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Commit ab55415c authored by Franck Dary's avatar Franck Dary
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Refactored some code

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maca_common/CMakeLists.txt
+ 2
− 0
View file @ ab55415c
...@@ -14,3 +14,5 @@ install(TARGETS macaon_convert_embeddings DESTINATION bin) ...@@ -14,3 +14,5 @@ install(TARGETS macaon_convert_embeddings DESTINATION bin)
#compiling library #compiling library
add_library(maca_common STATIC ${SOURCES}) add_library(maca_common STATIC ${SOURCES})
target_link_libraries(maca_common fasttext) target_link_libraries(maca_common fasttext)
target_link_libraries(maca_common transition_machine)
target_link_libraries(maca_common trainer)
maca_common/include/programOptionsTemplates.hpp 0 → 100644
+ 24
− 0
View file @ ab55415c
/// \file programOptionsTemplates.hpp
/// \author Franck Dary
/// @version 1.0
/// @date 2019-05-27
#ifndef PROGRAMOPTIONSTEMPLATES_HPP
#define PROGRAMOPTIONSTEMPLATES_HPP
#include <boost/program_options.hpp>
namespace po = boost::program_options;
void loadTrainProgramParameters(int argc, char * argv[]);
po::options_description getTrainOptionsDescription();
po::variables_map checkOptions(po::options_description & od, int argc, char ** argv);
void updatePaths();
void launchTraining();
void createExpPath();
void launchTraining();
void createTemplatePath();
void loadTrainProgramParameters(int argc, char * argv[]);
void removeTemplatePath();
#endif
maca_common/src/programOptionsTemplates.cpp 0 → 100644
+ 323
− 0
View file @ ab55415c
/// \file programOptionsTemplates.cpp
/// \author Franck Dary
/// @version 1.0
/// @date 2019-05-27
#include <cstdio>
#include <cstdlib>
#include <boost/program_options.hpp>
#include "programOptionsTemplates.hpp"
#include "BD.hpp"
#include "Config.hpp"
#include "TransitionMachine.hpp"
#include "Trainer.hpp"
#include "ProgramParameters.hpp"
namespace po = boost::program_options;
/// @brief Get the list of mandatory and optional macaon_train program arguments.
///
/// @return The lists.
po::options_description getTrainOptionsDescription()
{
po::options_description desc("Command-Line Arguments ");
po::options_description req("Required");
req.add_options()
("expName", po::value<std::string>()->required(),
"Name of this experiment")
("templateName", po::value<std::string>()->required(),
"Name of the template folder")
("tm", po::value<std::string>()->required(),
"File describing the Tape Machine we will train")
("bd", po::value<std::string>()->required(),
"BD file that describes the multi-tapes buffer")
("mcd", po::value<std::string>()->required(),
"MCD file that describes the input")
("train,T", po::value<std::string>()->required(),
"Training corpus formated according to the MCD");
po::options_description opt("Optional");
opt.add_options()
("help,h", "Produce this help message")
("debug,d", "Print infos on stderr")
("randomDebug", "Print infos on stderr with a probability of randomDebugProbability")
("randomDebugProbability", po::value<float>()->default_value(0.001),
"Probability that debug infos will be printed")
("printEntropy", "Print mean entropy and standard deviation accross sequences")
("dicts", po::value<std::string>()->default_value(""),
"The .dict file describing all the dictionaries to be used in the experiement. By default the filename specified in the .tm file will be used")
("featureModels", po::value<std::string>()->default_value(""),
"For each classifier, specify what .fm (feature model) file to use. By default the filename specified in the .cla file will be used. Example : --featureModel Parser=parser.fm,Tagger=tagger.fm")
("optimizer", po::value<std::string>()->default_value("amsgrad"),
"The learning algorithm to use : amsgrad | adam | sgd")
