
- unsigned nonz = 0;
- if (!quiet) nonz = (unsigned)lambdas.num_nonzero();
-
- if (!quiet) {
- cerr << _p5 << _p << "WEIGHTS" << endl;
- for (vector<string>::iterator it = print_weights.begin(); it != print_weights.end(); it++) {
- cerr << setw(18) << *it << " = " << lambdas.get(FD::Convert(*it)) << endl;
- }
- cerr << " ---" << endl;
- cerr << _np << " 1best avg score: " << score_avg;
- cerr << _p << " (" << score_diff << ")" << endl;
- cerr << _np << " 1best avg model score: " << model_avg;
- cerr << _p << " (" << model_diff << ")" << endl;
- cerr << " avg # pairs: ";
- cerr << _np << npairs/(float)in_sz << endl;
- cerr << " avg # rank err: ";
- cerr << rank_errors/(float)in_sz;
- if (faster_perceptron) cerr << " (meaningless)";
- cerr << endl;
- cerr << " avg # margin viol: ";
- cerr << margin_violations/(float)in_sz << endl;
- if (batch) cerr << " batch loss: " << batch_loss << endl;
- cerr << " k-best loss imp: " << ((float)kbest_loss_improve/in_sz)*100 << "%" << endl;
- cerr << " non0 feature count: " << nonz << endl;
- cerr << " avg list sz: " << list_sz/(float)in_sz << endl;
- cerr << " avg f count: " << f_count/(float)list_sz << endl;
- }
-
- pair<score_t,score_t> remember;
- remember.first = score_avg;
- remember.second = model_avg;
- all_scores.push_back(remember);
- if (score_avg > max_score) {
- max_score = score_avg;
- best_it = t;
- }
time (&end);
- float time_diff = difftime(end, start);
- overall_time += time_diff;
- if (!quiet) {
- cerr << _p2 << _np << "(time " << time_diff/60. << " min, ";
- cerr << time_diff/in_sz << " s/S)" << endl;
- }
- if (t+1 != T && !quiet) cerr << endl;
-
- if (noup) break;
+ time_t time_diff = difftime(end, start);
+ total_time += time_diff;
+ cerr << "(time " << time_diff/60. << " min, ";
+ cerr << time_diff/input_sz << " s/S)" << endl;
+ if (t+1 != T) cerr << endl;
- // write weights to file
- if (select_weights == "best" || keep) {
+ if (keep) { // keep intermediate weights
lambdas.init_vector(&decoder_weights);
string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz";
Weights::WriteToFile(w_fn, decoder_weights, true);
}
- if (check) cout << "---" << endl;
-
} // outer loop
- if (average) w_average /= (weight_t)T;
-
- if (!noup) {
- if (!quiet) cerr << endl << "Writing weights file to '" << output_fn << "' ..." << endl;
- if (select_weights == "last" || average) { // last, average
- WriteFile of(output_fn); // works with '-'
- ostream& o = *of.stream();
- o.precision(17);
- o << _np;
- if (average) {
- for (SparseVector<weight_t>::iterator it = w_average.begin(); it != w_average.end(); ++it) {
- if (it->second == 0) continue;
- o << FD::Convert(it->first) << '\t' << it->second << endl;
- }
- } else {
- for (SparseVector<weight_t>::iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
- if (it->second == 0) continue;
- o << FD::Convert(it->first) << '\t' << it->second << endl;
- }
- }
- } else if (select_weights == "VOID") { // do nothing with the weights
- } else { // best
- if (output_fn != "-") {
- CopyFile("weights."+boost::lexical_cast<string>(best_it)+".gz", output_fn);
- } else {
- ReadFile bestw("weights."+boost::lexical_cast<string>(best_it)+".gz");
- string o;
- cout.precision(17);
- cout << _np;
- while(getline(*bestw, o)) cout << o << endl;
- }
- if (!keep) {
- for (unsigned i = 0; i < T; i++) {
- string s = "weights." + boost::lexical_cast<string>(i) + ".gz";
- unlink(s.c_str());
- }
- }
- }
- if (!quiet) cerr << "done" << endl;
+ // final weights
+ if (average) {
+ w_average /= T;
+ w_average.init_vector(decoder_weights);
+ } else if (!keep) {
+ lambdas.init_vector(decoder_weights);
}
+ if (average || !keep)
+ Weights::WriteToFile(output_fn, decoder_weights, true);
- if (!quiet) {
- cerr << _p5 << _np << endl << "---" << endl << "Best iteration: ";
- cerr << best_it+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl;
- cerr << "This took " << overall_time/60. << " min." << endl;
- }
+ cerr << endl << "---" << endl << "Best iteration: ";
+ cerr << best_iteration+1 << " [GOLD = " << best*100 << "]." << endl;
+ cerr << "This took " << total_time/60. << " min." << endl;
+
+ return 0;
}
diff --git a/training/dtrain/dtrain.h b/training/dtrain/dtrain.h
index 07bd9b65..0bbb5c9b 100644
--- a/training/dtrain/dtrain.h
+++ b/training/dtrain/dtrain.h
@@ -1,9 +1,6 @@
#ifndef _DTRAIN_H_
#define _DTRAIN_H_
-#define DTRAIN_DOTS 10 // after how many inputs to display a '.'
