void RecursiveRLSWrapper<T>::train(const gMat2D<T> &X, const gMat2D<T> &y)
{
this->opt->removeOpt("split");
this->opt->removeOpt("paramsel");
this->opt->removeOpt("optimizer");
this->opt->removeOpt("kernel");
SplitHo<T> splitTask;
GurlsOptionsList* split = splitTask.execute(X, y, *(this->opt));
this->opt->addOpt("split", split);
const gMat2D<unsigned long>& split_indices = split->getOptValue<OptMatrix<gMat2D<unsigned long> > >("indices");
const gMat2D<unsigned long>& split_lasts = split->getOptValue<OptMatrix<gMat2D<unsigned long> > >("lasts");
const unsigned long n = X.rows();
const unsigned long d = X.cols();
const unsigned long t = y.cols();
const unsigned long last = split_lasts.getData()[0];
const unsigned long nva = n-last;
unsigned long* va = new unsigned long[nva];
copy(va, split_indices.getData()+last, nva);
gMat2D<T>* Xva = new gMat2D<T>(nva, d);
gMat2D<T>* yva = new gMat2D<T>(nva, t);
subMatrixFromRows(X.getData(), n, d, va, nva, Xva->getData());
subMatrixFromRows(y.getData(), n, t, va, nva, yva->getData());
gMat2D<T>* XtX = new gMat2D<T>(d, d);
gMat2D<T>* Xty = new gMat2D<T>(d, t);
dot(X.getData(), X.getData(), XtX->getData(), n, d, n, d, d, d, CblasTrans, CblasNoTrans, CblasColMajor);
dot(X.getData(), y.getData(), Xty->getData(), n, d, n, t, d, t, CblasTrans, CblasNoTrans, CblasColMajor);
GurlsOptionsList* kernel = new GurlsOptionsList("kernel");
kernel->addOpt("XtX", new OptMatrix<gMat2D<T> >(*XtX));
kernel->addOpt("Xty", new OptMatrix<gMat2D<T> >(*Xty));
kernel->addOpt("Xva", new OptMatrix<gMat2D<T> >(*Xva));
kernel->addOpt("yva", new OptMatrix<gMat2D<T> >(*yva));
nTot = n;
this->opt->addOpt("kernel", kernel);
ParamSelHoPrimal<T> paramselTask;
this->opt->addOpt("paramsel", paramselTask.execute(X, y, *(this->opt)));
RLSPrimalRecInit<T> optimizerTask;
this->opt->addOpt("optimizer", optimizerTask.execute(X, y, *(this->opt)));
}
void KernelWrapper<T>::setSigma(double value)
{
if(this->opt->hasOpt("paramsel.sigma"))
this->opt->template getOptValue<OptNumber>("paramsel.sigma") = value;
else
{
GurlsOptionsList* paramsel = this->opt->template getOptAs<GurlsOptionsList>("paramsel");
paramsel->addOpt("sigma", new OptNumber(value));
}
setNSigma(1);
}
gurls::GurlsOptionsList* train(T* X, T* y, unsigned long n, unsigned long d, unsigned long t,
std::string algorithm, std::string kernel, std::string problem, std::string savefile)
{
gMat2D<T> Xtr(X, n, d, false);
gMat2D<T> ytr(y, n, t, false);
GurlsWrapper<T> *gurlsWrap;
try
{
if(algorithm=="krls")
gurlsWrap= new KernelRLSWrapper<T>("Train");
else{
algorithm="krls";
gurlsWrap= new KernelRLSWrapper<T>("Train");
std::cout<<"algorithm forced to krls"<<std::endl;
}
GurlsOptionsList *retopt = new GurlsOptionsList(gurlsWrap->getOpt());
gurlsWrap->loadOpt(*retopt, false);
if(kernel=="linear" && algorithm=="krls")
dynamic_cast<KernelRLSWrapper<T>*>(gurlsWrap)->setKernelType(KernelWrapper<T>::LINEAR);
else if(algorithm=="krls")
dynamic_cast<KernelRLSWrapper<T>*>(gurlsWrap)->setKernelType(KernelWrapper<T>::RBF);
if(problem=="classification"){
gurlsWrap->setProblemType(gurlsWrap->CLASSIFICATION);}
else if(problem=="regression")
gurlsWrap->setProblemType(gurlsWrap->REGRESSION);
else{
const char * probList[] = { "Classification", "Regression"};
typename KernelWrapper<T>::ProblemType prob =gurlsWrap->problemTypeFromData(Xtr, ytr);
std::cout<<"Problem automatically set to: "<<probList[prob];