("loss", po::value<std::string>()->default_value("neglogsoftmax"),
"The loss function to use : neglogsoftmax | weighted")
("dev", po::value<std::string>()->default_value(""),
"Development corpus formated according to the MCD")
("lang", po::value<std::string>()->default_value("fr"),
"Language you are working with")
("nbiter,n", po::value<int>()->default_value(5),
"Number of training epochs (iterations)")
("iterationSize", po::value<int>()->default_value(-1),
"The number of examples for each iteration. -1 means the whole training set")
("lr", po::value<float>()->default_value(0.001),
"Learning rate of the optimizer")
("seed,s", po::value<int>()->default_value(100),
"The random seed that will initialize RNG")
("nbTrain", po::value<int>()->default_value(0),
"The number of models that will be trained, with only the random seed changing")
("duplicates", po::value<bool>()->default_value(true),
"Remove identical training examples")
("showFeatureRepresentation", po::value<int>()->default_value(0),
"For each state of the Config, show its feature representation")
("interactive", po::value<bool>()->default_value(true),
"Is the shell interactive ? Display advancement informations")
("randomEmbeddings", po::value<bool>()->default_value(false),
"When activated, the embeddings will be randomly initialized")
("randomParameters", po::value<bool>()->default_value(true),
"When activated, the parameters will be randomly initialized")
("sequenceDelimiterTape", po::value<std::string>()->default_value("EOS"),
"The name of the buffer's tape that contains the delimiter token for a sequence")
("sequenceDelimiter", po::value<std::string>()->default_value("1"),
"The value of the token that act as a delimiter for sequences")
("batchSize", po::value<int>()->default_value(50),
"The size of each minibatch (in number of taining examples)")
("dictCapacity", po::value<int>()->default_value(50000),
"The maximal size of each Dict (number of differents embeddings).")
("tapeToMask", po::value<std::string>()->default_value("FORM"),
"The name of the Tape for which some of the elements will be masked.")
("maskRate", po::value<float>()->default_value(0.0),
"The rate of elements of the Tape that will be masked.")
("printTime", "Print time on stderr.")
("featureExtraction", "Use macaon only as a feature extractor, print corpus to stdout.")
("devEvalOnGold", "If true, dev accuracy will be computed on gold configurations.")
("devLoss", po::value<bool>()->default_value(false),
"Compute and print total loss on dev for every epoch.")
("shuffle", po::value<bool>()->default_value(true),
"Shuffle examples after each iteration");
po::options_description oracle("Oracle related options");
oracle.add_options()
("epochd", po::value<int>()->default_value(3),
"Number of the first epoch where the oracle will be dynamic")
("proba", po::value<float>()->default_value(0.9),
"The probability that the dynamic oracle will chose the predicted action");
po::options_description ams("Amsgrad family optimizers");
ams.add_options()
("b1", po::value<float>()->default_value(0.9),
"beta1 parameter for the Amsgtad or Adam optimizer")
("b2", po::value<float>()->default_value(0.999),
"beta2 parameter for the Amsgtad or Adam optimizer")
("bias", po::value<float>()->default_value(1e-8),
"bias parameter for the Amsgtad or Adam or Adagrad optimizer");
po::options_description ga("Genetic algorithm related options");
ga.add_options()
("nbIndividuals", po::value<int>()->default_value(5),
"Number of individuals that will be used to take decisions");
desc.add(req).add(opt).add(oracle).add(ams).add(ga);
return desc;
}
/// @brief Store the program arguments inside a variables_map
///
/// @param od The description of all the possible options.
/// @param argc The number of arguments given to this program.
/// @param argv The values of arguments given to this program.