-#define DTRAIN_SCALE 100000
-
#include <iomanip>
#include <climits>
#include <string.h>
@@ -25,113 +22,92 @@ namespace po = boost::program_options;
namespace dtrain
{
-
-inline void register_and_convert(const vector<string>& strs, vector<WordID>& ids)
-{
- vector<string>::const_iterator it;
- for (it = strs.begin(); it < strs.end(); it++)
- ids.push_back(TD::Convert(*it));
-}
-
-inline string gettmpf(const string path, const string infix)
-{
- char fn[path.size() + infix.size() + 8];
- strcpy(fn, path.c_str());
- strcat(fn, "/");
- strcat(fn, infix.c_str());
- strcat(fn, "-XXXXXX");
- if (!mkstemp(fn)) {
- cerr << "Cannot make temp file in" << path << " , exiting." << endl;
- exit(1);
- }
- return string(fn);
-}
-
-typedef double score_t;
-
struct ScoredHyp
{
- vector<WordID> w;
- SparseVector<double> f;
- score_t model;
- score_t score;
- unsigned rank;
+ vector<WordID> w;
+ SparseVector<weight_t> f;
+ weight_t model, gold;
+ size_t rank;
};
-struct LocalScorer
+inline void
+PrintWordIDVec(vector<WordID>& v, ostream& os=cerr)
{
- unsigned N_;
- vector<score_t> w_;
-
- virtual score_t
- Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned rank, const unsigned src_len)=0;
-
- virtual void Reset() {} // only for ApproxBleuScorer, LinearBleuScorer
-
- inline void
- Init(unsigned N, vector<score_t> weights)
- {
- assert(N > 0);
- N_ = N;
- if (weights.empty()) for (unsigned i = 0; i < N_; i++) w_.push_back(1./N_);
- else w_ = weights;
- }
-
- inline score_t
- brevity_penalty(const unsigned hyp_len, const unsigned ref_len)
- {
- if (hyp_len > ref_len) return 1;
- return exp(1 - (score_t)ref_len/hyp_len);
+ for (size_t i = 0; i < v.size(); i++) {
+ os << TD::Convert(v[i]);
+ if (i < v.size()-1) os << " ";
}
-};
+}
-struct HypSampler : public DecoderObserver
-{
- LocalScorer* scorer_;
- vector<WordID>* ref_;
- unsigned f_count_, sz_;
- virtual vector<ScoredHyp>* GetSamples()=0;
- inline void SetScorer(LocalScorer* scorer) { scorer_ = scorer; }
- inline void SetRef(vector<WordID>& ref) { ref_ = &ref; }
- inline unsigned get_f_count() { return f_count_; }
- inline unsigned get_sz() { return sz_; }
-};
+inline ostream& _np(ostream& out) { return out << resetiosflags(ios::showpos); }
+inline ostream& _p(ostream& out) { return out << setiosflags(ios::showpos); }
+inline ostream& _p4(ostream& out) { return out << setprecision(4); }
-struct HSReporter
+bool
+dtrain_init(int argc, char** argv, po::variables_map* conf)
{
- string task_id_;
-
- HSReporter(string task_id) : task_id_(task_id) {}
-
- inline void update_counter(string name, unsigned amount) {
- cerr << "reporter:counter:" << task_id_ << "," << name << "," << amount << endl;
+ po::options_description opts("Configuration File Options");
+ opts.add_options()
+ ("bitext,b", po::value<string>(), "bitext")
+ ("decoder_conf,C", po::value<string>(), "configuration file for decoder")
+ ("iterations,T", po::value<size_t>()->default_value(15), "number of iterations T (per shard)")
+ ("k", po::value<size_t>()->default_value(100), "size of kbest list")
+ ("learning_rate,l", po::value<weight_t>()->default_value(0.00001), "learning rate")
+ ("l1_reg,r", po::value<weight_t>()->default_value(0.), "l1 regularization strength")
+ ("margin,m", po::value<weight_t>()->default_value(1.0), "margin for margin perceptron")
+ ("score,s", po::value<string>()->default_value("chiang"), "per-sentence BLEU approx.")
+ ("N", po::value<size_t>()->default_value(4), "N for BLEU approximation")
+ ("input_weights,w", po::value<string>(), "input weights file")
+ ("average,a", po::bool_switch()->default_value(true), "output average weights")
+ ("keep,K", po::bool_switch()->default_value(false), "output a weight file per iteration")
+ ("struct,S", po::bool_switch()->default_value(false), "structured SGD with hope/fear")
+ ("output,o", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT")
+ ("disable_learning,X", po::bool_switch()->default_value(false), "disable learning")
+ ("output_data,D", po::value<string>()->default_value(""), "output data to STDOUT; arg. is 'kbest', 'default' or 'all'")
+ ("print_weights,P", po::value<string>()->default_value("EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV"),
+ "list of weights to print after each iteration");
+ po::options_description clopts("Command Line Options");
+ clopts.add_options()
+ ("conf,c", po::value<string>(), "dtrain configuration file")
+ ("help,h", po::bool_switch(), "display options");
+ opts.add(clopts);
+ po::store(parse_command_line(argc, argv, opts), *conf);
+ cerr << "dtrain" << endl << endl;
+ if ((*conf)["help"].as<bool>()) {
+ cerr << opts << endl;
+