gurlsWrap->setProblemType(prob);}
gurlsWrap->train(Xtr, ytr);
if(savefile!="")
retopt->save(savefile);
delete gurlsWrap;
return retopt;
}
catch (gException& e)
{
std::cout << e.getMessage() << std::endl;
delete gurlsWrap;
throw e;
}
}
void RLSlinear::trainModel(gMat2D<float> &X, gMat2D<float> &Y, bool newGURLS)
{
if(modelLinearRLS!=NULL)
delete modelLinearRLS;
if (newGURLS==false)
{
OptTaskSequence *seq = new OptTaskSequence();
OptProcess* process_train = new OptProcess();
OptProcess* process_predict = new OptProcess();
*seq << "kernel:linear" << "split:ho" << "paramsel:hodual";
*process_train << GURLS::computeNsave << GURLS::compute << GURLS::computeNsave;
*process_predict << GURLS::load << GURLS::ignore << GURLS::load;
*seq<< "optimizer:rlsdual"<< "pred:dual";
*process_train<<GURLS::computeNsave<<GURLS::ignore;
*process_predict<<GURLS::load<<GURLS::computeNsave;
GurlsOptionsList * processes = new GurlsOptionsList("processes", false);
processes->addOpt("train",process_train);
processes->addOpt("test",process_predict);
modelLinearRLS = new GurlsOptionsList(className, true);
modelLinearRLS->addOpt("seq", seq);
modelLinearRLS->addOpt("processes", processes);
RLS.run(X,Y,*modelLinearRLS,"train");
}
else
{
unsigned long n = X.rows();
unsigned long d = X.cols();
unsigned long t = Y.cols(); // should be 1
modelLinearRLS = train(X.getData(), Y.getData(), n, d, t, "krls", "linear");
}
}
void RecursiveRLSWrapper<T>::retrain()
{
GurlsOptionsList* kernel = this->opt->template getOptAs<GurlsOptionsList>("kernel");
const gMat2D<T> &Xva = kernel->getOptValue<OptMatrix<gMat2D<T> > >("Xva");
const gMat2D<T> &yva = kernel->getOptValue<OptMatrix<gMat2D<T> > >("yva");
const unsigned long nva = Xva.rows();
this->opt->removeOpt("paramsel");
this->opt->removeOpt("optimizer");
GurlsOptionsList* split = this->opt->template getOptAs<GurlsOptionsList>("split");
split->removeOpt("indices");
split->removeOpt("lasts");
gMat2D<unsigned long>* indices = new gMat2D<unsigned long>(1, nTot);
gMat2D<unsigned long>* lasts = new gMat2D<unsigned long>(1, 1);
set(indices->getData(), 0ul, nTot-nva);
unsigned long * it = indices->getData() + (nTot-nva);
for(unsigned long i=0; i<nva; ++i, ++it)
*it = i;
lasts->getData()[0] = (nTot-nva);
split->addOpt("indices", new OptMatrix<gMat2D<unsigned long> >(*indices));
split->addOpt("lasts", new OptMatrix<gMat2D<unsigned long> >(*lasts));
ParamSelHoPrimal<T> paramselTask;
this->opt->addOpt("paramsel", paramselTask.execute(Xva, yva, *(this->opt)));
RLSPrimalRecInit<T> optimizerTask;
gMat2D<T> emptyMat;
this->opt->addOpt("nTot", new OptNumber(nTot));
this->opt->addOpt("optimizer", optimizerTask.execute(emptyMat, emptyMat, *(this->opt)));
this->opt->removeOpt("nTot");
}
/**
* Main function
*/
int main(int argc, char *argv[])
{
string xtr_file, xte_file, ytr_file, yte_file;
// check that all inputs are given
if(argc<4)
{
std::cout << "========================================================================================"<< std::endl
<< " Wrong parameters number ("<<argc <<")." << std::endl
<< " Provide a valid path for training, test and output files using the following syntax:" << std::endl
<< " \n\n\t " << argv[0] << " xtr xte ytr yte" << std::endl
<< "========================================================================================" << std::endl << std::endl;
return 0;
}
// get file names from input
xtr_file = argv[1];
xte_file = argv[2];
