moses-smt/mosesdecoder
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Merge github.com:moses-smt/mosesdecoder into hieu_opt_input

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Hieu Hoang 2013-08-12 13:04:29 +01:00
commit 249aefc0c3
6 changed files with 85 additions and 121 deletions
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4
mira/Decoder.cpp
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@ -144,7 +144,7 @@ vector< vector<const Word*> > MosesDecoder::runDecoder(const std::string& source
m_manager->CalcNBest(nBestSize, nBestList, distinct);
// optionally print nbest to file (to extract scores and features.. currently just for sentence bleu scoring)
if (filename != "") {
/*if (filename != "") {
ofstream out(filename.c_str());
if (!out) {
ostringstream msg;
@ -154,7 +154,7 @@ vector< vector<const Word*> > MosesDecoder::runDecoder(const std::string& source
// TODO: handle sentence id (for now always 0)
//OutputNBest(out, nBestList, StaticData::Instance().GetOutputFactorOrder(), 0, false);
out.close();
}
}*/
// read off the feature values and bleu scores for each sentence in the nbest list
Moses::TrellisPathList::const_iterator iter;

139
mira/Main.cpp
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@ -78,8 +78,8 @@ int main(int argc, char** argv)
size_t mixingFrequency;
size_t weightDumpFrequency;
string weightDumpStem;
bool scale_margin, scale_margin_precision;
bool scale_update, scale_update_precision;
bool scale_margin;
bool scale_update;
size_t n;
size_t batchSize;
bool distinctNbest;
@ -103,7 +103,7 @@ int main(int argc, char** argv)
bool perceptron_update;
bool hope_fear;
bool model_hope_fear;
int hope_n, fear_n;
size_t hope_n, fear_n;
size_t bleu_smoothing_scheme;
float min_oracle_bleu;
float minBleuRatio, maxBleuRatio;
@ -122,17 +122,19 @@ int main(int argc, char** argv)
string moses_src;
float sigmoidParam;
float bleuWeight, bleuWeight_hope, bleuWeight_fear;
bool bleu_weight_lm, bleu_weight_lm_adjust;
bool bleu_weight_lm;
float bleu_weight_lm_factor;
bool l1_regularize, l2_regularize, l1_reg_sparse, l2_reg_sparse;
float l1_lambda, l2_lambda;
bool most_violated, most_violated_reg, all_violated, max_bleu_diff, one_against_all;
bool most_violated, most_violated_reg, all_violated, max_bleu_diff;
bool feature_confidence, signed_counts;
float decay_core, decay_sparse, core_r0, sparse_r0;
bool selective, summed;
float bleu_weight_fear_factor;
bool hildreth;
float add2lm;
// compute real sentence Bleu scores on complete translations, disable Bleu feature
bool realBleu, disableBleuFeature;
bool rescaleSlack;
bool makePairs;
@ -147,12 +149,14 @@ int main(int argc, char** argv)
("l1sparse", po::value<bool>(&l1_reg_sparse)->default_value(true), "L1-regularization for sparse weights only")
("l2sparse", po::value<bool>(&l2_reg_sparse)->default_value(true), "L2-regularization for sparse weights only")
("mv-reg", po::value<bool>(&most_violated_reg)->default_value(false), "Regularize most violated constraint")
("most-violated", po::value<bool>(&most_violated)->default_value(false), "Add most violated constraint")
("all-violated", po::value<bool>(&all_violated)->default_value(false), "Add all violated constraints")
("feature-confidence", po::value<bool>(&feature_confidence)->default_value(false), "Confidence-weighted learning")
("signed-counts", po::value<bool>(&signed_counts)->default_value(false), "Use signed feature counts for CWL")
("dbg", po::value<bool>(&debug)->default_value(true), "More debug output")
("make-pairs", po::value<bool>(&makePairs)->default_value(true), "Make pairs of hypotheses for 1slack")
("debug", po::value<bool>(&debug)->default_value(true), "More debug output")
("rescale-slack", po::value<bool>(&rescaleSlack)->default_value(false), "Rescale slack in 1-slack formulation")
("disable-bleu-feature", po::value<bool>(&disableBleuFeature)->default_value(false), "Disable the Bleu feature")