///
/// @return The variables map
po::variables_map checkOptions(po::options_description & od, int argc, char ** argv)
{
po::variables_map vm;
try {po::store(po::parse_command_line(argc, argv, od), vm);}
catch(std::exception& e)
{
std::cerr << "Error: " << e.what() << "\n";
od.print(std::cerr);
exit(1);
}
if (vm.count("help"))
{
std::cout << od << "\n";
exit(0);
}
try {po::notify(vm);}
catch(std::exception& e)
{
std::cerr << "Error: " << e.what() << "\n";
od.print(std::cerr);
exit(1);
}
return vm;
}
/// @brief Set all the usefull paths relative to expPath
void updatePaths()
{
const char * MACAON_DIR = std::getenv("MACAON_DIR");
std::string slash = "/";
ProgramParameters::langPath = MACAON_DIR + slash + ProgramParameters::lang + slash;
ProgramParameters::expPath = ProgramParameters::langPath + "bin/" + ProgramParameters::expName + slash;
ProgramParameters::templatePath = ProgramParameters::langPath + ProgramParameters::templateName + slash;
ProgramParameters::tmFilename = ProgramParameters::expPath + ProgramParameters::tmName;
ProgramParameters::bdFilename = ProgramParameters::expPath + ProgramParameters::bdName;
ProgramParameters::mcdFilename = ProgramParameters::expPath + ProgramParameters::mcdName;
ProgramParameters::trainFilename = ProgramParameters::expPath + ProgramParameters::trainName;
ProgramParameters::devFilename = ProgramParameters::expPath + ProgramParameters::devName;
ProgramParameters::newTemplatePath = ProgramParameters::langPath + "bin/" + ProgramParameters::baseExpName + slash;
}
/// @brief Create the folder containing the current experiment from the template frolder
void createExpPath()
{
// If this is the case, the training should resume where it was stopped.
// No need to recreate the expPath.
if (ProgramParameters::newTemplatePath == ProgramParameters::expPath)
return;
std::string decode = "\
#! /bin/bash\n\
\n\
if [ \"$#\" -lt 2 ]; then\n\
echo \"Usage : $0 input mcd\"\n\
exit\n\
fi\n\
\n\
INPUT=$1\n\
MCD=$2\n\
\n\
shift\n\
shift\n\
ARGS=\"\"\n\
for arg in \"$@\"\n\
do\n\
ARGS=\"$ARGS $arg\"\n\
done\n\
\n\
macaon_decode --lang " + ProgramParameters::lang + " --tm machine.tm --bd test.bd -I $INPUT --mcd $MCD --expName " + ProgramParameters::expName + "$ARGS";
if (system(("rm -r " + ProgramParameters::expPath + " 2> /dev/null").c_str())){}
if (system(("mkdir " + ProgramParameters::expPath).c_str())){}
if (system(("cp -r " + ProgramParameters::newTemplatePath + "* " + ProgramParameters::expPath + ".").c_str())){}
if (system(("echo \'" + decode + "\' > " + ProgramParameters::expPath + "decode.sh").c_str())){}
if (system(("chmod +x " + ProgramParameters::expPath + "decode.sh").c_str())){}
if (system(("ln -f -s " + ProgramParameters::expPath + "decode.sh " + ProgramParameters::langPath + "bin/maca_tm_" + ProgramParameters::expName).c_str())){}
ProgramParameters::tapeSize = getNbLines(ProgramParameters::trainFilename);
ProgramParameters::devTapeSize = ProgramParameters::devFilename.empty() ? 0 : getNbLines(ProgramParameters::devFilename);
ProgramParameters::readSize = ProgramParameters::tapeSize;
}
/// @brief Train a model according to all the ProgramParameters
void launchTraining()
{
TransitionMachine transitionMachine(true);
BD trainBD(ProgramParameters::bdFilename, ProgramParameters::mcdFilename);
Config trainConfig(trainBD, ProgramParameters::trainFilename);
std::unique_ptr<BD> devBD;
std::unique_ptr<Config> devConfig;
std::unique_ptr<Trainer> trainer;
if(ProgramParameters::devFilename.empty())
{
trainer.reset(new Trainer(transitionMachine, trainBD, trainConfig));
}
else
{
devBD.reset(new BD(ProgramParameters::bdFilename, ProgramParameters::mcdFilename));
devConfig.reset(new Config(*devBD.get(), ProgramParameters::devFilename));
trainer.reset(new Trainer(transitionMachine, trainBD, trainConfig, devBD.get(), devConfig.get()));
}
trainer->train();
}
void createTemplatePath()
{
if (system(("rm -r " + ProgramParameters::newTemplatePath + " 2> /dev/null").c_str())){}
