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

mn_naive_bayes_csr_online.cpp

/* file: mn_naive_bayes_csr_online.cpp */
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/*
! Content:
! C++ example of Naive Bayes classification in the online processing mode.
!
! The program trains the Naive Bayes model on a supplied training data set in
! compressed sparse rows (CSR)__format and then performs classification of
! previously unseen data.
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::algorithms::multinomial_naive_bayes;
typedef float algorithmFPType; /* Algorithm floating-point type */
/* Input data set parameters */
const string trainDatasetFileNames[4] =
{
"../data/online/naivebayes_train_csr_1.csv", "../data/online/naivebayes_train_csr_2.csv",
"../data/online/naivebayes_train_csr_3.csv", "../data/online/naivebayes_train_csr_4.csv"
};
const string trainGroundTruthFileNames[4] =
{
"../data/online/naivebayes_train_labels_1.csv", "../data/online/naivebayes_train_labels_2.csv",
"../data/online/naivebayes_train_labels_3.csv", "../data/online/naivebayes_train_labels_4.csv"
};
string testDatasetFileName = "../data/online/naivebayes_test_csr.csv";
string testGroundTruthFileName = "../data/online/naivebayes_test_labels.csv";
const size_t nTrainVectorsInBlock = 8000;
const size_t nTestObservations = 2000;
const size_t nClasses = 20;
const size_t nBlocks = 4;
training::ResultPtr trainingResult;
classifier::prediction::ResultPtr predictionResult;
CSRNumericTablePtr trainData[nBlocks];
CSRNumericTablePtr testData;
void trainModel();
void testModel();
void printResults();
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 10,
&trainDatasetFileNames[0], &trainDatasetFileNames[1],
&trainDatasetFileNames[2], &trainDatasetFileNames[3],
&trainGroundTruthFileNames[0], &trainGroundTruthFileNames[1],
&trainGroundTruthFileNames[2], &trainGroundTruthFileNames[3],
&testDatasetFileName, &testGroundTruthFileName);
trainModel();
testModel();
printResults();
return 0;
}
void trainModel()
{
/* Create an algorithm object to train the Naive Bayes model */
training::Online<algorithmFPType, training::fastCSR> algorithm(nClasses);
for(size_t i = 0; i < nBlocks; i++)
{
/* Read trainDatasetFileNames and create a numeric table to store the input data */
trainData[i] = CSRNumericTablePtr(createSparseTable<float>(trainDatasetFileNames[i]));
FileDataSource<CSVFeatureManager> trainLabelsSource(trainGroundTruthFileNames[i],
DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
trainLabelsSource.loadDataBlock(nTrainVectorsInBlock);
/* Pass a training data set and dependent values to the algorithm */
algorithm.input.set(classifier::training::data, trainData[i]);
algorithm.input.set(classifier::training::labels, trainLabelsSource.getNumericTable());
/* Build the Naive Bayes model */
algorithm.compute();
}
/* Finalize the Naive Bayes model */
algorithm.finalizeCompute();
/* Retrieve the algorithm results */
trainingResult = algorithm.getResult();
}
void testModel()
{
/* Read testDatasetFileName and create a numeric table to store the input data */
testData = CSRNumericTablePtr(createSparseTable<float>(testDatasetFileName));
/* Create an algorithm object to predict Naive Bayes values */
prediction::Batch<algorithmFPType, prediction::fastCSR> algorithm(nClasses);
/* Pass a testing data set and the trained model to the algorithm */
algorithm.input.set(classifier::prediction::data, testData);
algorithm.input.set(classifier::prediction::model, trainingResult->get(classifier::training::model));
/* Predict Naive Bayes values */
algorithm.compute();
/* Retrieve the algorithm results */
predictionResult = algorithm.getResult();
}
void printResults()
{
FileDataSource<CSVFeatureManager> testGroundTruth(testGroundTruthFileName,
DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
testGroundTruth.loadDataBlock(nTestObservations);
printNumericTables<int, int>(testGroundTruth.getNumericTable().get(),
predictionResult->get(classifier::prediction::prediction).get(),
"Ground truth", "Classification results",
"NaiveBayes classification results (first 20 observations):", 20);
}

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