("real-bleu", po::value<bool>(&realBleu)->default_value(false), "Compute real sentence Bleu on complete translations")
("add2lm", po::value<float>(&add2lm)->default_value(0.0), "Add the specified amount to all LM weights")
("hildreth", po::value<bool>(&hildreth)->default_value(false), "Prefer Hildreth over analytical update")
("selective", po::value<bool>(&selective)->default_value(false), "Build constraints for every feature")
@ -166,9 +170,10 @@ int main(int argc, char** argv)
("core-r0", po::value<float>(&core_r0)->default_value(1.0), "Start learning rate for core features")
("sparse-r0", po::value<float>(&sparse_r0)->default_value(1.0), "Start learning rate for sparse features")
("tie-bw-to-lm", po::value<bool>(&bleu_weight_lm)->default_value(false), "Make bleu weight depend on lm weight")
("adjust-bw", po::value<bool>(&bleu_weight_lm_adjust)->default_value(false), "Adjust bleu weight when lm weight changes")
("decay-core", po::value<float>(&decay_core)->default_value(0.01), "Decay for core feature learning rate")
("decay-sparse", po::value<float>(&decay_sparse)->default_value(0.01), "Decay for sparse feature learning rate")
("tie-bw-to-lm", po::value<bool>(&bleu_weight_lm)->default_value(true), "Make bleu weight depend on lm weight")
("bw-lm-factor", po::value<float>(&bleu_weight_lm_factor)->default_value(2.0), "Make bleu weight depend on lm weight by this factor")
("bw-factor-fear", po::value<float>(&bleu_weight_fear_factor)->default_value(1.0), "Multiply fear weight by this factor")
("accumulate-weights", po::value<bool>(&accumulateWeights)->default_value(false), "Accumulate and average weights over all epochs")
@ -190,12 +195,12 @@ int main(int argc, char** argv)
("dump-mixed-weights", po::value<bool>(&dumpMixedWeights)->default_value(false), "Dump mixed weights instead of averaged weights")
("epochs,e", po::value<size_t>(&epochs)->default_value(10), "Number of epochs")
("feature-cutoff", po::value<int>(&featureCutoff)->default_value(-1), "Feature cutoff as additional regularization for sparse features")
("fear-n", po::value<int>(&fear_n)->default_value(1), "Number of fear translations used")
("fear-n", po::value<size_t>(&fear_n)->default_value(1), "Number of fear translations used")
("help", po::value(&help)->zero_tokens()->default_value(false), "Print this help message and exit")
("history-bleu", po::value<bool>(&historyBleu)->default_value(false), "Use 1best translations to update the history")
("history-smoothing", po::value<float>(&historySmoothing)->default_value(0.9), "Adjust the factor for history smoothing")
("hope-fear", po::value<bool>(&hope_fear)->default_value(true), "Use only hope and fear translations for optimisation (not model)")
("hope-n", po::value<int>(&hope_n)->default_value(2), "Number of hope translations used")
("hope-n", po::value<size_t>(&hope_n)->default_value(2), "Number of hope translations used")
("input-file,i", po::value<string>(&inputFile), "Input file containing tokenised source")
("input-files-folds", po::value<vector<string> >(&inputFilesFolds), "Input files containing tokenised source, one for each fold")
("learner,l", po::value<string>(&learner)->default_value("mira"), "Learning algorithm")
@ -209,10 +214,10 @@ int main(int argc, char** argv)
("min-oracle-bleu", po::value<float>(&min_oracle_bleu)->default_value(0), "Set a minimum oracle BLEU score")
("min-weight-change", po::value<float>(&min_weight_change)->default_value(0.0001), "Set minimum weight change for stopping criterion")
("mira-learning-rate", po::value<float>(&mira_learning_rate)->default_value(1), "Learning rate for MIRA (fixed or flexible)")
("mixing-frequency", po::value<size_t>(&mixingFrequency)->default_value(1), "How often per epoch to mix weights, when using mpi")
("mixing-frequency", po::value<size_t>(&mixingFrequency)->default_value(10), "How often per epoch to mix weights, when using mpi")
("model-hope-fear", po::value<bool>(&model_hope_fear)->default_value(false), "Use model, hope and fear translations for optimisation")