ytr_file = argv[3];
yte_file = argv[4];
try
{
gMat2D<T> Xtr, Xte, ytr, yte;
// load data from file
Xtr.readCSV(xtr_file);
Xte.readCSV(xte_file);
ytr.readCSV(ytr_file);
yte.readCSV(yte_file);
// specify the task sequence
OptTaskSequence *seq = new OptTaskSequence();
*seq << "paramsel:loocvprimal" << "optimizer:rlsprimal" << "pred:primal" << "perf:macroavg";
// *seq << "kernel:linear" << "paramsel:loocvdual" << "optimizer:rlsdual" << "pred:dual" << "perf:macroavg";
// *seq << "paramsel:siglam"
// << "kernel:rbf"
// << "optimizer:rlsdual"
// << "predkernel:traintest"
// << "pred:dual"
// << "perf:macroavg";
GurlsOptionsList * process = new GurlsOptionsList("processes", false);
// defines instructions for training process
OptProcess* process1 = new OptProcess();
*process1 << GURLS::computeNsave << GURLS::computeNsave << GURLS::ignore << GURLS::ignore;
// *process1 << GURLS::computeNsave << GURLS::computeNsave << GURLS::computeNsave << GURLS::ignore << GURLS::ignore;
// *process1 << GURLS::computeNsave
// << GURLS::computeNsave
// << GURLS::computeNsave
// << GURLS::ignore
// << GURLS::ignore
// << GURLS::ignore;
process->addOpt("one", process1);
// defines instructions for testing process
OptProcess* process2 = new OptProcess();
*process2 << GURLS::load << GURLS::load<< GURLS::computeNsave << GURLS::computeNsave;
// *process2 << GURLS::load << GURLS::load << GURLS::load<< GURLS::compute << GURLS::compute;
// *process2 << GURLS::load
// << GURLS::load
// << GURLS::ignore
// << GURLS::compute
// << GURLS::compute
// << GURLS::compute;
process->addOpt("two", process2);
// build an options' structure
GurlsOptionsList* opt = new GurlsOptionsList("GURLSlooprimal", true);
opt->addOpt("seq", seq);
opt->addOpt("processes", process);
GURLS G;
string jobId0("one");
string jobId1("two");
clock_t start = std::clock();
// run gurls for training
G.run(Xtr, ytr, *opt, jobId0);
// run gurls for testing
G.run(Xte, yte, *opt, jobId1);
cout << "Elased time is " << (clock() - start) / (double)(CLOCKS_PER_SEC) << endl;
cout << "Accuracy: ";
cout << OptMatrix<gMat2D<T> >::dynacast(GurlsOptionsList::dynacast(opt->getOpt("perf"))->getOpt("acc"))->getValue()[0][0] << endl;
}
catch (gException& e)
{
cout << e.getMessage() << endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
void RecursiveRLSWrapper<T>::update(const gVec<T> &X, const gVec<T> &y)
{
if(!this->trainedModel())
throw gException("Error, Train Model First");
RLSPrimalRecUpdate<T> optimizer;
const unsigned long d = X.getSize();
const unsigned long t = y.getSize();
gMat2D<T>X_mat(1, d);
copy(X_mat.getData(), X.getData(), d);
gMat2D<T>y_mat(1, t);
copy(y_mat.getData(), y.getData(), t);
GurlsOptionsList* ret = optimizer.execute(X_mat, y_mat, *(this->opt));
this->opt->removeOpt("optimizer");
this->opt->addOpt("optimizer", ret);
++nTot;
gMat2D<T>* xtx = new gMat2D<T>(d,d);
gMat2D<T>* xty = new gMat2D<T>(d,t);
dot(X.getData(), X.getData(), xtx->getData(), 1, d, 1, d, d, d, CblasTrans, CblasNoTrans, CblasColMajor);
dot(X.getData(), y.getData(), xty->getData(), 1, d, 1, t, d, t, CblasTrans, CblasNoTrans, CblasColMajor);
GurlsOptionsList* kernel = this->opt->template getOptAs<GurlsOptionsList>("kernel");
const gMat2D<T>& XtX = kernel->getOptValue<OptMatrix<gMat2D<T> > >("XtX");
const gMat2D<T>& Xty = kernel->getOptValue<OptMatrix<gMat2D<T> > >("Xty");
axpy(d*d, (T)1.0, XtX.getData(), 1, xtx->getData(), 1);