if (system(("mkdir " + ProgramParameters::newTemplatePath).c_str())){}
if (system(("cp -r " + ProgramParameters::templatePath + "* " + ProgramParameters::newTemplatePath + ".").c_str())){}
}
void removeTemplatePath()
{
if (system(("rm -r " + ProgramParameters::newTemplatePath + " 2> /dev/null").c_str())){}
}
void loadTrainProgramParameters(int argc, char * argv[])
{
auto od = getTrainOptionsDescription();
po::variables_map vm = checkOptions(od, argc, argv);
ProgramParameters::expName = vm["expName"].as<std::string>();
ProgramParameters::baseExpName = ProgramParameters::expName;
ProgramParameters::templateName = vm["templateName"].as<std::string>();
ProgramParameters::tmName = vm["tm"].as<std::string>();
ProgramParameters::bdName = vm["bd"].as<std::string>();
ProgramParameters::mcdName = vm["mcd"].as<std::string>();
ProgramParameters::debug = vm.count("debug") == 0 ? false : true;
ProgramParameters::randomDebug = vm.count("randomDebug") == 0 ? false : true;
ProgramParameters::printEntropy = vm.count("printEntropy") == 0 ? false : true;
ProgramParameters::printTime = vm.count("printTime") == 0 ? false : true;
ProgramParameters::featureExtraction = vm.count("featureExtraction") == 0 ? false : true;
ProgramParameters::devEvalOnGold = vm.count("devEvalOnGold") == 0 ? false : true;
ProgramParameters::trainName = vm["train"].as<std::string>();
ProgramParameters::devName = vm["dev"].as<std::string>();
ProgramParameters::lang = vm["lang"].as<std::string>();
ProgramParameters::nbIter = vm["nbiter"].as<int>();
ProgramParameters::seed = vm["seed"].as<int>();
ProgramParameters::batchSize = vm["batchSize"].as<int>();
ProgramParameters::dictCapacity = vm["dictCapacity"].as<int>();
ProgramParameters::nbTrain = vm["nbTrain"].as<int>();
ProgramParameters::removeDuplicates = vm["duplicates"].as<bool>();
ProgramParameters::interactive = vm["interactive"].as<bool>();
ProgramParameters::shuffleExamples = vm["shuffle"].as<bool>();
ProgramParameters::devLoss = vm["devLoss"].as<bool>();
ProgramParameters::randomEmbeddings = vm["randomEmbeddings"].as<bool>();
ProgramParameters::randomParameters = vm["randomParameters"].as<bool>();
ProgramParameters::sequenceDelimiterTape = vm["sequenceDelimiterTape"].as<std::string>();
ProgramParameters::sequenceDelimiter = vm["sequenceDelimiter"].as<std::string>();
ProgramParameters::learningRate = vm["lr"].as<float>();
ProgramParameters::randomDebugProbability = vm["randomDebugProbability"].as<float>();
ProgramParameters::beta1 = vm["b1"].as<float>();
ProgramParameters::beta2 = vm["b2"].as<float>();
ProgramParameters::bias = vm["bias"].as<float>();
ProgramParameters::nbIndividuals = vm["nbIndividuals"].as<int>();
ProgramParameters::optimizer = vm["optimizer"].as<std::string>();
ProgramParameters::dicts = vm["dicts"].as<std::string>();
ProgramParameters::loss = vm["loss"].as<std::string>();
ProgramParameters::dynamicEpoch = vm["epochd"].as<int>();
ProgramParameters::dynamicProbability = vm["proba"].as<float>();
ProgramParameters::tapeToMask = vm["tapeToMask"].as<std::string>();
ProgramParameters::maskRate = vm["maskRate"].as<float>();
ProgramParameters::showFeatureRepresentation = vm["showFeatureRepresentation"].as<int>();
ProgramParameters::iterationSize = vm["iterationSize"].as<int>();
std::string featureModels = vm["featureModels"].as<std::string>();
if (!featureModels.empty())
{
auto byClassifiers = split(featureModels, ',');
for (auto & classifier : byClassifiers)
{
auto parts = split(classifier, '=');
if (parts.size() != 2)
{
fprintf(stderr, "ERROR (%s) : wrong format for argument of option featureModels. Aborting.\n", ERRINFO);
exit(1);
}
ProgramParameters::featureModelByClassifier[parts[0]] = parts[1];
}
}
}
trainer/src/macaon_train.cpp
+ 2
− 305
View file @ ab55415c
...@@ -5,254 +5,12 @@ ...@@ -5,254 +5,12 @@
#include <cstdio> #include <cstdio>
#include <cstdlib> #include <cstdlib>
#include <boost/program_options.hpp>
#include "BD.hpp" #include "BD.hpp"
#include "Config.hpp" #include "Config.hpp"