+ return false;
}
- inline void update_gcounter(string name, unsigned amount) {
- cerr << "reporter:counter:Global," << name << "," << amount << endl;
+ if (conf->count("conf")) {
+ ifstream f((*conf)["conf"].as<string>().c_str());
+ po::store(po::parse_config_file(f, opts), *conf);
}
-};
+ po::notify(*conf);
+ if (!conf->count("decoder_conf")) {
+ cerr << "Missing decoder configuration." << endl;
+ cerr << opts << endl;
-inline ostream& _np(ostream& out) { return out << resetiosflags(ios::showpos); }
-inline ostream& _p(ostream& out) { return out << setiosflags(ios::showpos); }
-inline ostream& _p2(ostream& out) { return out << setprecision(2); }
-inline ostream& _p5(ostream& out) { return out << setprecision(5); }
+ return false;
+ }
+ if (!conf->count("bitext")) {
+ cerr << "No input given." << endl;
+ cerr << opts << endl;
-inline void printWordIDVec(vector<WordID>& v, ostream& os=cerr)
-{
- for (unsigned i = 0; i < v.size(); i++) {
- os << TD::Convert(v[i]);
- if (i < v.size()-1) os << " ";
+ return false;
+ }
+ if ((*conf)["output_data"].as<string>() != "") {
+ if ((*conf)["output_data"].as<string>() != "kbest" &&
+ (*conf)["output_data"].as<string>() != "default" &&
+ (*conf)["output_data"].as<string>() != "all") {
+ cerr << "Wrong 'output_data' argument: ";
+ cerr << (*conf)["output_data"].as<string>() << endl;
+ return false;
+ }
}
-}
-template<typename T>
-inline T sign(T z)
-{
- if (z == 0) return 0;
- return z < 0 ? -1 : +1;
+ return true;
}
-
} // namespace
#endif
diff --git a/training/dtrain/lplp.rb b/training/dtrain/lplp.rb
index 86e835e8..cf28b477 100755
--- a/training/dtrain/lplp.rb
+++ b/training/dtrain/lplp.rb
@@ -1,4 +1,4 @@
-# lplp.rb
+#!/usr/bin/env ruby
# norms
def l0(feature_column, n)
@@ -19,7 +19,8 @@ end
# stats
def median(feature_column, n)
- return feature_column.concat(0.step(n-feature_column.size-1).map{|i|0}).sort[feature_column.size/2]
+ return feature_column.concat(0.step(n-feature_column.size-1).map{|i|0})
+ .sort[feature_column.size/2]
end
def mean(feature_column, n)
@@ -28,7 +29,7 @@ end
# selection
def select_k(weights, norm_fun, n, k=10000)
- weights.sort{|a,b| norm_fun.call(b[1], n) <=> norm_fun.call(a[1], n)}.each { |p|
+ weights.sort{|a,b| norm_fun.call(b[1], n)<=>norm_fun.call(a[1], n)}.each { |p|
puts "#{p[0]}\t#{mean(p[1], n)}"
k -= 1
if k == 0 then break end
@@ -84,17 +85,16 @@ def _test()
end
#_test()
-
def usage()
puts "lplp.rb <l0,l1,l2,linfty,mean,median> <cut|select_k> <k|threshold> <#shards> < <input>"
puts " l0...: norms for selection"
puts "select_k: only output top k (according to the norm of their column vector) features"
puts " cut: output features with weight >= threshold"
- puts " n: if we do not have a shard count use this number for averaging"
+ puts " n: number of shards for averaging"
exit 1
end
-if ARGV.size < 4 then usage end
+usage if ARGV.size<4
norm_fun = method(ARGV[0].to_sym)
type = ARGV[1]
x = ARGV[2].to_f
diff --git a/training/dtrain/parallelize.rb b/training/dtrain/parallelize.rb
index 82600009..128f3f98 100755
--- a/training/dtrain/parallelize.rb
+++ b/training/dtrain/parallelize.rb
@@ -1,155 +1,134 @@
#!/usr/bin/env ruby
require 'trollop'
+require 'zipf'
-def usage
- STDERR.write "Usage: "
- STDERR.write "ruby parallelize.rb -c <dtrain.ini> [-e <epochs=10>] [--randomize/-z] [--reshard/-y] -s <#shards|0> [-p <at once=9999>] -i <input> -r <refs> [--qsub/-q] [--dtrain_binary <path to dtrain binary>] [-l \"l2 select_k 100000\"] [--extra_qsub \"-l virtual_free=24G\"]\n"
- exit 1
+conf = Trollop::options do
+ opt :conf, "dtrain configuration", :type => :string, :short => '-c'
+ opt :input, "input as bitext (f ||| e)", :type => :string, :short => '-i'
+ opt :epochs, "number of epochs", :type => :int, :default => 10
+ opt :randomize, "randomize shards once", :type => :bool, :default => false, :short => '-z'
+ opt :reshard, "randomize after each epoch", :type => :bool, :default => false, :short => '-y'
+ opt :shards, "number of shards", :type => :int, :short => '-s'
+ opt :weights, "input weights for first epoch", :type => :string, :default => '', :short => '-w'
+ opt :lplp_args, "arguments for lplp.rb", :type => :string, :default => "l2 select_k 100000", :short => '-l'
+ opt :per_shard_decoder_configs, "give custom decoder config per shard", :type => :string, :short => '-o'
+ opt :processes_at_once, "jobs to run at oce", :type => :int, :default => 9999, :short => '-p'
+ opt :qsub, "use qsub", :type => :bool, :default => false, :short => '-q'
+ opt :qsub_args, "extra args for qsub", :type => :string, :default => "h_vmem=5G", :short => 'r'