axpy(d*t, (T)1.0, Xty.getData(), 1, xty->getData(), 1);
kernel->removeOpt("XtX");
kernel->addOpt("XtX", new OptMatrix<gMat2D<T> >(*xtx));
kernel->removeOpt("Xty");
kernel->addOpt("Xty", new OptMatrix<gMat2D<T> >(*xty));
unsigned long proportion = static_cast<unsigned long>(gurls::round(1.0/this->opt->getOptAsNumber("hoproportion")));
if(nTot % proportion == 0)
{
const gMat2D<T>& Xva = kernel->getOptValue<OptMatrix<gMat2D<T> > >("Xva");
const gMat2D<T>& yva = kernel->getOptValue<OptMatrix<gMat2D<T> > >("yva");
const unsigned long nva = Xva.rows();
const unsigned long nva_new = nva+1;
gMat2D<T>* Xva_new = new gMat2D<T>(nva_new, d);
const T* old_it = Xva.getData();
T* new_it = Xva_new->getData();
for(const T* end = new_it+(nva_new*d); new_it< end; old_it+=nva, new_it +=nva_new)
copy(new_it, old_it, nva);
copy(Xva_new->getData()+nva, X.getData(), d, nva_new, 1);
kernel->removeOpt("Xva");
kernel->addOpt("Xva", new OptMatrix<gMat2D<T> >(*Xva_new));
gMat2D<T>* yva_new = new gMat2D<T>(nva_new, t);
old_it = yva.getData();
new_it = yva_new->getData();
for(const T* end = new_it+(nva_new*t); new_it< end; old_it+=nva, new_it +=nva_new)
copy(new_it, old_it, nva);
copy(yva_new->getData()+nva, y.getData(), t, nva_new, 1);
kernel->removeOpt("yva");
kernel->addOpt("yva", new OptMatrix<gMat2D<T> >(*yva_new));
}
}
/**
* Main function
*/
int main(int argc, char *argv[])
{
string xtr_file, xte_file, ytr_file, yte_file;
// check that all inputs are given
if(argc<4)
{
std::cout << "========================================================================================"<< std::endl
<< " Wrong parameters number ("<<argc <<")." << std::endl
<< " Provide a valid path for training, test and output files using the following syntax:" << std::endl
<< " \n\n\t " << argv[0] << " xtr xte ytr yte" << std::endl
<< "========================================================================================" << std::endl << std::endl;
return 0;
}
// get file names from input
xtr_file = argv[1];
xte_file = argv[2];
ytr_file = argv[3];
yte_file = argv[4];
try
{
gMat2D<T> *Xtr, *Xte, *ytr, *yte;
// load data from file
Xtr = readFile<T>(xtr_file);
Xte = readFile<T>(xte_file);
ytr = readFile<T>(ytr_file);
yte = readFile<T>(yte_file);
// specify the task sequence
OptTaskSequence *seq = new OptTaskSequence();
seq->addTask("paramsel:loocvprimal");
seq->addTask("optimizer:rlsprimal");
seq->addTask("pred:primal");
seq->addTask("perf:macroavg");
seq->addTask("perf:precrec");
GurlsOptionsList * process = new GurlsOptionsList("processes", false);
// defines instructions for training process
std::vector<double> process1;
process1.push_back(GURLS::computeNsave);
process1.push_back(GURLS::computeNsave);
process1.push_back(GURLS::ignore);
process1.push_back(GURLS::ignore);
process1.push_back(GURLS::ignore);
process->addOpt("one", new OptNumberList(process1));
// defines instructions for testing process
std::vector<double> process2;
process2.push_back(GURLS::load);
process2.push_back(GURLS::load);
process2.push_back(GURLS::computeNsave);
process2.push_back(GURLS::computeNsave);
process2.push_back(GURLS::computeNsave);
process->addOpt("two", new OptNumberList(process2));
// build an options' structure
GurlsOptionsList* opt = new GurlsOptionsList("GURLSlooprimal", false);
opt->addOpt("seq", seq);
opt->addOpt("processes", process);
GURLS G;
string jobId0("one");
string jobId1("two");