("moses-src", po::value<string>(&moses_src)->default_value(""), "Moses source directory")
("nbest,n", po::value<size_t>(&n)->default_value(1), "Number of translations in n-best list")
("nbest,n", po::value<size_t>(&n)->default_value(30), "Number of translations in n-best list")
("normalise-weights", po::value<bool>(&normaliseWeights)->default_value(false), "Whether to normalise the updated weights before passing them to the decoder")
("normalise-margin", po::value<bool>(&normaliseMargin)->default_value(false), "Normalise the margin: squash between 0 and 1")
("perceptron-learning-rate", po::value<float>(&perceptron_learning_rate)->default_value(0.01), "Perceptron learning rate")
@ -224,30 +229,27 @@ int main(int argc, char** argv)
("prune-zero-weights", po::value<bool>(&pruneZeroWeights)->default_value(false), "Prune zero-valued sparse feature weights")
("reference-files,r", po::value<vector<string> >(&referenceFiles), "Reference translation files for training")
("reference-files-folds", po::value<vector<string> >(&referenceFilesFolds), "Reference translation files for training, one for each fold")
("kbest", po::value<bool>(&kbest)->default_value(false), "Select hope/fear pairs from a list of nbest translations")
("kbest", po::value<bool>(&kbest)->default_value(true), "Select hope/fear pairs from a list of nbest translations")
("scale-by-inverse-length", po::value<bool>(&scaleByInverseLength)->default_value(false), "Scale BLEU by (history of) inverse input length")
("scale-by-input-length", po::value<bool>(&scaleByInputLength)->default_value(false), "Scale BLEU by (history of) input length")
("scale-by-input-length", po::value<bool>(&scaleByInputLength)->default_value(true), "Scale BLEU by (history of) input length")
("scale-by-avg-input-length", po::value<bool>(&scaleByAvgInputLength)->default_value(false), "Scale BLEU by average input length")
("scale-by-avg-inverse-length", po::value<bool>(&scaleByAvgInverseLength)->default_value(false), "Scale BLEU by average inverse input length")
("scale-by-x", po::value<float>(&scaleByX)->default_value(1), "Scale the BLEU score by value x")
("scale-lm", po::value<bool>(&scale_lm)->default_value(false), "Scale the language model feature")
("scale-factor-lm", po::value<float>(&scale_lm_factor)->default_value(2), "Scale the language model feature by this factor")
("scale-by-x", po::value<float>(&scaleByX)->default_value(0.1), "Scale the BLEU score by value x")
("scale-lm", po::value<bool>(&scale_lm)->default_value(true), "Scale the language model feature")
("scale-factor-lm", po::value<float>(&scale_lm_factor)->default_value(0.5), "Scale the language model feature by this factor")
("scale-wp", po::value<bool>(&scale_wp)->default_value(false), "Scale the word penalty feature")
("scale-factor-wp", po::value<float>(&scale_wp_factor)->default_value(2), "Scale the word penalty feature by this factor")
("scale-margin", po::value<bool>(&scale_margin)->default_value(0), "Scale the margin by the Bleu score of the oracle translation")
("scale-margin-precision", po::value<bool>(&scale_margin_precision)->default_value(0), "Scale margin by precision of oracle")
("scale-update", po::value<bool>(&scale_update)->default_value(0), "Scale update by Bleu score of oracle")
("scale-update-precision", po::value<bool>(&scale_update_precision)->default_value(0), "Scale update by precision of oracle")
("sentence-level-bleu", po::value<bool>(&sentenceBleu)->default_value(true), "Use a sentences level Bleu scoring function")
("shuffle", po::value<bool>(&shuffle)->default_value(false), "Shuffle input sentences before processing")
("sigmoid-param", po::value<float>(&sigmoidParam)->default_value(1), "y=sigmoidParam is the axis that this sigmoid approaches")
("slack", po::value<float>(&slack)->default_value(0.01), "Use slack in optimiser")
("slack", po::value<float>(&slack)->default_value(0.05), "Use slack in optimiser")