+ opt :dtrain_binary, "path to dtrain binary", :type => :string, :short => '-d'
end
-opts = Trollop::options do
- opt :config, "dtrain config file", :type => :string
- opt :epochs, "number of epochs", :type => :int, :default => 10
- opt :lplp_args, "arguments for lplp.rb", :type => :string, :default => "l2 select_k 100000"
- opt :randomize, "randomize shards before each epoch", :type => :bool, :short => '-z', :default => false
- opt :reshard, "reshard after each epoch", :type => :bool, :short => '-y', :default => false
- opt :shards, "number of shards", :type => :int
- opt :processes_at_once, "have this number (max) running at the same time", :type => :int, :default => 9999
- opt :input, "input", :type => :string
- opt :references, "references", :type => :string
- opt :qsub, "use qsub", :type => :bool, :default => false
- opt :dtrain_binary, "path to dtrain binary", :type => :string
- opt :extra_qsub, "extra qsub args", :type => :string, :default => ""
- opt :per_shard_decoder_configs, "give special decoder config per shard", :type => :string, :short => '-o'
- opt :first_input_weights, "input weights for first iter", :type => :string, :default => '', :short => '-w'
-end
-usage if not opts[:config]&&opts[:shards]&&opts[:input]&&opts[:references]
-
dtrain_dir = File.expand_path File.dirname(__FILE__)
-if not opts[:dtrain_binary]
+if not conf[:dtrain_binary]
dtrain_bin = "#{dtrain_dir}/dtrain"
else
- dtrain_bin = opts[:dtrain_binary]
+ dtrain_bin = conf[:dtrain_binary]
end
-ruby = '/usr/bin/ruby'
lplp_rb = "#{dtrain_dir}/lplp.rb"
-lplp_args = opts[:lplp_args]
-cat = '/bin/cat'
+lplp_args = conf[:lplp_args]
-ini = opts[:config]
-epochs = opts[:epochs]
-rand = opts[:randomize]
-reshard = opts[:reshard]
-predefined_shards = false
+dtrain_conf = conf[:conf]
+epochs = conf[:epochs]
+rand = conf[:randomize]
+reshard = conf[:reshard]
+predefined_shards = false
per_shard_decoder_configs = false
-if opts[:shards] == 0
+if conf[:shards] == 0
predefined_shards = true
num_shards = 0
- per_shard_decoder_configs = true if opts[:per_shard_decoder_configs]
+ per_shard_decoder_configs = true if conf[:per_shard_decoder_configs]
else
- num_shards = opts[:shards]
+ num_shards = conf[:shards]
end
-input = opts[:input]
-refs = opts[:references]
-use_qsub = opts[:qsub]
-shards_at_once = opts[:processes_at_once]
-first_input_weights = opts[:first_input_weights]
-opts[:extra_qsub] = "-l #{opts[:extra_qsub]}" if opts[:extra_qsub]!=""
+input = conf[:input]
+use_qsub = conf[:qsub]
+shards_at_once = conf[:processes_at_once]
+first_input_weights = conf[:weights]
`mkdir work`
-def make_shards(input, refs, num_shards, epoch, rand)
+def make_shards input, num_shards, epoch, rand
lc = `wc -l #{input}`.split.first.to_i
index = (0..lc-1).to_a
index.reverse!
index.shuffle! if rand
shard_sz = (lc / num_shards.to_f).round 0
leftover = lc - (num_shards*shard_sz)
- leftover = 0 if leftover < 0
+ leftover = [0, leftover].max
in_f = File.new input, 'r'
in_lines = in_f.readlines
- refs_f = File.new refs, 'r'
- refs_lines = refs_f.readlines
shard_in_files = []
- shard_refs_files = []
in_fns = []
- refs_fns = []
- new_num_shards = 0
+ real_num_shards = 0
0.upto(num_shards-1) { |shard|
break if index.size==0
- new_num_shards += 1
- in_fn = "work/shard.#{shard}.#{epoch}.in"
+ real_num_shards += 1
+ in_fn = "work/shard.#{shard}.#{epoch}"
shard_in = File.new in_fn, 'w+'
in_fns << in_fn
- refs_fn = "work/shard.#{shard}.#{epoch}.refs"
- shard_refs = File.new refs_fn, 'w+'
- refs_fns << refs_fn
0.upto(shard_sz-1) { |i|
j = index.pop
+ break if !j
shard_in.write in_lines[j]
- shard_refs.write refs_lines[j]
}
shard_in_files << shard_in
- shard_refs_files << shard_refs
}
while leftover > 0
j = index.pop
shard_in_files[-1].write in_lines[j]
- shard_refs_files[-1].write refs_lines[j]
leftover -= 1
end
- (shard_in_files + shard_refs_files).each do |f| f.close end
+ shard_in_files.each do |f| f.close end
in_f.close
- refs_f.close
- return in_fns, refs_fns, new_num_shards
+ return in_fns, real_num_shards
end
input_files = []
-refs_files = []
if predefined_shards
- input_files = File.new(input).readlines.map {|i| i.strip }
- refs_files = File.new(refs).readlines.map {|i| i.strip }
+ input_files = File.new(input).readlines.map { |i| i.strip }
if per_shard_decoder_configs
- decoder_configs = File.new(opts[:per_shard_decoder_configs]).readlines.map {|i| i.strip}
+ decoder_configs = ReadFile.readlines_strip(conf[:per_shard_decoder_configs]
+ ).map { |i| i.strip }
end
num_shards = input_files.size
else
- input_files, refs_files, num_shards = make_shards input, refs, num_shards, 0, rand
+ input_files, num_shards = make_shards input, num_shards, 0, rand