// run gurls for training
G.run(*Xtr, *ytr, *opt, jobId0);
// run gurls for testing
G.run(*Xte, *yte, *opt, jobId1);
}
catch (gException& e)
{
cout << e.getMessage() << endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
void KernelRLSWrapper<T>::train(const gMat2D<T> &X, const gMat2D<T> &y)
{
this->opt->removeOpt("split");
this->opt->removeOpt("optimizer");
const unsigned long nlambda = static_cast<unsigned long>(this->opt->getOptAsNumber("nlambda"));
const unsigned long nsigma = static_cast<unsigned long>(this->opt->getOptAsNumber("nsigma"));
OptTaskSequence *seq = new OptTaskSequence();
GurlsOptionsList * process = new GurlsOptionsList("processes", false);
OptProcess* process1 = new OptProcess();
process->addOpt("one", process1);
this->opt->addOpt("seq", seq);
this->opt->addOpt("processes", process);
if(this->kType == KernelWrapper<T>::LINEAR)
{
if(nlambda > 1ul)
{
*seq << "split:ho" << "kernel:linear" << "paramsel:hodual";
*process1 << GURLS::computeNsave << GURLS::computeNsave << GURLS::computeNsave;
}
else if(nlambda == 1ul)
{
if(this->opt->hasOpt("paramsel.lambdas"))
{
*seq << "kernel:linear";
*process1 << GURLS::computeNsave;
}
else
throw gException("Please set a valid value for the regularization parameter, calling setParam(value)");
}
else
throw gException("Please set a valid value for NParam, calling setNParam(value)");
}
else if(this->kType == KernelWrapper<T>::RBF)
{
if(nlambda > 1ul)
{
if(nsigma > 1ul)
{
*seq << "split:ho" << "paramsel:siglamho" << "kernel:rbf";
*process1 << GURLS::computeNsave << GURLS::computeNsave << GURLS::computeNsave;
}
else if(nsigma == 1ul)
{
if(this->opt->hasOpt("paramsel.sigma"))
{
*seq << "split:ho" << "kernel:rbf" << "paramsel:hodual";
*process1 << GURLS::computeNsave << GURLS::computeNsave << GURLS::computeNsave;
}
else
throw gException("Please set a valid value for the kernel parameter, calling setSigma(value)");
}
else
throw gException("Please set a valid value for NSigma, calling setNSigma(value)");
}
else if(nlambda == 1ul)
{
if(nsigma == 1ul)
{
if(this->opt->hasOpt("paramsel.sigma") && this->opt->hasOpt("paramsel.lambdas"))
{
*seq << "split:ho" << "kernel:rbf";
*process1 << GURLS::computeNsave << GURLS::computeNsave;
}
else
throw gException("Please set a valid value for kernel and regularization parameters, calling setParam(value) and setSigma(value)");
}
else
throw gException("Please set a valid value for NSigma, calling setNSigma(value)");
}
else
throw gException("Please set a valid value for NParam, calling setNParam(value)");
}
*seq << "optimizer:rlsdual";
*process1 << GURLS::computeNsave;
GURLS G;
G.run(X, y, *(this->opt), "one");
}
int main(int argc, char *argv[])
{
srand(static_cast<unsigned int>(time(NULL)));
cout.precision(4);
cout.width(11);
cout << fixed << right << endl;
string input_folder("../data");
if (argc < 2)
{
cout << "========================================================================================"<< endl
<< " WARNING: No input folder provided. " << endl
<< " Using the default option: \'" << input_folder << "\'" << endl
<< " Please make sure such folder exists or provide a valid path using the following syntax:" << endl
<< " " << argv[0]
<< " <path-to-a-valid-input-folder>" << endl
<< "========================================================================================" << endl << endl;
}
else
input_folder = argv[1];
try
{
gMat2D<T> Xtr, Xte, ytr, yte;
