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#ifndef _DTRAIN_H_
#define _DTRAIN_H_
#include <iomanip>
#include <climits>
#include <string.h>
#include <boost/algorithm/string.hpp>
#include <boost/regex.hpp>
#include <boost/algorithm/string/regex.hpp>
#include <boost/program_options.hpp>
#include "decoder.h"
#include "ff_register.h"
#include "sentence_metadata.h"
#include "verbose.h"
#include "viterbi.h"
using namespace std;
namespace po = boost::program_options;
namespace dtrain
{
struct Hyp
{
Hyp() {}
Hyp(vector<WordID> w, SparseVector<weight_t> f, weight_t model, weight_t gold,
size_t rank) : w(w), f(f), model(model), gold(gold), rank(rank) {}
vector<WordID> w;
SparseVector<weight_t> f;
weight_t model, gold;
size_t rank;
};
bool
dtrain_init(int argc,
char** argv,
po::variables_map* conf)
{
po::options_description opts("Configuration File Options");
opts.add_options()
("bitext,b", po::value<string>(),
"bitext, source and references in a single file [e ||| f]")
("decoder_conf,C", po::value<string>(),
"decoder configuration file")
("iterations,T", po::value<size_t>()->default_value(15),
"number of iterations T")
("k", po::value<size_t>()->default_value(100),
"sample size per input (e.g. size of k-best lists)")
("unique_kbest", po::bool_switch()->default_value(true),
"unique k-best lists")
("forest_sample", po::bool_switch()->default_value(false),
"sample k hyptheses from forest instead of using k-best list")
("learning_rate,l", po::value<weight_t>()->default_value(0.00001),
"learning rate [only meaningful if margin>0 or input weights are given]")
("l1_reg,r", po::value<weight_t>()->default_value(0.),
"l1 regularization strength [see Tsuruoka, Tsujii and Ananiadou (2009)]")
("adadelta,D", po::bool_switch()->default_value(false),
"use AdaDelta dynamic learning rates")
("adadelta_decay", po::value<weight_t>()->default_value(0.9),
"decay for AdaDelta algorithm")
("adadelta_input", po::value<string>()->default_value(""),
"input for AdaDelta's parameters, two files: file.gradient, and file.update")
("adadelta_output", po::value<string>()->default_value(""),
"prefix for outputting AdaDelta's parameters")
("margin,m", po::value<weight_t>()->default_value(1.0),
"margin for margin perceptron [set =0 for standard perceptron]")
("cut,u", po::value<weight_t>()->default_value(0.1),
"use top/bottom 10% (default) of k-best as 'good' and 'bad' for pair sampling, 0 to use all pairs")
("adjust,A", po::bool_switch()->default_value(false),
"adjust cut for optimal pos. in k-best to cut")
("all,A", po::bool_switch()->default_value(false),
"update using all pairs, ignoring margin and threshold")
("score,s", po::value<string>()->default_value("nakov"),
"per-sentence BLEU (approx.)")
("nakov_fix", po::value<weight_t>()->default_value(1.0),
"add to reference length [see score.h]")
("chiang_decay", po::value<weight_t>()->default_value(0.9),
"decaying factor for Chiang's approx. BLEU")
("N", po::value<size_t>()->default_value(4),
"N for BLEU approximation")
("input_weights,w", po::value<string>(),
"weights to initialize model")
("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 [as weights.T.gz]")
("structured,S", po::bool_switch()->default_value(false),
"structured prediction objective [hope/fear] w/ SGD")
("pro_sampling", po::bool_switch()->default_value(false),
"updates from pairs selected as shown in Fig.4 of (Hopkins and May, 2011) [Gamma=max_pairs (default 5000), Xi=cut (default 50); threshold default 0.05]")
("threshold", po::value<weight_t>()->default_value(0.),
"(min.) threshold in terms of gold score for pair selection")
("max_pairs",
po::value<size_t>()->default_value(numeric_limits<size_t>::max()),
"max. number of updates/pairs")
("batch,B", po::bool_switch()->default_value(false),
"perform batch updates")
("output,o", po::value<string>()->default_value("-"),
"output weights file, '-' for STDOUT")
("disable_learning,X", po::bool_switch()->default_value(false),
"fix model")
("output_updates,U", po::value<string>()->default_value(""),
"output updates (diff. vectors) [to filename]")
("output_raw,R", po::value<string>()->default_value(""),
"output raw data (e.g. k-best lists) [to filename]")
("stop_after", po::value<size_t>()->default_value(numeric_limits<size_t>::max()),
"only look at this number of segments")
("print_weights", 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 << setprecision(3) << opts << endl;
return false;
}
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;
return false;
}
if (!conf->count("bitext")) {
cerr << "No input bitext." << endl;
cerr << opts << endl;
return false;
}
return true;
}
} // namespace
#endif
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