#include "TransitionMachine.hpp" #include "TransitionMachine.hpp"
#include "Trainer.hpp" #include "Trainer.hpp"
#include "ProgramParameters.hpp" #include "ProgramParameters.hpp"
#include "programOptionsTemplates.hpp"
namespace po = boost::program_options;
/// @brief Get the list of mandatory and optional program arguments.
///
/// @return The lists.
po::options_description getOptionsDescription()
{
po::options_description desc("Command-Line Arguments ");
po::options_description req("Required");
req.add_options()
("expName", po::value<std::string>()->required(),
"Name of this experiment")
("templateName", po::value<std::string>()->required(),
"Name of the template folder")
("tm", po::value<std::string>()->required(),
"File describing the Tape Machine we will train")
("bd", po::value<std::string>()->required(),
"BD file that describes the multi-tapes buffer")
("mcd", po::value<std::string>()->required(),
"MCD file that describes the input")
("train,T", po::value<std::string>()->required(),
"Training corpus formated according to the MCD");
po::options_description opt("Optional");
opt.add_options()
("help,h", "Produce this help message")
("debug,d", "Print infos on stderr")
("randomDebug", "Print infos on stderr with a probability of randomDebugProbability")
("randomDebugProbability", po::value<float>()->default_value(0.001),
"Probability that debug infos will be printed")
("printEntropy", "Print mean entropy and standard deviation accross sequences")
("dicts", po::value<std::string>()->default_value(""),
"The .dict file describing all the dictionaries to be used in the experiement. By default the filename specified in the .tm file will be used")
("featureModels", po::value<std::string>()->default_value(""),
"For each classifier, specify what .fm (feature model) file to use. By default the filename specified in the .cla file will be used. Example : --featureModel Parser=parser.fm,Tagger=tagger.fm")
("optimizer", po::value<std::string>()->default_value("amsgrad"),
"The learning algorithm to use : amsgrad | adam | sgd")
("loss", po::value<std::string>()->default_value("neglogsoftmax"),
"The loss function to use : neglogsoftmax | weighted")
("dev", po::value<std::string>()->default_value(""),
"Development corpus formated according to the MCD")
("lang", po::value<std::string>()->default_value("fr"),
"Language you are working with")
("nbiter,n", po::value<int>()->default_value(5),
"Number of training epochs (iterations)")
("iterationSize", po::value<int>()->default_value(-1),
"The number of examples for each iteration. -1 means the whole training set")
("lr", po::value<float>()->default_value(0.001),
"Learning rate of the optimizer")
("seed,s", po::value<int>()->default_value(100),
"The random seed that will initialize RNG")
("nbTrain", po::value<int>()->default_value(0),
"The number of models that will be trained, with only the random seed changing")
("duplicates", po::value<bool>()->default_value(true),
"Remove identical training examples")
("showFeatureRepresentation", po::value<int>()->default_value(0),
"For each state of the Config, show its feature representation")
("interactive", po::value<bool>()->default_value(true),
"Is the shell interactive ? Display advancement informations")
("randomEmbeddings", po::value<bool>()->default_value(false),
"When activated, the embeddings will be randomly initialized")
("randomParameters", po::value<bool>()->default_value(true),
"When activated, the parameters will be randomly initialized")
("sequenceDelimiterTape", po::value<std::string>()->default_value("EOS"),
"The name of the buffer's tape that contains the delimiter token for a sequence")
("sequenceDelimiter", po::value<std::string>()->default_value("1"),
"The value of the token that act as a delimiter for sequences")
("batchSize", po::value<int>()->default_value(50),
"The size of each minibatch (in number of taining examples)")
("dictCapacity", po::value<int>()->default_value(50000),
"The maximal size of each Dict (number of differents embeddings).")