("sparse-average", po::value<bool>(&sparseAverage)->default_value(false), "Average weights by the number of processes")
("sparse-no-average", po::value<bool>(&sparseNoAverage)->default_value(false), "Don't average sparse weights, just sum")
("stop-weights", po::value<bool>(&weightConvergence)->default_value(true), "Stop when weights converge")
("verbosity,v", po::value<int>(&verbosity)->default_value(0), "Verbosity level")
("weight-dump-frequency", po::value<size_t>(&weightDumpFrequency)->default_value(1), "How often per epoch to dump weights (mpi)")
("weight-dump-frequency", po::value<size_t>(&weightDumpFrequency)->default_value(2), "How often per epoch to dump weights (mpi)")
("weight-dump-stem", po::value<string>(&weightDumpStem)->default_value("weights"), "Stem of filename to use for dumping weights");
po::options_description cmdline_options;
@ -396,7 +398,7 @@ int main(int argc, char** argv)
// initialise Moses
// add references to initialize Bleu feature
boost::trim(decoder_settings);
decoder_settings += " -mira -distinct-nbest -references";
decoder_settings += " -mira -n-best-list - " + boost::lexical_cast<string>(n) + " distinct -weight-bl 1 -references";
if (trainWithMultipleFolds) {
decoder_settings += " ";
decoder_settings += referenceFilesFolds[myFold];
@ -442,9 +444,8 @@ int main(int argc, char** argv)
if (normaliseMargin)
cerr << "sigmoid parameter: " << sigmoidParam << endl;
}
optimiser = new MiraOptimiser(slack, scale_margin, scale_margin_precision,
scale_update, scale_update_precision, boost, normaliseMargin, sigmoidParam);
learning_rate = mira_learning_rate;
optimiser = new MiraOptimiser(slack, scale_margin, scale_update, boost, normaliseMargin, sigmoidParam);
learning_rate = mira_learning_rate;
perceptron_update = false;
} else if (learner == "perceptron") {
if (rank == 0) {
@ -469,9 +470,9 @@ int main(int argc, char** argv)
cerr << "Info: Setting batch size to 1 for perceptron update" << endl;
}
if (hope_n == -1)
if (hope_n == 0)
hope_n = n;
if (fear_n == -1)
if (fear_n == 0)
fear_n = n;
if (model_hope_fear || kbest)
@ -556,9 +557,10 @@ int main(int argc, char** argv)
}
bleuWeight = lmSum * bleu_weight_lm_factor;
cerr << "Set bleu weight to lm weight * " << bleu_weight_lm_factor << endl;
if (!kbest) cerr << "Set bleu weight to lm weight * " << bleu_weight_lm_factor << endl;
}
// bleu weights can be set separately for hope and fear; otherwise they are both set to 'lm weight * bleu_weight_lm_factor'
if (bleuWeight_hope == -1) {
bleuWeight_hope = bleuWeight;
}
@ -566,8 +568,10 @@ int main(int argc, char** argv)
bleuWeight_fear = bleuWeight;
}
bleuWeight_fear *= bleu_weight_fear_factor;
cerr << "Bleu weight: " << bleuWeight << endl;
cerr << "Bleu weight fear: " << bleuWeight_fear << endl;
if (!kbest) {
cerr << "Bleu weight: " << bleuWeight << endl;
cerr << "Bleu weight fear: " << bleuWeight_fear << endl;
}
if (decode_hope || decode_fear || decode_model) {
size_t decode = 1;
@ -697,7 +701,6 @@ int main(int argc, char** argv)
vector<size_t> ref_ids;
size_t actualBatchSize = 0;
vector<size_t>::const_iterator current_sid_start = sid;
size_t examples_in_batch = 0;
bool skip_example = false;
for (size_t batchPosition = 0; batchPosition < batchSize && sid
@ -802,27 +805,11 @@ int main(int argc, char** argv)
ref_length = decoder->getClosestReferenceLength(*sid, oracle.size());
avg_ref_length = ref_length;
float hope_length_ratio = (float)oracle.size()/ref_length;
int oracleSize = (int)oracle.size();
cerr << endl;
// count sparse features occurring in hope translation
featureValuesHope[batchPosition][0].IncrementSparseHopeFeatures();
float precision = bleuScoresHope[batchPosition][0];
if (historyBleu || simpleHistoryBleu) {
precision /= decoder->getTargetLengthHistory();
} else {
if (scaleByAvgInputLength) precision /= decoder->getAverageInputLength();
else if (scaleByAvgInverseLength) precision /= (100/decoder->getAverageInputLength());
precision /= scaleByX;
}