end
0.upto(epochs-1) { |epoch|
puts "epoch #{epoch+1}"
pids = []
input_weights = ''
- if epoch > 0 then input_weights = "--input_weights work/weights.#{epoch-1}" end
+ input_weights = "--input_weights work/weights.#{epoch-1}" if epoch>0
weights_files = []
shard = 0
remaining_shards = num_shards
while remaining_shards > 0
shards_at_once.times {
break if remaining_shards==0
- qsub_str_start = qsub_str_end = ''
- local_end = ''
+ qsub_str_start = qsub_str_end = local_end = ''
if use_qsub
- qsub_str_start = "qsub #{opts[:extra_qsub]} -cwd -sync y -b y -j y -o work/out.#{shard}.#{epoch} -N dtrain.#{shard}.#{epoch} \""
+ qsub_str_start = "qsub -l #{conf[:qsub_args]} -cwd -sync y -b y -j y\
+ -o work/out.#{shard}.#{epoch}\
+ -N dtrain.#{shard}.#{epoch} \""
qsub_str_end = "\""
local_end = ''
else
local_end = "2>work/out.#{shard}.#{epoch}"
end
if per_shard_decoder_configs
- cdec_cfg = "--decoder_config #{decoder_configs[shard]}"
+ cdec_conf = "--decoder_conf #{decoder_configs[shard]}"
else
- cdec_cfg = ""
+ cdec_conf = ""
end
- if first_input_weights!='' && epoch == 0
+ if first_input_weights != '' && epoch == 0
input_weights = "--input_weights #{first_input_weights}"
end
pids << Kernel.fork {
- `#{qsub_str_start}#{dtrain_bin} -c #{ini} #{cdec_cfg} #{input_weights}\
- --input #{input_files[shard]}\
- --refs #{refs_files[shard]}\
+ `#{qsub_str_start}#{dtrain_bin} -c #{dtrain_conf} #{cdec_conf}\
+ #{input_weights}\
+ --bitext #{input_files[shard]}\
--output work/weights.#{shard}.#{epoch}#{qsub_str_end} #{local_end}`
}
weights_files << "work/weights.#{shard}.#{epoch}"
@@ -159,10 +138,11 @@ end
pids.each { |pid| Process.wait(pid) }
pids.clear
end
- `#{cat} work/weights.*.#{epoch} > work/weights_cat`
- `#{ruby} #{lplp_rb} #{lplp_args} #{num_shards} < work/weights_cat > work/weights.#{epoch}`
+ `cat work/weights.*.#{epoch} > work/weights_cat`
+ `ruby #{lplp_rb} #{lplp_args} #{num_shards} < work/weights_cat\
+ > work/weights.#{epoch}`
if rand and reshard and epoch+1!=epochs
- input_files, refs_files, num_shards = make_shards input, refs, num_shards, epoch+1, rand
+ input_files, num_shards = make_shards input, num_shards, epoch+1, rand
end
}
diff --git a/training/dtrain/sample.h b/training/dtrain/sample.h
new file mode 100644
index 00000000..1249e372
--- /dev/null
+++ b/training/dtrain/sample.h
@@ -0,0 +1,59 @@
+#ifndef _DTRAIN_SAMPLE_H_
+#define _DTRAIN_SAMPLE_H_
+
+#include "kbest.h"
+
+#include "score.h"
+
+namespace dtrain
+{
+
+struct ScoredKbest : public DecoderObserver
+{
+ const size_t k_;
+ size_t feature_count_, effective_sz_;
+ vector<ScoredHyp> samples_;
+ Scorer* scorer_;
+ vector<Ngrams>* ref_ngs_;
+ vector<size_t>* ref_ls_;
+
+ ScoredKbest(const size_t k, Scorer* scorer) :
+ k_(k), scorer_(scorer) {}
+
+ virtual void
+ NotifyTranslationForest(const SentenceMetadata& /*smeta*/, Hypergraph* hg)
+ {
+ samples_.clear(); effective_sz_ = feature_count_ = 0;
+ KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,
+ KBest::FilterUnique, prob_t, EdgeProb> kbest(*hg, k_);
+ for (size_t i = 0; i < k_; ++i) {
+ const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,
+ KBest::FilterUnique, prob_t, EdgeProb>::Derivation* d =
+ kbest.LazyKthBest(hg->nodes_.size() - 1, i);
+ if (!d) break;
+ ScoredHyp h;
+ h.w = d->yield;
+ h.f = d->feature_values;
+ h.model = log(d->score);
+ h.rank = i;
+ h.gold = scorer_->Score(h.w, *ref_ngs_, *ref_ls_);
+ samples_.push_back(h);
+ effective_sz_++;
+ feature_count_ += h.f.size();
+ }
+ }
+
+ vector<ScoredHyp>* GetSamples() { return &samples_; }
+ inline void SetReference(vector<Ngrams>& ngs, vector<size_t>& ls)
+ {
+ ref_ngs_ = &ngs;
+ ref_ls_ = &ls;
+ }
+ inline size_t GetFeatureCount() { return feature_count_; }
+ inline size_t GetSize() { return effective_sz_; }
+};
+
+} // namespace
+
+#endif
+
diff --git a/training/dtrain/score.h b/training/dtrain/score.h
index 1cdd3fa9..ca3da39b 100644
--- a/training/dtrain/score.h
+++ b/training/dtrain/score.h
@@ -6,35 +6,28 @@
namespace dtrain
{
-
struct NgramCounts
{
- unsigned N_;
- map<unsigned, score_t> clipped_;
- map<unsigned, score_t> sum_;
+ size_t N_;
+ map<size_t, weight_t> clipped_;
+ map<size_t, weight_t> sum_;
+
+ NgramCounts() {}
- NgramCounts(const unsigned N) : N_(N) { Zero(); }
+ NgramCounts(const size_t N) : N_(N) { Zero(); }
inline void
operator+=(const NgramCounts& rhs)
{
if (rhs.N_ > N_) Resize(rhs.N_);
- for (unsigned i = 0; i < N_; i++) {
+ for (size_t i = 0; i < N_; i++) {
this->clipped_[i] += rhs.clipped_.find(i)->second;
this->sum_[i] += rhs.sum_.find(i)->second;
}
}
- inline const NgramCounts
- operator+(const NgramCounts &other) const
- {
