C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

impl_als_csr_batch.cpp

/* file: impl_als_csr_batch.cpp */
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/*
! Content:
! C++ example of the implicit alternating least squares (ALS) algorithm in
! the batch processing mode.
!
! The program trains the implicit ALS model on a training data set.
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::data_management;
using namespace daal::algorithms::implicit_als;
/* Input data set parameters */
string trainDatasetFileName = "../data/batch/implicit_als_csr.csv";
typedef float algorithmFPType; /* Algorithm floating-point type */
/* Algorithm parameters */
const size_t nFactors = 2;
NumericTablePtr dataTable;
ModelPtr initialModel;
training::ResultPtr trainingResult;
void initializeModel();
void trainModel();
void testModel();
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &trainDatasetFileName);
initializeModel();
trainModel();
testModel();
return 0;
}
void initializeModel()
{
/* Read trainDatasetFileName from a file and create a numeric table to store the input data */
dataTable = NumericTablePtr(createSparseTable<float>(trainDatasetFileName));
/* Create an algorithm object to initialize the implicit ALS model with the default method */
training::init::Batch<algorithmFPType, training::init::fastCSR> initAlgorithm;
initAlgorithm.parameter.nFactors = nFactors;
/* Pass a training data set and dependent values to the algorithm */
initAlgorithm.input.set(training::init::data, dataTable);
/* Initialize the implicit ALS model */
initAlgorithm.compute();
initialModel = initAlgorithm.getResult()->get(training::init::model);
}
void trainModel()
{
/* Create an algorithm object to train the implicit ALS model with the default method */
training::Batch<algorithmFPType, training::fastCSR> algorithm;
/* Pass a training data set and dependent values to the algorithm */
algorithm.input.set(training::data, dataTable);
algorithm.input.set(training::inputModel, initialModel);
algorithm.parameter.nFactors = nFactors;
/* Build the implicit ALS model */
algorithm.compute();
/* Retrieve the algorithm results */
trainingResult = algorithm.getResult();
}
void testModel()
{
/* Create an algorithm object to predict recommendations of the implicit ALS model */
prediction::ratings::Batch<> algorithm;
algorithm.parameter.nFactors = nFactors;
algorithm.input.set(prediction::ratings::model, trainingResult->get(training::model));
algorithm.compute();
NumericTablePtr predictedRatings = algorithm.getResult()->get(prediction::ratings::prediction);
printNumericTable(predictedRatings, "Predicted ratings:");
}

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