if (scale_margin_precision || scale_update_precision) {
if (historyBleu || simpleHistoryBleu || scaleByAvgInputLength || scaleByAvgInverseLength) {
cerr << "Rank " << rank << ", epoch " << epoch << ", set hope precision: " << precision << endl;
((MiraOptimiser*) optimiser)->setPrecision(precision);
}
}
vector<const Word*> bestModel;
if (debug_model || historyBleu || simpleHistoryBleu) {
// MODEL (for updating the history only, using dummy vectors)
@ -837,9 +824,9 @@ int main(int argc, char** argv)
}
// FEAR
float fear_length_ratio = 0;
//float fear_length_ratio = 0;
float bleuRatioHopeFear = 0;
int fearSize = 0;
//int fearSize = 0;
cerr << "Rank " << rank << ", epoch " << epoch << ", " << fear_n << "best fear translations" << endl;
vector< vector<const Word*> > outputFear = decoder->getNBest(input, *sid, fear_n, -1.0, bleuWeight_fear,
featureValuesFear[batchPosition], bleuScoresFear[batchPosition], modelScoresFear[batchPosition],
@ -849,8 +836,8 @@ int main(int argc, char** argv)
ref_length = decoder->getClosestReferenceLength(*sid, fear.size());
avg_ref_length += ref_length;
avg_ref_length /= 2;
fear_length_ratio = (float)fear.size()/ref_length;
fearSize = (int)fear.size();
//fear_length_ratio = (float)fear.size()/ref_length;
//fearSize = (int)fear.size();
cerr << endl;
for (size_t i = 0; i < fear.size(); ++i)
delete fear[i];
@ -994,11 +981,11 @@ int main(int argc, char** argv)
HypothesisQueue queueHope(hope_n);
HypothesisQueue queueFear(fear_n);
cerr << endl;
if (most_violated || all_violated || one_against_all) {
if (most_violated || all_violated) {
float bleuHope = -1000;
float bleuFear = 1000;
size_t indexHope = -1;
size_t indexFear = -1;
int indexHope = -1;
int indexFear = -1;
vector<float> bleuHopeList;
vector<float> bleuFearList;
@ -1029,32 +1016,24 @@ int main(int argc, char** argv)
}
float currentViolation = 0;
float minimum_bleu_diff = 0.01;
for (size_t i=0; i<bleuScores[batchPosition].size(); ++i) {
float bleuDiff = bleuHope - bleuScores[batchPosition][i];
float modelDiff = modelScores[batchPosition][indexHope] - modelScores[batchPosition][i];
if (bleuDiff > epsilon) {
if (one_against_all && bleuDiff > minimum_bleu_diff) {
cerr << ".. adding pair";
bleuHopeList.push_back(bleuHope);
bleuFearList.push_back(bleuScores[batchPosition][i]);
indexHopeList.push_back(indexHope);
indexFearList.push_back(i);
} else if (modelDiff < bleuDiff) {
float diff = bleuDiff - modelDiff;
if (diff > epsilon) {
if (all_violated) {
cerr << ".. adding pair";
bleuHopeList.push_back(bleuHope);
bleuFearList.push_back(bleuScores[batchPosition][i]);
indexHopeList.push_back(indexHope);
indexFearList.push_back(i);
} else if (most_violated && diff > currentViolation) {
currentViolation = diff;
bleuFear = bleuScores[batchPosition][i];
indexFear = i;
cerr << "Rank " << rank << ", epoch " << epoch << ", current violation: " << currentViolation << " (" << modelDiff << " >= " << bleuDiff << ")" << endl;
}
if ((bleuDiff > epsilon) && (modelDiff < bleuDiff)) {
float diff = bleuDiff - modelDiff;
if (diff > epsilon) {
if (all_violated) {
cerr << ".. adding pair";
bleuHopeList.push_back(bleuHope);
bleuFearList.push_back(bleuScores[batchPosition][i]);
indexHopeList.push_back(indexHope);
indexFearList.push_back(i);
}
else if (most_violated && diff > currentViolation) {
currentViolation = diff;
bleuFear = bleuScores[batchPosition][i];
indexFear = i;
cerr << "Rank " << rank << ", epoch " << epoch << ", current violation: " << currentViolation << " (" << modelDiff << " >= " << bleuDiff << ")" << endl;
}
}
}

13
mira/MiraOptimiser.cpp
View File

@ -356,15 +356,6 @@ size_t MiraOptimiser::updateWeightsAnalytically(
cerr << "Rank " << rank << ", epoch " << epoch << ", normalised constraint: " << modelScoreDiff << " >= " << loss << " (current violation: " << diff << ")" << endl;
}
if (m_scale_margin) {
diff *= bleuScoreHope;
cerr << "Rank " << rank << ", epoch " << epoch << ", scaling margin with oracle bleu score " << bleuScoreHope << endl;