Xtr.readCSV(input_folder+"/Xtr.txt");
Xte.readCSV(input_folder+"/Xte.txt");
ytr.readCSV(input_folder+"/ytr.txt");
yte.readCSV(input_folder+"/yte.txt");
// cout << "Xtr = " << endl << Xtr << endl;
// cout << "Xte = " << endl << Xte << endl;
GurlsOptionsList opt("ExampleExperiment", true);
OptTaskSequence *seq = new OptTaskSequence();
*seq << "paramsel:fixsiglam" << "kernel:rbf" << "optimizer:rlsdual" << "predkernel:traintest" << "pred:dual" << "perf:macroavg";
opt.addOpt("seq", seq);
GurlsOptionsList * process = new GurlsOptionsList("processes", false);
OptProcess* process1 = new OptProcess();
*process1 << GURLS::computeNsave << GURLS::computeNsave << GURLS::computeNsave << GURLS::ignore << GURLS::ignore << GURLS::ignore;
process->addOpt("one", process1);
OptProcess* process2 = new OptProcess();
*process2 << GURLS::load << GURLS::load << GURLS::load << GURLS::computeNsave << GURLS::computeNsave << GURLS::computeNsave;
process->addOpt("two", process2);
opt.addOpt("processes", process);
std::cout << opt << std::endl;
std::string jobid1("one");
std::string jobid2("two");
GURLS G;
G.run(Xtr, ytr, opt, jobid1);
std::cout << std::endl;
G.run(Xte, yte, opt, jobid2);
std::cout << opt << std::endl;
// opt.save("par1.txt");
// GurlsOptionsList *s1 = new GurlsOptionsList("dummy", false);
// s1->load("par1.txt");
// std::cout << *s1 << std::endl;
}
catch (gException& e)
{
cout << e.getMessage() << endl;
return -1;
}
return 0;
}
void GurlsOptionsList::copyOpt(string key, const GurlsOptionsList &from)
{
const GurlsOption* toCopy = from.getOpt(key);
GurlsOption* newOpt = NULL;
switch(toCopy->getType())
{
case StringOption:
newOpt = new OptString(OptString::dynacast(toCopy)->getValue());
break;
case NumberOption:
newOpt = new OptNumber(OptNumber::dynacast(toCopy)->getValue());
break;
case StringListOption:
newOpt = new OptStringList(OptStringList::dynacast(toCopy)->getValue());
break;
case NumberListOption:
newOpt = new OptNumberList(OptNumberList::dynacast(toCopy)->getValue());
break;
case FunctionOption:
newOpt = new OptFunction(OptFunction::dynacast(toCopy)->getName());
break;
case ProcessOption:
newOpt = new OptProcess(*OptProcess::dynacast(toCopy));
break;
case MatrixOption:
case VectorOption:
{
const OptMatrixBase* base = dynamic_cast<const OptMatrixBase*>(toCopy);
if(base == NULL)
throw gException(Exception_Illegal_Dynamic_Cast);
#ifdef _BGURLS
if(base->hasBigArray())
{
switch(base->getMatrixType())
{
case OptMatrixBase::ULONG:
newOpt = copyOptMatrix<BigArray<unsigned long> >(toCopy);
break;
case OptMatrixBase::FLOAT:
newOpt = copyOptMatrix<BigArray<float> >(toCopy);
break;
case OptMatrixBase::DOUBLE:
newOpt = copyOptMatrix<BigArray<double> >(toCopy);
break;
}
}
else
#endif
{
switch(base->getMatrixType())
{
case OptMatrixBase::ULONG:
newOpt = copyOptMatrix<gMat2D<unsigned long> >(toCopy);
break;
case OptMatrixBase::FLOAT:
newOpt = copyOptMatrix<gMat2D<float> >(toCopy);
break;
case OptMatrixBase::DOUBLE:
newOpt = copyOptMatrix<gMat2D<double> >(toCopy);
break;
}
}
}
break;
case OptListOption:
{
const GurlsOptionsList* toCopy_list = GurlsOptionsList::dynacast(toCopy);
newOpt = new GurlsOptionsList(*toCopy_list);
}
break;
case OptArrayOption:
newOpt = new OptArray(*OptArray::dynacast(toCopy));
break;
case TaskSequenceOption:
newOpt = new OptTaskSequence(*OptTaskSequence::dynacast(toCopy));
break;
case TaskOption:
newOpt = new OptTask(*OptTask::dynacast(toCopy));