("tapeToMask", po::value<std::string>()->default_value("FORM"),
"The name of the Tape for which some of the elements will be masked.")
("maskRate", po::value<float>()->default_value(0.0),
"The rate of elements of the Tape that will be masked.")
("printTime", "Print time on stderr.")
("featureExtraction", "Use macaon only as a feature extractor, print corpus to stdout.")
("devEvalOnGold", "If true, dev accuracy will be computed on gold configurations.")
("devLoss", po::value<bool>()->default_value(false),
"Compute and print total loss on dev for every epoch.")
("shuffle", po::value<bool>()->default_value(true),
"Shuffle examples after each iteration");
po::options_description oracle("Oracle related options");
oracle.add_options()
("epochd", po::value<int>()->default_value(3),
"Number of the first epoch where the oracle will be dynamic")
("proba", po::value<float>()->default_value(0.9),
"The probability that the dynamic oracle will chose the predicted action");
po::options_description ams("Amsgrad family optimizers");
ams.add_options()
("b1", po::value<float>()->default_value(0.9),
"beta1 parameter for the Amsgtad or Adam optimizer")
("b2", po::value<float>()->default_value(0.999),
"beta2 parameter for the Amsgtad or Adam optimizer")
("bias", po::value<float>()->default_value(1e-8),
"bias parameter for the Amsgtad or Adam or Adagrad optimizer");
po::options_description ga("Genetic algorithm related options");
ga.add_options()
("nbIndividuals", po::value<int>()->default_value(5),
"Number of individuals that will be used to take decisions");
desc.add(req).add(opt).add(oracle).add(ams).add(ga);
return desc;
}
/// @brief Store the program arguments inside a variables_map
///
/// @param od The description of all the possible options.
/// @param argc The number of arguments given to this program.
/// @param argv The values of arguments given to this program.
///
/// @return The variables map
po::variables_map checkOptions(po::options_description & od, int argc, char ** argv)
{
po::variables_map vm;
try {po::store(po::parse_command_line(argc, argv, od), vm);}
catch(std::exception& e)
{
std::cerr << "Error: " << e.what() << "\n";
od.print(std::cerr);
exit(1);
}
if (vm.count("help"))
{
std::cout << od << "\n";
exit(0);
}
try {po::notify(vm);}
catch(std::exception& e)
{
std::cerr << "Error: " << e.what() << "\n";
od.print(std::cerr);
exit(1);
}
return vm;
}
/// @brief Set all the usefull paths relative to expPath
void updatePaths()
{
const char * MACAON_DIR = std::getenv("MACAON_DIR");
std::string slash = "/";
ProgramParameters::langPath = MACAON_DIR + slash + ProgramParameters::lang + slash;
ProgramParameters::expPath = ProgramParameters::langPath + "bin/" + ProgramParameters::expName + slash;
ProgramParameters::templatePath = ProgramParameters::langPath + ProgramParameters::templateName + slash;
ProgramParameters::tmFilename = ProgramParameters::expPath + ProgramParameters::tmName;
ProgramParameters::bdFilename = ProgramParameters::expPath + ProgramParameters::bdName;
ProgramParameters::mcdFilename = ProgramParameters::expPath + ProgramParameters::mcdName;
ProgramParameters::trainFilename = ProgramParameters::expPath + ProgramParameters::trainName;
ProgramParameters::devFilename = ProgramParameters::expPath + ProgramParameters::devName;
ProgramParameters::newTemplatePath = ProgramParameters::langPath + "bin/" + ProgramParameters::baseExpName + slash;
}
/// @brief Create the folder containing the current experiment from the template frolder
void createExpPath()
{
// If this is the case, the training should resume where it was stopped.