- NgramCounts result = *this;
- result += other;
- return result;
- }
-
inline void
- operator*=(const score_t rhs)
+ operator*=(const weight_t rhs)
{
for (unsigned i = 0; i < N_; i++) {
this->clipped_[i] *= rhs;
@@ -43,7 +36,7 @@ struct NgramCounts
}
inline void
- Add(const unsigned count, const unsigned ref_count, const unsigned i)
+ Add(const size_t count, const size_t ref_count, const size_t i)
{
assert(i < N_);
if (count > ref_count) {
@@ -57,40 +50,23 @@ struct NgramCounts
inline void
Zero()
{
- for (unsigned i = 0; i < N_; i++) {
+ for (size_t i = 0; i < N_; i++) {
clipped_[i] = 0.;
sum_[i] = 0.;
}
}
inline void
- One()
- {
- for (unsigned i = 0; i < N_; i++) {
- clipped_[i] = 1.;
- sum_[i] = 1.;
- }
- }
-
- inline void
- Print()
- {
- for (unsigned i = 0; i < N_; i++) {
- cout << i+1 << "grams (clipped):\t" << clipped_[i] << endl;
- cout << i+1 << "grams:\t\t\t" << sum_[i] << endl;
- }
- }
-
- inline void Resize(unsigned N)
+ Resize(size_t N)
{
if (N == N_) return;
else if (N > N_) {
- for (unsigned i = N_; i < N; i++) {
+ for (size_t i = N_; i < N; i++) {
clipped_[i] = 0.;
sum_[i] = 0.;
}
} else { // N < N_
- for (unsigned i = N_-1; i > N-1; i--) {
+ for (size_t i = N_-1; i > N-1; i--) {
clipped_.erase(i);
sum_.erase(i);
}
@@ -99,123 +75,344 @@ struct NgramCounts
}
};
-typedef map<vector<WordID>, unsigned> Ngrams;
+typedef map<vector<WordID>, size_t> Ngrams;
inline Ngrams
-make_ngrams(const vector<WordID>& s, const unsigned N)
+MakeNgrams(const vector<WordID>& s, const size_t N)
{
Ngrams ngrams;
vector<WordID> ng;
for (size_t i = 0; i < s.size(); i++) {
ng.clear();
- for (unsigned j = i; j < min(i+N, s.size()); j++) {
+ for (size_t j = i; j < min(i+N, s.size()); j++) {
ng.push_back(s[j]);
ngrams[ng]++;
}
}
+
return ngrams;
}
inline NgramCounts
-make_ngram_counts(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned N)
+MakeNgramCounts(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref,
+ const size_t N)
{
- Ngrams hyp_ngrams = make_ngrams(hyp, N);
- Ngrams ref_ngrams = make_ngrams(ref, N);
+ Ngrams hyp_ngrams = MakeNgrams(hyp, N);
NgramCounts counts(N);
- Ngrams::iterator it;
- Ngrams::iterator ti;
+ Ngrams::iterator it, ti;
for (it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++) {
- ti = ref_ngrams.find(it->first);
- if (ti != ref_ngrams.end()) {
- counts.Add(it->second, ti->second, it->first.size() - 1);
- } else {
- counts.Add(it->second, 0, it->first.size() - 1);
+ size_t max_ref_count = 0;
+ for (auto r: ref) {
+ ti = r.find(it->first);
+ if (ti != r.end())
+ max_ref_count = max(max_ref_count, ti->second);
}
+ counts.Add(it->second, min(it->second, max_ref_count), it->first.size()-1);
}
+
return counts;
}
-struct BleuScorer : public LocalScorer
+class Scorer
{
- score_t Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref_len);
- score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
- void Reset() {}
-};
+ protected:
+ const size_t N_;
+ vector<weight_t> w_;
+
+ public:
+ Scorer(size_t n): N_(n)
+ {
+ for (size_t i = 1; i <= N_; i++)
+ w_.push_back(1.0/N_);
+ }
-struct StupidBleuScorer : public LocalScorer
-{
- score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
- void Reset() {}
-};
+ inline bool
+ Init(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls,
+ size_t& hl,
+ size_t& rl,
+ size_t& M,
+ vector<weight_t>& v,
+ NgramCounts& counts)
+ {
+ hl = hyp.size();
+ if (hl == 0) return false;
+ rl = BestMatchLength(hl, ref_ls);
+ if (rl == 0) return false;
+ counts = MakeNgramCounts(hyp, ref_ngs, N_);
+ if (rl < N_) {
+ M = rl;
+ for (size_t i = 0; i < M; i++) v.push_back(1/((weight_t)M));
+ } else {
+ M = N_;
+ v = w_;
+ }
-struct FixedStupidBleuScorer : public LocalScorer
-{
- score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
- void Reset() {}
-};
+ return true;
+ }
-struct SmoothBleuScorer : public LocalScorer
-{
- score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
- void Reset() {}
-};
+ inline weight_t
+ BrevityPenalty(const size_t hl, const size_t rl)
+ {
+ if (hl > rl)
+ return 1;
-struct SumBleuScorer : public LocalScorer
-{
- score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
- void Reset() {}
+ return exp(1 - (weight_t)rl/hl);
+ }
+
+ inline size_t
+ BestMatchLength(const size_t hl,
+ const vector<size_t>& ref_ls)
+ {
+ size_t m;
+ if (ref_ls.size() == 1) {
+ m = ref_ls.front();
+ } else {
+ size_t i = 0, best_idx = 0;
+ size_t best = numeric_limits<size_t>::max();
+ for (auto l: ref_ls) {
+ size_t d = abs(hl-l);
+ if (d < best) {
+ best_idx = i;
+ best = d;
+ }
+ i += 1;
+ }
+ m = ref_ls[best_idx];
+ }
+
+ return m;
+ }
+
+ virtual weight_t