break;
case TaskIDOption:
case GenericOption:
break;
}
if(newOpt != NULL)
addOpt(key, newOpt);
}
/**
* Main function
*/
int main(int argc, char *argv[])
{
string xtr_file, xte_file, ytr_file, yte_file;
// check that all inputs are given
if(argc<4)
{
std::cout << "========================================================================================"<< std::endl
<< " Wrong parameters number ("<<argc <<")." << std::endl
<< " Provide a valid path for training, test and output files using the following syntax:" << std::endl
<< " \n\n\t " << argv[0] << " xtr xte ytr yte" << std::endl
<< "========================================================================================" << std::endl << std::endl;
return 0;
}
// get file names from input
xtr_file = argv[1];
xte_file = argv[2];
ytr_file = argv[3];
yte_file = argv[4];
try
{
gMat2D<T> Xtr, Xte, ytr, yte;
// load data from file
Xtr.readCSV(xtr_file);
Xte.readCSV(xte_file);
ytr.readCSV(ytr_file);
yte.readCSV(yte_file);
// specify the task sequence
OptTaskSequence *seq = new OptTaskSequence();
OptTaskSequence *seq2 = new OptTaskSequence();
*seq << "paramsel:loocvprimal" << "optimizer:rlsprimal" << "pred:primal" << "perf:precrec";
*seq2 << "pred:primal"<< "perf:macroavg";
GurlsOptionsList * process = new GurlsOptionsList("processes", false);
GurlsOptionsList * processbis = new GurlsOptionsList("processes2", false);
// defines instructions for training process
OptProcess* process1 = new OptProcess();
*process1 << GURLS::computeNsave << GURLS::computeNsave << GURLS::ignore << GURLS::ignore;
process->addOpt("one", process1);
// defines instructions for testing process
OptProcess* process2 = new OptProcess();
*process2 << GURLS::load << GURLS::load << GURLS::computeNsave << GURLS::computeNsave;
process->addOpt("two", process2); // defines instructions for testing process
OptProcess* process3 = new OptProcess();
*process3 << GURLS::load << GURLS::computeNsave;
processbis->addOpt("three", process3);
// build an options' structure
GurlsOptionsList* opt = new GurlsOptionsList("GURLSlooprimal", true);
opt->addOpt("seq", seq);
opt->addOpt("processes", process);
GURLS G;
string jobId0("one");
string jobId1("two");
string jobId2("three");
// run gurls for training
G.run(Xtr, ytr, *opt, jobId0);
// run gurls for testing with precrec
G.run(Xte, yte, *opt, jobId1);
GurlsOptionsList* opt2 = new GurlsOptionsList(*opt);
opt2->removeOpt("seq");
opt2->addOpt("seq", seq2);
opt2->removeOpt("processes");
opt2->addOpt("processes", processbis);
// run gurls for testing with macroavg
G.run(Xte, yte, *opt2, jobId2);
// Results visualization
/*** Debug code
opt->printAll();
int optType = (*(opt->getOpt("perf.acc"))).getType(); // Get referenced option type
OptMatrix<gMat2D<double> >* temp = (opt->getOptAs<OptMatrix<gMat2D<double> > >("perf.acc"));
int ty = temp->getMatrixType();
cout << "Matrix type: " << ty << endl;
double *acc = temp->getValue().getData();
***/
double *acc_precrec = (opt->getOptAs<OptMatrix<gMat2D<double> > >("perf.ap"))->getValue().getData();
cout << "precision recall (perf.ap):" << endl;
// Print accuracy values
for (int i=0 ; i<4 ; i++)
cout << acc_precrec[i] << "\t";
cout << endl;
double *acc_macroavg = (opt2->getOptAs<OptMatrix<gMat2D<double> > >("perf.acc"))->getValue().getData();
cout << "Macro-average (perf.acc):" << endl;
// Print accuracy values
for (int i=0 ; i<4 ; i++)