}
if (m_scale_margin_precision) {
diff *= (1+m_precision);
cerr << "Rank " << rank << ", epoch " << epoch << ", scaling margin with 1+precision: " << (1+m_precision) << endl;
}
if (diff > epsilon) {
// squash it between 0 and 1
//diff = tanh(diff);
@ -401,10 +392,6 @@ size_t MiraOptimiser::updateWeightsAnalytically(
cerr << "Rank " << rank << ", epoch " << epoch << ", scaling update with oracle bleu score " << bleuScoreHope << endl;
alpha *= bleuScoreHope;
}
if (m_scale_update_precision) {
cerr << "Rank " << rank << ", epoch " << epoch << ", scaling update with 1+precision: " << (1+m_precision) << endl;
alpha *= (1+m_precision);
}
cerr << "Rank " << rank << ", epoch " << epoch << ", clipped/scaled alpha: " << alpha << endl;

30
mira/Optimiser.h
View File

@ -69,16 +69,21 @@ public:
MiraOptimiser() :
Optimiser() { }
MiraOptimiser(
float slack, bool scale_margin, bool scale_margin_precision,
bool scale_update, bool scale_update_precision, bool boost, bool normaliseMargin, float sigmoidParam) :
MiraOptimiser(float slack) :
Optimiser(),
m_slack(slack),
m_scale_margin(false),
m_scale_update(false),
m_boost(false),
m_normaliseMargin(false),
m_sigmoidParam(1.0) { }
MiraOptimiser(float slack, bool scale_margin, bool scale_update,
bool boost, bool normaliseMargin, float sigmoidParam) :
Optimiser(),
m_slack(slack),
m_scale_margin(scale_margin),
m_scale_margin_precision(scale_margin_precision),
m_scale_update(scale_update),
m_scale_update_precision(scale_update_precision),
m_precision(1),
m_boost(boost),
m_normaliseMargin(normaliseMargin),
m_sigmoidParam(sigmoidParam) { }
@ -148,21 +153,16 @@ public:
m_slack = slack;
}
void setPrecision(float precision) {
m_precision = precision;
}
private:
// regularise Hildreth updates
float m_slack;
// scale margin with BLEU score or precision
bool m_scale_margin, m_scale_margin_precision;
// scale update with oracle BLEU score or precision
bool m_scale_update, m_scale_update_precision;
// scale margin with BLEU score
bool m_scale_margin;
float m_precision;
// scale update with oracle BLEU score
bool m_scale_update;
// boosting of updates on misranked candidates
bool m_boost;

4
mira/training-expt.perl
View File

@ -289,8 +289,8 @@ my $learner = &param("train.learner", "mira");
my $batch = &param("train.batch", 1); # don't print this param twice (when printing training file)
my $extra_args = &param("train.extra-args");
my $by_node = &param("train.by-node");
my $slots = &param("train.slots",8);
my $jobs = &param("train.jobs",8);
my $slots = &param("train.slots",10);
my $jobs = &param("train.jobs",10);
my $mixing_frequency = &param("train.mixing-frequency", 1); # don't print this param twice
my $weight_dump_frequency = &param("train.weight-dump-frequency", 1); # don't print this param twice
my $burn_in = &param("train.burn-in");

16
scripts/ems/experiment.perl
View File

@ -1698,14 +1698,8 @@ sub write_mira_config {
my $tuning_decoder_settings = &check_and_get("TUNING:decoder-settings");
my $start_weights = &backoff_and_get("TUNING:start-weight-config");
my $tuning_settings = &check_and_get("TUNING:tuning-settings");
my $jobs = 10; # this overwrites the default in training-expt.perl
if ($tuning_settings =~ /^(.*)--jobs (\d+)(.*)$/) {
$jobs = $2;
$tuning_settings = $1.$3;
$tuning_settings =~ s/ +/ /;
$tuning_settings =~ s/^ //;
$tuning_settings =~ s/ $//;
}
my $parallel_settings = &backoff_and_get("TUNING:parallel-settings");
my $use_jackknife = &backoff_and_get("TUNING:use-jackknife");
# are we tuning a meta feature?
@ -1794,7 +1788,11 @@ sub write_mira_config {
}
print CFG "decoder-settings=".$tuning_decoder_settings." -text-type \"dev\"\n";
print CFG "hours=48 \n";
print CFG "jobs=$jobs \n";
if ($parallel_settings) {
foreach my $setting (split(" ", $parallel_settings)) {
print CFG $setting."\n";
}
}
print CFG "extra-args=".$tuning_settings."\n\n";
print CFG "[devtest] \n";
if (&get("TRAINING:hierarchical-rule-set")) {