// No need to recreate the expPath.
if (ProgramParameters::newTemplatePath == ProgramParameters::expPath)
return;
std::string decode = "\
#! /bin/bash\n\
\n\
if [ \"$#\" -lt 2 ]; then\n\
echo \"Usage : $0 input mcd\"\n\
exit\n\
fi\n\
\n\
INPUT=$1\n\
MCD=$2\n\
\n\
shift\n\
shift\n\
ARGS=\"\"\n\
for arg in \"$@\"\n\
do\n\
ARGS=\"$ARGS $arg\"\n\
done\n\
\n\
macaon_decode --lang " + ProgramParameters::lang + " --tm machine.tm --bd test.bd -I $INPUT --mcd $MCD --expName " + ProgramParameters::expName + "$ARGS";
if (system(("rm -r " + ProgramParameters::expPath + " 2> /dev/null").c_str())){}
if (system(("mkdir " + ProgramParameters::expPath).c_str())){}
if (system(("cp -r " + ProgramParameters::newTemplatePath + "* " + ProgramParameters::expPath + ".").c_str())){}
if (system(("echo \'" + decode + "\' > " + ProgramParameters::expPath + "decode.sh").c_str())){}
if (system(("chmod +x " + ProgramParameters::expPath + "decode.sh").c_str())){}
if (system(("ln -f -s " + ProgramParameters::expPath + "decode.sh " + ProgramParameters::langPath + "bin/maca_tm_" + ProgramParameters::expName).c_str())){}
ProgramParameters::tapeSize = getNbLines(ProgramParameters::trainFilename);
ProgramParameters::devTapeSize = ProgramParameters::devFilename.empty() ? 0 : getNbLines(ProgramParameters::devFilename);
ProgramParameters::readSize = ProgramParameters::tapeSize;
}
/// @brief Train a model according to all the ProgramParameters
void launchTraining()
{
TransitionMachine transitionMachine(true);
BD trainBD(ProgramParameters::bdFilename, ProgramParameters::mcdFilename);
Config trainConfig(trainBD, ProgramParameters::trainFilename);
std::unique_ptr<BD> devBD;
std::unique_ptr<Config> devConfig;
std::unique_ptr<Trainer> trainer;
if(ProgramParameters::devFilename.empty())
{
trainer.reset(new Trainer(transitionMachine, trainBD, trainConfig));
}
else
{
devBD.reset(new BD(ProgramParameters::bdFilename, ProgramParameters::mcdFilename));
devConfig.reset(new Config(*devBD.get(), ProgramParameters::devFilename));
trainer.reset(new Trainer(transitionMachine, trainBD, trainConfig, devBD.get(), devConfig.get()));
}
trainer->train();
}
void createTemplatePath()
{
if (system(("rm -r " + ProgramParameters::newTemplatePath + " 2> /dev/null").c_str())){}
if (system(("mkdir " + ProgramParameters::newTemplatePath).c_str())){}
if (system(("cp -r " + ProgramParameters::templatePath + "* " + ProgramParameters::newTemplatePath + ".").c_str())){}
}
void removeTemplatePath()
{
if (system(("rm -r " + ProgramParameters::newTemplatePath + " 2> /dev/null").c_str())){}
}
/// @brief Train a TransitionMachine to predict and add information to a structured input file, by using annotated examples. /// @brief Train a TransitionMachine to predict and add information to a structured input file, by using annotated examples.