cout << acc_macroavg[i] << "\t";
cout << endl;
}
catch (gException& e)
{
cout << e.getMessage() << endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
int main(int argc, char *argv[])
{
if(argc < 3)
{
cout << "Usage: " << argv[0] << " <input dir> <shared dir>." << endl;
cout << "\t - <input dir> is the directory where the csv data files reside" << endl;
cout << "\t - <shared dir> is a directory accessible by all processes (all processes must be able to access the same path)" << endl;
return 0;
}
srand(static_cast<unsigned int>(time(NULL)));
cout.precision(4);
cout.width(11);
cout << fixed << right << endl;
path input_directory(argv[1]);
path shared_directory(argv[2]);
int numprocs;
int myid;
MPI_Init(&argc, &argv);
// Find out the number of processes
MPI_Comm_size(MPI_COMM_WORLD, &numprocs);
// Get the process rank
MPI_Comm_rank(MPI_COMM_WORLD, &myid);
if(myid ==0)
cout << "Loading Xtr..." << endl;
BigArray<T> Xtr(path(shared_directory / "Xtr.h5").native(), 0, 0);
Xtr.readCSV(path(input_directory / "Xtr.txt").native());
if(myid ==0)
cout << "Loading Xte..." << endl;
BigArray<T> Xte(path(shared_directory / "Xte.h5").native(), 0, 0);
Xte.readCSV(path(input_directory / "Xte.txt").native());
if(myid ==0)
cout << "Loading ytr..." << endl;
BigArray<T> ytr(path(shared_directory / "ytr.h5").native(), 0, 0);
ytr.readCSV(path(input_directory / "ytr.txt").native());
if(myid ==0)
cout << "Loading yte..." << endl;
BigArray<T> yte(path(shared_directory / "yte.h5").native(), 0, 0);
yte.readCSV(path(input_directory / "yte.txt").native());
BGurlsOptionsList opt("examplebgurls", shared_directory.native(), true);
// remove old experiments results
if(myid == 0 && boost::filesystem::exists(path(opt.getOptAsString("savefile"))))
boost::filesystem::remove(path(opt.getOptAsString("savefile")));
OptTaskSequence *seq = new OptTaskSequence();
*seq << "bigsplit:ho" << "bigparamsel:hoprimal" << "bigoptimizer:rlsprimal" << "bigpred:primal" << "bigperf:macroavg";
opt.addOpt("seq", seq);
GurlsOptionsList * process = new GurlsOptionsList("processes", false);
OptProcess* process1 = new OptProcess();
*process1 << GURLS::computeNsave << GURLS::computeNsave << GURLS::computeNsave << GURLS::ignore << GURLS::ignore;
process->addOpt("one", process1);
OptProcess* process2 = new OptProcess();
*process2 << GURLS::load << GURLS::load << GURLS::load << GURLS::computeNsave << GURLS::computeNsave;
process->addOpt("two", process2);
opt.addOpt("processes", process);
if(myid ==0)
std::cout << opt << std::endl;
std::string jobid1("one");
std::string jobid2("two");
BGURLS G;
// Run bgurls on the training set
// cout << "---Training..." << endl;
G.run(Xtr, ytr, opt, jobid1);
// cout << "---Training done..." << endl;
// if(myid ==0)
// std::cout << opt << std::endl;
// Run bgurls on the test set
// cout << "---Testing..." << endl;
G.run(Xte, yte, opt, jobid2);
// cout << "---Testing done..." << endl;
if(myid ==0)
{
const gMat2D<T>& acc = opt.getOptValue<OptMatrix<gMat2D<T> > >("perf.acc");
const int accs = acc.getSize();
std::cout.precision(4);
std::cout << std::endl << "Prediction accurcay is:" << std::endl;
std::cout << "\t";
for(int i=1; i<= accs; ++i)
std::cout << "Class " << i << "\t";
std::cout << std::endl << "\t";
for(int i=0; i< accs; ++i)
std::cout << acc.getData()[i]*100.0 << "%\t";
std::cout << std::endl;
}
BigArray<T>::releaseMPIData();
MPI_Finalize();
return EXIT_SUCCESS;
}