+ Score(const vector<WordID>&,
+ const vector<Ngrams>&,
+ const vector<size_t>&) = 0;
+
+ void
+ UpdateContext(const vector<WordID>& /*hyp*/,
+ const vector<Ngrams>& /*ref_ngs*/,
+ const vector<size_t>& /*ref_ls*/,
+ weight_t /*decay*/) {}
};
-struct SumExpBleuScorer : public LocalScorer
+/*
+ * 'fixed' per-sentence BLEU
+ * simply add 1 to reference length for calculation of BP
+ *
+ * as in "Optimizing for Sentence-Level BLEU+1
+ * Yields Short Translations"
+ * (Nakov et al. '12)
+ *
+ */
+class PerSentenceBleuScorer : public Scorer
{
- score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
- void Reset() {}
+ public:
+ PerSentenceBleuScorer(size_t n) : Scorer(n) {}
+
+ weight_t
+ Score(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls)
+ {
+ size_t hl, rl, M;
+ vector<weight_t> v;
+ NgramCounts counts;
+ if (!Init(hyp, ref_ngs, ref_ls, hl, rl, M, v, counts))
+ return 0.;
+ weight_t sum=0, add=0;
+ for (size_t i = 0; i < M; i++) {
+ if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.;
+ if (i > 0) add = 1;
+ sum += v[i] * log(((weight_t)counts.clipped_[i] + add)
+ / ((counts.sum_[i] + add)));
+ }
+
+ return BrevityPenalty(hl, rl+1) * exp(sum);
+ }
};
-struct SumWhateverBleuScorer : public LocalScorer
+
+/*
+ * BLEU
+ * 0 if for one n \in {1..N} count is 0
+ *
+ * as in "BLEU: a Method for Automatic Evaluation
+ * of Machine Translation"
+ * (Papineni et al. '02)
+ *
+ */
+
+class BleuScorer : public Scorer
{
- score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
- void Reset() {};
+ public:
+ BleuScorer(size_t n) : Scorer(n) {}
+
+ weight_t
+ Score(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls)
+ {
+ size_t hl, rl, M;
+ vector<weight_t> v;
+ NgramCounts counts;
+ if (!Init(hyp, ref_ngs, ref_ls, hl, rl, M, v, counts))
+ return 0.;
+ weight_t sum = 0;
+ for (size_t i = 0; i < M; i++) {
+ if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) return 0.;
+ sum += v[i] * log((weight_t)counts.clipped_[i]/counts.sum_[i]);
+ }
+
+ return BrevityPenalty(hl, rl) * exp(sum);
+ }
};
-struct ApproxBleuScorer : public BleuScorer
+/*
+ * original BLEU+1
+ * 0 iff no 1gram match ('grounded')
+ *
+ * as in "ORANGE: a Method for Evaluating
+ * Automatic Evaluation Metrics
+ * for Machine Translation"
+ * (Lin & Och '04)
+ *
+ */
+class OriginalPerSentenceBleuScorer : public Scorer
{
- NgramCounts glob_onebest_counts_;
- unsigned glob_hyp_len_, glob_ref_len_, glob_src_len_;
- score_t discount_;
+ public:
+ OriginalPerSentenceBleuScorer(size_t n) : Scorer(n) {}
+
+ weight_t
+ Score(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls)
+ {
+ size_t hl, rl, M;
+ vector<weight_t> v;
+ NgramCounts counts;
+ if (!Init(hyp, ref_ngs, ref_ls, hl, rl, M, v, counts))
+ return 0.;
+ weight_t sum=0, add=0;
+ for (size_t i = 0; i < M; i++) {
+ if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.;
+ if (i == 1) add = 1;
+ sum += v[i] * log(((weight_t)counts.clipped_[i] + add)/((counts.sum_[i] + add)));
+ }
- ApproxBleuScorer(unsigned N, score_t d) : glob_onebest_counts_(NgramCounts(N)), discount_(d)
- {
- glob_hyp_len_ = glob_ref_len_ = glob_src_len_ = 0;
- }
+ return BrevityPenalty(hl, rl) * exp(sum);
+ }
+};
- inline void Reset() {
- glob_onebest_counts_.Zero();
- glob_hyp_len_ = glob_ref_len_ = glob_src_len_ = 0.;
- }
+/*
+ * smooth BLEU
+ * max is 0.9375 (with N=4)
+ *
+ * as in "An End-to-End Discriminative Approach
+ * to Machine Translation"
+ * (Liang et al. '06)
+ *
+ */
+class SmoothPerSentenceBleuScorer : public Scorer
+{
+ public:
+ SmoothPerSentenceBleuScorer(size_t n) : Scorer(n) {}
+
+ weight_t
+ Score(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls)
+ {
+ size_t hl=hyp.size(), rl=BestMatchLength(hl, ref_ls);
+ if (hl == 0 || rl == 0) return 0.;
+ NgramCounts counts = MakeNgramCounts(hyp, ref_ngs, N_);
+ size_t M = N_;
+ if (rl < N_) M = rl;
+ weight_t sum = 0.;
+ vector<weight_t> i_bleu;
+ for (size_t i=0; i < M; i++)
+ i_bleu.push_back(0.);
+ for (size_t i=0; i < M; i++) {
+ if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) {
+ break;
+ } else {
+ weight_t i_score = log((weight_t)counts.clipped_[i]/counts.sum_[i]);
+ for (size_t j=i; j < M; j++) {
+ i_bleu[j] += (1/((weight_t)j+1)) * i_score;
+ }
+ }
+ sum += exp(i_bleu[i])/pow(2.0, (double)(N_-i+2));
+ }
- score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned rank, const unsigned src_len);
+ return BrevityPenalty(hl, hl) * sum;
+ }
};
-struct LinearBleuScorer : public BleuScorer
+/*
+ * approx. bleu
+ * Needs some more code in dtrain.cc .
+ * We do not scaling by source lengths, as hypotheses are compared only
+ * within an kbest list, not globally.