/// ///
...@@ -262,68 +20,7 @@ void removeTemplatePath() ...@@ -262,68 +20,7 @@ void removeTemplatePath()
/// @return 0 if there was no crash. /// @return 0 if there was no crash.
int main(int argc, char * argv[]) int main(int argc, char * argv[])
{ {
auto od = getOptionsDescription(); loadTrainProgramParameters(argc, argv);
po::variables_map vm = checkOptions(od, argc, argv);
ProgramParameters::expName = vm["expName"].as<std::string>();
ProgramParameters::baseExpName = ProgramParameters::expName;
ProgramParameters::templateName = vm["templateName"].as<std::string>();
ProgramParameters::tmName = vm["tm"].as<std::string>();
ProgramParameters::bdName = vm["bd"].as<std::string>();
ProgramParameters::mcdName = vm["mcd"].as<std::string>();
ProgramParameters::debug = vm.count("debug") == 0 ? false : true;
ProgramParameters::randomDebug = vm.count("randomDebug") == 0 ? false : true;
ProgramParameters::printEntropy = vm.count("printEntropy") == 0 ? false : true;
ProgramParameters::printTime = vm.count("printTime") == 0 ? false : true;
ProgramParameters::featureExtraction = vm.count("featureExtraction") == 0 ? false : true;
ProgramParameters::devEvalOnGold = vm.count("devEvalOnGold") == 0 ? false : true;
ProgramParameters::trainName = vm["train"].as<std::string>();
ProgramParameters::devName = vm["dev"].as<std::string>();
ProgramParameters::lang = vm["lang"].as<std::string>();
ProgramParameters::nbIter = vm["nbiter"].as<int>();
ProgramParameters::seed = vm["seed"].as<int>();
ProgramParameters::batchSize = vm["batchSize"].as<int>();
ProgramParameters::dictCapacity = vm["dictCapacity"].as<int>();
ProgramParameters::nbTrain = vm["nbTrain"].as<int>();
ProgramParameters::removeDuplicates = vm["duplicates"].as<bool>();
ProgramParameters::interactive = vm["interactive"].as<bool>();
ProgramParameters::shuffleExamples = vm["shuffle"].as<bool>();
ProgramParameters::devLoss = vm["devLoss"].as<bool>();
ProgramParameters::randomEmbeddings = vm["randomEmbeddings"].as<bool>();
ProgramParameters::randomParameters = vm["randomParameters"].as<bool>();
ProgramParameters::sequenceDelimiterTape = vm["sequenceDelimiterTape"].as<std::string>();
ProgramParameters::sequenceDelimiter = vm["sequenceDelimiter"].as<std::string>();
ProgramParameters::learningRate = vm["lr"].as<float>();
ProgramParameters::randomDebugProbability = vm["randomDebugProbability"].as<float>();
ProgramParameters::beta1 = vm["b1"].as<float>();
ProgramParameters::beta2 = vm["b2"].as<float>();
ProgramParameters::bias = vm["bias"].as<float>();
ProgramParameters::nbIndividuals = vm["nbIndividuals"].as<int>();
ProgramParameters::optimizer = vm["optimizer"].as<std::string>();
ProgramParameters::dicts = vm["dicts"].as<std::string>();
ProgramParameters::loss = vm["loss"].as<std::string>();
ProgramParameters::dynamicEpoch = vm["epochd"].as<int>();
ProgramParameters::dynamicProbability = vm["proba"].as<float>();
ProgramParameters::tapeToMask = vm["tapeToMask"].as<std::string>();
ProgramParameters::maskRate = vm["maskRate"].as<float>();
ProgramParameters::showFeatureRepresentation = vm["showFeatureRepresentation"].as<int>();
ProgramParameters::iterationSize = vm["iterationSize"].as<int>();
std::string featureModels = vm["featureModels"].as<std::string>();
if (!featureModels.empty())
{
auto byClassifiers = split(featureModels, ',');
for (auto & classifier : byClassifiers)
{
auto parts = split(classifier, '=');
if (parts.size() != 2)
{
fprintf(stderr, "ERROR (%s) : wrong format for argument of option featureModels. Aborting.\n", ERRINFO);
exit(1);
}
ProgramParameters::featureModelByClassifier[parts[0]] = parts[1];
}
}
if (ProgramParameters::nbTrain) if (ProgramParameters::nbTrain)
{ {
......
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