C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

host_cancel_compute.cpp

/* file: host_cancel_compute.cpp */
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/*
! Content:
! C++ example demonstrates how to cancel algorithm's compute() call on a
! host application request by means of user-defined callback.
!
! The program trains the decision forest model on a training
! datasetFileName and cancels computation on application request.
!******************************************************************************/
#define DAAL_NOTHROW_EXCEPTIONS //it is required to get cancellation status as compute() return code rather than exception
#include "daal.h"
#include "service.h"
#include <time.h>
using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::algorithms::decision_forest::classification;
using namespace daal::services;
/* Input data set parameters */
const string trainDatasetFileName = "../data/batch/df_classification_train.csv";
const size_t categoricalFeaturesIndices[] = { 2 };
const size_t nFeatures = 3; /* Number of features in training and testing data sets */
/* Decision forest parameters */
const size_t nTrees = 10;
const size_t minObservationsInLeafNode = 8;
const size_t nClasses = 5; /* Number of classes */
training::ResultPtr trainModel();
void loadData(const std::string& fileName, NumericTablePtr& pData, NumericTablePtr& pDependentVar);
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &trainDatasetFileName);
training::ResultPtr trainingResult = trainModel();
return 0;
}
class ExampleApp : public daal::services::HostAppIface
{
public:
ExampleApp(double timeLimitSec) : _timeLimitSec(timeLimitSec), _bCancelled(false), _startTime(0){}
void start()
{
time(&_startTime);
_bCancelled = false;
}
virtual bool isCancelled() DAAL_C11_OVERRIDE
{
if(_bCancelled)
return true;
time_t now;
time(&now);
const double sec = difftime(now, _startTime);
if((sec >= _timeLimitSec) && !_bCancelled)
{
if(!_bCancelled)
{
_bCancelled = true;
std::cout << "Cancelled after " << sec << " seconds" << std::endl;
return true;
}
}
return false;
}
private:
time_t _startTime;
const double _timeLimitSec;
volatile bool _bCancelled;
};
training::ResultPtr trainModel()
{
/* Create Numeric Tables for training data and dependent variables */
NumericTablePtr trainData;
NumericTablePtr trainDependentVariable;
loadData(trainDatasetFileName, trainData, trainDependentVariable);
/* Create an algorithm object to train the decision forest classification model */
training::Batch<> algorithm(nClasses);
/* Pass a training data set and dependent values to the algorithm */
algorithm.input.set(classifier::training::data, trainData);
algorithm.input.set(classifier::training::labels, trainDependentVariable);
algorithm.parameter.nTrees = nTrees;
algorithm.parameter.featuresPerNode = nFeatures;
algorithm.parameter.minObservationsInLeafNode = minObservationsInLeafNode;
algorithm.parameter.varImportance = algorithms::decision_forest::training::MDI;
algorithm.parameter.resultsToCompute = algorithms::decision_forest::training::computeOutOfBagError;
ExampleApp host(10); /* set the time limit to work before cancelling equal to 10 sec */
algorithm.setHostApp(HostAppIfacePtr(&host, EmptyDeleter()));
host.start();
Status s;
do
{
/* Build the decision forest classification model */
std::cout << "compute()" << std::endl;
s = algorithm.compute();
}
while(s.ok());
if(!s)
{
std::cout << s.getDescription() << std::endl;
return training::ResultPtr();
}
/* Retrieve the algorithm results */
return algorithm.getResult();
}
void loadData(const std::string& fileName, NumericTablePtr& pData, NumericTablePtr& pDependentVar)
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> trainDataSource(fileName,
DataSource::notAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create Numeric Tables for training data and dependent variables */
pData.reset(new HomogenNumericTable<>(nFeatures, 0, NumericTable::notAllocate));
pDependentVar.reset(new HomogenNumericTable<>(1, 0, NumericTable::notAllocate));
NumericTablePtr mergedData(new MergedNumericTable(pData, pDependentVar));
/* Retrieve the data from input file */
trainDataSource.loadDataBlock(mergedData.get());
NumericTableDictionaryPtr pDictionary = pData->getDictionarySharedPtr();
for(size_t i = 0, n = sizeof(categoricalFeaturesIndices) / sizeof(categoricalFeaturesIndices[0]); i < n; ++i)
(*pDictionary)[categoricalFeaturesIndices[i]].featureType = data_feature_utils::DAAL_CATEGORICAL;
}

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