+ *
+ * as in "Online Large-Margin Training of Syntactic
+ * and Structural Translation Features"
+ * (Chiang et al. '08)
+ *
+
+ */
+class ApproxBleuScorer : public Scorer
{
- unsigned onebest_len_;
- NgramCounts onebest_counts_;
-
- LinearBleuScorer(unsigned N) : onebest_len_(1), onebest_counts_(N)
- {
- onebest_counts_.One();
- }
+ private:
+ NgramCounts context;
+ weight_t hyp_sz_sum;
+ weight_t ref_sz_sum;
+
+ public:
+ ApproxBleuScorer(size_t n) :
+ Scorer(n), context(n), hyp_sz_sum(0), ref_sz_sum(0) {}
+
+ weight_t
+ Score(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls)
+ {
+ size_t hl, rl, M;
+ vector<weight_t> v;
+ NgramCounts counts;
+ if (!Init(hyp, ref_ngs, ref_ls, hl, rl, M, v, counts))
+ return 0.;
+ counts += context;
+ weight_t sum = 0;
+ for (size_t i = 0; i < M; i++) {
+ if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) return 0.;
+ sum += v[i] * log((weight_t)counts.clipped_[i]/counts.sum_[i]);
+ }
- score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned rank, const unsigned /*src_len*/);
+ return BrevityPenalty(hyp_sz_sum+hl, ref_sz_sum+rl) * exp(sum);
+ }
- inline void Reset() {
- onebest_len_ = 1;
- onebest_counts_.One();
- }
+ void
+ UpdateContext(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls,
+ weight_t decay=0.9)
+ {
+ size_t hl, rl, M;
+ vector<weight_t> v;
+ NgramCounts counts;
+ Init(hyp, ref_ngs, ref_ls, hl, rl, M, v, counts);
+
+ context += counts;
+ context *= decay;
+ hyp_sz_sum += hl;
+ hyp_sz_sum *= decay;
+ ref_sz_sum += rl;
+ ref_sz_sum *= decay;
+ }
};
-
} // namespace
#endif
diff --git a/training/dtrain/update.h b/training/dtrain/update.h
new file mode 100644
index 00000000..83dc3186
--- /dev/null
+++ b/training/dtrain/update.h
@@ -0,0 +1,150 @@
+#ifndef _DTRAIN_UPDATE_H_
+#define _DTRAIN_UPDATE_H_
+
+namespace dtrain
+{
+
+bool
+_cmp(ScoredHyp a, ScoredHyp b)
+{
+ return a.gold > b.gold;
+}
+
+bool
+_cmpHope(ScoredHyp a, ScoredHyp b)
+{
+ return (a.model+a.gold) > (b.model+b.gold);
+}
+
+bool
+_cmpFear(ScoredHyp a, ScoredHyp b)
+{
+ return (a.model-a.gold) > (b.model-b.gold);
+}
+
+inline bool
+_good(ScoredHyp& a, ScoredHyp& b, weight_t margin)
+{
+ if ((a.model-b.model)>margin
+ || a.gold==b.gold)
+ return true;
+
+ return false;
+}
+
+inline bool
+_goodS(ScoredHyp& a, ScoredHyp& b)
+{
+ if (a.gold==b.gold)
+ return true;
+
+ return false;
+}
+
+/*
+ * multipartite ranking
+ * sort (descending) by bleu
+ * compare top X (hi) to middle Y (med) and low X (lo)
+ * cmp middle Y to low X
+ */
+inline size_t
+CollectUpdates(vector<ScoredHyp>* s,
+ SparseVector<weight_t>& updates,
+ weight_t margin=0.)
+{
+ size_t num_up = 0;
+ size_t sz = s->size();
+ sort(s->begin(), s->end(), _cmp);
+ size_t sep = round(sz*0.1);
+ for (size_t i = 0; i < sep; i++) {
+ for (size_t j = sep; j < sz; j++) {
+ if (_good((*s)[i], (*s)[j], margin))
+ continue;
+ updates += (*s)[i].f-(*s)[j].f;
+ num_up++;
+ }
+ }
+ size_t sep_lo = sz-sep;
+ for (size_t i = sep; i < sep_lo; i++) {
+ for (size_t j = sep_lo; j < sz; j++) {
+ if (_good((*s)[i], (*s)[j], margin))
+ continue;
+ updates += (*s)[i].f-(*s)[j].f;
+ num_up++;
+ }
+ }
+
+ return num_up;
+}
+
+inline size_t
+CollectUpdatesStruct(vector<ScoredHyp>* s,
+ SparseVector<weight_t>& updates,
+ weight_t unused=-1)
+{
+ // hope
+ sort(s->begin(), s->end(), _cmpHope);
+ ScoredHyp hope = (*s)[0];
+ // fear
+ sort(s->begin(), s->end(), _cmpFear);
+ ScoredHyp fear = (*s)[0];
+ if (!_goodS(hope, fear))
+ updates += hope.f - fear.f;
+
+ return updates.size();
+}
+
+inline void
+OutputKbest(vector<ScoredHyp>* s)
+{
+ sort(s->begin(), s->end(), _cmp);
+ size_t i = 0;
+ for (auto k: *s) {
+ cout << i << "\t" << k.gold << "\t" << k.model << " \t" << k.f << endl;
+ i++;
+ }
+}
+
+inline void
+OutputMultipartitePairs(vector<ScoredHyp>* s,
+ weight_t margin=0.,
+ bool all=true)
+{
+ size_t sz = s->size();
+ sort(s->begin(), s->end(), _cmp);
+ size_t sep = round(sz*0.1);
+ for (size_t i = 0; i < sep; i++) {
+ for (size_t j = sep; j < sz; j++) {
+ if (!all && _good((*s)[i], (*s)[j], margin))
+ continue;
+ cout << (*s)[i].f-(*s)[j].f << endl;
+ }
+ }
+ size_t sep_lo = sz-sep;
+ for (size_t i = sep; i < sep_lo; i++) {
+ for (size_t j = sep_lo; j < sz; j++) {
+ if (!all && _good((*s)[i], (*s)[j], margin))
+ continue;
+ cout << (*s)[i].f-(*s)[j].f << endl;
+ }
+ }
+}
+
+inline void
+OutputAllPairs(vector<ScoredHyp>* s)
+{
+ size_t sz = s->size();
+ sort(s->begin(), s->end(), _cmp);
+ for (size_t i = 0; i < sz-1; i++) {
+ for (size_t j = i+1; j < sz; j++) {
+ if ((*s)[i].gold == (*s)[j].gold)
+ continue;
+ cout << (*s)[i].f-(*s)[j].f << endl;
+ }
+ }
+}
+
+} // namespace
+
+#endif
+
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